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                                          <http://schema.org/name>        "Kuck, Maximilian"
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                "This dataset contains process data from milling operations performed on a three-axis milling machine (CMX 600V, DMG Mori), complementing DOI:10.35097/hvvwn1kfwf7qt48z. To reflect the diversity and variability of real-world milling scenarios, the dataset includes components, tools and machining strategies that are relevant to the industry. A total of 54 milling experiments are included. To support reproducibility and benchmarking applications, the dataset includes NC programs, labelled pictures, and detailed documentation. During processing, signals were acquired from the Siemens SINUMERIK 840D CNC system via the SINUMERIK Edge app 'Analyse MyWorkpiece/Capture', which enables the export of high-frequency data at 500 Hz in JSON format (.json). Additionally, force and acceleration data recorded by external sensors is provided in MATLAB format (.mat). Each experiment also includes a combined MATLAB file in which all recorded signals are synchronized." , "This dataset contains process data from milling operations performed on a three-axis milling machine (CMX 600V, DMG Mori) and is complementary to DOI:10.35097/hvvwn1kfwf7qt48z. The data was collected in a controlled laboratory setting using industrially relevant components, tools, and machining strategies to reflect the diversity and variability of practical milling scenarios. Process signals from the machine controller were recorded using a Siemens Industrial Edge device at a sampling rate of 500 Hz. Additional high-frequency force and acceleration data were measured using a force platform (Kistler Type Z 3393) and a spindle-mounted acceleration sensor (PCB Type 356A33). The analog signals were amplified via a charge amplifier (Kistler Type 5015A1000 K) and acquired using a Data Translation DT9836 data acquisition card at 10 kHz.\nRaw data from the force and acceleration sensors are provided as MATLAB timetable files (.mat). The process signals from the Siemens Industrial Edge are stored in JSON format (.json).\nIn addition, each experiment includes a consolidated MATLAB file in which all available signals (Edge and sensor data) are temporally aligned and stored as a single timetable object. To achieve this, the 500 Hz Edge signals were interpolated using the PCHIP method (Piecewise Cubic Hermite Interpolating Polynomial) to match the 10 kHz resolution of the sensor data. This unified file enables direct signal comparison across all sources and simplifies further analysis. \nIn total, 54 milling experiments are included. NC programs, labeled pictures, and detailed documentation are provided to support benchmarking applications.\n--------------------------------------------------------------------------------------------------------------------------\nDocuments:\n- Dataset: All data files organized by component type and trial ID\n- Descriptive: Design of Experiment, NC programs executed during the experiments (G-code), labeled pictures organized by component type and tools\nExperimental Setup:\n- Machines: CMX 600V (3-axis milling center)\n- Materials: S235JR (unalloyed structural steel), Al2007 T4 (aluminum alloy), POM-C (polyoxymethylene copolymer)\n- Cutting Tools:\n - HSS-Co8 end mill (TiAlN coating, Ø 5 mm, k10 tolerance)\n - Solid carbide end mill (TiSi coating, Ø 10 mm, f8 tolerance)\n - Solid carbide end mill (uncoated, Ø 5 mm, e8 tolerance)\n- Workspace blank dimensions: 150x75x50mm" , "Der vorliegende Datensatz enthält Prozessdaten, die auf einer dreiachs Fräsmaschine (CMX 600V, DMG Mori) durchgeführt wurden, und ergänzt DOI:10.35097/hvvwn1kfwf7qt48z. Um die Vielfalt und Variabilität realer Fräsbearbeitungen widerzuspiegeln, wurden industrierelevante Bauteile, Werkzeuge und Bearbeitungsstrategien angewendet. Insgesamt sind 54 Fräsversuche enthalten. Um eine Reproduzierbarkeit und Benchmarking-Anwendungen zu ermöglichen, umfasst der Datensatz NC-Programme, beschriftete Bilder und eine detaillierte Dokumentation. Während der Bearbeitung wurden die Signale der SINUMERIK 840D Steuerung über die SINUMERIK Edge App „Analyse MyWorkpiece/Capture” erfasst. Diese ermöglicht einen Datenexport mit 500 Hz im JSON-Format (.json). Zusätzlich werden die von externen Sensoren aufgezeichneten Kraft- und Beschleunigungsdaten im MATLAB-Dateiformat (.mat) bereitgestellt. Jeder Versuch umfasst zudem eine MATLAB-Datei, in der alle aufgezeichneten Signale synchron abgespeichert sind.";
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                "This dataset consists of raw and processed images obtained in droplet impingement experiments in a shadowgraphy method with additional color-coded glare points. The raw images are saved in the uncompressed file format .tif and processed images are saved as .png." , "The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface on the basis of monocular images obtained via optical measurement techniques.\nThe method is tested an evaluated at the example of liquid droplets impacting on structured solid substrates.\nThe droplet dynamics are captured through high-speed imaging in an extended shadowgraphy setup with additional glare points from lateral light sources that encode further three-dimensional information of the gas-liquid interface in the images.\nA neural network is trained for the physically correct reconstruction of the droplet dynamics on a labelled dataset generated by synthetic image rendering on the basis of gas-liquid interface shapes obtained from direct numerical simulation.\nThe employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. \nThe accurate reconstruction of the three-dimensional shape of the gas-liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach.\nThe introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation.\nBy the successful reconstruction of obscured areas in the input images, it is demonstrated that the neural network has the capability to learn a physically correct interpolation of missing data from the numerical simulation.\nFurthermore, the physically reasonable reconstruction of unknown gas-liquid interface shapes for drop impact regimes that were not contained in the training dataset indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.";
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                "For decades, scanning/transmission electron microscopy (S/TEM) techniques have been employed to analyze shear bands in metallic glasses and understand their formation in order to improve the mechanical properties of metallic glasses. However, due to a lack of direct information in reciprocal space, conventional S/TEM cannot characterize the local strain and atomic structure of amorphous materials, which are key to describe the deformation of glasses. For this work, we applied 4-dimensional STEM to map and directly correlate the local strain and the atomic structure at the nanometer scale in deformed metallic glasses. We observe residual strain fields with quadrupolar symmetry concentrated at dilated Eshelby inclusions. The strain fields percolate in a vortex-like manner building up the shear band. This provides a new understanding of the formation of shear bands in metallic glass." , "The data format is DM4 (Gatan Microscopy Suite Software) and processed by lab-written Matlab code. 4D-STEM measurements were conducted using a Themis Z double-corrected TEM (Thermofisher Scientific) operated at 300 kV in microprobe STEM mode with spot size 6 and a semi-convergence angle of 0.26 mrad giving rise to a diffraction-limited probe size of ~5 nm. 4D-STEM records local 2D diffraction patterns over a 2D array of probe positions by stepwise scanning of the probe. The method is called 4D-STEM referring to its typical 4D dataset (2D diffraction pattern on a 2D array of the sample). We used a OneView camera (Gatan Inc.) with a camera length of 1.15 m to record the diffraction patterns. This camera length was chosen to capture the first diffuse diffraction ring with a sufficient diameter on the camera to enhance the sensitivity for measuring distortions. The 2nd diffuse diffraction ring was also included. This preserves the capability for PDF analysis. 4D-STEM maps were acquired with a step size of 5.8 nm and a frame size of 900×500 pixels for the Fe85.2Si0.5B9.5P4Cu0.8 metallic glass ribbon and a step size of 9.7 nm and a frame size of 350×270 pixels for the Zr46Cu38Al8Ag8 bulk metallic glass with an exposure time of 3.3 ms per frame (frame rate of ~300 f/s). \nThe diffraction pattern of a typical amorphous material shows a diffuse ring pattern (Figure 1a). As described in previous works [23-24, 27], the local stress in the metallic glass induces a structural anisotropy, which results in an elliptic distortion of the diffraction ring leading to a deviation from the ideal circle as illustrated in Figure 1b (the diffraction pattern was artificially elongated for easy presentation). Therefore, the strain can be mapped by determining the ellipticity of the diffraction ring in each local diffraction pattern of the 4D-STEM dataset. Different from high-resolution (HR)TEM-based strain mapping methods such as geometric phase analysis (GPA), which analyzes real space atomic lattice displacements [49], the strain measurement used here analyzes the diffraction ring in the 4D-STEM data. It thus enables the capability to measure strain for amorphous materials and a large field of view (up to micrometers).";
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                "This dataset includes the simulation model of the demonstrator vehicle \"elf++\" for vehicle dynamics simulation in CarMaker. The demonstrator vehicle was developed in the research project e²-Lenk at the Institute of Vehicle System Technology (FAST) and at Schaeffler Hub for Advanced Research at KIT (SHARE at KIT) and subsequently used in the projects SmartLoad and FLOOW. The CarMaker model includes the vehicle geometry, the chassis kinematics, the powertrain, and the tire model, supplemented by the modeling of the steering system, the localization module, and the longitudinal and lateral dynamics control in MATLAB-Simulink. Other modules such as vehicle guidance controllers can be added to the Simulink model. The modeling status corresponds to the vehicle state at the end of the SmartLoad project." , "# Vehicle Dynamics Model of Demonstrator Vehicle \"elf++\"   \nEnglish version see below.\n## Einleitung\nDies ist ein Fahrdynamik-Simulationsmodell für das Demonstratorfahrzeug „elf++“, das mit CarMaker 8.0 und Matlab 2018b kompatibel ist. Das CarMaker-Projekt umfasst das Fahrzeugmodell *Demonstrator_vehicle_ELFpp* und den Testrun *Test_Runs_DrivingDynamicsArea*.\nDas Modell ermöglicht die Simulation von Fahrmanövern durch Vorgabe des zeitlichen Verlaufs von Lenkradwinkel und Geschwindigkeit. Weitere Funktionalitäten können im Simulink-Modell modular ergänzt und angepasst werden. Beispielhaft wurde ihm Rahmen folgender Veröffentlichung ein Fahrzeugführungsregler in das Modell integriert: [Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034).\nBei dem Demonstratorfahrzeug „elf++“ handelt es sich um ein Fahrzeug im Maßstab 1:1,5, das über eine Ackermannlenkung mit redundant verbautem Lenkaktor und radselektive Antriebe an der gelenkten Vorderachse verfügt.\n## Aufbau des integrierten Simulink-Modells *Demonstrator_vehicle_ELFpp*  \n### *Vehicle_Control*\nDieser Block umfasst die Funktionen der Fahrzeugregelung von der Sollwertvorgabe bis zur Ausgabe der Aktorstellgrößen.\n- #### *Target*\n Vorgabe des Lenkradwinkelverlaufs in rad und der Geschwindigkeit in m/s des zu fahrenden Manövers.\n- #### *Steering_controller*\n Lenkradwinkelregelung zur Vorgabe des Gesamtlenkmomentes.\n- #### *Steering_Actuator_Allocation*\n Aufteilung des Gesamtlenkmomentes auf die redundant verbauten Lenkaktoren.\n- #### *Velocity_controller*\n Geschwindigkeitsregelung zur Vorgabe des Gesamtantriebsmomentes.\n- #### *Driving_Actuator_Allocation*\n Aufteilung des Gesamtantriebsmomentes auf die radindividuellen Antriebe der Vorderachse.\n### *CarMaker*\nDieser Block stellt die Schnittstelle zu CarMaker dar. Die Aktorstellgrößen, die die Regler vorgeben, werden im *Torque_Limiter* den physikalischen Limits entsprechend beschränkt. Weitere Modifikationen gegenüber der CarMaker-Vorlage betreffen die Modellierung des Lenksystems. Ausgehend von den Aktormomenten und den Spurstangenkräften wird mittels eines 2DOF Modells die resultierende Lenkbewegung berechnet.\n### *Localization*\nZur Abbildung des auf dem Fahrzeug verbauten GNSS RTK Systems werden in diesem Block die Positionsmessgrößen in ihrer Frequenz reduziert.\n## Parameter und Einstellungen\n### *sim_settings.m*\nMit den Vorgaben in dieser Dateikönnen die Dauer des Simulationsdurchlaufes, sowie die Parameter des Modells angepasst werden, die die Einstelllungen der Regler und die fahrdynamischen Grenzen des Manövers betreffen.\n### *init_parameter* in Ordner *vehicle_control_data*\nDiese Datei definiert Konstanten und enthält fahrzeugspezifische Parameter.\n## Verwendung\nUm dieses Simulationsmodell zu verwenden, öffnen Sie das Simulink-Modell *Demonstrator_vehicle_ELFpp* und starten daraus CarMaker durch Klicken auf das CarMaker Symbol. In CarMaker öffnen Sie den Testrun *Test_Runs_DrivingDynamicsArea*. Definieren Sie dann die Fahrmanöver im *Target* Block, indem Sie den zeitlichen Verlauf von Lenkradwinkel und Geschwindigkeit angeben. Starten Sie die Simulation über die CarMaker GUI.\n## English version:\n## Introduction\nThis is a driving dynamics simulation model for the demonstrator vehicle “elf++” compatible with CarMaker 8.0 and Matlab 2018b. The CarMaker project includes the vehicle model *Demonstrator_vehicle_ELFpp* and the test run *Test_Runs_DrivingDynamicsArea*.\nThe model supports the simulation of driving maneuvers by specifying the course of the steering wheel angle and velocity. Further functionalities can be added and adapted modularly in the Simulink model. For example, a vehicle guidance controller was integrated into the model as part of the following publication: [Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034).\nThe demonstrator vehicle “elf++” is a vehicle on a scale of 1:1.5 that has Ackermann steering with a redundantly installed steering actuator and wheel-selective drives on the steered front axle.\n## Structure of the integrated simulink model *Demonstrator_vehicle_ELFpp*\n### *Vehicle_Control*\nThis block includes the functions of the vehicle control from the setpoint specification to the output of the actuator target values.\n- #### *Target*\n    Specification of the steering wheel angle course in rad and the speed in m/s of the maneuver to be driven.\n- #### *Steering_controller*\n Steering wheel angle control that outputs the total steering torque.\n- #### *Steering_Actuator_Allocation*\n Allocation of the total steering torque to the redundantly installed steering actuators.\n- #### *Velocity_controller*\n Velocity control that outputs the total drive  torque.\n- #### *Driving_Actuator_Allocation*\n Allocation of the total drive torque to the individual wheel drives of the front axle.\n### *CarMaker*\nThis block represents the interface to CarMaker. The actuator target values which the controllers specify are limited in the *Torque_Limiter* according to their physical limits. Further modifications compared to the CarMaker template concern the modeling of the steering system. Starting from the actuator torques and the tie rod forces, the resulting steering movement is calculated by means of a 2DOF model.\n### *Localization*\nTo represent the GNSS RTK system installed on the vehicle, the frequency of the position measurement variables is reduced in this block.\n## Parameter and Settings\n#### *sim_settings.m*\nIn this file, the duration of the simulation run can be adjusted, as well as the parameters of the model, which affect the settings of the controllers and the driving dynamic limits of the maneuver input.\n#### *init_parameter* in Ordner *vehicle_control_data*\nThis file contains constants and parameters specific to the vehicle.\n## Usage\nTo use this simulation model, open the Simulink model *Demonstrator_vehicle_ELFpp* and start CarMaker from it by clicking on the CarMaker icon. In CarMaker, open the test run *Test_Runs_DrivingDynamicsArea*. Then define the driving maneuvers in the *Target* block by specifying the course of steering wheel angle and velocity. Start the simulation via the CarMaker GUI." , "Dieser Datensatz enthält das Simulationsmodell des Demonstratorfahrzeugs \"elf++\" für die Fahrdynamiksimulation in CarMaker. Das Demonstratorfahrzeug wurde im Forschungsprojekt e²-Lenk am Institut für Fahrzeugsystemtechnik (FAST) und am Schaeffler Hub for Advanced Research am KIT (SHARE am KIT) entwickelt und anschließend in den Projekten SmartLoad und FLOOW eingesetzt. Die Modellierung in CarMaker umfasst die Fahrzeuggeometrie, die Fahrwerkskinematik, den Antriebsstrang und das Reifenmodell, ergänzt um die Modellierung des Lenksystems, des Lokalisierungsmoduls und der Längs- und Querdynamikregelung in MATLAB-Simulink. Das Simulink-Modell kann um weitere Module wie z.B. einen Fahrzeugführungsregler ergänzt werden. Der Modellierungsstand entspricht dem Fahrzeugzustand zum Ende des Projektes SmartLoad.";
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                "These are the experimental data for the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished on [arXiv](https://arxiv.org/) in 2023.\nYou can find the paper [here](https://doi.org/10.48550/arXiv.2307.11607) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text." , "# Experimental Data for the Paper \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\nThese are the experimental data for the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished at [arXiv](https://arxiv.org/) in 2023.\nIf we create multiple versions of this paper in the future, these experimental data will cover at least the first version.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 255 h.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [`2212360a32`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/2212360a320121862556bc8d54da45cb6dc8ee93).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation.py`) is [`487b8ba04d`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/487b8ba04d513881be48fb2bc7b1c1dca416d122).\nWe also tagged both commits (`run-2023-06-23` and `evaluation-2023-07-04`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth files are CSVs only containing numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column corresponds to a feature.\nThe first line in each CSV contains the names of the features as strings; for `_y.csv` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the dataset).\n  Might also be an empty list, i.e., `[]` if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1,1]`): The training-set objective value of the feature set.\n  Three methods (FCBF, MI, Model Importance) have the range `[0,1]`, while two methods (mRMR, Greedy Wrapper) have the range `[-1,1]`.\n- `test_objective` (float in `[-1,1]` ): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  For white-box feature-selection methods, this measures one solver call.\n  In contrast, wrapper feature selection can call the solver multiple times, and we record the total runtime of the \"Greedy Wrapper\" algorithm.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver; for wrapper feature selection, this is only the status of the last solver call.\n  - 0 = (proven as) optimal\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `random_forest_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `{1, ..., 10}`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for simultaneous search and always `10` for sequential search.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for simultaneous search but always sum-aggregation for sequential search.\n  (In fact, sequential search only optimizes individual feature sets anyway, so the aggregation does not matter for the search.)\n- `search_name` (string, 2 different values): The name of the search method for alternatives (`search_sequentially` or `search_simultaneously`).\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= Model Gain), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0,4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1,1000]`): The number of iterations in case wrapper feature selection was used, missing value (empty string) in the other cases.\n  This column does not exist in result files not containing wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that lists of feature indices are treated as such (rather than strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```";
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                "This code has been used for the numerical experiments in the thesis \"High-frequency wave-propagation: error analysis for analytical and numerical approximations\" by Julian Baumstark, see https://www.doi.org/10.5445/IR/1000149719." , "The scripts are intended to reproduce the figures from the thesis\n\"High-frequency wave-propagation: error analysis for analytical and numerical approximations\"\nby Julian Baumstark\n################################################################################\n#### Requirements\nThe codes are tested on \n    Ubuntu 18.04 LTS\nwith \n    Matlab R2020a \n################################################################################\n#### Generation of figures\nWith this code Figures \n    3.1, 3.2, 4.1, 4.2, 4.3, 5.1, and 5.2\ncan be generated. \nThe calculation of the data for Figure 4.1, 4.2, 4.3, 5.1 and 5.2 takes a few days and requires a storage space of about 140 GB.\n#### Figure 3.1 and 3.2\nIn order to obtain Figure 3.X (X=1 or 2) run the command\n    visualize_Fig3X.m\nin Matlab. \n#### Figure 4.1, 4.2 and 4.3\nOpen folder \"Chapter_4\" in Matlab.\nIn order to obtain Figure 4.X (X=1 or 2 or 3) first run the command\n    kleingordon_analytical_approx.m \nto compute the data. Then, run\n    visualize_Fig4X.m\nin Matlab. \n#### Figure 5.1 and 5.2\nOpen folder \"Chapter_5\" in Matlab.\nIn order to obtain Figure 5.X (X=1 or 2) first run the command\n    kleingordon_numerical_approx.m \nto compute the data. Then, run\n    visualize_Fig5X.m\nin Matlab.";
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                "The data set consists of five types. \n1) In table AnthroDemoMaxForcesTable.mat the anthropometric data, the gender and the maximum grip and push forces of the subjects are listed. \n2) In Matrix resultsMatrix.mat the mechanical impedance and the phase angle of the measurements of the subjects study are summarized. The resulting mechanical impedance and the phase angle are already calibrated in each direction, and can be used directly from the matrix. The matrix consists of three dimensions, the first is the mechanical impedance and the phase angle in the direction of excitation (zh), the second dimension is the cross-axial mechanical impedance and the phase angle in xh-direction, and the third dimension is the cross-axial mechanical impedance and the phase angle in yh-direction. The directions are named according to the coordinate system of DIN 5349:2001. Each row of the matrix represents one measurement. The following data information and results are in the corresponding columns:\n- 1: number of the subect\n- 2: is 0 if the grip and push forces during the measurements are within the range of 5N and 14%, otherwise a 1 is at this position. This can be used for data exclusion.  \n- 3: predefined push force value\n- 4: predefined grip force value\n- 5: experiment number according to the resultsMatrix for each subject\n- 6: sequence of the taken measurements of each subject, due to a randomized test plan\n- 7: mean measured grip force\n- 8: median measured grip force\n- 9: standard deviation of the measured grip force\n- 10: mean measured push force\n- 11: median measured push force\n- 12: standard deviation of the measured push force\n- 13: mechanical impedance at 10 Hz\n- 14: phase angle at 10 Hz\n- 15: mechanical impedance at 15 Hz\n- 16: phase angle at 15 Hz\n- 13:2:209 mechanical impedance between 10-500 Hz in 5 Hz steps\n- 14:2:210 phase angle of the mechanical impedance between 10-500 Hz in 5 Hz steps\n3) The VPAmatrix.mat is according to resultsMatrix.mat, instead of the mechanical impedance in positions 13:2:209 the vibration power absorption values are listed and in positions 14:2:210 the weighted vibration power absorption according to ISO10068:2012 is listed. Here, only the direction of excitation is evaluated. \n4) Raw data, the data name consists of the time stamp and is named afterwards in the same order as the matrix: Px, where x is the number of the subject, followed by the push and then the grip force and the position in the matrix. In each struct the metadata, the time and the signals are saved. The excitation in the subject study was applied along the forearm axis (zh according to DIN5349:2001). Therefore, the signals az_top and az_bottom are the acceleration at the top and bottom of the measuring handle in excitation direction. The signals Fz_top and Fz_bottom are the forces in the direction of excitation, both are used to calculate the mechanical impedance and vibration power absorption in the excitation direction (dimension 1 in the resultsMatrix.mat file). The signals used for the second dimension of the resultsMatrix are Fx_top, Fx_bottom, az_top, az_bottom, for the third dimension Fy_top, Fy_bottom, az_top, az_bottom are used.\n5) emptyMeasurement.mat is a measurement of the unloaded handle while excited with the same signal as in the subject study. This file can be used for the calibration of the handle." , "The data set contains the raw as well as the evaluated data from a subject study with 6 subjects to measure the mechanical impedance and the vibration power absorbtion while varying the grip and push forces in ten levels between 10-135N and 10-110N respectively. The measuring handle on which the study was conducted complies with the specifications in ISO10068:2012. The subjects were randomised to hold the force levels while vibrations were applied translatory along the lower arm axis (zh-axis according to DIN5349:2001) to their hand-arm system via a shaker system, with the posture of the subjects lying within the ranges specified in ISO 10819:2022. The excitation was a multisine with frequencies between 10 to 500 Hz in 5 Hz steps with an root mean square value of 40m/s². The acquisition time of each measurement is 16.5 sec, for the evaluation the data is cutted to exclude the first 4 sec and the last 1.5 sec.";
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                "These are the experimental data for the paper> Bach, Jakob, and Klemens Böhm. \"Alternative feature selection with user control\"\npublished in the [International Journal of Data Science and Analytics](https://link.springer.com/journal/41060) in 2024.\nYou can find the paper [here](https://doi.org/10.1007/s41060-024-00527-8) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text." , "# Experimental Data for the Paper \"Alternative feature selection with user control\"\nThese are the experimental data for the paper> Bach, Jakob, and Klemens Böhm. \"Alternative feature selection with user control\"\npublished in the [International Journal of Data Science and Analytics](https://link.springer.com/journal/41060) in 2024.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 255 h.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [`2212360a32`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/2212360a320121862556bc8d54da45cb6dc8ee93).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation_journal.py`) is [`758b71e70b`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/758b71e70b941a54c88bda17a5591698dafcc03c).\nWe also tagged both commits (`run-2023-06-23` and `evaluation-2024-03-19`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation_journal.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth files are CSVs only containing numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column corresponds to a feature.\nThe first line in each CSV contains the names of the features as strings; for `_y.csv` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation_journal.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the dataset).\n  Might also be an empty list, i.e., `[]` if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1,1]`): The training-set objective value of the feature set.\n  Three feature-selection methods (*FCBF*, *MI*, *Model Importance*) have the range `[0,1]`, while two methods (*mRMR*, *Greedy Wrapper*) have the range `[-1,1]`.\n- `test_objective` (float in `[-1,1]` ): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  The interpretation of this value depends on the feature-selection method.\n  For white-box feature-selection methods, this value corresponds to one solver call.\n  For wrapper feature selection, we record the total runtime of the *Greedy Wrapper* algorithm, which calls the solver and trains prediction models multiple times.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver; for wrapper feature selection, this is only the status of the last solver call and only refers to a satisfiability problem rather than the (black-box) optimization problem.\n  - 0 = (proven as) optimal\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `random_forest_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `{1, ..., 10}`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for simultaneous search, but always `10` for sequential search.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for simultaneous search but always sum-aggregation for sequential search.\n  (In fact, sequential search only optimizes individual feature sets anyway, so the aggregation does not matter for the search.)\n- `search_name` (string, 2 different values): The name of the search method for alternatives (`search_sequentially` or `search_simultaneously`).\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= *Model Gain* in the paper), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0,4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1,1000]`): The number of iterations in case wrapper feature selection was used, missing value (empty string) in the other cases.\n  This column does not exist in result files not containing wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that lists of feature indices are treated as such (rather than strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```";
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                "Ziel des Datensatzes ist das Training sowie die Validierung von Modellen zur Prognose von Zeitreihen für Fräsprozesse. Hierfür wurden an einer DMG CMX 600 V durch eine Siemens Industrial Edge Prozesse mit einer Abtastrate von 500 Hz aufgenommen. Es wurde ein Prozess für das Modelltraining und ein Prozess für die Validierung aufgenommen, welche sowohl für die Bearbeitung von Stahl sowie von Aluminium verwendet wurden. Es wurden mehrere Aufnahmen mit und ohne Werkstück (Aircut) aufgenommen, um möglichst viele Fälle abdecken zu können." , "Documents:\n-Design of Experiments: Information on the paths such as the technological values of the experiments\n-Recording information: Information about the recordings with comments\n-Data: All recorded datasets. The first level contains the folders for training and  validation both with and without the workpiece. In the next level, the individual test executions are located. The individual recordings are stored in the form of a JSON file. This consists of a header with all relevant information such as the signal sources. This is followed by the entries of the recorded time series.\n-NC-Code: NC programs executed on the machine\n-Workpiece: Pictures of the raw parts as well as the machined workpieces. The pictures show the unfinished part on the left, the training part in the middle and a part with two validation runs on the right.\nExperimental data:\n-Machine: DMG CMX 600 V\n-Material: S235JR, 2007 T4\n-Tools:\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 5mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 10mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 20mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 10mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n-Workpiece blank dimensions: 150x75x50mm\nLicense: This work is licensed under a Creative Commons Attribution 4.0 International License. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)." , "The aim of the data set is the training as well as the validation of models for the prediction of time series for milling processes. For this purpose, processes with a sampling rate of 500 Hz were recorded on a DMG CMX 600 V by a Siemens Industrial Edge. A process for model training and a process for validation were recorded, which were used for both steel and aluminum machining. Several recordings were made with and without the workpiece (aircut) in order to cover as many cases as possible.";
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                "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d." , "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only.";
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                "Drug-induced differential gene expression analysis (DGEA) is an essential tool for uncovering the molecular basis of phenotypic changes in cells upon drug treatment, and ultimately for understanding the mechanisms of individual tumor responses to anticancer drugs. Performing high-throughput DGEA after drug treatment is challenging due to the very high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells.\nWe introduce a novel, miniaturized method operating at the nanoliter scale for drug-induced DGEA. This innovative approach facilitates high-throughput and parallel analysis of the drug response of cells derived from patients, effectively circumventing issues related to limited samples and the laborious nature of traditional protocols.\nThe method is based on the Droplet Microarray (DMA) platform, a microscope glass slide with a pattern of hydrophilic spots separated by a superhydrophobic background, which enables the formation of droplets suitable for testing a minute number of cells with compounds. DMA allows for phenotypic analysis using microscopy, followed by obtaining cDNA from the treated cells and DGEA. The procedure involves cell lysis for mRNA isolation and cDNA conversion on DMA, followed by pooling of the droplets and subjecting them to qPCR analysis. \nIn this work, we demonstrate the protocol for drug-induced DGEA on the DMA platform using cell lines and primary patient-derived chronic lymphocytic leukemia (CLL) cells. The methodology established here is critical for performing DGEA on a limited number of cells, with potential applications in functional precision oncology. In this way, this method helps to gain insights from the molecular profiling of unique patient-derived samples after drug treatment in vitro, which is essential for understanding individual tumor response to anticancer drugs." , "The provided data are technical repeats from the research experimental details. including qPCR data and image analysis for cellular viability.";
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                "Research data, research results, and publications of the PhD thesis entitled 'Validation Framework for RDF-based Constraint Languages', submitted to the Department of Economics and Management at the Karlsruhe Institute of Technology (KIT)." , "# PhD Thesis---* Title: **[Validation Framework for RDF-based Constraint Languages](http://dx.doi.org/10.5445/IR/1000056458)*** Author: **[Thomas Hartmann](http://www.dr-thomashartmann.de/)*** Examination Date: 08.07.2016* University: [Karlsruhe Institute of Technology (KIT)](https://www.kit.edu/english/)* Chair: [Institute of Applied Informatics and Formal Description Methods](http://www.aifb.kit.edu/web/Hauptseite/en)* Department: [Department of Economics and Management](http://www.wiwi.kit.edu/english/index.php)* 1. Advisor: [Prof. Dr. York Sure-Vetter](http://www.aifb.kit.edu/web/York_Sure-Vetter/en), Karlsruhe Institute of Technology* 2. Advisor: [Prof. Dr. Kai Eckert](https://www.hdm-stuttgart.de/forschung_transfer/forschungsthemen/metadatenmanagement/team/eckert), Stuttgart Media University---***PhD Thesis Download**** [http://dx.doi.org/10.5445/IR/1000056458](http://dx.doi.org/10.5445/IR/1000056458)***Publications**** Complete set of publications: [publications](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/publications)***Research Data, Research Results, and Publications**** Link to  the KIT research data repository: [http://dx.doi.org/10.5445/BWDD/11](http://dx.doi.org/10.5445/BWDD/11)---***RDF Validation Requirements Database**** [http://purl.org/net/rdf-validation](http://purl.org/net/rdf-validation)***Validation Environment**** Demo: [http://purl.org/net/rdfval-demo](http://purl.org/net/rdfval-demo)* Source code: [software/rdf-validator](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/software/rdf-validator)---***Chapter 2: Foundations for RDF Validation**** XML validation: [chapter/chapter-2/xml-validation](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-2/xml-validation)***Chapter 3: Vocabularies for Representing Research Data and Related Metadata**** RDF vocabularies commonly used to represent different types of research data and related metadata: [chapter/chapter-3/common-vocabularies](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-3/common-vocabularies)* Complete running example in RDF: [chapter/chapter-3/common-vocabularies/running-example](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-3/common-vocabularies/running-example)***Chapter 4: RDFication of XML Enabling to use RDF Validation Technologies**** Evaluation results: [chapter/chapter-4/evaluation](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-4/evaluation) ***Chapter 6: Consistent Validation across RDF-based Constraint Languages**** Constraint languages implementations: [chapter/chapter-6/constraint-languages-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-6/constraint-languages-implementations)***Chapter 7: Validation Framework for RDF-based Constraint Languages****  Formal specification, HTML documentation, and UML class diagram of the RDF Constraints Vocabulary (RDF-CV): [chapter/chapter-7/rdf-constraints-vocabulary](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-7/rdf-constraints-vocabulary)* Generic SPIN mappings for constraint types: [chapter/chapter-7/generic-SPIN-mappings/RDF-CV-2-SPIN.ttl](https://github.com/github-thomas-hartmann/phd-thesis/blob/master/chapter/chapter-7/generic-SPIN-mappings/RDF-CV-2-SPIN.ttl)***Chapter 8: The Role of Reasoning for RDF Validation**** Implementations for all constraint types expressible by OWL 2 QL, OWL 2 DL, and DSP as well as for major constraint types representable by ReSh and ShEx:  [chapter/chapter-8/constraint-types-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-8/constraint-types-implementations)* Implementation of reasoning capabilities for all reasoning constraint types for which OWL 2 QL and OWL 2 DL reasoning may be performed: [chapter\\chapter-8\\reasoning-constraint-types-implementations/OWL2-Reasoning-2-SPIN.ttl](https://github.com/github-thomas-hartmann/phd-thesis/blob/master/chapter/chapter-8/reasoning-constraint-types-implementations/OWL2-Reasoning-2-SPIN.ttl)* Validation and reasoning implementations of constraint types:  [chapter/chapter-8/constraint-types-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-8/constraint-types-implementations)***Chapter 9: Evaluating the Usability of Constraint Types for Assessing RDF Data Quality**** Implementations of all 115 constraints: [chapter/chapter-9/constraints](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/constraints)* Evaluation results for each QB data set grouped by SPARQL endpoint: [chapter/chapter-9/evaluation/data-sets/QB](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/evaluation/data-sets/QB)* Vocabulary implementations: [chapter/chapter-9/vocabularies/implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/vocabularies/implementations)***Appendix**** Link to appendix: [http://dx.doi.org/10.5445/IR/1000054062](http://dx.doi.org/10.5445/IR/1000054062)";
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                "\"Entwicklung eines Eignungstests fuer die Zulassung zum Sportstudium\" in Sportunterricht, 25, (1976), 5 , S. 148-153Verf. beschreibt einen Eignungstest, der zum Wintersemester 1975/76 erstmals bei maennlichen und weiblichen Bewerbern fuer das Sportstudium angewandt wurde. Die leistungsdiagnostische Aufgabe des Tests besteht in der Feststellung der motorischen Grundeigenschaftenin Relation zu den Anforderungen der Grundkurse. Es werden keine sportmotorischen Fertigkeiten, sondern motorische Eigenschaften wie Ausdauer, Geschicklichkeit und Schnelligkeit in Spielsituationen geprueft. Die Tests bestehen aus den Untergruppen Haro-Fitness-Test, Geraetturnen, Leichtathletik, Schwimmen, Spiele und setzen sich aus verschiedenen Einzeltests zusammen. Alle Ergebnisse werden in Testpunkte umgerechnet, von denen zum Schluss eine Mindestzahl erreicht werden muss. Gewisse Schwaechen in Einzeltests koennen in anderen wettgemacht werden. Zur Erstellung der Normen wurden zwei Probedurchlaeufe in Beziehung zu bereits gewonnenen Erfahrungswerten gebracht. Von jedem Item wurde die Standardabweichung ermittelt, die Messwerte in eine Standardnormalverteilung transformiert und die T-Werte ermittelt. Die Ergebnisse der Einzeltests werden in diesen Werten ausgedrueckt und fuer jede Untergruppe das arithmetische Mittel gebildet. Das Mittel aller fuenf Mittelwerte bildet das Gesamtergebnis.";
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                "# Degree of substitution of cellulose acetate and other esters: Raw ATR-FTIR spectra and DS data obtained from IR integration, 1H NMR and 31P NMR  \n## Authors\nFrank Rhein, Timo Sehn, Michael A.R. Meier\n## About\nThis repository contains the raw data used in *\"Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectrosopy and machine learning\"*. For detailed information please refer to the original publication upon acceptance.\n## Datasets\nThe following data sets are compiled in this repository and published together with raw ATR-FTIR spectra:\n- `Wolfs.A`: DOI https://doi.org/10.1002/pol.20230220 \n    - Raw ATR-FTIR data\n    - DS data from IR integration, 1H NMR, 31P NMR methods\n- `Wolfs.B`: DOI https://doi.org/10.1039/D1GC01508G\n    - Raw ATR-FTIR data\n    - DS data from 1H NMR, 31P NMR methods\n- `Sehn.A`, `Sehn.B`, `Sehn.C`: DOI https://doi.org/10.1021/acs.biomac.3c00762 \n    - Raw ATR-FTIR data\n    - DS data from 1H NMR method\nAn overview is given in `data_overview.xlsx`.\n## Structure\nRaw ATR-FTIR data is contained in the `IR_data_..` folders, while DS values from different methods are found in the respective `.txt` files. > Note: `IR_DS` refers to the integration based method reported in https://doi.org/10.1002/pol.20230220  \n## Methods\nFor more information on data generation and analytical setups please refer to the original publications referenced above. DS data based on 1H NMR data is obtained according to the newly reported evaluation routine.  \n## Evaluation Scripts\nThe Python scripts for data analysis are available on Github under https://github.com/pdhs-group/DS_IR_ML";
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                "The dataset was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anoma-lies in the workpiece the dataset can be applied for anomaly detection. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning." , "The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. \nDuring each milling run the milling tool moved along the longitudinal side and then was moved back alongside the workpiece. This way machining started always on the same side of the workpiece. \nTable 1 provides an overview of the milling runs. Run 1 to 4 were performed with a HSS tool with a diameter of 10 mm. The tool in use was an end mill (HSS-E-SPM HPC 10 mm) developed by Hoffmann Group. During the first three runs with this end mill no tool breakage occurred. However, in run 4 the tool broke. Runs 5 and 6 were performed by milling with an end mill of the same tool series (HSS-E-SPM HPC 8 mm) that just differs in tool diameter. In contrast to this run 7 was performed by using a solid carbid tool (Solid carbide roughing end mill HPC 8 mm). Cutting with SC tools provides much higher productivity with the downside being higher tool price. In our case the SC end mill performed cuts with a feed rate of 1150 mm/min compared to 191 mm/min achieved by a HSS end mill of the same diameter. Tool breakages were recorded on all runs with end mills of diameter 8 mm. \nTable 1. overview of the data folders \nfolder name | number of json files | tool diameter | tool breakage | tool type\nrun 1  2 10 mm No HSS\nrun 2 2 10 mm No HSS\nrun 3 2 10 mm No HSS\nrun 4 2 10 mm Yes HSS\nrun 5 2 8 mm Yes HSS\nrun 6 3 8 mm Yes HSS\nrun 7  1 8 mm Yes SC\nEach json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2.\nTable 2. recorded signals during milling\nSignal index in payload | Signal name | Signal Address |Type\n13-18 VelocityFeedForward VEL_FFW|1* double\n19-24 Power POWER|1* string\n25-30 CountourDeviation CONT_DEV|1* double\n38-43 TorqueFeedForward TORQUE_FFW|1* double\n44-49 Encoder1Position ENC1_POS|1* double\n56-61 Load LOAD|1* double\n68-73 Torque TORQUE|1* double\n68-91 Current CURRENT|1* double\n* 1 represents x-axis, 2 represents y-axis, 3 represents z-axis and 6 represents spindle-axis.\nNote that our milling center has 3 axis and therefore values for axes 4 and 5 are null.";
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                "Ice crystal formation in mixed-phase clouds is initiated by specific aerosol particles, termed ice-nucleating particles (INPs). Only a tiny fraction of all aerosol particles are INPs, providing a challenge for contemporary INP measurement techniques. Models have shown that the presence of INPs in clouds can impact their radiative properties and induce precipitation formation. However, for a qualified implementation of INPs in models, measurement techniques able to accurately detect the temperature-dependent INP concentration are needed. Here we present measurements of INP concentrations in ambient air under conditions relevant to mixed-phase clouds from a total of ten INP methods over two weeks in October 2018 at the Puy de Dôme observatory in central France. A special focus in this intercomparison campaign was placed on having overlapping sampling periods. Although a variety of different measurement principles were used, the majority of the data show INP concentrations within a factor of 5 of one another, demonstrating the suitability of the instruments to derive model-relevant INP data.\nLower values of comparability are likely due to instrument-specific features such as aerosol lamina spreading in continuous-flow diffusion chambers, demonstrating the need to account for such phenomena when interpreting INP concentration data from online instruments. Moreover, consistently higher INP concentrations were observed from aerosol filters collected on the rooftop at the Puy de Dôme station without the use of an aerosol inlet." , "Ice-nucleating particle concentration measurements from two continuous flow diffusion chambers (CFDC; Colorado State University-Continuous Flow Diffusion Chamber, CSU-CFDC; Spectrometer for Ice Nuclei, SPIN) and an expansion chamber (Portable Ice Nucleation Experiment, PINE) and seven offline freezing techniques using water suspensions of filter-collected particles sampled (FRankfurt Ice Nuclei Deposition FreezinG Experiment, FRIDGE; Ice Nucleation Droplet Array, INDA; Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology, INSEKT; Ice Spectrometer, IS; Leipzig Ice Nucleation Array, LINA; LED based Ice Nucleation Detection Apparatus LINDA; Micro-Orifice Uniform Deposit Impactor–Droplet Freezing Technique, UNAM-MOUDI-DFT).";
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                          "384000 bytes";
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        <http://schema.org/identifier>  "https://doi.org/10.35097/1876";
        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "Ice-nucleating particles" , "Intercomparison campaign" , "Puy de Dôme" , "Geological Science";
        <http://schema.org/name>        "The Puy de Dôme ICe Nucleation Intercomparison Campaign (PICNIC): Comparison between online and offline methods in ambient air";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/NpymwswSktofdqID" .
<https://radar.kit.edu/id/QfOFGsYhIeVfuLgN>
        a                               <http://schema.org/Dataset>;
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                [ a                           <http://schema.org/Person>;
                  <http://schema.org/name>    "Höpfner, Michael";
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                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Glatthor, Norbert"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Atmosphärische Spurenstoffe und Fernerkundung"
                                                  ];
                                          <http://schema.org/familyName>  "Glatthor";
                                          <http://schema.org/givenName>   "Norbert";
                                          <http://schema.org/name>        "Glatthor, Norbert"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Atmosphärische Spurenstoffe und Fernerkundung"
                                                  ];
                                          <http://schema.org/familyName>  "Höpfner";
                                          <http://schema.org/givenName>   "Michael";
                                          <http://schema.org/name>        "Höpfner, Michael";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-4174-9531"
                                        ];
        <http://schema.org/datePublished>
                "2023-12-05";
        <http://schema.org/description>
                ":title = \"OCS 60deg longitude 10deg latitude mean profiles from MIPAS single retrievals\";\n  :institution = \"Karlsruhe Institute of Technology - Institute for Meteorology and Climate Research\";\n  :organization = \"KIT/IMK-ASF\";\n  :global_target_name = \"OCS\";\n  :source = \"Satellite MIPAS on Envisat\";\n  :type_of_data = \"MEASUREMENTS\";\n  :experiment = \"MIPAS\";\n  :platform = \"SATELLITE\";\n  :name_of_platform = \"ENVISAT\";\n  :original ungridded retrievals = \"https://www.imk-asf.kit.edu/english/308.php\"\n  :data_version = \"V5H_OCS_20,V5R_OCS_221,V5R_OCS_222,V5R_OCS_120\";\n  :author = \"SAT-group at KIT/IMK-ASF\";\n  :institute = \"KIT/IMK-ASF\";\n  :address = \"P.O.B. 3640, 76021 Karlsruhe, Germany\";\n  :email = \"norbert.glatthor@kit.edu, michael.hoepfner@kit.edu\";\n  :distribution_statement = \"Please follow data policy instructions for the use of this data, to be found on the data distribution site.\";\n  :references = \"Glatthor et al., 2015 (https://doi.org/10.1002/2015GL066293), Glatthor et al., 2017 (https://doi.org/10.5194/acp-17-2631-2017) \";\n  :comment = \"Gridded from data which was retrieved from single scan retrievals\";\n  :file_creation_time = \"Wed Oct  9 18:31:05 2019\";\n  :file_modification_time = \" \";\n  :file_modification_description = \"negative values have been skipped for averaging\";";
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        <http://schema.org/name>        "Combined gridded carbonyl sulfide (OCS) dataset from MIPAS";
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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                                        ];
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                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0006-9824-2797"
                                        ];
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                                          <http://schema.org/name>        "Hita Garcia, Francisco";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-4709-3083"
                                        ];
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                                        ];
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                                          <http://schema.org/familyName>  "van de Kamp";
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                                          <http://schema.org/name>        "van de Kamp, Thomas";
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                                        ];
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                                        ];
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                                        ];
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                                          <http://schema.org/familyName>  "Lösel";
                                          <http://schema.org/givenName>   "Philipp D.";
                                          <http://schema.org/name>        "Lösel, Philipp D.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0506-1171"
                                        ];
        <http://schema.org/datePublished>
                "2025-05-16";
        <http://schema.org/description>
                "Processed micro-tomographic scan of the ant Tapinoma simrothi,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                        ];
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
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                ];
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                                        ];
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                                                  ];
                                          <http://schema.org/familyName>  "Röckel";
                                          <http://schema.org/givenName>   "Thomas";
                                          <http://schema.org/name>        "Röckel, Thomas";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0002-2467-2189"
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                                        ];
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                                        ];
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                                          <http://schema.org/name>        "Lösel, Philipp D.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0506-1171"
                                        ];
        <http://schema.org/datePublished>
                "2025-05-16";
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                "In the frame of the PRESLHY project more than 100 experiments on hydrogen – air flame propagation regimes in a shock tube at cryogenic temperatures were made with the combustion tube at the KIT HYKA test site. More than half of the experiments were made at cryogenic temperatures (between approx. 90 K and 130 K). The rest experiments were conducted at ambient conditions as the reference data. It was however possible to provide experimental data at various H2 concentrations from 8 to 60 Vol. %H2 and 3 blockage ratios (BR = 0, 0.3 and 0.6). During the course of the experiments, many experimental difficulties and peculiarities specific for cryogenic temperatures were encountered. Critical conditions for flame acceleration to the speed of sound and to the detonation onset at cryo-temperatures were experimentally found. It was also found that the danger of hydrogen combustion and explosion in terms of maximum combustion pressure is much higher than at ambient temperatures because of several times higher density of the combusted substance. Then, even for sonic deflagration at cryogenic temperatures, the maximum pressure might be higher than the detonation pressure at ambient conditions. The data obtained in current work can be used for safety distance assessment for LH2 safety applications." , "7zip-files:\nThe naming convention of the corresponding 7zip-files largely follows the one provided in the Data Management Plan at ambient temperature 293K reads as follows:\nPT0-25.7z\nwith PT indicating the PRESHY tube, the index 0 means a smooth tube with blockage ratio BR = 0%, then the test number 25 corresponds to certain hydrogen concentration of 15%H2.\nFor cryogenic temperatures, it reads as follows: \nPT30C-25.7z\nwith the meaning the PRESLHY tube experiment number 25 in an obstructed tube with blockage ratio BR = 30% at cryogenic temperature (prefix C). The experimental conditions should be addressed to the test number 25 with hydrogen concentration 15.2%H2 \nEach data file consist of original recording “.TPC”-file converted to a CSV format file with extension “.ASC”.\nThe structure of these files is simple. They are columnar comma separated CSV tables. The first line contains the time and names of the channel: \nTime, TRG, P01, P02, P03, P04, P05, P06, P07, P08, P09, P10, P11, P12, P13, F01, F02, F03, F04, F05, F07, F08, F09, F10, F11, F12, F13, F14, F15\n“TRG” is the trigger signal; “P” is the pressure sensor; “F” is the light sensor (InGaAs). \nThe second line contains the unit for each sensor:\ns, V, bar, bar, bar, bar, bar, bar, bar, bar, bar, bar, bar, bar, bar, V, V, V, V, V, V, V, V, V, V, V, V, V, V\nThe third line shows the start time of each channel. Because of the simultaneous data acquisition, the start times are identical for all channels and can be omitted. \nThe “Time” column gives the time relative to the trigger event. The trigger event is recorded in the “TRG” column. In fact, it should be synchronized with a spark plug but actually, it can be a delay between the trigger signal and spark actuation. Even, it might be no ignition phenomena. \nNext lines give the signal value for each sensor versus time (in first column).\nWMF:\nThe original results are also presented in graphic form as so called distance  time diagrams. The diagram for each test reads as follows:\nPT0-24-long-RT.wmf\nWith the same meaning as for the raw data file with an additional index long or short according to captured registration time and prefix RT means distance (radius)  time diagram. WMF means the Windows Meta File format. This number corresponds to the test PT0-24 for smooth tube with blockage BR = 0% at ambient temperature wth hydrogen concentration 15.2%. The distance-time diagram shows pressure - and photodiode signals - time histories (horizontal axis, t in seconds) placed vertically in accordance with the real position in a tube (vertical axis, X in m). The pink line goes through the points corresponding to the flame arrival time based on photodiode signals (blue lines). The slope dX/dt of the flame trajectory in X-t coordinates is the flame velocity Uf.";
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                                          <http://schema.org/name>        "Jordan, T."
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                "The presented video files demonstrate the behavior of combustion, its speed and acceleration, under various initial conditions: temperature, pressure, block ratio. The corresponding data (temperature, pressure, concentrations)  in distribution and ignition modes are also presented." , "In the frame of the PRESLHY project W.P. 5.3. a series of experiments was carried out. The phenomena of cryogenic hydrogen combustion in a layer geometry relevant to flame spread over the spill of LH2 and flame acceleration and deflagration-detonation-transition for cryogenic hydrogen-air clouds in an enclosure were studied. The objective of work was completing the experimental database on cryogenic LH2 combustion, including laminar steady state and turbulent combustion and detonation of LH2 and gaseous hydrogen in air at cryogenic temperatures.";
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                "PSC volume density profiles retrieved from single MIPAS/Envisat limb scans. Two profiles are provided, 0: retrieved with assumption of 3um radius particles, 1: retrieved with assumption of 0.2um radius particles. These profiles are min/max limits for the volume densities with res1 < 5-10 um3 cm-3. For STS, profiles res1 are best suited. Profiles with res1 > 5-10 um3 cm-3 contain ice and in this case the real volume densities may be even larger.CAUTION: at 26 km and 28 km, retrieval instabilities may be present in case of thick ice PSCs below. For more information, see article:\nThe MIPAS/Envisat climatology (2002–2012) of polar stratospheric cloud (PSC) volume density profiles, M. Hoepfner, T. Deshler, M. Pitts, L. Poole, R. Spang, G. Stiller, T. v. Clarmann, Atmos. Meas. Tech., 2018, https://doi.org/10.5194/amt-2018-163" , "A global data set of vertical profiles of polar stratospheric cloud (PSC) volume density has been derived from Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) space-borne infrared limb measurements between 2002 and 2012. To develop a well characterized and efficient retrieval scheme, systematic tests based on limb-radiance simulations for PSCs from in-situ balloon observations have been performed . The finally selected wavenumber range was 831–832.5 cm-1.\nOptical constants of nitric acid trihydrate (NAT) have been used to derive maximum and minimum profiles of volume density which are compatible with MIPAS observations under the assumption of small, non-scattering and larger, scattering PSC particles. These max/min profiles deviate from their mean value at each altitude by about 40-45%, which is attributed as the maximum systematic error of the retrieval. Further, the retrieved volume density profiles are characterized by a random error due to instrumental noise of 0.02–0.05 μm3cm-3, a detection limit of about 0.1–0.2 μm3cm-3 and a vertical resolution of around 3 km. Comparisons with co-incident observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite showed good agreement regarding the vertical profile shape. Quantitatively, in the case of supercooled ternary solution (STS) PSCs, the CALIOP dataset fits to the MIPAS retrievals obtained under the assumptions of small particles. Unlike for STS and NAT, in the case of ice PSCs the MIPAS retrievals are limited by the clouds becoming optically thick in the limb-direction. In these cases, the MIPAS volume densities represent lower limits. Among other interesting features, this climatology helps to study quantitatively the on-set of PSC formation very near to the South Pole and the large variability of the PSC volume densities between different Arctic stratospheric winters.";
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                                          <http://schema.org/name>        "Azuma, Fumika";
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                "Processed micro-tomographic scan of the ant Orectognathus versicolor,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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        <http://schema.org/name>        "2 Inverse vulcanization of norbornenylsilanes";
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                "# Experimental Data for the Paper \"Using Constraints to Discover Sparse and Alternative Subgroup Descriptions\" (Version 2)\nThese are the experimental data for the second version (v2) of the paper> Bach, Jakob. \"Using Constraints to Discover Sparse and Alternative Subgroup Descriptions\"\npublished on [arXiv](https://arxiv.org/) in 2025.\nIf we create further versions of this paper in the future, these experimental data may cover them as well.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery) for the code and instructions to reproduce the experiments.\nWe obtained the experimental results on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 160 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8` for the main experiments and `3.9` for the competitor-runtime experiments.\nWith this configuration, running the main experimental pipeline (`main_experiments/run_experiments.py`) took about 34 hours.\nNote that the experimental data originate from multiple pipeline runs, as we have two experimental pipelines, and we reran one of them to include additional subgroup-discovery methods:\n- The commit hash for the last run of the competitor-runtime experimental pipeline (`competitor_runtime_experiments/run_competitor_runtime_experiments.py`) is [1a026326b3](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/1a026326b34246a9fcf4df658a99a898b5fd38e1) (tag: `competitor-runtime-2025-01-12`).\n- The commit hash for the last run of the main experimental pipeline (`main_experiments/run_experiments.py`) except the subgroup-discovery methods *BSD* and *SD-Map* is [0a57bcd529](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/0a57bcd52938dce6285e8113d777360c2b17f30f) (tag: `run-2024-05-13`).\n  These data are identical to the [experimental data for v1 of the paper](https://doi.org/10.35097/caKKJCtoKqgxyvqG).\n- The commit hash for the last run of the main experimental pipeline (`main_experiments/run_experiments.py`) for the (new) subgroup-discovery methods *BSD* and *SD-Map* is [50dd82e0fc](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/50dd82e0fcd175a4068ef4ce838dcacdc1045fc9) (tag: `run-2025-01-21-arXiv-v2`).\nThe main experimental pipeline did not change between the last two mentioned commits (except for refactorings and to include two new subgroup-discovery methods),\nand the competitor-runtime experimental pipeline changed neither.\nThus, using the tag `run-2025-01-21-arXiv-v2` to reproduce all experiments should yield the same results (except runtimes and timeout-affected results).\nThe commit hash for the last run of the two evaluation pipelines (`main_experiments/run_evaluation_arxiv.py` and `competitor_runtime_experiments/run_competitor_runtime_evaluation_arxiv.py`) is [bc3aafc904](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/bc3aafc904cc27214d458f93782104484ffc372d) (tag: `evaluation-2025-02-16-arXiv-v2`).\nThe experimental data are stored in five folders, i.e., `competitor-runtime-datasets/`, `competitor-runtime-results/`, `datasets/`, `plots/`, and `results/`.\nFurther, the console output of `main_experiments/run_evaluation_arxiv.py` is stored in `Evaluation_console_output_main.txt`,\nand the console output of `competitor_runtime_experiments/run_competitor_runtime_evaluation_arxiv.py` is stored in `Evaluation_console_output_competitor_runtimes.txt`.\nWe manually copied both evaluation outputs from the console to a file.\nIn the following, we describe the structure and content of each data file.\n## `competitor-runtime-datasets/`\nThese are the input data for the competitor-runtime experimental pipeline `competitor_runtime_experiments/run_competitor_runtime_experiments.py`.\nThey were obtained with the script `competitor_runtime_experiments/prepare_competitor_runtime_datasets.py`.\nThe folder structure of `competitor-runtime-datasets/` is similar to that of the dataset folder of the main experiments (`datasets/`), so please consult the corresponding section of this document for more details on the contained file types.\nThe main difference is that only five of the 27 PMLB datasets are included, plus the `iris` dataset provided by `scikit-learn`.\n## `competitor-runtime-results/`\nThese are the output data of the competitor-runtime experimental pipeline in the form of CSVs, produced by the script `competitor_runtime_experiments/run_competitor_runtime_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `competitor_runtime_experiments/run_competitor_runtime_evaluation_arxiv.py`.\nThe remaining 325 files are subsets of the results.\nThe competitor-runtime experimental pipeline parallelizes over 6 datasets, 5 cross-validation folds, and 17 subgroup-discovery methods.\nThus, a full cross-product would yield `6 * 5 * 17 = 510` files containing subsets of the results, but some subgroup-discovery methods timed out on some datasets,\nso the corresponding result files are missing.\nEach row in a result file corresponds to one subgroup.\nOne can identify individual subgroup-discovery runs with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- subgroup-discovery method `sd_name`\n- feature-cardinality threshold `param.k`\nThe remaining column, `fitting_time`, represents the evaluation metric.\nIn detail, all result files for the competitor-runtime experiments contain the following columns (whose names are consistent with the main experiments):\n- `fitting_time` (non-negative float): The runtime (in seconds) of the subgroup-discovery method.\n- `dataset_name` (string, 6 different values): The name of the dataset used for subgroup discovery.\n- `split_idx` (int in `[0, 4]`): The index of the cross-validation fold of the dataset used for subgroup discovery.\n- `sd_name` (string, 17 different values): The name of the subgroup-discovery method, consisting of the package name and the algorithm name (e.g., `sd4py.Beam`).\n- `param.k` (int in `[1, 5]`): The feature-cardinality threshold for subgroup descriptions.\n## `datasets/`\nThese are the input data for the main experimental pipeline `main_experiments/run_experiments.py`, i.e., the prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 27 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `main_experiments/prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth file types are CSVs that only contain numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column either corresponds to a feature (in `_X`) or the target (in `_y`).\nThe first line in each `_X` file contains the names of the features as strings; for `_y` files, there is only one column, always named `target`.\nFor the prediction target, we ensured that the minority (i.e., less frequent) class is the positive class (i.e., has the class label `1`), so the labeling may differ from PMLB.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `plots/`\nThese are the output files of the main evaluation pipeline `main_experiments/run_evaluation_arxiv.py`.\nWe include these plots in our paper.\n## `results/`\nThese are the output data of the main experimental pipeline in the form of CSVs, produced by the script `main_experiments/run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `main_experiments/run_evaluation_arxiv.py`.\nThe remaining files are subsets of the results, as the main experimental pipeline parallelizes over 27 datasets, 5 cross-validation folds, and 8 subgroup-discovery methods.\nThus, there are `27 * 5 * 8 = 1080` files containing subsets of the results.\nEach row in a result file corresponds to one subgroup.\nOne can identify individual subgroup-discovery runs with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- subgroup-discovery method `sd_name`\n- feature-cardinality threshold `param.k` (missing value if no feature-cardinality constraint employed)\n- solver timeout `param.timeout` (missing value if not solver-based search)\n- number of alternatives `param.a` (missing value if only original subgroup searched)\n- dissimilarity threshold `param.tau_abs` (missing value if only original subgroup searched)\nFor each value combination of these seven columns, there is either one subgroup (search for original subgroups)\nor six subgroups (search for alternative subgroup descriptions, in which case the column `alt.number` identifies individual subgroups within a search run).\nFurther, note that the last four mentioned columns contain missing values, which should be treated as a category on their own.\nIn particular, if you use `groupby()` from `pandas` for analyzing the results and you want to include any of the last four mentioned columns in the grouping,\nyou should either fill in the missing values with an (arbitrary) placeholder value or use the parameter `dropna=False`,\nbecause the grouping (by default) ignores the rows with missing values in the group columns otherwise.\nThe remaining columns represent results and evaluation metrics.\nIn detail, all result files contain the following columns:\n- `objective_value` (float `>= -0.25` + missing values): Objective value of the subgroup-discovery method on the training set.\n  Usually quantifies WRAcc (in `[-0.25, 0.25]`) when searching original subgroups and normalized Hamming similarity (in `[0, 1]`) when searching alternative subgroup descriptions.\n  First exception: The subgroup-discovery method *MORS* has missing values since *MORS* does not explicitly compute an objective when searching for subgroups.\n  Second exception: The subgroup-discovery methods *BSD* and *SD-Map* use WRAcc times the number of data objects (a dataset-dependent constant) as objective,\n  so the objective value is higher than for the remaining subgroup-discovery methods.\n- `optimization_status` (string, 2 different values + missing values): For *SMT*, `sat` if optimal solution found and `unknown` if timeout.\n  Missing value for all other subgroup-discovery methods (which do not use solver timeouts).\n- `optimization_time` (non-negative float): The optimization runtime (in seconds) inside the subgroup-discovery method, i.e., without pre- and post-processing steps.\n- `fitting_time` (non-negative float): The complete runtime (in seconds) of the subgroup-discovery method (as reported in the paper), i.e., including pre- and post-processing steps.\n  Very similar to `optimization_time` except for *SMT* as the subgroup-discovery method, which may spend a considerable amount of time formulating the optimization problem.\n- `train_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the training set.\n- `test_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the test set.\n- `train_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the training set.\n- `test_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the test set.\n- `box_lbs` (list of floats, e.g., `[-inf, 0, -inf, -2, 8]`): The lower bounds for each feature in the subgroup description.\n  Negative infinity if a feature's lower bound did not exclude any data objects from the subgroup.\n- `box_ubs` (list of floats, e.g., `[inf, 10, inf, 5, 9]`): The upper bounds for each feature in the subgroup description.\n  Positive infinity if a feature's upper bound did not exclude any data objects from the subgroup.\n- `selected_feature_idxs` (list of non-negative ints, e.g., `[0, 4, 5]`): The indices (starting from 0) of the features selected (= restricted) in the subgroup description.\n  Is an empty list, i.e., `[]`, if no feature was restricted (so the subgroup contains all data objects).\n- `dataset_name` (string, 27 different values): The name of the `PMLB` dataset used for subgroup discovery.\n- `split_idx` (int in `[0, 4]`): The index of the cross-validation fold of the dataset used for subgroup discovery.\n- `sd_name` (string, 8 different values): The name of the subgroup-discovery method (`Beam`, `BI`, `BSD`, `MORS`, `PRIM`, `Random`, `SD-Map`, or `SMT`).\n- `param.k` (int in `[1, 5]` + missing values): The feature-cardinality threshold for subgroup descriptions.\n  Missing value if unconstrained subgroup discovery.\n  Always `3` if alternative subgroup descriptions searched.\n- `param.timeout` (int in `[1, 2048]` + missing values): For *SMT*, solver timeout (in seconds) for optimization (not including time for formulating the optimization problem).\n  Missing value for all other subgroup-discovery methods.\n- `alt.hamming` (float in `[0, 1]` + missing values): Normalized Hamming similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.jaccard` (float in `[0, 1]` + missing values): Jaccard similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.number` (int in `[0, 5]` + missing values): The number of the current alternative if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n  Thus, original subgroups either have `0` or a missing value in this column (i.e., for experimental settings where alternative subgroup descriptions searched, there is no separate search for an original subgroup, only a joint sequential search for original and alternatives).\n- `param.a` (int with value `5` + missing values): The number of desired alternative subgroup descriptions, not counting the original (zeroth) subgroup description.\n  Missing value if only original subgroup searched.\n- `param.tau_abs` (int in `[1, 3]` + missing values) The dissimilarity threshold for alternatives, corresponding to the absolute number of features that have to be deselected from the original subgroup description and each prior alternative.\n  Missing value if only original subgroup searched.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the columns `box_lbs`, `box_ubs`, and `selected_feature_idxs`\n(which represent lists and whose values are quoted except for empty lists).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that the lists of feature indices are treated as lists (rather than plain strings):\n```python\nimport ast\nresults['selected_feature_idxs'] = results['selected_feature_idxs'].apply(ast.literal_eval)\n```\nNote that this conversion does not work for `box_lbs` and `box_ubs`, where the lists not only contain ordinary numbers but also `-inf`, and `inf`;\nsee [this *Stack Overflow* post](https://stackoverflow.com/questions/64773836/error-converting-string-list-to-list-when-it-contains-inf) for potential alternatives." , "These are the experimental data for the second version (v2) of the paper> Bach, Jakob. \"Using Constraints to Discover Sparse and Alternative Subgroup Descriptions\"\nThis version of the paper was published on [arXiv](https://arxiv.org/) in 2025.\nYou can find the paper [here](https://doi.org/10.48550/arXiv.2406.01411) and the code [here](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text.";
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                "### Supplementary Research Data for the dissertation\n### \"Comprehensive insights into the impedimetric characterization of dielectric thin films\"\n### by Benedikt Sapotta\nImportant: The impedance spectra are disclosed as .DTA files which can be accessed with e.g. the text editor\n### Content\n#### 1 Estimation of IDE cell constant\n- python script to calculate an IDE cell constant based on https://doi.org/10.1016/0925-4005(95)85053-8\n#### 2 Estimation of effective permittivity of coated IDE\n- python script to calculate the effective permittivity of a coated IDE structure base on https://doi.org/10.1016/j.sna.2011.09.033 and https://doi.org/10.1016/j.sna.2004.01.040\n- functionality of the python script is demonstrated by reproducing figure 7 and 8 from https://doi.org/10.1016/j.sna.2011.09.033\n#### 3 Uncoated IDE data\n- light and scanning electron micrographs of the uncoated IDE structures\n- impedance spectra discussed in chapter 5.2 together with the data fit results using ECM 1\n- impedance spectra and evaluation procedure discussed in chapter 5.3\n - python scripts to compute the reference conductivities \"Expected Conductivities.py\" and reference permittivities \"Expected Permittivities.py\"\n - data fit results to the aqueous KCl solutions copied into an excel file\n - python script to compute the capacitance C\\_Par+C\\_Geo from the DRM impedance spectra \"Capacitance\\_Calculator.py\"\n - python script to compute the relative permittivities of the DRMs from C\\_Par+C\\_Geo \"Permittivity\\_Calculator.py\"\n - python script to compute the electrolytic condudctivity of the CRMs from the R\\_E best fit estimates \"Conductivity\\_Calculator.py\"\n#### 4 HKUST\\-1 coated IDE data\n- data and evaluation procedures for the coated 5 µm electrode chip discussed in chapter 6.1 \n - impedance spectra collected with the\n  - DRMs: 1-butanol, ethanol and air to obtain C\\_Par and K\\_Cell of the electrode chip\n  - linker precursor solution as a function of the growth cycles (1-205)\n  - metal precursor solution as a function of the growth cycles (1-205)\n - 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spectra were recorded after the 300, 600 and 900 seconds after the initial exposure to the linker solution, only the 900 s data was evaluated\n   - single frequency impedance data was recorded during the initial 300 seconds of the linker solution exposure step at 100 kHz\n  - metal precursor solution as a function of the growth cycle (1-200)\n   - spectra were recorded after 200, 400 and 600 seconds after initial exposure to the metal solution, only the 600 s data was evaluated\n   - single frequency impedance data was recorded during the initial 200 seconds of the metal solution exposure step at 100 kHz\n  - testing solutions as a function of the growth cycle\n   - spectra were recorded after 180, 240 and 300 seconds after initial exposure to the respective testing solution, only the 300 s data was evaluated\n   - single frequency impedance data was recorded during the initial 180 seconds of each testing solution exposure step at 1 MHz\n - ECM 2 fit results to the metal and linker data copied into the excel file \"Growth\\_Data\\_BatchFit\\_Results.xlsx\"\n - ECM 1 fit results to the testing solution spectra recorded before the HKUST-1 coating process copied into the excel file \"Cycle\\_0\\_DataFits.xlsx\"\n - ECM 2 fit results to the testing solution spectra recorded with the HKUST-1 coated electrode structure copied into the excel files \"Cycle\\_50DataFits.xlsx\",  \"Cycle\\_100\\_Data\\_Fits.xlsx\", \"Cycle 150 Data Fits.xlsx\" and \"Cycle\\_200DataFits.xlsx\",\n - python script for the monte carlo modelling to obtain the coating permittivities and conductivities during the exposure with the testing solutions \"MC\\_modelling\\_testing\\_data.py\" which is an extension of \"MC\\_modelling\\_growth\\_data.py\"\n - a simplified data flow chart as .png to facilitate the understanding of \"MC\\_modelling\\_testing\\_data.py\"\n - python scripts to compute the testing solution permittivities \"Medium\\_RelativePermittvity\\_Calculator.py\" and conductivities \"Medium\\_Conductivity\\_Calculator.py\"\n - an assortment of scanning electron micrographs depicting the coated IDE structures\n#### 5 ZIF\\-8 coated IDE data\n- impedance spectra collected with the\n - 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                "This dataset contains process data from around six hours of milling operations performed on a three-axis horizontal milling machine (DMC 60 H, Deckel Maho). To reflect the diversity and variability of real-world milling scenarios, components, tools and machining strategies relevant to the industry were used. A total of 33 milling experiments are included, covering three component types and eight anomaly types. To support full reproducibility and benchmarking applications, the dataset includes NC programs, CAD models and detailed documentation. During processing, signals were acquired from the Siemens SINUMERIK 840D CNC system via the SINUMERIK Edge app 'Analyse MyWorkpiece/Capture', which enables the export of high-frequency data at 500 Hz in JSON format (.json). Additionally, force and acceleration data recorded by external sensors is provided in MATLAB format (.mat). Each experiment also includes a combined MATLAB file in which all recorded signals are synchronized." , "This dataset contains process data from around six hours of milling operations performed on a three-axis horizontal milling machine (DMC 60 H, Deckel Maho). The data was collected in a laboratory setting using industry relevant components, tools and machining strategies in order to reflect the diversity and variability of practical milling scenarios. Controller-side process signals were acquired from the Siemens SINUMERIK 840D CNC system via the SINUMERIK Edge app \"Analyze MyWorkpiece/Capture\", which enables high-frequency data export at 500 Hz. Additional force measurements were recorded using a force measurement platform (Kistler Type 9255C), and accelerations were captured using a sensor mounted on the main spindle (PCB Type 356A33). The force platform signals were amplified using a charge amplifier (Kistler Type 5015A1000 K) before being acquired at a sampling rate of 10 kHz useing a data acquisition card (Data Translation DT9836; referred to as DAC). The acceleration sensor was connected to the same DAC via a Kistler coupler (type 5122).\nRaw data from the force and acceleration sensors is provided in the form of MATLAB timetable files (.mat). The process signals from the Siemens SINUMERIK Edge are stored in JSON format (.json). Preprocessed Edge data is also available as structured CSV files:\n- hfdata.csv: high-frequency controller signals (e.g., position, current, torque) for the X, Y and Z axes as well as the main spindle\n- hfblockevent.csv: executed NC code extracted from the machine log\n- header.csv: recording metadata\nIn addition, each experiment includes a consolidated MATLAB file (.mat), in which all available Edge and DAC signals are synchronized and stored as a single timetable object. To achieve this, the 500 Hz Edge signals were interpolated using the PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) method to match the 10 kHz resolution of the sensor data. This unified file allows signals to be compared directly across all sources and simplifies further analysis. A MATLAB figure (Sync_Plot.fig) is also included to visualize and confirm the successful synchronization between the sensor and Edge signals.\nA total of, 33 milling experiments are included, covering three component types and eight anomaly types. NC programs, 3D CAD models (.stp), and detailed documentation are provided to support full reproducibility and benchmarking applications.\n--------------------------------------------------------------------------------------------------------------------------\nDocuments:\n- Dataset: All data files organized by component type and trial ID\n- Descriptive:\n   - Design of Experiment (process parameters and configuration details for each experimental trial)\n   - Cutting Tool information\n   - Part description: NC programs executed during the experiments (G-code) and 3D CAD models (.stp), organised by component type\nExperimental Setup:\n- Machine: DMC 60 H (retrofitted 3-axis horizontal milling center)\n- Materials: S235JR (unalloyed structural steel), Al2007 T4 (aluminum alloy), POM-C (polyoxymethylene copolymer)\n- Cutting Tools:\n   - HSS-Co8 end mill (TiAlN coating, Ø 20 mm, k10 tolerance)\n   - Solid carbide end mill (TiSi coating, Ø 10 mm, f8 tolerance)\n   - Solid carbide end mill (uncoated, Ø 5 mm, e8 tolerance)\n- Workpiece blank dimensions: 150x75x50mm" , "Der vorliegende Datensatz enthält etwa sechs Stunden Prozessdaten, die bei Fräsbearbeitungen auf einer DMC 60 H von Deckel Maho aufgezeichnet wurden. Um die Vielfalt und Variabilität realer Fräsbearbeitungen widerzuspiegeln, wurden branchenrelevante Bauteile, Werkzeuge und Bearbeitungsstrategien verwendet. Insgesamt sind 33 Fräsversuche enthalten, die drei Bauteiltypen und acht Anomalietypen abdecken. Der Datensatz umfasst NC-Programme, CAD-Modelle und eine detaillierte Dokumentation, um eine vollständige Reproduzierbarkeit und eine Anwendung als Benchmark zu ermöglichen. Während der Bearbeitung wurden die Signale der SINUMERIK 840D Steuerung über die SINUMERIK Edge App „Analyse MyWorkpiece/Capture” erfasst. Diese ermöglicht einen Datenexport mit 500 Hz im JSON-Format (.json). Zusätzlich werden die von externen Sensoren aufgezeichneten Kraft- und Beschleunigungsdaten im MATLAB-Dateiformat (.mat) bereitgestellt. Jeder Versuch umfasst zudem eine MATLAB-Datei, in der alle aufgezeichneten Signale synchron abgespeichert sind.";
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                "A commercial 20 Ah pouch cell (SPB58253172P2, Enertech International, Inc.), which consists of a NMC111-LMO cathode blend and a graphite anode[1] is characterized thermally. The cell opening is performed at a State of Charge (SoC) of 0 % in an inert environment in a glove box. After disassembly, the anode and cathode sheets were triple washed with DMC and left to dry. For the density and heat capacity measurements, the coatings of both anode and cathode sheets were scraped off. For the thermal diffusivity measurements 18 mm double coated coins were punched from the sheets. All probes were exposed to air only very shortly before inserting them into the measurement devices. \nThe experimental analysis is performed according to Oehler et al.[2]. The density measurement is done with a Micro Ultrapyc 1200e Gas (Quantachrome Instruments) pycnometer in nitrogen gas. The heat capacity is measured in a Q2000 (TA Instruments) differential scanning calorimeter in a temperature range from -40 °C to 60 °C. The thermal diffusivity measurement of the double coated coins is performed in helium atmosphere in a LFA 467 Hyperflash (NETZSCH-Gerätebau GmbH) in a temperature range from -20 °C to 60 °C. To calculate the thermal conductivity of the coatings via a serial connection[3], additional knowledge of the layer thicknesses, porosities of the coatings and the thermal material properties of the current collectors are needed. The thicknesses of the coatings and current collectors are measured with a Micromar 40 EWR (Mahr GmbH). A Balance XPE206DR precision scale (Mettler Toledo) was used to measure the mass of the coating probes. The porosity of the coating was then determined gravimetrically. All measurements were conducted at least three times. Error propagation of the standard deviation was performed for the calculated values. The measurement of the density and heat capacities of the current collectors as well as pouch foil and separator were performed as previously described. The current collector materials are assumed to be copper and aluminum and the thermal conductivities at 0 °C are taken from literature[4]. \n[1] L. Cloos, J. Langer, M. Schiffler, A. Weber, Th. Wetzel, J. Electrochem. Soc. 2024, 171, 040538.\n[2] D. Oehler, P. Seegert, T. Wetzel, Energy Technol. 2021, 9, 2000574.\n[3] A. Loges, S. Herberger, D. Werner, T. Wetzel, Journal of Power Sources 2016, 325, 104–115.\n[4] P. Stephan, S. Kabelac, M. Kind, D. Mewes, K. Schaber, T. Wetzel, Eds. , VDI-Wärmeatlas: Fachlicher Träger VDI-Gesellschaft Verfahrenstechnik und Chemieingenieurwesen, Springer Berlin Heidelberg, Berlin, Heidelberg, 2019." , "Temperature strongly influences the electrical and aging behavior of lithium-ion battery cells. Therefore, modeling of the thermal behavior of these cells is needed to depict the behavior adequately. Thermal models require the knowledge of the thermal material properties of the cell. Here, we present the thermal material properties of the components of a commercial NMC111-LMO / graphite lithium-ion battery cell. A gas pycnometer is used to measure the density and a differential scanning calorimeter to measure the heat capacity of the coatings. With knowledge of the properties of the double sided coated current collector and its thermal diffusivity measured with a laser flash apparatus, the thermal conductivity of the coatings can be calculated. The standard deviation with error propagation amounts to maximum of ≈ 8.5 %.";
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                                          <http://schema.org/givenName>   "Chandan";
                                          <http://schema.org/name>        "Sarkar, Chandan";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0001-4374-0231"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "van de Kamp";
                                          <http://schema.org/givenName>   "Thomas";
                                          <http://schema.org/name>        "van de Kamp, Thomas";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7390-1318"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Lösel";
                                          <http://schema.org/givenName>   "Philipp D.";
                                          <http://schema.org/name>        "Lösel, Philipp D.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0506-1171"
                                        ];
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                "2025-05-07";
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                "Processed micro-tomographic scan of the ant Camponotus japonicus,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/name>    "DFG";
                                          <http://schema.org/sameAs>  "https://ror.org/018mejw64"
                                        ];
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                                          <http://schema.org/name>    "BMBF";
                                          <http://schema.org/sameAs>  "https://ror.org/04pz7b180"
                                        ];
        <http://schema.org/identifier>  "https://doi.org/10.35097/drbrr30c96ys8g0h";
        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "anatomy" , "tomography" , "synchrotron" , "formicidae" , "biodiversity" , "camponotus japonicus" , "insect" , "digitization" , "antscan" , "biology" , "ant" , "micro-CT" , "Biology";
        <http://schema.org/name>        "OKENT0105311-Camponotus.japonicus";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/drbrr30c96ys8g0h" .
<https://radar.kit.edu/id/hej4msrycaju14g7>
        a                               <http://schema.org/Dataset>;
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                ];
        <http://schema.org/copyrightHolder>
                [ a                           <http://schema.org/Organization>;
                  <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                  <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Zuber";
                                          <http://schema.org/givenName>   "Marcus";
                                          <http://schema.org/name>        "Zuber, Marcus";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-2429-6171"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Faragó";
                                          <http://schema.org/givenName>   "Tomáš";
                                          <http://schema.org/name>        "Faragó, Tomáš";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0006-9824-2797"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Azuma";
                                          <http://schema.org/givenName>   "Fumika";
                                          <http://schema.org/name>        "Azuma, Fumika";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-5275-8775"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Katzke";
                                          <http://schema.org/givenName>   "Julian";
                                          <http://schema.org/name>        "Katzke, Julian";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-1447-9660"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Hita Garcia";
                                          <http://schema.org/givenName>   "Francisco";
                                          <http://schema.org/name>        "Hita Garcia, Francisco";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-4709-3083"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "van de Kamp";
                                          <http://schema.org/givenName>   "Thomas";
                                          <http://schema.org/name>        "van de Kamp, Thomas";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7390-1318"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Lösel";
                                          <http://schema.org/givenName>   "Philipp D.";
                                          <http://schema.org/name>        "Lösel, Philipp D.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0506-1171"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Economo";
                                          <http://schema.org/givenName>   "Evan P.";
                                          <http://schema.org/name>        "Economo, Evan P.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7402-0432"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Sarkar";
                                          <http://schema.org/givenName>   "Chandan";
                                          <http://schema.org/name>        "Sarkar, Chandan";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0001-4374-0231"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Hamann";
                                          <http://schema.org/givenName>   "Elias";
                                          <http://schema.org/name>        "Hamann, Elias";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-0623-9069"
                                        ];
        <http://schema.org/datePublished>
                "2025-05-14";
        <http://schema.org/description>
                "Processed micro-tomographic scan of the ant Pseudolasius sp ,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
        <http://schema.org/distribution>
                [ a                               <http://schema.org/DataDownload>;
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                          "463668736 bytes";
                  <http://schema.org/contentUrl>  "https://www.radar-service.eu/radar-backend/archives/hej4msrycaju14g7/versions/1/content";
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                          "application/x-tar"
                ];
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                                          <http://schema.org/name>    "DFG";
                                          <http://schema.org/sameAs>  "https://ror.org/018mejw64"
                                        ];
        <http://schema.org/funder>      [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "BMBF";
                                          <http://schema.org/sameAs>  "https://ror.org/04pz7b180"
                                        ];
        <http://schema.org/identifier>  "https://doi.org/10.35097/hej4msrycaju14g7";
        <http://schema.org/isAccessibleForFree>
                true;
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        <http://schema.org/name>        "CASENT0877597-Pseudolasius.sp.";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                                        ];
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/hej4msrycaju14g7" .
<https://radar.kit.edu/id/WCGCNRlQUyWGNbjD>
        a                               <http://schema.org/Dataset>;
        <http://schema.org/alternateName>
                "Bad Dürkheimer Bewegungscheck";
        <http://schema.org/contributor>
                [ a                         <http://schema.org/Organization>;
                  <http://schema.org/name>  "Motor Research Data Repository, Karlsruhe Institute of Technology"
                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Fehr, Ulrich"
                ];
        <http://schema.org/creator>     [ a                         <http://schema.org/Organization>;
                                          <http://schema.org/name>  "Fehr, Ulrich"
                                        ];
        <http://schema.org/datePublished>
                "2024-06-10";
        <http://schema.org/description>
                "Aim\nDue to the increasing spread of the coronavirus and the accompanying containment measures, physical activity opportunities for children were severely restricted. The present study focuses on changes in children?s physical fitness during this period.\n\nMethods\nWe conducted an almost full census of 131 primary school children in the 2nd grade in Bad Dürkheim, assessing their physical fitness immediately before the school closures on 16 March 2020 and after the end of the summer holidays in Rhineland-Palatinate, Germany. The standardised German motor skills test with representative reference values was used. For evaluation, we used repeated measures analysis of variance.\n\nResults\nWe found a significant increase in physical fitness and BMI for the total sample during the observation period. Only in the dimensions of speed and endurance did the children show less development than would have been expected for their age. The proportion of overweight children increased from 17.7 to 23.8%.\n\nConclusion\nThe results show that the frequently formulated fear that the Corona-related restrictions in spring/summer 2020 could have negative effects on children?s physical development is not completely justified. However, in the view of the authors, the developments in BMI and endurance diagnosed in the short observation period should definitely be  understood as a warning signal in the case of further restricted physical activity offers due to school closures, suspended sport clubs." , "Longitudinal section (trend study)" , "Measurement/Test";
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                          "300544 bytes";
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                ];
        <http://schema.org/identifier>  "https://doi.org/10.35097/WCGCNRlQUyWGNbjD";
        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "overweight" , "SARS-CoV-2" , "BMI" , "fitness" , "Life Science" , "Sports" , "motor capability";
        <http://schema.org/name>        "Entwicklung der körperlichen Leistungsfähigkeit bei Kindern während der coronabedingten Einschränkungen im Frühjahr/Sommer 2020.";
        <http://schema.org/publisher>   [ a                         <http://schema.org/Thing>;
                                          <http://schema.org/name>  "Motor Research Data Repository, Karlsruher Institute of Technology"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/WCGCNRlQUyWGNbjD" .
<https://radar.kit.edu/id/6uy81h4dhhfqyyq5>
        a                               <http://schema.org/Dataset>;
        <http://schema.org/copyrightHolder>
                [ a                           <http://schema.org/Organization>;
                  <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                  <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                ];
        <http://schema.org/copyrightHolder>
                [ a                           <http://schema.org/Organization>;
                  <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                  <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "van de Kamp";
                                          <http://schema.org/givenName>   "Thomas";
                                          <http://schema.org/name>        "van de Kamp, Thomas";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7390-1318"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Hita Garcia";
                                          <http://schema.org/givenName>   "Francisco";
                                          <http://schema.org/name>        "Hita Garcia, Francisco";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-4709-3083"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Faragó";
                                          <http://schema.org/givenName>   "Tomáš";
                                          <http://schema.org/name>        "Faragó, Tomáš";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0006-9824-2797"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Sarkar";
                                          <http://schema.org/givenName>   "Chandan";
                                          <http://schema.org/name>        "Sarkar, Chandan";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0001-4374-0231"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Zuber";
                                          <http://schema.org/givenName>   "Marcus";
                                          <http://schema.org/name>        "Zuber, Marcus";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-2429-6171"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Hamann";
                                          <http://schema.org/givenName>   "Elias";
                                          <http://schema.org/name>        "Hamann, Elias";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-0623-9069"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Economo";
                                          <http://schema.org/givenName>   "Evan P.";
                                          <http://schema.org/name>        "Economo, Evan P.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7402-0432"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Katzke";
                                          <http://schema.org/givenName>   "Julian";
                                          <http://schema.org/name>        "Katzke, Julian";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-1447-9660"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Azuma";
                                          <http://schema.org/givenName>   "Fumika";
                                          <http://schema.org/name>        "Azuma, Fumika";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-5275-8775"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Lösel";
                                          <http://schema.org/givenName>   "Philipp D.";
                                          <http://schema.org/name>        "Lösel, Philipp D.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0506-1171"
                                        ];
        <http://schema.org/datePublished>
                "2025-05-08";
        <http://schema.org/description>
                "Processed micro-tomographic scan of the ant Camponotus vestitus strophiatus,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
        <http://schema.org/distribution>
                [ a                               <http://schema.org/DataDownload>;
                  <http://schema.org/contentSize>
                          "13340167168 bytes";
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                          "application/x-tar"
                ];
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                                          <http://schema.org/name>    "BMBF";
                                          <http://schema.org/sameAs>  "https://ror.org/04pz7b180"
                                        ];
        <http://schema.org/funder>      [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "DFG";
                                          <http://schema.org/sameAs>  "https://ror.org/018mejw64"
                                        ];
        <http://schema.org/identifier>  "https://doi.org/10.35097/6uy81h4dhhfqyyq5";
        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "Biology" , "digitization" , "insect" , "micro-CT" , "biodiversity" , "camponotus vestitus strophiatus" , "anatomy" , "ant" , "tomography" , "biology" , "formicidae" , "antscan" , "synchrotron";
        <http://schema.org/name>        "CASENT0744748-Camponotus.vestitus.strophiatus";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/6uy81h4dhhfqyyq5" .
<https://radar.kit.edu/id/hedvCarywckcNjDm>
        a                               <http://schema.org/Dataset>;
        <http://schema.org/contributor>
                [ a                               <http://schema.org/Person>;
                  <http://schema.org/familyName>  "Students from KIT and Université Grenoble Alpes";
                  <http://schema.org/name>        "Students from KIT and Université Grenoble Alpes"
                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Geophysical Institute, KIT"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Pan";
                                          <http://schema.org/givenName>   "Yudi";
                                          <http://schema.org/name>        "Pan, Yudi"
                                        ];
        <http://schema.org/datePublished>
                "2023-06-21";
        <http://schema.org/description>
                "Field seismic data (in su format) and the corresponding reports on it.\n1. 'CloseMesh.su' is the data acquired on a fine-mesh acquisition geometry during different days.\n2. 'LargeMesh.su' is the data acquired on a large-mesh acquisition geometry on a same day.\n3. 'report_header_information.pdf' describes the header information of the su files.\n4. 'Report_Rheinstetten KIT.pdf' is a report on the field measurement.";
        <http://schema.org/distribution>
                [ a                               <http://schema.org/DataDownload>;
                  <http://schema.org/contentSize>
                          "6845756928 bytes";
                  <http://schema.org/contentUrl>  "https://www.radar-service.eu/radar-backend/archives/hedvCarywckcNjDm/versions/1/content";
                  <http://schema.org/encodingFormat>
                          "application/x-tar"
                ];
        <http://schema.org/identifier>  "https://doi.org/10.35097/1246";
        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "Physics";
        <http://schema.org/name>        "3D shallow-seismic data acquired at Rheinstetten";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/hedvCarywckcNjDm" .
<https://radar.kit.edu/id/91vkxbwmnpkg8zms>
        a                               <http://schema.org/Dataset>;
        <http://schema.org/copyrightHolder>
                [ a                           <http://schema.org/Organization>;
                  <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                  <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
                ];
        <http://schema.org/copyrightHolder>
                [ a                           <http://schema.org/Organization>;
                  <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                  <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Faragó";
                                          <http://schema.org/givenName>   "Tomáš";
                                          <http://schema.org/name>        "Faragó, Tomáš";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0006-9824-2797"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Azuma";
                                          <http://schema.org/givenName>   "Fumika";
                                          <http://schema.org/name>        "Azuma, Fumika";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-5275-8775"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Zuber";
                                          <http://schema.org/givenName>   "Marcus";
                                          <http://schema.org/name>        "Zuber, Marcus";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-2429-6171"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Katzke";
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                "# Experimental Data for the Paper \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\" (Version 3)\nThese are the experimental data for the third version (v3) of the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished on [arXiv](https://arxiv.org/) in 2025.\nIf we create further versions of this paper in the future, these experimental data may cover them as well.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nWe obtained the experimental results on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 160 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 249 hours.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [b4083afcc4](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/b4083afcc41613912129e11b69580f11752ba060).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation_arxiv.py`) is [7d027c2382](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/7d027c23820fce067cce8953fdfdac719722949b).\nWe also tagged both commits (`run-2024-09-28-arXiv-v3` and `evaluation-2024-12-08-arXiv-v3`).\nThe experimental data are stored in three folders, i.e., `datasets/`, `plots/`, and `results/`.\nFurther, the console output of `run_evaluation_arxiv.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., the prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth file types are CSVs that only contain numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column either corresponds to a feature (in `_X`) or the target (in `_y`).\nThe first line in each `_X` file contains the names of the features as strings; for `_y` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `plots/`\nThese are the output files of the evaluation pipeline `run_evaluation_arxiv.py`.\nWe include these plots in our paper.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation_arxiv.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of non-negative ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the corresponding dataset).\n  Might also be an empty list, i.e., `[]`, if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1, 1]` + missing values): The training-set objective value of the feature set.\n  Three feature-selection methods (*FCBF*, *MI*, *Model Importance*) have the range `[0, 1]`, while two methods (*mRMR*, *Greedy Wrapper*) have the range `[-1, 1]`.\n- `test_objective` (float in `[-1, 1]` + missing values): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  The interpretation of this value depends on the search method for alternatives and the feature-selection method.\n  (1a) In solver-based search for alternatives in combination with white-box feature-selection methods, this value corresponds to one solver call.\n  (1b) In solver-based search for alternatives in combination with wrapper feature selection, we record the total runtime of the *Greedy Wrapper* algorithm, which calls the solver and trains prediction models multiple times.\n  (2) In heuristic search for alternatives (algorithms *Greedy Balancing* and *Greedy Replacement*), we record the total runtime of the heuristic search algorithms.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver-based or heuristic search method for alternatives.\n  For *Greedy Wrapper*, this is only the status of the last solver call (last iteration) and refers to optimizing similarity to the previous best solution (under swap constraints) rather than optimizing feature-set quality.\n  - 0 = (proven as) optimal; cannot occur for heuristic search methods\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible; cannot occur for heuristic search methods\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1, 1]` + missing values): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1, 1]` + missing values): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1, 1]` + missing values): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n  Not evaluated in the current version of our paper, as the observed trends are similar to those with decision trees.\n- `random_forest_test_mcc` (float in `[-1, 1]` + missing values): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n  Not evaluated in the current version of our paper, as the observed trends are similar to those with decision trees.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `[1, 10]`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for solver-based simultaneous search and *Greedy Balancing*, but always `10` for solver-based sequential search and *Greedy Replacement*.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for solver-based simultaneous search but always sum-aggregation for the remaining search methods\n  (where the value of this parameter does not matter for the search but needs to be specified for compatibility reasons).\n- `search_name` (string, 4 different values):\n  The name of the search method for alternatives (`search_greedy_balancing`, `search_greedy_replacement`, `search_sequentially`, or `search_simultaneously`).\n  *Greedy Balancing* and *Greedy Replacement* are (solver-free) heuristics and are only combined with two feature-selection methods (*MI* and *Model Importance*).\n  The other two values denote solver-based search here (though in the paper, we also categorize optimization problems as sequential/simultaneous, no matter how they are solved) and are combined with all five feature-selection methods.\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= *Model Gain* in the paper), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0, 4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1, 1000]` + missing values): The number of iterations if *Greedy Wrapper* is used for feature selection, missing value (empty string) in the other cases.\n  This column does not exist in result files that do not contain wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```All result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that the lists of feature indices are treated as lists (rather than plain strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```" , "These are the experimental data for the third version (v3) of the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\nThis version of the paper was published on [arXiv](https://arxiv.org/) in 2025.\nYou can find the paper [here](https://doi.org/10.48550/arXiv.2307.11607) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text.";
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                "Simulation results tagged f05864 (see appendix A) generated using Inovesa (10.5281/zenodo.1243264). The data is usable utilizeing e.g. the scripts provided at https://github.com/Inovesa/examples.";
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                "### **DATA SET DESCRIPTION**\n### **1. Spatial coverage**\nMeasurements were collected during the CROSSINN field campaign in the Inn Valley, Austria. The focus of the campaign was located at a mild valley curvature approximately 20 km northeast of Innsbruck. To optimize deployment requirements (power, permits, etc.) as well as to reduce demand for additional KITcube flux towers, all instruments were deployed as close as possible to three pre-existing i-Box flux towers (CS-VF0, CS-SF1, CS-NF27).\n### **2. Temporal coverage**\n01.08.2019 - 13.10.2019\n### **3. Instrument details and data file structure**\nAvailable here are archived directories, corresponding to a particular device (WLS200s-115, WLS200s-124, WLS200s-125), including the coplanar-retrieved (i.e. dual-Doppler) output and additional four text files.\n***WLS200s_xyz_YYYY-MM-DD.zip*** daily .zip files contain raw measurements collected with three WLS200s Windcube Doppler lidars, IDs xyz=115, 124, 125 (Leosphere). All three Windcubes used Windforge v3.1.1 for data acquisition. The raw measurements are organized into daily .zip files, each containing folder 'wind_and_aerosol_data' with separate .nc.gz files for each 1-min direct and indirect RHI scans in hourly folders, along with other support data in folders 'environmental data', 'scan type', 'resolutions', and 'settings'.\n***WLS200s_coplanar_retrieval_YYYYMMDD.zip*** daily .zip files contain the coplanar-retrieved wind field organized into hourly NetCDF files. The retrieval algorithm by Haid (2019) was used to obtain the wind field.\n***WLS200s_miscellaneous.zip*** file contains text files of the type 'WLS200s-xyz_scans_with_faulty_acquisition.txt'. These files contain time stamps (in UTC) of RHI scans affected by an occasionally faulty acquisition, resulting in two adjacent time stamps having recorded a same elevation angle. This typically affected pcolor/contour plotting routines. The issue was most common with WLS200s-125. On the other hand, the text file 'ceilometer_precipitation_flag.txt' contains the time stamps (in UTC) marked as experiencing either precipitation or sufficiently deep cloud cover. Specifically, if a given 1-min vertical profile of uncalibrated CHM15k backscatter up to 4 km above ground level, was characterized by more than 20% of backscatter coefficients exceeding 55 dB, it was deemed as ”precipitating”. Valid only for Kolsass.\n### **4. Locations**\nWith the exception of the WLS200s-115 (@ Hochhaeuser) and WLS200s-125 (@ Mairbach) Windcube Doppler lidars, all other KITcube instruments were deployed at Kolsass, the headquarter site for the field campaign. All sondes were also launched and tracked from Kolsass.\nKolsass - 47.305290°N / 11.622231°E\nHochhaeuser - 47.286182°N / 11.631232°E\nMairbach - 47.326664°N / 11.608736°E\n### **5. Contact**\nContact nevio.babic@kit.edu for any questions regarding the data sets.\n### **6. References**\nHaid, M., 2019: marenha/doppler_wind_lidar_toolbox: First release (v1.0.0). Zenodo, doi:10.5281/zenodo.3583083" , "The data sets found here were collected with the KITcube measurement platform in the Inn Valley, Austria, from 1 Aug 2019 to 13 Oct 2019, within the CROSSINN field campaign (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements). The goal of the campaign was to study the multi-scale three-dimensional flow structure and the impact of the flow on the mountain atmospheric boundary layer (MoBL) structure and evolution across the Inn Valley. Further details of the campaign can be found in Adler et al (2020).\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1" , "Die hier zu findenden Datensätze wurden mit der KITcube-Messplattform im Inntal, Österreich, vom 1.8.2019 bis 13.10.2019, im Rahmen der CROSSINN-Feldkampagne (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) erhoben. Das Ziel der Kampagne war es, die multiskalige dreidimensionale Strömungsstruktur und den Einfluss der Strömung auf die Struktur und Entwicklung der atmosphärischen Grenzschicht (MoBL) über dem Inntal zu untersuchen. Weitere Details der Kampagne sind in Adler et al. (2020) zu finden.\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1";
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                "Direct numerical simulation (DNSs) are used to systematically investigate applicability of minimal channel approach for characterization of roughness-induced drag in irregular rough surfaces. Roughness is generated mathematically using a random algorithm, in which the power spectrum (PS) and probability density function (PDF) of surface height function can be prescribed. 12 different combinations of PS and PDF are examined and both transitionally and fully rough regimes are investigated (roughness heights varies in the range $k^+$ = 25 -- 100).\n    It is demonstrated that both roughness function ($\\Delta U^+$) and zero-plane displacement can be predicted within $\\pm5\\%$ accuracy using DNS in properly sized minimal channels. Notably, the predictions do not deteriorate when a limited range of large horizontal roughness scales are filtered out due to the small channel size (here up to 10\\% of original roughness height spectral energy based on 2D PS). Additionally, examining the results obtained from different random realizations of roughness shows that a certain combination of PDF and PS leads to a nearly unique $\\Delta U^+$ for deterministically different surface topographies.\n    In addition to the global flow properties, the distribution of time-averaged surface force exerted by the roughness onto the fluid is calculated and compared for different cases. \n    It is shown that patterns of surface force distribution over irregular rough surfaces can be well captured when the sheltering effect is taken into account. This is made possible applying the sheltering model proposed by Yang et al. to each specific roughness topography.\n    Furthermore, an analysis of the coherence function between roughness height and surface force distributions reveals that the coherence drops at larger streamwise wavelengths, which can be an indication that very large horizontal scales are less dominant in contributing to the skin friction drag. \n    Finally, some existing roughness correlations are assessed using the present roughness dataset, and it is shown that the correlation predictions for the values of equivalent sand-grain roughness mainly lie within $\\pm30\\%$ error in comparison to the DNS results." , "These files contain the data used in the publication:\n“DNS-based characterization of pseudo-random roughness in minimal channels”\nJ. Yang, A. Stroh, D. Chung and P. Forooghi\npublished in Journal of Fluid Mechanics\ndoi:10.1017/jfm.2022.331\nNumerical Details:\nThe carried out DNS is based on a pseudo-spectral solver for incompressible boundary layer flows developed at KTH/Stockholm. The Navier-Stokes equations are numerically integrated using the velocity-vorticity formulation by a spectral method with Fourier decomposition in the horizontal directions and Chebyshev discretization in the wall-normal direction. For temporal advancement, the convection and viscous terms are discretized using the 3rd order Runge-Kutta and Crank-Nicolson methods, respectively. The simulation domain represents an turbulent channel flow with periodic boundary conditions applied in streamwise and spanwise directions, while the wall-normal extension of the domain is bounded by no-slip boundary conditions at the upper and lower domain wall. The flow is driven by a prescribed constant pressure gradient (CPG). The friction Reynolds number for the present case is fixed to Re_τ = 500. The structured surface is introduced through an immersed boundary method (IBM) based on the method proposed by Goldstein et al. (1993) and is essentially a proportional controller which imposes zero velocity in the solid region of the numerical domain. \nData Files:\nThe data files are saved and labeled in *.mat files. Each file contains MATLAB data consisting of the roughness height distribution and corresponding coordinates. The roughness structures are non-dimensionalized with the channel half height δ.\nReference:\nPlease provide a reference to the article above when using this data.\nPlease direct questions regarding numerical setup/data to Jiasheng Yang (jiasheng.yang@kit.edu)";
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                                        ];
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                                        ];
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                                        ];
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                                        ];
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                                          <http://schema.org/name>        "Economo, Evan P.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-7402-0432"
                                        ];
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                                          <http://schema.org/name>        "Faragó, Tomáš";
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                                        ];
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                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-5503-9816"
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                                          <http://schema.org/givenName>   "Grace C. E.";
                                          <http://schema.org/name>        "Porter, Grace C. E."
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        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
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                                          <http://schema.org/givenName>   "Matthew";
                                          <http://schema.org/name>        "Salter, Matthew"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
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                                          <http://schema.org/givenName>   "Romy";
                                          <http://schema.org/name>        "Ullrich, Romy";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-8162-4354"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
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                                          <http://schema.org/givenName>   "Robert";
                                          <http://schema.org/name>        "Wagner, Robert"
                                        ];
        <http://schema.org/datePublished>
                "2023-06-21";
        <http://schema.org/description>
                "This dataset contains all the measurements used for the analysis of the respective publication. More details on the measurement setup etc. can be found in there.\nThe data contains of three subfolders, one including the AIDA data, one the INKA and one the mul-NIPI data. Each folder contains one txt file for each experiment of the measurement campaign (see Table 2 in the publication). The name of the txt file represents the sample and the aerosolisation technique used (in case of the AIDA and INKA data). \nBelow we give a short explanation on the data in each folder:\nAIDA:\nEach data file consists of 6 columns: temperature T ([T]=K), the uncertainty of T deltaT, the surface active site density nS ([ns]=m-2), the uncertainty of ns deltanS, the ice nucleation active site density per mass of sea salt nm ([nm]=g-1), the uncertainty of nm deltanm. The AIDA ns data were corrected for the background ice nucleation mode observed in the reference experiments with purely inorganic Sigma-Aldrich sea salt solution droplets (see Sect. 2.4 in the manuscript).\nThere was no signal above background for the following experiments (and therefore no data file exists): SM100a AEGOR, SM10 AEGOR, SML8 AEGOR.\nINKA:\nEach data file consists of 2 columns: temperature T ([T]=°C), the surface active site density ns ([ns]=m-2).\nThere was no signal for the following experiments (and therefore no data file exists): Sigma sea salt nebuliser, SM100a AEGOR, SML8 AEGOR.\nNIPI:\nData files consist at least of two columns (T and FF) and additional of the following (depending on the dataset): temperature T ([T]=°C), concentration of ice nucleation particles INP ([INP]=L-1), frozen fraction FF, the ice nucleation active site density per mass of sea salt nm ([nm]=g-1), the freezing point depression corrected temperature Corrected T ([Corrected T]=°C), the upper limit of the ice nucleation active site density per mass of sea salt nm ([nm]=g-1), the lower limit of the ice nucleation active site density per mass of sea salt nm ([nm]=g-1).";
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        <http://schema.org/keywords>    "Geological Science" , "marine" , "aerosol particles" , "algae" , "ice nucleation" , "Arctic";
        <http://schema.org/name>        "Dataset ice nucleating activity of Arctic sea surface microlayer samples and marine algal cultures";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/SnSKfQeCcToshRHH" .
<https://radar.kit.edu/id/jhurtyz0ar4hhw84>
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                ];
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                          ];
                  <http://schema.org/familyName>  "Ehrenberg";
                  <http://schema.org/givenName>   "Helmut";
                  <http://schema.org/name>        "Ehrenberg, Helmut"
                ];
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                  <http://schema.org/name>    "Geiß, David";
                  <http://schema.org/sameAs>  "https://orcid.org/0009-0000-9727-1875"
                ];
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                                                  ];
                                          <http://schema.org/familyName>  "Geiß";
                                          <http://schema.org/givenName>   "David";
                                          <http://schema.org/name>        "Geiß, David";
                                          <http://schema.org/sameAs>      "http://orcid.org/0009-0000-9727-1875"
                                        ];
        <http://schema.org/datePublished>
                "2025-09-12";
        <http://schema.org/description>
                "X-ray powder diffraction (XRD): Data was processed using WinXPow to transform the obtained .raw data into .xy data avoiding negative intensities. Data was plotted using OriginLab.\n57Fe Mössbauer spectroscopy: The spectra were fitted with WinNormos and plotted using OriginLab.\nX-ray absorption spectroscopy (XAS): All XAS spectra were analyzed and processed utilizing the ATHENA software package. The X-ray absorption near edge spectra (XANES) regions of the XAS spectra were acquired by subtracting the pre-edge background from the overall absorption and normalizing to the spline fit using the ATHENA software package.";
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        <http://schema.org/isAccessibleForFree>
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        <http://schema.org/keywords>    "Engineering";
        <http://schema.org/name>        "Lithiumrecycling aus Batteriematerialien mit einem mechanochemischen Ansatz";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
                                        ];
        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/jhurtyz0ar4hhw84" .
<https://radar.kit.edu/id/zIGnSndGzafCzzrK>
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                  <http://schema.org/familyName>  "Gatti";
                  <http://schema.org/givenName>   "Davide";
                  <http://schema.org/name>        "Gatti, Davide"
                ];
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                  <http://schema.org/familyName>  "Schäfer";
                  <http://schema.org/givenName>   "Kay";
                  <http://schema.org/name>        "Schäfer, Kay";
                  <http://schema.org/sameAs>      "http://orcid.org/0000-0002-1704-8233"
                ];
        <http://schema.org/copyrightHolder>
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                  <http://schema.org/name>    "Neuhauser, Jonathan";
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                ];
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                                          <http://schema.org/familyName>  "Neuhauser";
                                          <http://schema.org/givenName>   "Jonathan";
                                          <http://schema.org/name>        "Neuhauser, Jonathan";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-3288-6056"
                                        ];
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                "# 🔬️ Experiment Evaluation\nPython module for the evaluation of lab experiments.\nThe module implements functions to import meta-data of measurements, filters to search for subsets of them and routines to import and plot data from this meta-data. It works well in its original context but is currently in open alpha since it will be restructured in order to be compatible with new lab environments.\nExamples of its usage in scientific works will soon be published by the author that can be used to reference it. Feel free to use it for your own projects and to ask questions. For now you can cite this repository as source.\n# 💻️ Installation\nYou need a running python3 installation on your OS. The module was written on Debian/GNU-Linux, was tested on Windows and should also run on other OS.\nIt is recommended to work in an virtual environment (see the official python documentation -> from bash: `python3 -m venv exp_env` `source exp_env/bin/activate`) or conda installation.\n## Dependencies\nDependencies are the usual scientific modules like numpy, matplotlib, pandas but also astropy. See the `requirements.txt` from that you should be able to install the library with\n```\npip install pip -U # Update pip itself\npip install -r /path/to/requirements.txt\n```\nAlternatively you can also install the required modules from the shell etc.. The author recommends to also install jupyter that includes the interactive ipython:\n```\n# Example via pip\npip install jupyter\npip install numpy\npip install matplotlib\npip install scipy\npip install pandas\npip install astropy\npip install mplcursors\npip install pynufft\n# pip install python-slugify  # make sure this version of slugify is installed and not 'slugify'\n```\n## The module itself\nInside your virtual environment there is a folder `exp_env/lib/python3.../site-packages`. Place the file `experiment_evaluation.py` inside this folder (or a new sub-folder with all your personal scientific code) to make it accessible.\nFrom within your code (try it from an interactive ipython session) you should now be able to import it via:\n```\nimport experiment_evaluation as ee\n# or from subfolder: import my_scientific_modules.experiment_evaluation as ee\n```\n### Matplotlib style\nIn order to use the fancy custom styles (for example for consistent looking graphs throughout your publication) it is advised to use matplotlib styles. For the provided styles, copy the custom styles \"thesis_default.mplstyle\" etc. from the folder `stylelib` inside your matplotlib library folder:\n`lib/python3.9/site-packages/matplotlib/mpl-data/stylelib/*.mplstyle`\n# 🧑‍💻 Usage\nA good way to learn its usage is to have a look at the [example](examples/example_experiment_evaluation.ipynb) file. But since the module is work in progress we first explain some concepts.\n## ✨ Why meta-data?\nThe module automates several steps of experiment evaluations. But the highlight is its capability to handle experimental meta-data. This enables the user to automatically choose and plot data with a question in mind (example: plot all EQE-curves at -2V and 173Hz) instead of repeatedly choosing files manually. For calculations that need more than one measurement this becomes extremely useful but also for implementing statistics.\nMeta data include things like experimental settings (applied voltage on a diode, time of the measurement, temperature etc.), the experimentalist and technical informations (file-format etc., manufacturer experimental device).\nThe module includes some generic functions but to use it for your specific lab environment you might need to add experiment and plot specific functions.\n## 💾️ How to save your experiment files?\nIn general lab measurement files stem from different devices and export routines. So frankly speaking lab-data is often a mess! But to use automatic evaluation tools some sort of system to recognize the measurement-type and store the meta-data is needed. In an ideal world a lab would decide on one file format for all measurements and labels them systematically. To include different data-types and their meta-data within one file-type there exists the *.asdf (advanced scientific data format, see their [documentation](https://asdf.readthedocs.io/en/stable/index.html) for further insight). So if you are just starting with your PhD try to use this file format everywhere ;).\nAlso to make experiments distinguishable every experiment needs an unique identifier. So you also should number every new experiment with an increasing number and the type of the experiment.\nExample of useful file naming for EQE measurements: `Nr783_EQE.asdf`\nIn the case of my PhD I decided to use what I found: store the different file formats, store them in folders with the name of the experiment and include meta-data in the file-names (bad example: `EQE/Nr783_3volt_pix1.csv`). This was not the best idea (so learn from what I learned :P)\nTo handle that mess, this module therefore implements also some regular-expressions to extract meta-data from file-names (`ee.meta_from_filename()`), but in general it is advised to store all meta-data in the file-header (with the exception of the unique identifier and experiment type). Like this you could store your files in whatever folder structure you like and still find them from within the script. The module then imports meta-data from the files into a database and you can do fancy data-science with your data!\n## 📑️ Database\nFor calculations and filtering of datasets the meta-data and data needs to be accessible in a machine readable form. For the time being the module imports all meta-data into a pandas DataFrame that represents our database (For very large datasets this would possibly be needed to be changed). For this we have to name the root folder that includes all experiment files/folders.\n**Hint**: If you did not follow the unique labeling/numbering for all your experiments you can still use this module by choosing a root folder that only includes the current experiment.\n```\nfrom pathlib import Path\nmeasurement_root_folder = Path(\"/home/PhD/Data/\")\n```\nWe can specify some pre-filtering for the specific experiment we want to evaluate:\n```\n# make use of the '/' operator to build OS independant paths\nmeasurement_folder = measurement_root_folder / \"LaserLab\" / \"proximity-sensor\" / \"OPD-Lens\" / \"OPD-Lens_v2\" \n# Define some pre-filter\ndevices = [nr for nr in range(1035, 1043)]  # Unique sample numbers of the experiment listed by list-comprehension \nexplst = \"Mervin Seiberlich\"\n```\nThen we import the metadata into the pandas DataFrame database via `ee.list_measurements()` and call it *meta-table*:\n```\nmeta_table = ee.list_measurements(measurement_root_folder, devices, experimentalist=explst, sort_by=[\"measurement_type\", \"nr\", \"pix\", \"v\"])\n```\n### 💡️ Advanced note:\nInternally `ee.list_measurements()` uses custom functions to import the experiment specific meta-data. Have a look into the source-code and search for `read_meta` for an example how this works in detail. With the *.asdf file-format only the generalized import function would be needed.\n## Import data and meta-data\nTo import now some measurement data for plotting we use the information inside *meta_table* with custom import routines and python dictionaries implementing our filters: \n```\n# Distinguish between reference and other measurments\nlens = {\"nr\":devices[:5]}\nref = {\"nr\":devices[5:]}\n# Select by bias and compare reference samples with lens (**dict unpacks the values to combine two or mor dictionaries)\neqe_lens_0V = ee.import_eqe(meta_table, mask_dict={**lens, **{\"v\":0}})\neqe_ref_0V = ee.import_eqe(meta_table, mask_dict={**ref, **{\"v\":0}})\n```\nThis yields python lists `eqe_lens_0V = [table1, table2, ... tableN]` with the selected data ready for plotting (Lists are maybe not smart for huge dataset and some N-dimensional object can replace this in future). Note: The tables inside the list are [astropy.QTable()](https://docs.astropy.org/en/stable/table/index.html) objects including the data and meta-data, as well as units!\nSo with this few lines of code you already did some advanced data filtering and import!\n## 🌡️ Physical units\nThe module astropy includes a submodule astropy.units. Since we deal with real world data, it is a good idea to also include units in calculations.\n```\nimport astropy.units as u\n# Radius of one microlens:\nr = 98 * u.um\n```\n## 📝️ Calculations\nIf you have to repeatedly do some advanced calculations or fits for some plots, include them as functions in the source-code. An example would be `ee.pink_noise()`\n## 📊️ Plots\nFor plotting there exists many modules in python. Due to its grate power we use matplotlib. This comes with the cost of some complexity (definitely have a look at its [documentation!](https://matplotlib.org)). But this enables us for example to have a consistence color style, figure-size and text-size in large projects like a PhD-thesis:\n```\nmpl.style.use([\"thesis_default\", \"thesis_talk\"])   # We use style-sheets to set things like figure-size and text-size, see https://matplotlib.org/stable/tutorials/introductory/customizing.html#composing-styles\nw,h = plt.rcParams['figure.figsize'] # get the default size for figures to scale plots accordingly\n```\nIn order to not invent the wheel over and over again it makes sense to wrap some plotting routines for each experiment inside some custom functions. For further detail see the [documentation/recommended function signature for matplotlib specialized functions](https://matplotlib.org/2.0.2/faq/usage_faq.html#coding-styles). This enables easy experiment-type specific plotting (even with statistics) once all functions are set up:\n```\n# %% plot eqe statistics\nfig, ax = plt.subplots(1,1, figsize=(w, h), layout=\"constrained\")\nee.plot_eqe(ax, eqe_lens_0V, statistics=True, color=\"tab:green\", plot_type=\"EQE\", marker=True, ncol=2)\nee.plot_eqe(ax, eqe_ref_0V, statistics=True, color=\"tab:blue\", plot_type=\"EQE\", marker=True, ncol=2)\nax.set_ylim(0,60)\nax.legend([\"Lens\",\"$1\\sigma - Lens$\", \"Reference\", \"$1\\sigma - reference$\"], loc=\"lower left\", ncol=2)\n```\nOnly 5 lines of code! For further details see the [example](examples/example_experiment_evaluation.ipynb) file!\n# 📖 FAQ\n1. How do I get further information about the functions of the module?\nUse the doc-string! In jupyter-lab or ipython type for example `ee.plot_eqe?` to read more about the custom plot function for eqe measurements\n2. How do I exclude some specific measurements (outliers, false_measurements)?\nImport functions now implement an `exclude` argument comprising a list of dictionarys to exclude specific data.\n```\ndata_dark_pcbm_ref = ee.import_iV(meta_table, A=0.01*u.cm**2, mask_dict={\"nr\":PCBM, **pix}, exclude=[{\"c\":[25,150]}, {\"solventOnly\":True}])\n```\nSometimes it is helpful to only append single measurements by the += operator though. Here is an example:\n```\ndata_ldr_0V = ee.import_ldr(meta_table, mask_dict={\"nr\":[1035, 1036, 1037, 1038, 1039, 1040, 1041], \"v\":0, \"custompower\":[0, 9.5]})\ndata_ldr_0V += ee.import_ldr(meta_table, mask_dict={\"nr\":1042, \"pix\":1, \"v\":0, \"custompower\":[0, 9.5]})\n```\n# 👥️ Contributors and acknowledgment\nMany question were answered by searching through forums and the author is very thankful for the culture of openness in the FOSS community.\nA warm thank you goes to [Jyh-Miin Lin](https://github.com/jyhmiinlin) who provided a helpful example and explanations on how to use the excellent [pynufft](https://github.com/jyhmiinlin/pynufft) package for non-uniform FFT.\n# 💌️ License\nLicense:\nCopyright (C) 2022-2023 Mervin Seiberlich\nAuthor - Mervin Seiberlich\nThis file is part of experiment_evaluation.\nexperiment_evaluation is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\nexperiment_evaluation is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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                                          <http://schema.org/sameAs>      "http://orcid.org/This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model.\nIt has been used in [1,1a]. Here, only the noise-free BSPs are provided.\nDue to its size, this is a multi-part dataset.\nPart 1: https://doi.org/10.5445/IR/1000156139\nPart 2: https://doi.org/10.5445/IR/1000156554\nPart 3: https://doi.org/10.5445/IR/1000156555\nPart 4: https://doi.org/10.5445/IR/1000156556\nPart 5: https://doi.org/10.5445/IR/1000156557\nEach archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:\n- heart.vtp:\n  A triangle mesh of the heart including the point data:\n  - ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\n  - class: Boundary regions used as input for the computation of fiber orientations [3].\n  - trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).\n- heart_transform_matrices.mat:\n  A 1 x 3 cell array containing 4 x 4 transformation matrices that\n  describe the pose of the heart within the torso.\n  Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.\n- actTimes.mat:\n  A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5]\n  (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).\n- bsp.mat:\n  - bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with\n         scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].  - bspEnd: Time index of the end of depolarization (largest scaled activation time).\nThe archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:\n- torso.vtp:\n  A triangle mesh of the torso including the point data:\n  - electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).\n- heart_meanshape.vtp:\n  A triangle mesh of the mean shape of the statistical shape model [7,8].  \n- heart_shapemodel.mat:\n  - pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).\n  - var: A numModes x 1 vector of variances.\n  - weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.\n- heart_alignment_matrix.mat:\n  A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).\n- heart_transform_params.mat:\n  A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).\n- fiber_angles.mat:\n  - alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.\n  - alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.\n- actTimeScalings.mat:\n  - A numModels x numFoci matrix of factors used to scale the activation times.\n- tmv_template.mat:\n  The transmembrane voltage time course used to compute BSPs.\n- heart_classes.vtp:\n  A coarse triangle mesh of the mean shape used for fuzzy classification.\n- heart_classes_subdiv.vtp:\n  A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates.\n[1] https://doi.org/10.48550/arXiv.2209.08095\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\n[2] https://doi.org/10.1016/j.media.2021.102247\n[3] https://github.com/KIT-IBT/LDRB_Fibers\n[4] https://github.com/KIT-IBT/FIM_Eikonal\n[5] https://doi.org/10.1137/120881956\n[6] https://doi.org/10.1016/j.cmpb.2007.09.004\n[7] https://doi.org/10.5281/zenodo.4506463\n[8] https://doi.org/10.1016/j.media.2015.08.009"
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                                          <http://schema.org/name>        "Azuma, Fumika";
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                "Processed micro-tomographic scan of the ant Nylanderia yambaru,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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                "The results are combined in three zip files, named according to the recipe.\nWithin each zip file, samples are named according to the following procedure:\nRecipe (BC for basic cake, PC for pound cake or B for brioche);\nProduct (c for cake, alias baked product and d for dough/batter);\nTreatment (Blank for untreated sample, A-O for lipase or QC for quality control);\nReplicate (a-c for triplicate and s for spiked sample a).\ne.g. sample BCdE_b is a batter sample of basic cake treated with lipase E" , "Lipases are promising improvers of cake batter and baking properties. Their suitability for use in various cake formulations cannot be predicted yet, because the reactions that lead to macroscopic effects need to be unravelled. Therefore, the lipidome of three different cake recipes with and without lipase treatment was assessed by ultra high performance liquid chromatography-mass spectrometry before and after baking. By comparing the reaction patterns of seven different lipases in the recipes with known effects on texture, we show that lipase substrate specificity impacts baking quality. Key reactions for the recipes were identified with the help of principal component analysis. In the eggless basic cake, glyceroglycolipids are causal for baking improvement. In pound cake, lysoglycerophospholipids were linked to textural effects. Lipase substrate specificity was shown to be dependent on the recipe. Further research is needed to understand how recipes can be adjusted to achieve optimal lipase substrate specificity for desirable batter and baking properties.";
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                "2023-06-21";
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                "In dieser Arbeit werden zwei Aspekte der dynamischen Natur von proteinen mit Hilfe von Enhanced Sampling Methoden untersucht. Zunächst wurden Mikrosekunden-Molekulardynamiksimulationen von Harzianin HK VI (HZ) in Wechselwirkung mit einer Dimyristoylphosphatidylcholin-Doppelschicht unter der Bedingung eines niedrigen Peptid-zu-Lipid Verhältnisses durchgeführt. Zwei Orientierungen des HZ-Moleküls in der Doppelschicht wurden gefunden und charakterisiert. In der Ausrichtung senkrecht zur Doppelschicht-Oberfläche induziert HZ eine lokale Ausdünnung der Doppelschicht. Wird HZ parallel zu seiner Oberfläche in die Doppelschicht eingesetzt, befindet es sich nahezu vollständig im hydrophoben Bereich der Doppelschicht.\nEine ausgedehnte Sampling lieferte qualitative Ergebnisse und zeigte, dass die letztgenannte Orientierung ein globales Minimum der freien Energie ist. Die sekundäre Struktur von HZ wurde charakterisiert, und es wurde festgestellt, dass sie sich in der 3_10-helikalen Familie befindet.\nZweitens wurde die Thiol-Disulfid-Austauschreaktion in Modellsystemen und kleinen Peptiden mit Hilfe eines kombinierten QM/MM-Metadynamikschemas untersucht. Die freien Energielandschaften dieser Systeme wurden generiert, und liefern die Strukturen von Reaktanden und Produkten mit atomaren Details, sowie die Höhen der freien Energiebarrieren (oder Aktivierungsenergien), die dem spontanen Austausch entgegenwirken. Ein QM/MM-Schema mit rein klassischem Wasser erwies sich als effiziente und präzise Kompromisslösung.\nDie Berechnungen ergaben den erwarteten symmetrischen Trisulfid-Übergangszustand und seine Struktur und Energiebarriere waren für die intramolekularen Thiol-Disulfid-Reaktionen in Modellpeptiden sehr ähnlich. Während CXC-Disulfidbindungen als sterisch ungünstig eingestuft wurden, wurde CXXC gegenüber längerfristigen Disulfidbindungen entlang des Peptidrückgrats bevorzugt, was der hohen Häufigkeit von CXXC-Motiven in Redox-Proteinen entspricht. Eine direkte Anwendung auf ein reales Protein wurde ebenfalls in Form von Force-Clamp Simulationen durchgeführt. Das Protokoll ermöglichte die Simulation der Disulfidbindungsisomerisierung in einem einzelnen Protein und reproduzierte die in einem AFM-Experiment beobachtete Regioselektivität." , "In this work two aspects of the dynamic nature of proteins are investigated using enhanced sampling techniques.\nFirstly, microsecond molecular dynamics simulations of harzianin HK VI (HZ) interacting with a dimyristoylphosphatidylcholine bilayer were performed at the condition of low peptide-to-lipid ratio. Two orientations of HZ molecule in the bilayer were found and characterized. In the orientation perpendicular to the bilayer surface, HZ induces a local thinning of the bilayer. When inserted into the bilayer parallel to its surface, HZ is located nearly completely within the hydrophobic region of the bilayer. An extended sampling simulation provided qualitative results and showed the latter orientation to be a global minimum of free energy.\nThe secondary structure of HZ was characterized, and it was found to be located in the 3_10 -helical family.\nSecondly, the thiol–disulfide exchange reaction in model systems and small peptides was investigated by means of a combined QM/MM metadynamics scheme. The free energy landscapes of these systems were generated, providing the structures of reactants and products with atomic detail, as well as the heights of free energy barriers (or, activation energies) opposing the spontaneous exchange. A QM/MM scheme with purely classical water turned out to be an efficient and accurate compromise solution. The calculations yielded the expected symmetric trisulfide transition state, and its structure and energy barrier were very similar for the intramolecular thiol–disulfide reactions in model peptides. While CXC disulfide bonds were found sterically unfavorable, CXXC were favored over longer-range disulfide bonds along the peptide backbone, in line with the high abundance of CXXC motifs in redox proteins. A direct application in a real protein was also performed through force-clamp simulations. The protocol allowed for simulation of the disulfide bond isomerization in a single protein, reproducing the regioselectivity observed in an AFM experiment." , "The data is storedd divided into three separate folders, which follow the structure of the thesis.";
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                "In der Dissertation  \"Timing Sensitive Dependency Analysis and its Application to Software Security\" präsentiere ich neue Verfahren zur statischen Analyse von Ausführungszeit-sensitiver Informationsflusskontrolle in Softwaresystemen. Ich wende diese Verfahren an zur Analyse nebenläufiger Java-Programme, sowie zur Analyse von Ausführungszeit-Seitenkanälen in Implementierungen kryptographischer Primitive.\nIn diesem VirtualBox Maschinen-Abbild stelle ich Implementierungen aller neuen Algorithmen dieser Dissertation bereit, zusammen mit randomisierten Test-Eigenschaften für alle formalen Observationen der Dissertation." , "This is the artifact corresponding to the thesis\n  \"Timing Sensitive Dependency Analysis and its Application to Software Security\" by Martin Hecker\nThe Virtual Box virtual machine image\n  ```dissertation-vm```\ncan be used by logging in via\n  user:     ```hecker```\n  password: ```hecker```\nIn the users home directory, you will find in\n * ```randomized-tests.git/```\n   — in the language Haskell —  both implementations of the submission's algorithms, and randomized tests that verify the submission's Observations.\n * ```randomized-tests.git/src/test/Program/Properties/DissObservations.hs```\n   contains the randomized tests corresponding to the dissertations Observations.\nThe results can be reproduces within the virtual machine image by help of the provided shell runners. From the users home directory, run\n  * ```./randomized-tests```\n    to once run all randomized tests except those marked 'slow', which are randomized tests that may run several minutes some randomized inputs.\n  * ```./randomized-tests-slow```\n    to once run all those randomized tests marked 'slow'.\n  * ```./randomized-tests-forever``` or ```./randomized-tests-slow-forever```\n    to run the randomized tests forever, only terminating if there was found a counter-example to the submission's Observations." , "In my thesis \"Timing Sensitive Dependency Analysis and its Application to Software Security\", I present new methods for the static analysis of timing sensitive information flow control in software systems. I apply these methods in the analysis of concurrent Java programs, as well as the analysis of timing side-channels in implementations of cryptographic primitives.\nIn this VirtualBox machine image, I provide Implementations for all new Algorithms of my thesis, and randomized test properties for all formal Observations in the thesis.";
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                "There are 15 matlab data files in this folder.\nMatlab files titled \"2D_vars_<simulation_name>_CCN\" are 2D variables on an unprocessed grid\nMatlab files tiled \"3D_CRH_vars_<simulation_name>_CCN.mat\" are 3D variables averaged on CRH blocks via the second block averaging method (see Sec 3 of paper for description)\nMatlab files tiled \"3D_CRH_vars_<simulation_name>_CCN_default_block_averaging.mat\" are 3D variables averaged on CRH blocks via the traditional (Bretherton et al.,2005) block averaging method";
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                "# Micro Gas Turbine Data\n## Overview\nThese experimental data support the paper *\"Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines\", presented at 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore.*\nThe data was collected from a commercial micro gas turbine designed for residential use, generating approximately 3 kW of electrical power. Its purpose was to model the turbine's behavior over time using machine learning techniques.\n## Folder Structure\n- `data`: Contains 8 experimental time series data in CSV format, collected from the micro gas turbine.\n- `plots`: Includes results from experiments and the code used to generate plots from the paper. \n- `plots/create_plots.ipynb`: A Jupyter notebook containing code to create the plots.\n## Time Series Data\nEach time series represents a separate experiment where the input control voltage was varied over time, and the resulting output electrical power of the micro gas turbine was measured. The data has a resolution of approximately 1 second and is structured in a CSV file with the following columns:\n- `time`: Time in seconds, denoted as $t$.\n- `input_voltage`: Input control voltage in volts, representing the control signal $x_t$.\n- `el_power`: electrical power in watts, representing the output signal $y_t$.\n## Prediction Task\nThe data was used for a time-series prediction task, aiming to predict `el_power` based on `input_voltage`. In the paper, the objective was to forecast the output $y_t$ given the control inputs $x_t, x_{t-1}, \\dots, x_{t-N+1}$.\n### Additional Information\nRequirements for running `create_plots.ipynb`:\n- Python 3.8.17\n- Jupyter Notebook 6.5.4\n- Pandas 1.2.2\n- Matplotlib 3.5.2\n- Seaborn 0.12.2\n**When using this dataset, please cite the following paper:**\n*Pawel Bielski, Aleksandr Eismont, Jakob Bach, Florian Leiser, Dustin Kottonau, and Klemens Böhm. 2024. Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines, 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore.*\nFor more details and the code used in the experiments, visit the GitHub [repository](https://github.com/Energy-Theory-Guided-Data-Science/Gas-Turbine)." , "These are experimental data for the paper:\nPawel Bielski, Aleksandr Eismont, Jakob Bach, Florian Leiser, Dustin Kottonau, and Klemens Böhm. 2024. Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines, 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore\nThe data consist of:\n1. experimental time series data collected from a micro gas turbine\n2. results from the experiments and the corresponding code to create plots used in the paper\nThe corresponding GitHub repository: \nhttps://github.com/Energy-Theory-Guided-Data-Science/Gas-Turbine";
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                "These are the experimental data for the paper> Bach, Jakob. \"Subgroup Discovery with Small and Alternative Feature Sets\"\nThe paper was accepted at the conference [SIGMOD 2025](https://2025.sigmod.org/).\nYou can find the paper [here](https://doi.org/10.1145/3725358) and the code [here](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text." , "# Experimental Data for the Paper \"Subgroup Discovery with Small and Alternative Feature Sets\"\nThese are the experimental data for the paper> Bach, Jakob. \"Subgroup Discovery with Small and Alternative Feature Sets\"\naccepted at the conference [SIGMOD 2025](https://2025.sigmod.org/).\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery) for the code and instructions to reproduce the experiments.\nWe obtained the experimental results on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 160 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the main experimental pipeline (`main_experiments/run_experiments.py`) took about 34 hours.\nNote that the current experimental data originate from two separate runs of the main experimental pipeline.\nIn particular, we repeated the same experiments for two additional subgroup-discovery methods later, without rerunning the previously included methods.\nThe commit hash for the last run of the main experimental pipeline (`main_experiments/run_experiments.py`) except the subgroup-discovery methods *BSD* and *SD-Map* is [0a57bcd529](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/0a57bcd52938dce6285e8113d777360c2b17f30f) (tag: `run-2024-05-13`).\nThe commit hash for the last run of the main experimental pipeline (`main_experiments/run_experiments.py`) for the (additional) subgroup-discovery methods *BSD* and *SD-Map* is [50dd82e0fc](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/50dd82e0fcd175a4068ef4ce838dcacdc1045fc9) (tag: `run-2025-01-21-SIGMOD-2025`).\nHowever, the main experimental pipeline did not change between these two runs (except for refactorings and including the two additional subgroup-discovery methods).\nThus, using the tag `run-2025-01-21-SIGMOD-2025` to reproduce all experiments should yield the same results (except runtimes and timeout-affected results).\nThe commit hash for the last run of the main evaluation pipeline (`main_experiments/run_evaluation_sigmod.py`) is [8e8e1f5e54](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/8e8e1f5e54ac7644fc34f57cae2ad39b6fc1d453) (tag: `evaluation-2025-03-23-SIGMOD-2025`).\nThe experimental data are stored in three folders, i.e., `datasets/`, `plots/`, and `results/`.\nFurther, the console output of `main_experiments/run_evaluation_sigmod.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the main experimental pipeline `main_experiments/run_experiments.py`, i.e., the prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 27 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `main_experiments/prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth file types are CSVs that only contain numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column either corresponds to a feature (in `_X`) or the target (in `_y`).\nThe first line in each `_X` file contains the names of the features as strings; for `_y` files, there is only one column, always named `target`.\nFor the prediction target, we ensured that the minority (i.e., less frequent) class is the positive class (i.e., has the class label `1`), so the labeling may differ from PMLB.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `plots/`\nThese are the output files of the evaluation pipeline `main_experiments/run_evaluation_sigmod.py`.\nWe include these plots in our paper.\n## `results/`\nThese are the output data of the main experimental pipeline in the form of CSVs, produced by the script `main_experiments/run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `main_experiments/run_evaluation_sigmod.py`.\nThe remaining files are subsets of the results, as the main experimental pipeline parallelizes over 27 datasets, 5 cross-validation folds, and 8 subgroup-discovery methods.\nThus, there are `27 * 5 * 8 = 1080` files containing subsets of the results.\nEach row in a result file corresponds to one subgroup.\nOne can identify individual subgroup-discovery runs with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- subgroup-discovery method `sd_name`\n- feature-cardinality threshold `param.k` (missing value if no feature-cardinality constraint employed)\n- solver timeout `param.timeout` (missing value if not solver-based search)\n- number of alternatives `param.a` (missing value if only original subgroup searched)\n- dissimilarity threshold `param.tau_abs` (missing value if only original subgroup searched)\nFor each value combination of these seven columns, there is either one subgroup (search for original subgroups)\nor six subgroups (search for alternative subgroup descriptions, in which case the column `alt.number` identifies individual subgroups within a search run).\nFurther, note that the last four mentioned columns contain missing values, which should be treated as a category on their own.\nIn particular, if you use `groupby()` from `pandas` for analyzing the results and you want to include any of the last four mentioned columns in the grouping,\nyou should either fill in the missing values with an (arbitrary) placeholder value or use the parameter `dropna=False`,\nbecause the grouping (by default) ignores the rows with missing values in the group columns otherwise.\nThe remaining columns represent results and evaluation metrics.\nIn detail, all result files contain the following columns:\n- `objective_value` (float `>= -0.25` + missing values): Objective value of the subgroup-discovery method on the training set.\n  Usually quantifies WRAcc (in `[-0.25, 0.25]`) when searching original subgroups and normalized Hamming similarity (in `[0, 1]`) when searching alternative subgroup descriptions.\n  First exception: The subgroup-discovery method *MORS* has missing values since *MORS* does not explicitly compute an objective when searching for subgroups.\n  Second exception: The subgroup-discovery methods *BSD* and *SD-Map* use WRAcc times the number of data objects (a dataset-dependent constant) as objective,\n  so the objective value is higher than for the remaining subgroup-discovery methods.\n- `optimization_status` (string, 2 different values + missing values): For *SMT*, `sat` if optimal solution found and `unknown` if timeout.\n  Missing value for all other subgroup-discovery methods (which do not use solver timeouts).\n- `optimization_time` (non-negative float): The optimization runtime (in seconds) inside the subgroup-discovery method, i.e., without pre- and post-processing steps.\n- `fitting_time` (non-negative float): The complete runtime (in seconds) of the subgroup-discovery method (as reported in the paper), i.e., including pre- and post-processing steps.\n  Very similar to `optimization_time` except for *SMT* as the subgroup-discovery method, which may spend a considerable amount of time formulating the optimization problem.\n- `train_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the training set.\n- `test_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the test set.\n- `train_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the training set.\n- `test_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the test set.\n- `box_lbs` (list of floats, e.g., `[-inf, 0, -inf, -2, 8]`): The lower bounds for each feature in the subgroup description.\n  Negative infinity if a feature's lower bound did not exclude any data objects from the subgroup.\n- `box_ubs` (list of floats, e.g., `[inf, 10, inf, 5, 9]`): The upper bounds for each feature in the subgroup description.\n  Positive infinity if a feature's upper bound did not exclude any data objects from the subgroup.\n- `selected_feature_idxs` (list of non-negative ints, e.g., `[0, 4, 5]`): The indices (starting from 0) of the features selected (= restricted) in the subgroup description.\n  Is an empty list, i.e., `[]`, if no feature was restricted (so the subgroup contains all data objects).\n- `dataset_name` (string, 27 different values): The name of the `PMLB` dataset used for subgroup discovery.\n- `split_idx` (int in `[0, 4]`): The index of the cross-validation fold of the dataset used for subgroup discovery.\n- `sd_name` (string, 8 different values): The name of the subgroup-discovery method (`Beam`, `BI`, `BSD`, `MORS`, `PRIM`, `Random`, `SD-Map`, or `SMT`).\n- `param.k` (int in `[1, 5]` + missing values): The feature-cardinality threshold for subgroup descriptions.\n  Missing value if unconstrained subgroup discovery.\n  Always `3` if alternative subgroup descriptions searched.\n- `param.timeout` (int in `[1, 2048]` + missing values): For *SMT*, solver timeout (in seconds) for optimization (not including time for formulating the optimization problem).\n  Missing value for all other subgroup-discovery methods.\n- `alt.hamming` (float in `[0, 1]` + missing values): Normalized Hamming similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.jaccard` (float in `[0, 1]` + missing values): Jaccard similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.number` (int in `[0, 5]` + missing values): The number of the current alternative if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n  Thus, original subgroups either have `0` or a missing value in this column (i.e., for experimental settings where alternative subgroup descriptions searched, there is no separate search for an original subgroup, only a joint sequential search for original and alternatives).\n- `param.a` (int with value `5` + missing values): The number of desired alternative subgroup descriptions, not counting the original (zeroth) subgroup description.\n  Missing value if only original subgroup searched.\n- `param.tau_abs` (int in `[1, 3]` + missing values) The dissimilarity threshold for alternatives, corresponding to the absolute number of features that have to be deselected from the original subgroup description and each prior alternative.\n  Missing value if only original subgroup searched.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the columns `box_lbs`, `box_ubs`, and `selected_feature_idxs`\n(which represent lists and whose values are quoted except for empty lists).\nThe first line in each result file contains the column names.\nYou can use the following code to make 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Their weight was measured (XS205, Mettler Toledo), and they were dried overnight at 120 °C under high vacuum before being transferred into an Ar glovebox (water and oxygen content under 0.1 ppm). All capacities were calculated based on the measured weight of the assembled electrodes.\nThe separator compositions were as follows: borosilicate glass (Whatman GF/A, porosity 91%, thickness 260 mum, m.p. > 300 °C), polyethylene terephthalate/Al2O3 (Viledon FS 3005-25, porosity 55%, thickness 25 mum, m.p. ~250 °C), polypropylene (Celgard 4560, porosity 55%, thickness 110 mum, m.p. ~150 °C), and polytetrafluoroethylene (Omnipore JVWP04700, porosity 88%, thickness 30 mum, m.p. ~300 °C). The HTT test cells contain the four separators mentioned above as sheets with a diameter of 21.6 mm. The cells were assembled in a glovebox under an Ar atmosphere, according to literature.[36] Temperature-stable separator housings and sealing ring materials made of polyether ether ketone (PEEK) were used for the selected temperature range.\nGases used for calibration were purchased from Air Liquide or Westfalen AG. The quantities and purities of the gases were: Ar (99.999%); calibration gas 1: Ar (main component, 99.999%), CH4 (5025 ppm, 99.5%), C2H4 (5025 ppm, 99.5%) and CO2 (5025 ppm, 99.5%); calibration gas 2: Ar (main component, 99.999%), H2 (5005ppm, 99.999%), CO (4991 ppm, 99.0%) and O2 (4700 ppm, 99.999%). Chemicals used for calibration were purchased and used and without further purification: dimethyl carbonate (99% dry, Acros Organics), Lithium hexafluorophosphate (99.99%, Sigma Aldrich), and Lithium peroxide (95%, ABCR). The conductive salt decomposition gas POF3 is calibrated.\nSetup for Temperature-induced Stress Test and Electrochemical characterization:\nThe operando electrochemical and gas analysis setup couples a potentiostat and mass spectrometer. The HTT cell (EL-Cell) connects the lithium-ion battery with quick connectors with a heated transfer line and the mass spectrometer (MS, Omnistar GSD 320, Pfeiffer Vacuum). The parameters of the mass spectrometer are:\nParameter Value\nMultiple Ion Detection (MID) m/z: 2, 16, 18, 26, 27, 28, 30, 32, 36, 44, 67, 69, 77, 78, 85, 88, 90, 104, 107, 118, 126, 155\nEnergy of the electron beam 70 eV\nDetector C-SEM\nCapillary temperature 200 °C\nInput heating 120 °C\nScan rate 500 ms\nAr flow rate 0.65 mL min-1\nThe heating profile of the HTT cell during temperature-induced stress test are:\nParameter Value\nStart temperature 25 °C\nHeating rate 2 °C min 1\nTest temperature 132 °C\nHold temperature at test temperature while recording the open-circuit voltage 60 min\nEnd temperature 25 °C\nAll assembled cells were cycled with a Gamry potentiostat (5000 E) in a self-built temperature chamber at 25 °C in the voltage range between 3.0 V and 4.2 V. The cycling procedure was identical for all experiments is as follows:\nParameter Value\nTemperature 25 °C\nRest time 6 h\nFormation, 1 cycle C/10\nHeating step after formation starts at ~3.7 V (~SOC 50%) OCV for 10 h\nCycle after temperature induced stress test C/10\nCharge / Discharge CC+CV / CC\nU(cut-off) 4.2 V / 3.0 V\nFor more information about experimental details, see in the Experimental Section in the manuscript and the Supporting Information." , "Separators in lithium-ion batteries are considered to be (electro-)chemically inert to typical operating conditions. Yet, temperature abuse tests at elevated temperatures of ca. 60 to 132 °C show that the choice of separator material has a decisive influence on battery behavior and degradation. Using online electrochemical mass spectrometry, we analyzed the evolution of cell voltage and gas products during and after thermal abuse for different separators. Polypropylene and polytetrafluoroethylene seem stable, whereas glass fiber and polyethylene terephthalate separators were reactive and caused additional gas release. Polyethylene terephthalate produced significantly more gas, and the separator failed mechanically, causing unreproducible and drastic performance losses. The CO2 content of polyethylene terephthalate separator is four times higher than that of glass fiber. Yet, a five times higher amount of POF3 was detected for glass fiber. The evolved gas amounts of CO2 or POF3 were modest for polypropylene and polytetrafluoroethylene.";
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                "Processed micro-tomographic scan of the ant Lasius neglectus,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                "see README.pdf" , "The archaeal proton pump bacteriorhodopsin uses sunlight to build up a proton gradient across the cell membrane. The mechanism of energy conversion is based on a well investigated photocycle. Despite over 45 years of research on this bioenergetical model system, there is still little information available about the last step of the photocycle: the O→ground (bR) state transition. It is merely assumed that a long-range proton transfer recovers the bR state of bacteriorhodopsin. This work aims to shed further light on the mechanistic details and thermodynamic/kinetic features of the O→bR transition. In order to achieve this, computational methods were used as a viable complement to the experiment since the handling of membrane proteins and the targeted objectives pose a quite involved task to experimental techniques. Initially, structural models for the O, bR and the O* intermediate state were obtained by employing enhanced sampling molecular dynamics simulations. For the simulation of the (long- range) proton transfer reactions, a hybrid QM/MM setup based on the semiempirical quantum chemistry method DFTB3 and the CHARMM36 force field was used. The change of Gibbs free energy associated with the proton transfer was resolved by employing free energy techniques with an advanced reaction coordinate based on the center of excess charge representation. The results indicate that the O→bR conversion represents a slightly exergonic process in which the O* state constitutes a metastable intermediate. Concerning the proton transfer pathway, an interesting finding was made: The long-range proton transfer is accomplished via a proton hole/in- verse Grotthuss mechanism. Moreover, this charge transfer is accompanied by the reorientation of a functionally important, positively charged arginine side chain as well as by the formation of the proton release group. In the course of these studies, DFTB3 was furthermore benchmarked for proton transfer reactions. The method proved to be efficient for the description of these reactions with a small tendency to underestimate reaction barriers. In order to correct for this error, which results from missing short-range Pauli repulsion in DFTB3, the empirical Delta-Pauli correction was developed. However, until now, there were no parameters available for this model. Hence, in this work the first parameter set for CHNO-based molecular systems was derived and benchmarked on relevant organic/biomolecular systems. The results show that Delta-Pauli successfully introduces short-range Pauli repulsion to DFTB3. As a consequence, Delta-Pauli improves the description of molecular systems regarding non-covalent interactions, inter- as well as intramolecular reaction barriers and equilibrium geometries." , "Die archaeale Protonenpumpe Bacteriorhodopsin nutzt Sonnenlicht um einen Protonengradienten über der Zellmembran aufzubauen. Der Mechanismus der Energieumwandlung basiert auf einem gut erforschten Photozyklus. Obwohl schon über 45 Jahre an diesem bioenergetischen Modellsystem geforscht wurde, sind noch immer wenig Informationen über den letzten Schritt dieses Photozykluses vorhanden: den Übergang vom O zum Grundzustand (bR). Es wird jedoch angenommen, dass ein langreichweitiger Protonentransfer den bR Zustand wiederherstellt. Diese Arbeit zielt darauf ab, weiteren Aufschluss über die mechanistischen Details und thermodynamischen/kinetischen Eigenschaften des O→bR Übergangs zu geben. Um dies zu bewerkstelligen, wurden computergestütze Rechenmethoden als brauchbare Ergänzung zum Experiment verwendet, da die Handhabung von Membranproteinen und die gesetzten Ziele für experimentelle Methoden eine schwierige Aufgabe darstellen. Zunächst wurden Strukturmodelle mit Hilfe von enhanced sampling Methoden für den O, bR und den O* Intermediatszustand erstellt. Für die Simulation der (langreichweitigen) Protonentransfer- reaktionen wurde ein hybrides QM/MM Set-up basierend auf der semiempirischen Quantenchemie- Methode DFTB3 und dem CHARMM36 Kraftfeld verwendet. Die mit dem Protonentransfer assoziierte Änderung der Gibbs’schen Freien Energie wurde mit Hilfe von Freie-Energie-Techniken in Kombination mit einer fortgeschrittenen Reaktionskoordinate, die auf der center of excess charge- Darstellung basiert, aufgeklärt. Die Ergebnisse weisen darauf hin, dass es sich bei der O→bR Konversion um einen leicht exergonen Prozess handelt, in dem der O* Zustand ein metastabiler Zwischenzustand bildet. Bezüglich des Protonentransfer-Pfads wurde eine interessante Feststellung gemacht: Der langreichweitige Protonentransfer wird über einen Protonen-Loch-/inversen Grotthuss-Mechanismus vollzogen. Zudem wird dieser Ladungstransfer begleitet von der Umorientierung einer funktionell wichtigen, positiv geladenen Arginin Seitenkette sowie der Ausbildung der proton release group. Im Zuge dieser Studien, wurde des Weiteren die Genauigkeit von DFTB3 für Protonentransferreaktionen evaluiert. Die Methode bewies sich als leistungsstark für die Beschreibung dieser Reaktionen mit einer geringfügigen Tendenz für die Unterschätzung von Reaktionsbarrieren. Um diesen Fehler zu korrigieren, der aus der Unterschätzung der kurzreichweitigen Pauli Repulsion in DFTB3 resultiert, wurde die empirische Delta-Pauli Korrektur entwickelt. Allerdings gab es bisher keine Parameter für dieses Model. Daher wurde in dieser Arbeit der erste Parametersatz für CHNO-basierte Molekülsysteme entworfen und an relevanten organischen/biochemischen Systemen evaluiert. Die Ergebnisse zeigen, dass Delta-Pauli erfolgreich kurzreichweitige Pauli Repulsion in DFTB3 einführt. Dies hat zur Folge, dass Delta-Pauli die Beschreibung von Molekülsystemen bezüglich nichtkovalenter Wechselwirkungen, inter- und intramolekularen Reaktionsbarrieren und Gleichgewichtsgeometrien verbessert.";
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                "# Simulated 2D RASER MRI dataset for AI-driven artefact correction\nData for AI-driven artefact correction in 2D RASER MRI images. \nRandom images are generated with basic shapes and image transformations. 30 projections of each image are taken, and undergo a RASER (Radiowave amplification by the stimulated emission of radiation) [1] simulation in MATLAB. The data is divided into 3 subsets: \n- 10k_images.7z    --> standard random images\n- 10k_images_WithPump.zip  --> projections experience parahydrogen pumping\n- 1k_images_20TPI.zip   --> high total population inversion (TPI) variations of +/- 20%\n## File format\nFolder structure: ```{subset}/image{#}/{TPI value}/{filename.csv}```\nEach folder contains the following files:\n- A(0).csv       --> Signal amplitude\n- d(0).csv       --> TPI evolution\n- meta.csv       --> Meta information\n- output(Real and Imag).csv   --> Simulated RASER signal\n- Phi(0).csv       --> Signal phase\n## Data loading\nScripts for data loading are provided with the code at [github.com/mobecks/raser-mri-ai](https://github.com/mobecks/raser-mri-ai).\n## References\n[1] Sören Lehmkuhl et al., RASER MRI: Magnetic resonance images formed spontaneously exploiting cooperative nonlinear interaction.Sci. Adv.8,eabp8483(2022). DOI:10.1126/sciadv.abp8483";
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                "# Experimental Data for the Paper \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\" (Version 2)\nThese are the experimental data for the second version (v2) of the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\n, published at [arXiv](https://arxiv.org/) in 2024.\nIf we create further versions of this paper in the future, these experimental data may cover them as well.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 255 h.\nNote that the data originate from two separate runs of the experimental pipeline since we re-ran a subset of the experimental tasks to include heuristic search methods for alternatives.\nOnly two out of five feature-selection methods were affected by these changes (as the heuristics do not work for the others), so we did not run the full pipeline again.\nResults for the feature-selection methods `FCBFSelector`, `GreedyWrapperSelector`, and `MRMRSelector` base on a run of the experimental pipeline (`run_experiments.py`) at the commit [`2212360a32`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/2212360a320121862556bc8d54da45cb6dc8ee93).\nResults for the feature-selection methods `MISelector` and `ModelImportanceSelector` base on a run of the experimental pipeline (`run_experiments.py`) at the commit [`ae73417c9c`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/ae73417c9cf64aedd77e02a50a3df8955e177251).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation_arxiv.py`) is [`898e9ebedf`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/898e9ebedfae642f4c6be80244b79fdce3920500).\nWe also tagged all relevant commits (`run-2023-06-23`, `run-2024-01-23`, and `evaluation-2024-02-01`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation_arxiv.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth files are CSVs only containing numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column corresponds to a feature.\nThe first line in each CSV contains the names of the features as strings; for `_y.csv` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation_arxiv.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the dataset).\n  Might also be an empty list, i.e., `[]` if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1,1]`): The training-set objective value of the feature set.\n  Three feature-selection methods (*FCBF*, *MI*, *Model Importance*) have the range `[0,1]`, while two methods (*mRMR*, *Greedy Wrapper*) have the range `[-1,1]`.\n- `test_objective` (float in `[-1,1]` ): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  The interpretation of this value depends on the search method for alternatives and the feature-selection method.\n  (1a) In solver-based search for alternatives in combination with white-box feature-selection methods, this value corresponds to one solver call.\n  (1b) In solver-based search for alternatives in combination with wrapper feature selection, we record the total runtime of the *Greedy Wrapper* algorithm, which calls the solver and trains prediction models multiple times.\n  (2) In heuristic search for alternatives (algorithms *Greedy Balancing* and *Greedy Replacement*), we record the total runtime of the heuristic search algorithms.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver-based or heuristic search method for alternatives; for wrapper feature selection, this is only the status of the last solver call and only refers to a satisfiability problem rather than the (black-box) optimization problem.\n  - 0 = (proven as) optimal; cannot occur for heuristic search methods\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible; cannot occur for heuristic search methods\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `random_forest_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `{1, ..., 10}`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for solver-based simultaneous search and *Greedy Balancing*, but always `10` for solver-based sequential search and *Greedy Replacement*.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for solver-based simultaneous search but always sum-aggregation for the remaining search methods (where the aggregation does not matter for the search).\n- `search_name` (string, 4 different values):\n  The name of the search method for alternatives (`search_greedy_balancing`, `search_greedy_replacement`, `search_sequentially`, or `search_simultaneously`).\n  *Greedy Balancing* and *Greedy Replacement* are (solver-free) heuristics and are only combined with two feature-selection methods (*MI* and *Model Importance*).\n  The other two values denote solver-based search here (though in the paper, we also categorize optimization problems as sequential/simultaneous, no matter how they are solved) and are combined with all five feature-selection methods.\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= *Model Gain* in the paper), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0,4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1,1000]`): The number of iterations in case wrapper feature selection was used, missing value (empty string) in the other cases.\n  This column does not exist in result files not containing wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that lists of feature indices are treated as such (rather than strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```" , "These are the experimental data for the second version (v2) of the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished on [arXiv](https://arxiv.org/) in 2024.\nYou can find the paper [here](https://doi.org/10.48550/arXiv.2307.11607) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text.";
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                                          <http://schema.org/sameAs>      "http://orcid.org/This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model.\nIt has been used in [1,1a]. Here, only the noise-free BSPs are provided.\nDue to its size, this is a multi-part dataset.\nPart 1: https://doi.org/10.5445/IR/1000156139\nPart 2: https://doi.org/10.5445/IR/1000156554\nPart 3: https://doi.org/10.5445/IR/1000156555\nPart 4: https://doi.org/10.5445/IR/1000156556\nPart 5: https://doi.org/10.5445/IR/1000156557\nEach archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:\n- heart.vtp:\n  A triangle mesh of the heart including the point data:\n  - ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\n  - class: Boundary regions used as input for the computation of fiber orientations [3].\n  - trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).\n- heart_transform_matrices.mat:\n  A 1 x 3 cell array containing 4 x 4 transformation matrices that\n  describe the pose of the heart within the torso.\n  Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.\n- actTimes.mat:\n  A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5]\n  (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).\n- bsp.mat:\n  - bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with\n         scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].  - bspEnd: Time index of the end of depolarization (largest scaled activation time).\nThe archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:\n- torso.vtp:\n  A triangle mesh of the torso including the point data:\n  - electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).\n- heart_meanshape.vtp:\n  A triangle mesh of the mean shape of the statistical shape model [7,8].  \n- heart_shapemodel.mat:\n  - pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).\n  - var: A numModes x 1 vector of variances.\n  - weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.\n- heart_alignment_matrix.mat:\n  A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).\n- heart_transform_params.mat:\n  A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).\n- fiber_angles.mat:\n  - alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.\n  - alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.\n- actTimeScalings.mat:\n  - A numModels x numFoci matrix of factors used to scale the activation times.\n- tmv_template.mat:\n  The transmembrane voltage time course used to compute BSPs.\n- heart_classes.vtp:\n  A coarse triangle mesh of the mean shape used for fuzzy classification.\n- heart_classes_subdiv.vtp:\n  A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates.\n[1] https://doi.org/10.48550/arXiv.2209.08095\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\n[2] https://doi.org/10.1016/j.media.2021.102247\n[3] https://github.com/KIT-IBT/LDRB_Fibers\n[4] https://github.com/KIT-IBT/FIM_Eikonal\n[5] https://doi.org/10.1137/120881956\n[6] https://doi.org/10.1016/j.cmpb.2007.09.004\n[7] https://doi.org/10.5281/zenodo.4506463\n[8] https://doi.org/10.1016/j.media.2015.08.009"
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                "This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model.\nIt has been used in [1,1a]. Here, only the noise-free BSPs are provided.\nDue to its size, this is a multi-part dataset.\nPart 1: https://doi.org/10.5445/IR/1000156139\nPart 2: https://doi.org/10.5445/IR/1000156554\nPart 3: https://doi.org/10.5445/IR/1000156555\nPart 4: https://doi.org/10.5445/IR/1000156556\nPart 5: https://doi.org/10.5445/IR/1000156557\nEach archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:\n- heart.vtp:\n  A triangle mesh of the heart including the point data:\n  - ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\n  - class: Boundary regions used as input for the computation of fiber orientations [3].\n  - trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).\n- heart_transform_matrices.mat:\n  A 1 x 3 cell array containing 4 x 4 transformation matrices that\n  describe the pose of the heart within the torso.\n  Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.\n- actTimes.mat:\n  A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5]\n  (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).\n- bsp.mat:\n  - bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with\n         scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].\n  - bspEnd: Time index of the end of depolarization (largest scaled activation time).\nThe archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:\n- torso.vtp:\n  A triangle mesh of the torso including the point data:\n  - electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).\n- heart_meanshape.vtp:\n  A triangle mesh of the mean shape of the statistical shape model [7,8].  \n- heart_shapemodel.mat:\n  - pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).\n  - var: A numModes x 1 vector of variances.\n  - weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.\n- heart_alignment_matrix.mat:\n  A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).\n- heart_transform_params.mat:\n  A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).\n- fiber_angles.mat:\n  - alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.\n  - alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.\n- actTimeScalings.mat:\n  - A numModels x numFoci matrix of factors used to scale the activation times.\n- tmv_template.mat:\n  The transmembrane voltage time course used to compute BSPs.\n- heart_classes.vtp:\n  A coarse triangle mesh of the mean shape used for fuzzy classification.\n- heart_classes_subdiv.vtp:\n  A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates.\n[1] https://doi.org/10.48550/arXiv.2209.08095\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\n[2] https://doi.org/10.1016/j.media.2021.102247\n[3] https://github.com/KIT-IBT/LDRB_Fibers\n[4] https://github.com/KIT-IBT/FIM_Eikonal\n[5] https://doi.org/10.1137/120881956\n[6] https://doi.org/10.1016/j.cmpb.2007.09.004\n[7] https://doi.org/10.5281/zenodo.4506463\n[8] https://doi.org/10.1016/j.media.2015.08.009" , "1.8 million ECGs derived from multiscale simulations of cardiac electrophysiology of ventricular extrasystoles. 1000 anatomical variants of a bi-ventricular mesh x 600 excitation origins x 3 heart posiitions in the torso.";
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                "--------------------------------------------------------------------------------\nDatabase: Ridge-Type Roughness - From Turbulent Channel Flow to Internal Combustion Engine\n--------------------------------------------------------------------------------\nThis database provides the data presented in the manuscript \"Ridge-type\nroughness – from turbulent channel flow to internal combustion engine\".\n(DOI:10.1007/s00348-021-03353-x)\nIt features experimental and numerical data  on two ridge-type geometries \nspecified in figures 1 and 2. \nThe data is provided as .dat files separated for geometries (trapezoidal grooves,\n rectangular grooves) and for the individual data sources (DNS, PTV, HW, dPdx, PIV). \nAn overview of all provided files is given in table 1. The nomenclature stated in\ntable 1 is the same as introduced in the manuscript in table 1.\nfigure 1: trapezoidal grooves\n```\n      _____s_____\n     /\\         /\\   |\n    /  \\       /  \\  |h\n___/    \\_____/    \\_|___\n```\n| ID                                                                   | flow type | s/δ     | h/δ     | tip angle[°] |\n|-----------------------------------------------|--------------|--------|---------|----------------|\n| trixx                                                              | channel    | 2.16   | 0.158  | 54.8               |\n| trixx                                                              | engine       | 1.52   | 0.376  | 65                  |\n______________________________________________________________\nfigure 2: rectangular grooves\n```\n  _______s______ \n    ____         ____\n   |    |       |    | \n   |    |       |    | h\n___|    |_______|    |_____\n   __w__\n```\n| ID                                                                   | flow type | s/δ      | h/δ     | w/δ    |\n|-----------------------------------------------|--------------|---------|--------|---------|\n| rectxx                                                           | channel    | 1.22    | 0.078 | 0.238  |\n| rectxx                                                           | engine       | 1.12    | 0.174 | 0.562  |\n__________________________________________________________\ntable 1: database case overview\n| filename                           | flow type | data source | Reb                  | Quantities stored                                          |\n|------------------------------|--------------|-----------------|-----------------|----------------------------------------------------|\n|                                              |                     |                          |                           |                                                                               |\n| refpiston.dat                  | engine       | PTV                 | 15000             | y,y/d99,z,z/d99,U,V,U/U0,(V-V0)/U0        |\n| refdns.dat                       | channel     | DNS                | 18000             | y/delta,U/U0,uu/U0^2,vv/U0^2,              |\n|                                             |                      |                           |                          |   ww/U0^2,uv/U0^2                                      |                                                                          \n|                                             |                      |                          |                           |                                                                               |                                     \n| tridpdx.dat                     | channel    | dPdx               | 5000-85000  | Reb, Retau, Cf                                                  |\n| trihw12.dat                    | channel    | HW                   | 12000              | z/delta,y/delta,U/U0                                     |\n| trihw18.dat                    | channel    | HW                   | 17900              | y/delta,z/delta,U/U0,urms/U0                  |\n| trihw37.dat                    | channel    | HW                   | 37500              | z/delta,y/delta,U/U0                                      |\n| trihw58.dat                    | channel   | HW                    | 58200              | z/delta,y/delta,U/U0                                      |\n| tridns.dat                        | channel   | DNS                  | 18000              | y/delta,z/delta,U/U0,V/U0,W/U0,              |\n|                                             |                    |                            |                           | uu/U0^2,vv/U0^2,ww/U0^2,uv/U0^2,     |            \n|                                             |                    |                            |                           | uw/U0^2,vw/U0^2                                           |\n| trispiv.dat                       | channel   | sPIV                  | 18000              | y,z,U,V,W                                                              |\n| tripiston.dat                  | engine      | PTV                   | 15000              | y,y/d99,z,z/d99,U,V,U/U0,(V-V0)/U0         |\n|                                             |                     |                           |                            |                                                                                |\n| rectdpdx.dat                  | channel   | dPdx               | 5000-85000    | Reb, Retau, Cf                                                  |\n| recthw.dat                      | channel   | HW                   | 18000               | y/delta,z/delta,U/U0,urms/U0                  |\n| rectdns.dat                     | channel   | DNS                 | 18000               | y/delta,z/delta,U/U0,V/U0,W/U0,             |\n|                                              |                     |                          |                            |    uu/U0^2,vv/U0^2,ww/U0^2,uv/U0^2, |\n|                                              |                     |                          |                            |   uw/U0^2,vw/U0^2                                        |\n| rectpiston.dat                | engine      | PTV                 | 15000               | y,y/d99,z,z/d99,U,V,U/U0,(V-V0)/U0         |\n______________________________________________________\nPTV: particle tracking velocimetry\nDNS: direct numerical simulation\ndPdx: pressure-drop measuremts \nHW: hot-wire measurements\nsPIV: stereoscopic particle image velocimetry\n--------------------------------------------------------------------------------------------\n                                                    lars.deyn@kit.edu";
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                                          <http://schema.org/sameAs>      "http://orcid.org/This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model.\nIt has been used in [1,1a]. Here, only the noise-free BSPs are provided.\nDue to its size, this is a multi-part dataset.\nPart 1: https://doi.org/10.5445/IR/1000156139\nPart 2: https://doi.org/10.5445/IR/1000156554\nPart 3: https://doi.org/10.5445/IR/1000156555\nPart 4: https://doi.org/10.5445/IR/1000156556\nPart 5: https://doi.org/10.5445/IR/1000156557\nEach archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:\n- heart.vtp:\n  A triangle mesh of the heart including the point data:\n  - ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\n  - class: Boundary regions used as input for the computation of fiber orientations [3].\n  - trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).\n- heart_transform_matrices.mat:\n  A 1 x 3 cell array containing 4 x 4 transformation matrices that\n  describe the pose of the heart within the torso.\n  Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.\n- actTimes.mat:\n  A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5]\n  (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).\n- bsp.mat:\n  - bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with\n         scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].  - bspEnd: Time index of the end of depolarization (largest scaled activation time).\nThe archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:\n- torso.vtp:\n  A triangle mesh of the torso including the point data:\n  - electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).\n- heart_meanshape.vtp:\n  A triangle mesh of the mean shape of the statistical shape model [7,8].  \n- heart_shapemodel.mat:\n  - pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).\n  - var: A numModes x 1 vector of variances.\n  - weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.\n- heart_alignment_matrix.mat:\n  A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).\n- heart_transform_params.mat:\n  A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).\n- fiber_angles.mat:\n  - alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.\n  - alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.\n- actTimeScalings.mat:\n  - A numModels x numFoci matrix of factors used to scale the activation times.\n- tmv_template.mat:\n  The transmembrane voltage time course used to compute BSPs.\n- heart_classes.vtp:\n  A coarse triangle mesh of the mean shape used for fuzzy classification.\n- heart_classes_subdiv.vtp:\n  A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates.\n[1] https://doi.org/10.48550/arXiv.2209.08095\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\n[2] https://doi.org/10.1016/j.media.2021.102247\n[3] https://github.com/KIT-IBT/LDRB_Fibers\n[4] https://github.com/KIT-IBT/FIM_Eikonal\n[5] https://doi.org/10.1137/120881956\n[6] https://doi.org/10.1016/j.cmpb.2007.09.004\n[7] https://doi.org/10.5281/zenodo.4506463\n[8] https://doi.org/10.1016/j.media.2015.08.009"
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                "# Pol-InSAR-Island dataset:\nThis folder contains multi-frequency Pol-InSAR data acquired by the F-SAR system of the German Aerospace Center (DLR) over Baltrum and corresponding land cover labels.\nData structure:\n- data\n  - FP1 # Flight path 1\n    - L # Frequency band\n      - T6 # Pol-InSAR data\n      - pauli.bmp # Pauli-RGB image of the master scene\n    - S\n      - ...\n  - FP2 # Flight path 2\n    - ...\n- label\n  - FP1\n    - label_train.bin\n    - ...\n  - FP2\n    - ...\nData format:\nThe data is provided as flat-binary raster files (.bin) with an accompanying ASCII header file (*.hdr) in ENVI-format.\nFor Pol-InSAR data the real and imaginary components of the diagonal elments and upper triangle elements of the 6 x 6 coherency matrix are stored in seperated files (T11.bin, T12_real.bin, T12_imag.bin,...)\nLand cover labels contained in label_train.bin and label_test.bin are encoded as integers using the following mapping:\n0 - Unassigned<br>\n1 - Tidal flat<br>\n2 - Water<br>\n3 - Coastal shrub<br>\n4 - Dense, high vegetation<br>\n5 - White dune<br>\n6 - Peat bog<br>\n7 - Grey dune<br>\n8 - Couch grass<br>\n9 - Upper saltmarsh<br>\n10 - Lower saltmarsh<br>\n11 - Sand<br>\n12 - Settlement" , "The strong scientific interest and the accompanying rapid development of machine learning, in particular deep learning, has led to a significant improvement in automatic image interpretation in recent years. Research generally focuses on classification or segmentation of optical images, but there are already several successful approaches that apply deep learning techniques to the analysis of PolSAR or Pol-InSAR images. While the success of learning-based methods for the analysis of optical images has been strongly driven by public benchmark datasets such as ImageNet and Cityscapes, which contain a large number of annotated training and test data, comparable datasets for the PolSAR and especially the Pol-InSAR domain are almost non-existent. This conclusion and the demand for large and representative expert-annotated benchmark datasets for the SAR community is also reached by Zhu et al. (2021) in their analysis of the current state of deep learning-based SAR image analysis. To fill this gap, this work presents a new multi-frequency Pol-InSAR benchmark dataset for training and testing learning-based methods. This dataset is intended to improve the development of new approaches or the adaptation and improvement of existing ones. Furthermore, a defined division of the data into training and testing sections will ensure the comparability of approaches of different works. \nFor the segmentation of PolSAR images, there already exist a few annotated datasets that are frequently referenced in the respective literature. These include the PolSF dataset, which contains PolSAR images from various spaceborne and airborne systems over San Francisco, the E-SAR dataset from Oberpfaffenhofen, and the Flevoland dataset, which contains an AIRSAR image depicting agricultural areas. The described datasets have several limitations that make them inadequate for evaluating learning-based image segmentation approaches. One weakness is the small amount of data, which is usually insufficient for training deep networks. Another problem is the lack of complexity of the segmentation task due to generic classes that are too easily distinguishable or spatial distributions of classes that are too simplistic, regular, and resembling each other. As a result, very high classification performances can already be achieved by simple classifiers, so that a comparison of more sophisticated classifiers, which are necessary for more challenging real-world tasks, is not possible. Another disadvantage of existing datasets is that the division into training and test areas is not fixed, which prevents the comparability of research that uses these datasets for evaluation. \nOur annotated, multi-frequency Pol-InSAR dataset named $\\textbf{Pol-InSAR-Island}$ provides information-rich data as well as a challenging segmentation task consisting of 12 land cover classes. The Pol-InSAR data of the dataset were acquired in April 2022, on behalf of the Lower Saxony Water Management, Coastal Defence and Nature Conservation Agency (NLWKN), with the airborne F-SAR system (Reigber, 2020) of the German Aerospace Center (DLR) during a measurement campaign over the German Wadden Sea. The basics necessary for this measurement campaign in terms of data acquisition and processing were developed within the GeoWAM project (Pinheiro, 2020; Schmitz, 2022). The data acquisition was performed simultaneously in S- and L-band. Interferometric analysis is enabled by imaging the area two times with a time offset of 12 minutes and a vertical baseline of 12m. The Pol-InSAR-Island dataset does not include all flight data of the measurement campaign, but only data sections that cover the island Baltrum. Based on co-registered interferometric image pairs the coherency matrix $\\mathbf{T}_6$ is calculated for each pixel, which is obtained from the scalar products of the Pauli scattering vectors $\\mathrm{k}_1$ and $\\mathrm{k}_2$:\n$\\mathbf{T}_6 = \\begin{bmatrix} \n                        \\mathrm{k}_1 \\\\ \\mathrm{k}_2\n                    \\end{bmatrix}\n                    \\begin{bmatrix}\n                        \\mathrm{k}_1^{*T} & \\mathrm{k}_2^{*T}\n                    \\end{bmatrix}\n                = \\begin{bmatrix}\n                    \\mathbf{T}_{11} & \\mathbf{\\Omega}_{12}\\\\\n                    \\mathbf{\\Omega}_{21} & \\mathbf{T}_{22}\n                \\end{bmatrix}.$\nSubsequently, this representation is projected from the slant-range to the ground-range geometry. The ground resolution of the geocoded products has a resolution of 1m x 1m. \nThe island of Baltrum is home to many different biotopes and vegetation species and is surrounded by the North Sea and tidal flats. The diverse land cover results in the target classes of the demanding Pol-InSAR-Island dataset. For a precise annotation of the data, an existing biotope type map, published by the the Lower Saxon Wadden Sea National Park Authority via Marine.Daten.Infrastruktur.Niedersachsen (MDI-NI), is used, which is a result of the Trilateral Monitoring and Assessment Program processing from 2013. In this map the island Baltrum is classified into 40 different biotope types. Since the biotope map was generated several years prior to the SAR measurement campaign, significant changes in the extent of the different biotope types are to be expected, making the map not sufficient for accurate annotation. Therefore, the map is revised within a semi-automatic process. Automatically, transition areas between different biotope types are removed and strongly similar biotope types are grouped into one class each. The decision about the grouping of biotope types is based on the semantic context and an analysis of the class separability based on polarimetric features. The analysis of class separability is performed visually based on a projection of the polarimetric features into a two-dimensional feature space using the UMAP algorithm. Manual improvements to the resulting annotation are made by visually interpreting the SAR images and simultaneously acquired optical images. The Pol-InSAR-Island dataset is split into two spatially disjoint subsets, using a chessboard grid that alternately defines training and test patches. Such a split pattern was chosen to ensure that all 12 classes are represented in both subsets and additionally mapped at different incidence angles. \nOverall, this results in a labeled multi-frequency Pol-InSAR dataset that provides a challenging segmentation task. The public availability of the dataset can make a crucial contribution to the further development and comparability of learning-based segmentation methods for Pol-InSAR images in the future.\nReferences\nPinheiro, M., Amao-Oliva, J., Scheiber, R., Jäger, M., Horn, R., Keller, M., Fischer, J. & Reigber, A. (2020). Dual-Frequency Airborne SAR for Large Scale Mapping of Tidal Flats. Remote Sensing, 12(11), 1827.\nReigber, A., Jäger, M., Fischer, J., Horn, R., Scheiber, R., Prats, P., & Nottensteiner, A. (2011). System Status and Calibration of the F-SAR Airborne SAR Instrument. 2011 IEEE International Geoscience and Remote Sensing Symposium, 1520-1523.\nSchmitz, S., Hammer, H., & Thiele, A. (2022). Multi-Frequency PolInSAR Data Are Advantageous for Land Cover Classification – A Visual and Quantitative Analysis. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, V-1-2022, 49–56.\nZhu, X. X., Montazeri, S., Ali, M., Hua, Y., Wang, Y., Mou, L., Shi, Y., Xu, F. & Bamler, R. (2021). Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives. IEEE Geoscience and Remote Sensing Magazine, 9(4), 143-172.";
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                "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d." , "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only.";
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                                          <http://schema.org/givenName>   "Michael";
                                          <http://schema.org/name>        "Frietsch, Michael"
                                        ];
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                                                    <http://schema.org/name>  "Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum"
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                                          <http://schema.org/familyName>  "Oncken";
                                          <http://schema.org/givenName>   "Onno";
                                          <http://schema.org/name>        "Oncken, Onno"
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                "2024-04-26";
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                "This is the data generated in the paper: \"Seismological evidence for a multifault network at the subduction interface\".\nPaper abstract:\nSubduction zones generate the largest earthquakes on Earth, yet their detailed structure, and its influence on seismic and aseismic slip, remains poorly understood. Geological studies of fossil subduction zones characterize the seismogenic interface as a 100m-1km thick zone1–3 within which deformation occurs mostly on meters-thick faults1,3–6. Conversely, seismological studies, with their larger spatial coverage and temporal resolution but lower spatial resolution, often image the seismogenic interface as a kilometers-wide band of seismicity7. Thus, how and when these meter-scale structures are active at the seismic-cycle timescale, and what influence they have on deformation is not known. Here, we detect these meters-thick faults with seismicity and show their influence on afterslip propagation. Using a local 3D velocity model and dense observations of over 1500 double-difference relocated earthquakes in Ecuador, we obtain an exceptionally detailed image of seismicity, showing that earthquakes occur sometimes on a single plane and sometimes on multiple meters-thick simultaneously active subparallel planes within the plate interface zone. This geometrical complexity impacts afterslip propagation, demonstrating the influence of fault continuity and structure on slip at the seismogenic interface. Our findings can therefore help create more realistic models of earthquake rupture, aseismic slip, and earthquake hazard in subduction zones." , "When using this data, please cite the paper \"Seismological evidence for a multifault network at the subduction interface\" (10.1038/s41586-024-07245-y)\nContents of this directory:\n1. Catalogue_NonLinLoc.csv: Catalogue of earthquakes located with NonLinLoc in a 1D model.\n2. Catalogue_full_picks.txt: Arrival times picked with machine-learning. For each earthquake, the header (lines starting with #) lists the origin time, longitude, latitude, depth, number of phases, and average horizontal uncertainty from NonLinLoc. For each pick, the station, traveltime, time residual from NonLinLoc and phase are listed.\n3. Catalogue_differential_arrivaltimes.txt: Cross-correlation differential times for all earthquake pairs with an interevent distance below 10 km. For each pair, the header (lines starting with #) lists the ID number of both events. For each pick, the station, differential time, correlation and phase are listed. To limit the file size, we only include cross-correlations above 0.6. \n4. Catalogue_tomoDD.csv: Catalogue of all earthquakes relocated with double-difference in tomoDD in a 3D model. The total relative location error, calculated using bootstrapping, refers to the location error of the event relative to the rest of its cluster in TomoDD. Since all events were relocated in a single cluster in TomoDD, this essentially corresponds to the average location error relative to all other events. For details on the calculation of errors, please refer to the online methods of Chalumeau et al. (2024).\n5. Errors_TomoDD.csv: Relative location errors for each event pair in TomoDD.\n6. Catalogue_families.csv: Families of similar earthquakes with cross-correlations above 0.75. For details on the creation of families, please refer to the online methods of Chalumeau et al. (2024).\n7. Stations.csv: List of station locations.\nIf you have any questions, please email Caroline Chalumeau [caroline.chalumeau@kit.edu / carochalu06@gmail.com] or Andreas Rietbrock [andreas.rietbrock@kit.edu]";
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                                          <http://schema.org/name>        "Hamann, Elias";
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                                          <http://schema.org/familyName>  "Lösel";
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                                          <http://schema.org/name>        "Lösel, Philipp D.";
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                "Processed micro-tomographic scan of the ant Dorylus fulvus,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                "Documents:\n-Design of Experiments: Information on the paths such as the technological values of the experiments\n-Recording information: Information about the recordings with comments\n-Data: All recorded datasets. The first level contains the folders for training and validation both with and without the workpiece. In the next level, the individual test executions are located. The individual recordings are stored in the form of a JSON file. This consists of a header with all relevant information such as the signal sources. This is followed by the entries of the recorded time series.\n-NC-Code: NC programs executed on the machine\nExperimental data:\n-Machine: Retrofitted DMC 60H\n-Material: S235JR, 2007 T4\n-Tools:\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 5mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 10mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 20mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 10mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n-Workpiece blank dimensions: 150x75x50mm\nLicense: This work is licensed under a Creative Commons Attribution 4.0 International\nLicense. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)." , "The aim of the dataset is to train and validate models for predicting time series for milling processes. For this purpose, processes were recorded at a sampling rate of 500 Hz by a Siemens Industrial Edge on a DMC 60H. The machine was upgraded in terms of control technology. Processes for model training and validation were recorded, suitable for both steel and aluminum machining. Several recordings were made with and without the workpiece (aircut) in order to cover as many cases as possible. This is the same series of experiments as in \"Training and validation dataset of milling processes for time series prediction\" with DOI 10.5445/IR/1000157789 and allows an investigation of the transferability of models between different machines." , "Das Ziel des Datensatzes ist das Training und die Validierung von Modellen zur Vorhersage von Zeitreihen für Fräsprozesse. Dazu wurden an einer DMC 60H Prozesse mit einer Abtastrate von 500 Hz durch eine Siemens Industrial Edge aufgenommen. Die Maschine wurde steuerungstechnisch aufgerüstet. Es wurden Prozesse für das Modelltraining und die Validierung aufgenommen, welche sowohl für die Bearbeitung von Stahl sowie von Aluminium verwendet wurden. Es wurden mehrere Aufnahmen mit und ohne Werkstück (Aircut) erstellt, um möglichst viele Fälle abdecken zu können. Es handelt sich um die gleiche Versuchsreihe wie in \"Training and validation dataset of milling processes for time series prediction\" mit der DOI 10.5445/IR/1000157789 und hat zum Ziel, eine Untersuchung der Übertragbarkeit von Modellen zwischen verschiedenen Maschinen zu ermöglichen.";
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                "In the present paper we consider a class of quasilinear wave equations on a smooth, bounded domain. We discretize it in space with isoparametric finite elements, and apply a semi-implicit Euler and midpoint rule as well as the exponential Euler and midpoint rule to obtain four fully discrete schemes. We derive rigorous error bounds of optimal order for the semi-discretization in space and the fully discrete methods in norms which are stronger than the classical $H^1\\times L^2$ energy norm under weak CFL-type conditions. To confirm our theoretical findings, we also present numerical experiments." , "This code is used for the numerical experiment in Section 2 of the paper\n\"Strong norm error bounds for quasilinear wave equations under weak CFL-type conditions\"\nby Benjamin Dörich. \nPart of the code relies on code written by J. Leibold in https://doi.org/10.5445/IR/1000130223 .\nThe computations are done in C++ using the Finite Element library deal.II; the plots then are generated with Python3.\nTo use this code, deal.II (release 9.5.1) has to be installed, cf. https://www.dealii.org/9.5.0\nIn order to compile the program, open a terminal session in this folderand call \"cmake -DDEAL_II_DIR=/path/to/deal.II .\" Next, call \"make release\" and \"make\".\nThen, one can run the commands (can be done in paralell)\n./main P1 \n./main P2 \n./main P3 \n./main P3_half\n./main euler\n./main midpoint\n./main alpha60P1\n./main alpha60P2\n./main alpha80P1\n./main alpha80P1\n./main alpha90P1\n./main alpha90P2\n./main alpha95P1\n./main alpha95P2\nto execute the code. This performs the computations and generates the files\nparameters_P1.ini_space_Q1.txt\nparameters_P2.ini_space_Q2.txt\nparameters_P3.ini_space_Q3.txt\nparameters_P3_half.ini_space_Q3.txt\nparameters_euler.ini_time_ImplEuler\nparameters_midpoint.ini_time_MidpointRule\nparameters_alpha_60_P1.ini_space_Q1.txt\nparameters_alpha_60_P1.ini_space_Q2.txt\nparameters_alpha_80_P1.ini_space_Q1.txt\nparameters_alpha_80_P2.ini_space_Q2.txt\nparameters_alpha_90_P1.ini_space_Q1.txt\nparameters_alpha_90_P2.ini_space_Q2.txt\nparameters_alpha_95_P1.ini_space_Q1.txt\nparameters_alpha_95_P2.ini_space_Q2.txt\nin the folder \"error\" containing the results of the numerical experiments.\nAfter that, the plots can be generated with the Python3 Script using in the terminal \npython3 wave_non_auto_error_plots.py\nin the folder \"tikz\".";
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                "Processed micro-tomographic scan of the ant Basiceros militaris,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                "Improvements to the signal-to-noise ratio of magnetic resonance detection lead to a strong reduction in measurement time, yet as a sole optimization goal for resonator design, it would be an oversimplification of the problem at hand. Multiple constraints, for example for field homogeneity and sample shape, suggest the use of numerical optimization to obtain resonator designs that deliver the intended improvement. Here we consider the 2D Lenz lens to be a sufficiently broadband flux transforming interposer between the sample and a radiofrequency (RF) circuit and to be a flexible and easily manufacturable device family with which to mediate different design requirements.We report on a method to apply topology optimization to determine the optimal layout of a Lenz lens and demonstrate realizations for both low- (45 MHz) and high-frequency (500 MHz) nuclear magnetic resonance." , "Folder Contains the Images, and the text file used for the corresponding images";
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                "--------------------------------------------------------------------------------\n            STATISTICS OF TURBULENT CHANNELS WITH AND WITHOUT \n                 DRAG REDUCTION AT CONSTANT POWER INPUT\n--------------------------------------------------------------------------------\n***This database features:***\n* high quality turbulent flow statistics for canonical channel flows driven at\n  Constant Power Input (CPI), Constant Flow Rate (CFR) and Constant Pressure\n  Gradient (CPG)\n* global energy budgets and single point Reynolds stress budgets of turbulent \n  channels driven at CPI in their canonical state or in a drag-reduced state \n  achieved via spanwise wall oscillations or opposition control\n* a comprehensive study of several predetermined and feedback active strategies \n  for turbulent drag reductions in channel, comprising parametric studies of the \n  increase in energy efficiency for different values of several control parameters  \n--------------------------------------------------------------------------------\n***Acknowledgment:***\nSupport through the Deutsche Forschungsgemeinschaft (DFG) project FR2823/5-1 is\ngratefully acknowledged. Computing time has been provided by the computational \nresource ForHLR Phase I funded by the Ministry of Science, Research and the Arts, \nBaden-Württemberg and DFG. \n--------------------------------------------------------------------------------\n***References:***\n* D. Gatti, A. Cimarelli, Y. Hasegawa, B. Frohnapfel & M. Quadrio, \n  (2018) \"Global energy fluxes in turbulent channels with flow \n  control\", Journal of Fluid Mechanics, Vol. 857, pag. 345-373, \n  doi: doi.org/10.1017/jfm.2018.749\n* M. Quadrio, B. Frohnapfel & Y. Hasegawa, (2016) \"Does the choice \n  of the forcing term affect flow statistics in DNS of turbulent \n  channel flows?\", European Journal of Mechanics B. / Fluids, Vol. \n  55, pag. 286-293, doi: doi.org/10.1016/j.euromechflu.2015\n* Y. Hasegawa, M. Quadrio & B. Frohnapfel, (2014) \"Numerical simulation \n  of turbulent duct flows with constant power input\", Journal of \n  Fluid Mechanics, Vol. 750, pag. 191-209, doi: doi.org/10.1017/jfm.2014.269\n* B. Frohnapfel, Y. Hasegawa & M. Quadrio, (2012) \"Money versus time: \n  evaluation of flow control in terms of energy consumption and \n  convenience\", Journal of Fluid Mechanics, Vol. 700, pag. 406-418\n  doi: doi.org/10.1017/jfm.2012.139\n--------------------------------------------------------------------------------\n***Content:***\nreference-stats         -->  statistics of turbulent channels at Retau=200, driven\n                             at Constant Flow Rate (CFR), Constant Pressure Gradient\n                             (CPG) and Constant Power Input (CPI). \nenergy-budgets          -->  global energy budgets and single-point Reynolds \n                             shear stress budgets of turbulent channels driven at \n                             Constant Power Input (CPI) with drag reduction \n                             achieved via spanwise wall oscillations (Quadrio &\n                             Ricco, JFM 2004) or opposition control (Choi, Moin &\n                             Kim, JFM 1994).\nflow-control-strategies -->  parametric studies for finding the maximum theoretical\n                             efficiency of following strategies for turbulent \n                             drag reduction at Constant Power Input (CPI):\n                             StTW-fz: streamwise-travelling waves of spanwise body force\n                             StTW-w : streamwise-travelling waves of spanwise wall velocity\n                             SpTW-fz: spanwise-travelling waves of spanwise body force\n                             SpTW-w : spanwise-travelling waves of spanwise wall velocity\n                             OC-v: opposition control based upon wall-normal velocity\n                             BF-w: near-wall body force damping of spanwise velocity fluctuations\nRefer to the readme files in each subdirectory for further information on each \ndatabase. \n--------------------------------------------------------------------------------\nChangelog:\nApr-23-2018: database uploaded\n--------------------------------------------------------------------------------\n                                                 davide.gatti@kit.edu\n                                           bettina.frohnapfel@kit.edu\n                                           maurizio.quadrio@polimi.it\n                                                ysk@iis.u-tokyo.ac.jp\n                                          andrea.cimarelli@unimore.it" , "This database contains the result of the Deutsche Forschungsgemeinschaft (DFG) project FR2823/5-1 \"Flow Control under Constant Power Input (CPI)\". Please refer to the README below for further information.";
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                "# `counterSharp` Experiment and Play Environment\nThis repository contains the reproducible experimental evaluation of the [counterSharp tool](https://github.com/samysweb/counterSharp).\nThe repository contains a Docker file which configures the counterSharp tool, two model counters (ApproxMC and Ganak) as well as the [tool by Dimovski et al.](https://github.com/aleksdimovski/probab_analyzer) for our experiments.\nFurthermore the repository contains the benchmarks on which we ran our experiments as well as the logs of our experiments and scripts for transforming the log files into LaTeX tables.\n## Getting Started\nIn order to pull and run the docker container from docker hub you should execute `docker run`. \nOr, you can load the archived and evaluated artifact into docker with\n```\ndocker load < countersharp-experiments.tar.gz\n```\nIf the image is loaded, `docker run` opens a shell allowing the execution of further commands:\n```bash\ndocker run -it -v `pwd`/results:/experiments/results samweb/countersharp-experiments\n```\nBy using a volume, the results are written to the host system rather than the docker container\nYou can remove the volume mounting option (`-v ...`), and create `/experiments/results` inside the container if you can spare the results.\nIf you are using the volume and run into permission problems, then you need to give rights via SELinux: `chcon -Rt svirt_sandbox_file_t `pwd`/results`.\nThis will create a writable folder `results` in your current folder which will hold any logs from experiments. \nA minimal example can be executed by running (this takes approximately **70 seconds**):\n```bash\n./showcase.sh\n```\nThis will create benchmark log files for the benchmarks `for_bounded_loop1.c` and `overflow.c` in the folder [`results`](results).\nFor example, `/experiments/results/for_bounded_loop1.c/0X/` contains five folder for the five repeated runs of the experiments on this file. \nEach folder `/experiments/results/for_bounded_loop1.c/0X/` contains the folders for the different tools, which includes the log and output files.\nA full run can be executed by running (this takes approximately a little under **2 days**):\n```bash\n./run-all.sh\n```\nAdditionally single benchmarks can be executed through the following commands:\n```bash\nrun-instance approx program.c \"[counterSharp arguments]\" # Runs countersharp with ApproxMC on program.c\nrun-instance ganak program.c \"[counterSharp arguments]\" # Runs countersharp with Ganak on program.c\nProbab.native -single -domain polyhedra program.c # Runs the tool by Dimovski et al. for deterministic programs\nProbab.native -single -domain polyhedra -nondet program.c # Runs the tool by Dimovski et al. for nondeterministic programs\n```\nFor example we can execute `run-instance approx /experiments/benchmarks/confidence.c \"--function testfun --unwind 1\"` to obtain the outcome of counterSharp and ApproxMC for the benchmark `confidence.c`.\nNote that the time information produced by runlim is always only for one part of the entire execution (i.e. for counterSharp or  one ApproxMC run or one Ganak run). The script `run-instance` is straightforwarded, we have the call to our tool [counterSharp](https://github.com/samysweb/counterSharp):\n```bash\npython3 -m counterSharp --amm /tmp/amm.dimacs --amh /tmp/amh.dimacs --asm /tmp/asm.dimacs --ash /tmp/ash.dimacs --con /tmp/con.dimacs -d $3 $2\n```\nwhich is followed by the call of `ApproxMC` oder `ganak`.\n## Benchmarks\nThe benchmarks are contained in the folder [`benchmarks`](benchmarks) which also includes an overview on the sources and modifications to the benchmarks  \nNote that benchmark versions for the tool by Dimovski et al. are contained in folder [`benchmarks-dimovski`](benchmarks-dimovski)\n## Benchmark Results\nThe results are contained in the folder [`results`](results) in which all logs from benchmark runs reside.\nThe log files from the evaluation are not available in the Docker Image, but just on GitHub.\nThe logs are split-up by benchmark instance (first level folder), run number (second level folder) and tool (third level folder)  \nFor example, the file `results/bwd_loop1a.c/01/approxmc/stdout.log` contains the stdout and stderr of running approxmc on the instance `bwd_loop1a.c` in run `01`  \n### Machine Details\nAll runs were executed on a Linux machine housing an Intel(R) Core(TM) i5-6500 CPU (3.20GHz) and 16GB of memory.\nNote that for every benchmark log `01/counterSharp/init.log` contains information on the machine used for benchmark execution as well as on the commits used in the experiments.\n## Running benchmarks\nFor all cases of automated benchmark execution we assume a CSV file containing relevant information on the instances to run: The first column is the benchmark's name, the second column are parameters passed to counterSharp (see `instances.csv`) or the tool by Dimovski (see `instances-dimovski.csv`).\nAll scripts produce benchmarking results for \"missing\" instances, i.e. instances for which no folder can be found in the `results` folder.\n- Run counterSharp on the benchmarks:  \n`run-counterSharp instances.csv`\n- Run [ApproxMC](https://github.com/meelgroup/ApproxMC) on benchmarks:  \n`run-approxmc instances.csv`\n - Only after counterSharp has been run\n- Run [GANAK](https://github.com/meelgroup/ganak) on benchmarks:  \n`run-ganak instances.csv`\n - Only after counterSharp has been run\n- Run [Dimovski's tool](https://github.com/aleksdimovski/probab_analyzer) on benchmarks:  \n`run-dimovski instances-dimovski.csv`\n## Log summarization\nSummarization is possible through the python script in `logParsing/parse.py` within the container.\nThe script takes as input a list of benchmarks to process and returns (parts of) a LaTeX table.\nNote, that there must exist logs for all benchmarks provided in the CSV file for the call to succeed!\n- **To obtain (sorted) results for deterministic benchmarks:**  \n`cat logParsing/deterministic-sorted.csv| python3 logParsing/parse.py results aggregate2`\n- **To obtain (sorted) results for nondeterministic benchmarks:**  \n`cat logParsing/nondeterministic-sorted.csv| python3 logParsing/parse.py results nondet`\n## Building the docker container\nAll tools are packaged into a Dockerfile which makes any installation unnecessary.\nThere is, however, the need for a running Docker installation.\nThe `Dockerfile` build depends on the accessibility of the following GitHub Repositories:\n- [CryptoMiniSat](https://github.com/msoos/cryptominisat)\n- [ApproxMC](https://github.com/meelgroup/approxmc)\n- [Ganak](https://github.com/meelgroup/ganak)\n- [Probab_Analyzer](https://github.com/aleksdimovski/probab_analyzer)\n- [counterSharp](https://github.com/samysweb/counterSharp)\nThe Docker image is hosted at [Dockerhub](https://hub.docker.com/repository/docker/samweb/countersharp-experiments).";
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                "Understanding the organizing principles of interacting electrons and the emergence of novel electronic phases is a central endeavor of condensed matter physics. Electronic nematicity, in which the discrete rotational symmetry in the electron fluid is broken while the translational one remains unaffected, is a prominent example of such a phase. It has proven ubiquitous in correlated electron systems, and is of prime importance to understand Fe-based superconductors.\nHere, we find that fluctuations of such broken symmetry are exceptionally strong over an extended temperature range above phase transitions in BaNi$_2$(As$_{1−x}$P$_x$)$_2$ , the nickel homologue to the Fe-based systems. This lends support to a type of electronic nematicity, dynamical in nature, which exhibits a particularly strong coupling to the underlying crystal lattice. Fluctuations between degenerate nematic configurations cause a splitting of phonon lines, without lifting degeneracies nor breaking symmetries, akin to spin liquids in magnetic systems" , "Raman scattering data";
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                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
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                                          <http://schema.org/givenName>   "Felix";
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                                          <http://schema.org/name>        "Philipp, Patrick"
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                "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d." , "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only.";
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                "Processed micro-tomographic scan of the ant Formica truncorum,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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                "Reconstructed ptychographic X-ray computed tomography data set acquired at Diamond Light Source, instrument I13-1 (proposal MG24079-1) of the activated hierarchical porous sample Ni/Al$_{2}$O$_{3}$-ha. The data set is part of an artificial coking study of nickel based catalysts, which were investigated by ptychographic X-ray computed tomography. Detailed information regarding sample preparation and experimental conditions are available in the respective publication and electronic supplementary information." , "The data contains the reconstructed tomogram slices in a \"*.tiff\" format as delta contrast tomogram. The correction of the delta tomogram and calculation of the electron density tomogram has to be done as described in the \"*_cutoffs.txt\" file. Information about required offset correction of the electron density tomograms is provided in the respective publication. Reconstructed X-ray ptychography projections were tomographically reconstructed using the Matlab script of the cSAXS group of the Swiss Light Source, as published in DOI:10.5281/zenodo.3539550 and DOI:10.1364/OE.27.036637. Ptychographic projections were obtained using the PtyRex package available at I13-1 beamline with 400 iterations.";
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                "2023-06-21";
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                "This data set contains path specifications of three variants of a circuit for driving tests on the driving dynamics area of the KIT Campus Ost.  The path specifications are used as input for a vehicle guidance controller to reproducibly let the automated vehicle follow the given path. Additionally, the three circuits are provided in a format that allows the integration of the tracks into the vehicle dynamics simulation tool CarMaker." , "Dieser Datensatz enthält Pfadvorgaben von drei Varianten eines Rundkurses für Fahrversuche auf der Dynamik-Testfläche des KIT Campus Ost.  Die Pfadvorgaben dienen als Input für eine automatisierte Fahrzeugführungsregelung, um Versuchsfahrzeuge reproduzierbar dem vorgegebenen Kurs folgen zu lassen. Ergänzend dazu sind die drei Kurse auch in einem Format enthalten, welches die Integration der Rundkurse im Gesamtfahrzeugsimulationstool CarMaker ermöglicht." , "# Three Variants of Circuits on Driving Dynamics Area of KIT \"Campus Ost\"\nThe English version can be found below.\n## Einleitung\nDie drei verschiedenen Kurse (*track_01*, *track_02*, *track_03*) unterscheiden sich in einzelnen Abschnitten, sodass je nach Variante z.B. engere Kurven vermieden werden oder längere gerade Abschnitte auftreten.\nDie Kurse finden in verschiedenen Projekten des Instituts für Fahrzeugsystemtechnik (FAST) und des Schaeffler Hub for Advanced Research am KIT (SHARE am KIT) Anwendung, beispielsweise zur Präsentation der Projektergebnisse im Projekt SmartLoad oder zur Erforschung neuer Ansätze zur Fahrzeugführungsregelung ([Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034)).\nDer Datensatz enthält die Kurse zum einen in Form einer Pfad-Definition als Input für einen Pfadfolgeregler und zum anderen als Straßen/Szenario-Definition zur Verwendung in der Gesamtfahrzeugsimulation mit CarMaker.\n## Format der Pfaddefinition\nDie Pfade werden definiert über ein Matlab mat-File in Form eines Structure Array, in welchem diskrete Stützstellen des Pfades enthalten sind.  Als Diskretisierung wurde ein Abstand zwischen den Stützstellen von ungefähr 0.3m gewählt.\nDas verwendete Koordinatensystem, in dem die Koordinaten der Stützstellen definiert sind, ist ein rechtshändiges kartesisches Koordinatensystem, dessen z-Achse nach oben zeigt. Dieses hat seinen Ursprung bei den globalen Koordinaten 49.022732549318°N, 8.432433939454015°E. Die X-Achse zeigt in Richtung des Punktes mit den Koordinaten 49.02303283495321°N, 8.431491841757758°E.\nAn jeder Stützstelle sind folgenden Dimensionen des Pfades definiert:\n- X-Koordinate in m\n- Y-Koordinate in m\n- Referenzgeschwindigkeit v in m/s\n- Pfad-Koordinate s in m\n- Bahnkrümmung in 1/m\n- Tangentiale Pfadorientierung in rad\n## Rundkurse in CarMaker\nDie Tracks können in die Gesamtfahrzeugsimulation mit CarMaker eingebunden werden. Somit kann beispielsweise ein Fahrzeugführungsregler anhand der Rundkurse simulativ optimiert werden und später in der Realität auf dem Testgelände validiert werden.  Die Tracks sind in zwei unterschiedlichen Arten modelliert. Zum einen als einspurige Straße (Index „Road“) und zum anderen als Mittellinie auf einer Dynamikfläche (Index „Line“). Letztere Variante lässt bei der Simulation größere Abweichungen vom Soll-Pfad zu, ohne dass es zum Abbruch des Simulationsdurchlaufes durch CarMaker kommt, womit sich diese Variante besser zur Entwicklung von Regler-Algorithmen eignet. \nDie Integration in ein CarMaker Projekt erfolgt durch Kopieren der zwölf Dateien (*Track_01_Line, Track_01_Line.rd5; Track_01_Road; Track_01_Road.rd5;…*) in den CarMaker Projektordner *…\\Data\\Road*.\nIn der CarMaker GUI kann der Kurs dann unter *Parameters -> Scenario/Road -> Load Road file* ausgewählt werden. Die erstellten *.rd5* Files basieren auf der CarMaker Version 8.0.\n## English version:\n## Introduction\nThe three different tracks (*track_01*, *track_02*, *track_03*) differ in some sections, so that depending on the variant, e.g. tighter curves are avoided or longer straight sections occur.\nThe circuits are used in various projects of the Institute of Vehicle System Technology (FAST) and the Schaeffler Hub for Advanced Research at KIT (SHARE at KIT), for example to present project results in the SmartLoad project or to research new approaches for vehicle guidance control ([Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034)).\nThe dataset contains the circuits in the form of a path definition as input for a path tracking controller and as a road/scenario definition for use in the vehicle dynamics simulation tool CarMaker.\n## Format of the path definition files\nThe paths are defined using a Matlab mat-file in the form of a structure array containing discrete grid points of the path.  As discretization a distance between the grid points of about 0.3m was chosen.\nThe coordinate system used to define the grid points is a right-handed cartesian coordinate system with the z-axis pointing upwards. It has its origin at the global coordinates 49.022732549318°N, 8.432433939454015°E. The x-axis points in the direction of the coordinates 49.02303283495321°N, 8.431491841757758°E.\nThe following dimensions of the path are defined at each grid point:\n- X-coordinate in m\n- Y-coordinate in m\n- Reference velocity v in m/s\n- Path-coordinate s in m\n- Path curvature in 1/m\n- Tangential path orientation in rad\n## Tracks in CarMaker\nThe tracks can be integrated into the vehicle dynamics simulation tool CarMaker. For example, a vehicle guidance controller can be simulatively optimized using the paths and later validated in reality on the driving dynamics area.  The tracks are modeled in two different ways. On the one hand as a single-lane road (index \"Road\") and on the other hand as a center line on a driving dynamics area (index \"Line\"). The latter variant allows larger deviations from the target path during the simulation without causing CarMaker to abort the simulation run, which makes this variant more suitable for the development of controller algorithms. \nThe integration into a CarMaker project is done by copying the twelve files *(Track_01_Line, Track_01_Line.rd5; Track_01_Road; Track_01_Road.rd5;...)* into the CarMaker project folder *...\\Data\\Road*.\nIn the CarMaker GUI the track can be selected under *Parameters -> Scenario/Road -> Load Road file*. The created *.rd5* files are based on CarMaker version 8.0.";
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                "During sliding of metallic surfaces, the near surfaces undergo significant changes in terms of topography, composition and microstructure. Since friction and wear behavior of the materials are strongly influenced by sub-surface deformations, it is fundamental to investigate these effects. Therefore, the present study aims towards a better understanding of the behavior of friction depending on well-defined initial microstructures. By performing sliding experiments on Au-Ni multilayer samples under ultrahigh vacuum (UHV) conditions, we observe that the individual layer thickness of multilayer systems has a strong influence on friction behavior due to the transition in the dominant deformation mechanism near the surface. The experiments reported here provide a new route for lowering the friction force of metallic material systems in dry contact by providing more stable microstructures and alloy formation. Through ultrafine grains present in the alloy formed by mechanical mixing the number of grain boundaries strongly increases and hence, grain boundary-mediated deformation results in the low friction coefficient." , "Friction data aquired by the homebuilt UHV microtribometer for a ruby sphere sliding against different Au-Ni multilayer samples. The data includes normal load, friction force and COF as a function of the mulitlayer thickness.";
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                                          <http://schema.org/familyName>  "Gerasimov";
                                          <http://schema.org/givenName>   "Michail";
                                          <http://schema.org/name>        "Gerasimov, Michail";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-9097-0630"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Röder";
                                          <http://schema.org/givenName>   "Fridolin";
                                          <http://schema.org/name>        "Röder, Fridolin"
                                        ];
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                                          <http://schema.org/affiliation>
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                                          <http://schema.org/name>        "Krewer, Ulrike";
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                "Due to its high energy density, lithium metal is a promising electrode for future energy storage. However, its practical capacity, cyclability and safety heavily depend on controlling its reactivity in contact with liquid electrolytes, which leads to the formation of a solid electrolyte interphase (SEI). In particular, there is a lack of fundamental mechanistic understanding of how the electrolyte composition impacts the SEI formation and its governing processes. Here, we present an in-depth model-based analysis of the initial SEI formation on lithium metal in a carbonate-based electrolyte. Thereby we reach for significantly larger length and time scales than comparable molecular dynamic studies. Our multiscale kinetic Monte Carlo/continuum model shows a layered, mostly inorganic SEI consisting of LiF on top of Li2CO3 and Li after 1 µs. Its formation is traced back to a complex interplay of various electrolyte and salt decomposition processes. We further reveal that low local Li+ concentrations result in a more mosaic-like, partly organic SEI and that a faster passivation of the lithium metal surface can be achieved by increasing the salt concentration. Based on this we suggest design strategies for SEI on lithium metal and make an important step towards knowledge-driven SEI engineering." , "The published source data file contains the underlying data of all figures of the manuscript and Supplementary Information of the publication “Knowledge-driven design of Solid-Electrolyte Interphases on lithium metal via multiscale modelling”. The source data of each figure is presented in a single sheet named after the corresponding figure. Legends are given if required. Please note that the source data of the kMC boxes is provided as xyz coordinates and that each species number corresponds to a chemical species (cf. legend).\nNumerical details: The kMC/continuum model was implemented in MATLAB R2021a and differential equations were solved with the ode15s solver. The calculations were performed on an i7-8000 CPU with 16 GB RAM. For detailed information on the modelling approach see the Method section in the manuscript and the Supplementary Information.";
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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        <http://schema.org/copyrightHolder>
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        <http://schema.org/name>        "Supplement: MIPAS VMR data and climatological circulation fields for publication 10.5194/acp-2019-704";
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                                          <http://schema.org/givenName>   "Birgit";
                                          <http://schema.org/name>        "Müller, Birgit";
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                                          <http://schema.org/name>        "Schilling, Frank";
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        <http://schema.org/name>        "DAMAST-KIT-1: Structural Core Analysis";
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                "This files contain the design of an modular linear stage for optoelectronic measurements as well as example software for measurements. It is build from open hardware parts (uStepperS and C-Beam Linear Actuator), commercial available parts, a designed shield for the hardware control as well as custom made laser-cut parts. The composition, the designed hardware and software is licenced under a free GPLv3 licence (see bottom of this page)." , "# Modular linear experiment stage\nThis files contain the design of an modular linear stage for optoelectronic measurements as well as example software for measurements. It is build from open hardware parts (uStepperS and C-Beam Linear Actuator), commercial available parts, a designed shield for the hardware control as well as custom made laser-cut parts. The composition, the designed hardware and software is licenced under a free GPLv3 licence (see bottom of this page).\nLicences of the used components:\n* [C-Beam Linear Actuator](https://openbuildspartstore.com/c-beam-tension-xl-linear-actuator-bundle/) OpenBuilds®, LLC design work(s) shared Open Source under the CC BY-SA 4.0 License Terms and Conditions\n* [uStepperS](https://github.com/uStepper/uStepperS) by ON Development is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n![modular_setup](https://codeberg.org/LibrEars/modular-linear-experiment-stage/media/branch/main/modular_setup.png)\n## Controle software for the 'uStepper S'\nThis module is used with the 'uStepper S' and the custom made shield for distance measuring with angle dependancy.\n### Usage\nYou can use the setup via the manual rotary encoder switch or via the serial commant (use arduino-GUI and serial window or see python example)\n```\n--- Proximity reference setup ------------------------------------\nManual mode:\n    Turn the knob to change the distance / angle\");\n    Click once to change the step size\");\n    Double-click to change between distance and rotation\");\n    Press > 1 second to set the distance / angle to 0\");\nPC mode (via serial):\n    Type the distance (mm), speed (mm/s) and angle (°), seperated by a commata\");\n    E.g. '20.0,40, 15'  means 20.00 mm (v=40mm/s for distance) and 15.00°\"\n------------------------------------\n```\nSomehow there is a bug with my board/software and setting the speed via stepper.setRPM(rpm) inside the code does not work properly. So do not expect the speed to be exact.\n### Installation\n#### uStepper S\nThe uStepper is programmed like an arduino bord with the Arduino IDE: https://www.arduino.cc/en/Main.Software\nTo install the uStepper into the arduino IDE follow: https://github.com/uStepper/uStepperS\n#### Rotary encoder for angle motor\nTo make the rotary encoder working, you may need to fix the Encoder.h (\\Arduino\\libraries\\Encoder\\utility\\interrupt_pins) library by adding the PB version of the microcontroller `__AVR_ATmega328PB__` to the list:\n```\n// Arduino Uno, Duemilanove, Diecimila, LilyPad, Mini, Fio, uStepperS, etc...\n#elif defined(__AVR_ATmega328P__) || defined(__AVR_ATmega328PB__) || defined(__AVR_ATmega168__) || defined(__AVR_ATmega8__)\n  #define CORE_NUM_INTERRUPT 2\n  #define CORE_INT0_PIN  2\n  #define CORE_INT1_PIN  3\n```\n### Interrupts\nAlso on the uStepperS board the [interrupts do not work](https://github.com/uStepper/uStepperS/issues/31). A workaround is to use [Pin Change Interrupts](https://www.teachmemicro.com/arduino-interrupt-tutorial/). They are different than normal Interrupts (\"might be a tiny bit slower and not that reliable because of that detection overhead (talking about micro seconds)\") but there is an [arduino library](https://www.arduino.cc/reference/en/libraries/pinchangeinterrupt/).\n→ seems to work with both motors and rotary encoder at the same time !\n### Distplay\nTo get the display running with the uStepperS, we need to change its lib rgb_lcd.h:\nThere is some wired behavior when using the serial of the uStepperS (ATmega328PB). Plugging the Grove-LCD RGB Backlight display in pins A1 (SDA0) and A2 (!!! this pin is changed [from A0 → A2 on my bord] but SCL0 is still there) the example code works. But pin D7 (SCL1) and D8 (SDA1) are somehow always HIGH which causes problems (not enough pins, strange behavior when uploading code with INX from ULN2003 driver on D7 &D8 ). \nTo circumvent this behavior the display should be attached to SCL1 /SDA1. This is not possible by default due to missing firmware. Therefore we need to add support for the microcontroller:\nSources:\n* https://github.com/MCUdude/MiniCore This lib adds support for the  ATmega328PB, in detail for serial communication with the lib Wire1.h\n* https://www.instructables.com/How-to-share-i2C-connection-on-Due/ To get the display running, we need to change its lib rgb_lcd.h\n1. Go to the folder \\Arduino\\libraries and copy the folder \\Grove_-_LCD_RGB_Backlight and rename it to \\Grove_-_LCD_RGB_Backlight_Serial1\n2. In the Folder \\Grove_-_LCD_RGB_Backlight_Serial1 rename the files rgb_lcd.cpp and rgb_lcd.h to rgb_lcd1.cpp and rgb_lcd1.h\n3. Open the file rgb_lcd1.cpp with an text editor and change the lines \n`#include <Wire.h>` to `#include <Wire1.h>`\n`#include \"rgb_lcd.h\"` to `#include \"rgb_lcd1.h\"`\n4. Also add the line ```extern TwoWire1 Wire1; // insert this line is the modification to include Wire1 (SDA1 / SCL1) .…```\n5. Then press ‘Ctrl+h’  to replace all function calls starting with `Wire.xxx()` with `Wire1.xxx()`\n! Make sure to not replace the lines added above a second time\n6. From now on in the arduino code always include `#include <Wire1.h>` and `#include \"rgb_lcd1.h\"`\n### Design shield for µStepperS\nIn order to also control a 28BYJ-48 stepper motor (e.g. for angle dependent reflectance measurements) or custom components that need high currents (e.g. high-power LEDs) a large part of the project was to design a shield.\n![shield](https://codeberg.org/LibrEars/modular-linear-experiment-stage/media/branch/main/shield.png)\n**Software**: https://www.kicad.org/\n* https://docs.kicad.org/5.0/en/getting_started_in_kicad/getting_started_in_kicad.html\n**Further reading**: \n* Digital pins (pullup & down resistors):\n  - https://www.mymakerstuff.de/2016/03/22/arduino-tutorium-kapitel-5-pull-up-und-pull-down-widerst%C3%A4nde/\n  - https://www.arduino.cc/en/Tutorial/Foundations/DigitalPins\n* Stepper Motor driver\n  - https://www.hwlibre.com/en/28byj-48/\n  - https://www.electronicoscaldas.com/datasheet/ULN2003A-PCB.pdf \n* Rotary encoder\n  - https://lastminuteengineers.com/rotary-encoder-arduino-tutorial/amp/ \n  - The switch needs a pullup resistor (similar to stop-switch)\n* Grove-LCD Display\n  - https://wiki.seeedstudio.com/Grove-LCD_RGB_Backlight/ \n# 💌️ License\nCopyright (C) 2022-2023 Mervin Seiberlich and Konstantinos Falis\nAuthors - Mervin Seiberlich, Konstantinos Falis\nThe Modular linear experiment stage is free hardware and software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\nexperiment_evaluation is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\nYou should have received a copy of the GNU General Public License along with experiment_evaluation. If not, see https://www.gnu.org/licenses/.";
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                "Brown carbon aerosol (BrC) is one major contributor to atmospheric air pollution in Europe, especially in winter. Therefore, we studied the chemical composition, diurnal variation, and sources of BrC from 17th February to 16th March at a rural location in southwest Germany. In total, 178 potential BrC molecules (including 7 nitro aromatic compounds, NACs) were identified in the particle phase comprising on average 63 ± 32 ng m−3, and 31 potential BrC (including 4 NACs) molecules were identified in the gas phase contributing on average 6.2 ± 5.0 ng m−3 during the whole campaign. The 178 potential BrC molecules only accounted for 2.3 ± 1.5 % of the total organic mass, but can explain 11 ± 11 % of the total BrC absorption at 370 nm, assuming an average mass absorption coefficient at 370 nm (MAC370) of 9.5 m2 g−1. A few BrC molecules dominated the total BrC absorption. In addition, diurnal variations show that gas phase BrC was higher at daytime and lower at night. It was mainly controlled by secondary formation (e.g. photooxidation) and particle-to-gas partitioning. Correspondingly, the particle phase BrC was lower at daytime and higher at nighttime. Secondary formation dominates the particle-phase BrC with 61 ± 21 %, while 39 ± 21 % originated from biomass burning. Furthermore, the particle-phase BrC showed decreasing light absorption due to photochemical aging. This study extends the current understanding of real-time behaviors of brown carbon aerosol in the gas and particle phase at a location characteristic for the central Europe." , "The data in the Excel files are related to the publication Jiang et al., ACPD, 2024. Each sheet of the Excel files corresponds to one plot in the publication as repeated below. The data originates mainly from aethalometer and FIGAERO-CIMS. The figures and captions from the publication are shown in the following to allow for a good understanding of the data files. Jiang, F., Saathoff, H., Ezenobi, U., Song, J., Zhang, H., Gao, L., and Leisner, T.,: Measurement report: Brown carbon aerosol in rural Germany: sources, chemistry, and diurnal variations, EGUsphere, https://doi.org/10.5194/egusphere-2024-1848, 2024, in review, 2024.";
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                "Using the calculator\n1. Open MATLAB and the CO2_Calculator.mlx file\n2. Open the “LIVE EDITOR” tab and press “RUN”\nNow a series Pop-Up windows will appear, asking for input. Please note that all inputs must be numeric, and a decimal dot is used instead of a comma (example: 20.5 and not 20,5). \n3. Enter which processes are included (1 for yes 0 for no)\n4. Enter the sheet thickness [mm] (1-6 in whole numbers) as well as the workpiece weight [kg]\n5. If laser cutting is included: Enter cutting length [mm]\n6. If bending is included: \na. Enter the number of bends (Max. 6)\nb. Enter the tensile strength [N/mm²] (dt. “Zugfestigkeit”)\nc. Enter the bending angle [mm] and bending length [mm] for each individual bend\n7. If welding is included:\na.  Enter number of welds (Max 6)\nb. Enter the shielding gas type (1: Ar + 2% CO2; 2: Ar + 18% CO2; 3: C25; Ar + 25% CO2; 4: opens a new window for your own specifications) Note: if unknown choose 1 for the lowest CO2 Emissions\nc. Enter the welding length [mm], the number of passes as well as the distance between the welds [mm] for each individual weld. Note: if the distance between the welds is not known, enter 0 or an estimate\n8. Enter if you want to save your results or not Note: please include “.xls” in the filename to save the file as Excel\nAfter everything is entered the results are presented in kg of emitted CO2 on the right, please scroll down.\nFor the welding process emitted fumes and particles (shielding gas 1&2) are presented as well." , "Nowadays sustainable design is a mandatory requirement in the product development pro-cess. For this reason, design methodologies are addressed to establish a close relationship between environmental, social and economic impact indicators and product features from early design stages, especially in those features related to its manufacturing. In this thesis, the design for manufacturing and assembly — DFMA methodology shall be investigated to sheet metal devices, integrating functional and component relationships to minimize particu-lar sustainability indicators such as energy consumption, carbon footprint, number of parts, required amount of material, assembly time and manufacturing costs. You shall be open to discuss with suppliers their energy consumption and compare various manufacturing pro-cess possibilities based on their carbon footprint and other sustainability indicators. The aim of this thesis is to support the design engineer with a guideline of how to be able to develop not only cost efficient concepts but in the same time be able to reduce the carbon footprint and other sustainability indicators of their product.";
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                "This Monte-Carlo simulation simulates nanoparticles at different temperatures. By applying a simulated annealing protocol, the user can find the most stable, entropically realistic structure of a nanoparticle." , "Simple energy model\nThe energy input is a simple mapping of coordination number to energy in eV. The input can be a direct mapping of every coordination number to a distinct energy. Alternatively, a linear energy increase with coordination number can be used, which speeds up the simulation.\nHigh performance\nBy using a simple energy model, the simulation can perform up to 10^11 iterations in one day. With a linear coordination number to energy mapping it can reach up to 10^12 iterations in one day. This allows for the simulation of larger nanoparticles.\nMultithreaded\nThe code allows for as many parallel simulations as cores available on the computing node. This enables the user to obtain a statistically relevant ensemble of low-energy structures. Adding parallel simulations has negligible increase in required RAM.\nTrack the Simulation using the .xyz-format\nThroughout the simulation it is possible to track the energy and save snapshots of the particle. Furthermore the particle with the lowest energy will be saved as an .xyz-file.\nSupport\nThe simulation can simulate the effect a support can have on the particle.\nA monometallic support can be created by giving a vector that is orthogonal to the support pane.\nUsage\nRequirements\nRust\nPython used version 3.11.10\nASE used version: ase-3.22.1\nInstall from git\ngit clone git@github.com:T-136/MC-Cluster.git\nBuild the binary\nBuild the program with:\ncargo build -r\nAfter the build step is complete the compiled program can be found in your project folder under \"./target/release/mc\".\nRun the simulation\n./target/release/MC-Cluster -a Pt,1000  --support Al,1,1,1  -t 1000 -i 1e7 -r 0-1  --e-cn ./example_data/cn_input_example.json -o 9/10 -g ./example_data/303030-grid --support-e 0 --xyz-trajectory\nUse \"-h\" or \"--help\" to see the available flags and how to use them.\n  -s, --start-cluster <START_CLUSTER>\n  -a, --atoms <ATOMS>\n          Atom name and number of that atom seperated by a comma. \"Pt,4000\"\n  -s, --support <SUPPORT>\n          When creating a new particle using the atoms flag, write the Atom name and a vector orthogonal to the support surface Al,1,1,1. When starting from a xyz file only the support atom is required\n  -f, --folder <FOLDER>\n          Output folder [default: ./sim/]\n  -i, --iterations <ITERATIONS>\n          Determines the length of the simulation\n  -b, --begin-temperature <BEGIN_TEMPERATURE>\n          Temperature where the annealing process starts [default: 5000]\n  -t, --temperature <TEMPERATURE>\n          Temperature at which the annealing process stops [default: 300]\n  -o, --optimization-cut-off-fraction <OPTIMIZATION_CUT_OFF_FRACTION>\n          Fraction of the simulation after which the annealing process is completed. After that, the temperature remains constant [default: 1 2]\n      --support-e <SUPPORT_E>\n          Support energy\n      --e-l-cn <E_L_CN>\n          File path or string containing a JSON-formatted list of energies\n      --e-cn <E_CN>\n          File path or string containing JSON-formatted energies\n  -r, --repetition <REPETITION>\n          How many times the same simulation is run. Multiple runs allow for convergence tests. The number will be part of the simulation folder name. After running `-r 0-1`, you can run `-r 1-2` and the previous simulation will not be overwritten [default: 0 1]\n  -g, --grid-folder <GRID_FOLDER>\n          Folder containing the setup files like neighbor sites. It can be created using the Python script `create_sites.py` [default: ../303030-pair]\n  -x, --xyz-trajectory\n          Track the simulation by taking snapshots of the cluster throughout the simulation\n      --heat-map\n          Generate a heat map\n  -h, --help\n          Print help\n  -V, --version\n          Print version";
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Installation of the toolbox is in accordance with the standard MATLAB® procedure for the installation of new toolboxes. \nAfter installation, phoRex provides an environment for Monte Carlo ray tracing simulations mapping radiation transport in 3D in channel-like geometries, for instance photoreactors. For the simulation of the isophotonic photoreactor a comprehensive live script example is given with the file example.mlx comprised in the toolbox. The example guides through the usage of the provided code in the context of quantum yield determination using the proposed isophotonic photoreactor.\nFurther, the comprised pre-processing script preProcessQY.m lines out how simulations are set up in the provided Monte Carlo ray tracing environment. This code example can be employed to understand how to set up own simulation cases for other use cases than the simulation of the isophotonic photoreactor proposed for the accurate determination of quantum yields. \nFor detailed information on the code structure of the Monte Carlo ray tracing approach itself, the author refers to the extensively commented source code given with the class definitions of the toolbox." , "Solar driven photocatalysis represents one way to address challenges arising from the need to substitute fossil energy carriers in the world’s economy. The developments in the field of photocatalysis heavily depend on new process designs whose development require methods for the determination of quantum yields and photoreaction kinetics as well as the ability to map radiation transport in complex photoreactors. However, the field of photoreaction engineering is an underdeveloped field lacking reliable and open access tools for radiation transport simulations and standardized photoreactors for quantum yield and photoreaction kinetic measurements. With this data set both a set of CAD files for the facile fabrication of an isophotonic photoreactor for the determination of quantum yields in gas, liquid, and multi-phase photoreactions via additive manufacturing as well as a comprehensive MATLAB®-based toolbox for radiation transport simulations in photoreactors are given. The proposed photoreactor is designed in a way that its reaction volume is isophotonic, which means that the reaction volume shows low gradients in the local volumetric rate of photon absorption. The toolbox allows the determination of radiation transport efficiencies within the isophotonic photoreactor and therewith provides the basis for meaningful data evaluation of experiments conducted with the isophotonic photoreactor. Beyond this concrete use case of the provided toolbox, the toolbox can also be employed for radiation transport simulations in many other use cases. Those range from the evaluation of experiments aiming for the determination of optical properties over light source design to the optimization of photoreactors. The data set therewith not only can support the further development of high quantum yield materials by material scientists in the field of photocatalysis but also can be used by chemical engineers working on new, high efficiency photo reactors or sophisticated light sources especially designed for specific photocatalysts and/or use cases." , "Sonnengestützte Fotosynthesen stellen einen Weg dar die Herausforderungen zu meistern, die aus der Notwendigkeit fossile Energieträger in der Weltwirtschaft zu ersetzen resultieren. Die Fortschritte im Bereich solarer Fotosynthesen hängen dabei stark von neuen Prozessdesigns ab. Deren Entwicklung setzt verlässliche Methoden zur Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken und die Fähigkeit Strahlungstransport in Fotoreaktoren akkurat abbilden zu können voraus. Das Gebiet der Fotoreaktionstechnik ist jedoch ein wenig entwickeltes Feld, in dem es an verlässlichen und offen zugänglichen Methoden für Strahlungstransportsimulationen und standardisierten Fotoreaktoren für die Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken fehlt. Mit dem vorliegenden Datensatz wird sowohl ein Satz von CAD Dateien zur Herstellung (via 3D Druck) eines Fotoreaktors für die akkurate Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken als auch eine umfassende, MATLAB®-basierte Toolbox für Strahlungstransportsimulationen bereitgestellt. Der vorgeschlagene Fotoreaktor weist eine isophotonische Reaktionszone auf, was bedeutet, dass die Reaktionszone nur kleine Gradienten in der lokalen volumetrischen Photonenabsorptionsrate aufweist. Die Toolbox erlaubt die Berechnung von Strahlungstransporteffizienzen im isophotonischen Fotoreaktor und stellt damit die Basis für eine sinnvolle Datenauswertung in Experimenten mit dem vorgeschlagenen Fotoreaktor dar. Über diesen konkreten Anwendungsfall der Toolbox hinaus, kann die Toolbox auch für andere Anwendungen im Bereich Strahlungstransportsimulationen eingesetzt werden. Diese reichen von der Auswertung von Versuchen zur Bestimmung von optischen Transporteigenschaften über die Auslegung von Lichtquellen hin zur Optimierung von Fotoreaktoren. Der bereitgestellte Datensatz kann damit nicht nur die Arbeit von Materialwissenschaftler*innen im Bereich der Entwicklung von Fotokatalysatoren mit hohen Quantenausbeuten unterstützen, sondern kann auch im Rahmen der Arbeit von Chemieingenieur*innen eingesetzt werden, die die Entwicklung von effizienten Fotoreaktoren und Lichtquellen für spezifische Fotokatalysatoren oder Anwendungsfälle vorantreiben.";
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                "Experimental data on the heat transfer coefficient during saturated flow boiling of CO$_2$ in a horizontal cylindrical tube (diameter 14 mm) with integrated metal sponges (open-cell metal foams) are presented in this publication. The nominal cell density of the sponges was 10 ppi (“pores per inch”) or 20 ppi. The mass flux varied from 25 kg m$^{-2}$ s$^{-1}$ to 125 kg m$^{-2}$ $\\,^{-1}$ and the vapor quality from 10% to 100%. The saturation pressure was either 12 bar, 19 bar or 26.5 bar. The length filled with sponge upstream of the heated part of the test section was 38 mm for the 10 ppi sponge and either 35 mm or 135 mm for the 20 ppi sponge. For the 20 ppi sample, the proportion of the test section filled with sponge was varied to investigate entrance effects. As a reference, the heat transfer coefficient during flow boiling in the empty tube was determined. In addition, geometric properties of the sponges relevant for the heat transfer coefficient (window diameter, strut diameter, porosity, specific surface area, and heat conductivity), were identified." , "# Sponges\n**10 ppi copper sponge made by replication technique**\n* total porosity: 91%\n* open porosity: between 85% and 88% \n* nominal cell density: approximately 10 pores per inch\n* mean strut diameter: 0.45 mm\n* mean window diameter: 1.6 mm\n**10 ppi plastic sponge made by 3D printing**\n* total and open porosity: 86%\n* nominal cell density: approximately 10 pores per inch\n* mean strut diameter: 0.6 mm\n* mean window diameter: 2.0 mm\n**20 ppi copper sponge made by replication technique**\n* total porosity: 90%\n* open porosity: between 84% and 87%\n* nominal cell size: approximately 20 pores per inch\n* mean strut diameter: 0.28 mm\n* mean window diameter: 1.0 mm\n# Operating conditions\n**two-phase flow boiling in test section filled with sponges**\n* pressure: 12 bar, 19 bar and 26.5 bar\n* mass flux: 25 kg m<sup>-2</sup> s<sup>-1</sup> to 125 kg m<sup>-2</sup> s<sup>-1</sup>\n* vapor quality: 10% to 100%\n* heat flux: 3 kW m<sup>-2</sup> to 65 kW m<sup>-2</sup>\n* boundary condition: \n    * constant wall temperature\n    * constant wall heat flux\n**two-phase flow boiling in empty tube**\n* pressure: 12 bar and 26.5 bar\n* mass flux: 25 kg kg m<sup>-2</sup> s<sup>-1</sup> to 150 kg m<sup>-2</sup> s<sup>-1</sup>\n* vapor quality: 10% to 100%\n* heat flux: 1 kW m<sup>-2</sup> to 40 kW m<sup>-2</sup>\n* boundary condition: \n    * constant wall temperature\n    * constant wall heat flux" , "In der hier vorgestellten Publikation werden experimentelle Daten zum Wärmeübergangs-koeffizienten beim gesättigten Strömungssieden von CO$_2$ in einem horizontalen zylindrischen Rohr (Durchmesser 14 mm) mit integrierten Metallschwämmen (offenzellige Metallschäume) präsentiert. Die nominelle Zelldichte der Schwämme betrug 10 ppi („Poren pro Zoll“) bzw. 20 ppi. Der Massenstrom wurde von 25 kg m$^{-2}$ s$^{-1}$ bis 125 kg m$^{-2}$ s$^{-1}$ variiert, der Strömungsdampfgehalt lag zwischen 10% und 100%. Der Sättigungsdruck betrug entweder 12 bar, 19 bar oder 26,5 bar. Die mit Schwämmen gefüllte Länge stromaufwärts der beheizten Zone betrug 38 mm beim 10 ppi Schwamm und entweder 35 mm oder 135 mm beim 20 ppi Schwamm. Für die 20 ppi Probe wurde der Anteil des mit Schwamm gefüllten Testabschnitts variiert, um Einlaufeffekte zu untersuchen. Als Referenz wurde der Wärmeübergangskoeffizient während des Strömungssiedens im leeren Rohr bestimmt. Darüber hinaus wurden die für den Wärme-übergangskoeffizient relevanten geometrische Eigenschaften der Schwämme (Fensterdurchmesser, Stegdurchmesser, Porosität, spezifische Oberfläche und Wärmeleitfähigkeit) ermittelt.";
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                "\"Zur Beweglichkeit von Kindern heute und vor zehn Jahren.\" In: Sportunterricht, 39 (1990) 10, S. 373-384\tDie Autoren sind am Institut fuer Sportwissenschaft der Universitaet Hannover taetig. Sie haben in einer empirischen Studie die Frage ueberprueft, ob sich die gegenwaertigen Lebensbedingungen von Grundschulkindern, die zunehmend mit dem viel diskutierten \"Bewegungsmangel\" beschrieben werden, auf die Entwicklung der Beweglichkeit auswirken. Hierzu haben sie im Herbst 1989 den Entwicklungsstand der Beweglichkeit im Bereich der grossen Gelenke von 74 achtjaehrigen Kindern heute mit den Unterlagen von 74 achtjaehrigen Kindern im Jahre 1979 verglichen. Die beiden Kindergruppen waren parallelisiert, d. h. es waren zwei parallele Gruppen hinsichtlich Geschlecht, Alter und Schule. Die Autoren gingen dabei von der Ueberlegung aus, dass eine hohe Beweglichkeit grundsaetzlich mit einer guten muskulaeren Situation verbunden ist. Diese wirkt sich langfristig gesehen positiv auf den Gesundheitszustand eines Menschen aus und gilt ausserdem als elementare Voraussetzung fuer qualitativ und quantitativ gute Bewegungsausfuehrungen im Sport. Zunaechst einmal wurde bei der Untersuchung der koerperliche Entwicklungsstand ermittelt, wobei sich Unterschiede zwischen den heutigen Grundschulkindern und den Grundschulkindern vor 10 Jahren ergaben. Die Jungen waren 1979 im geschlechtsspezifischen Vergleich bei gleichem Alter groesser und schwerer, hatten breitere Schultern und laengere Arme als die";
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                "# == This file is summarizing the content of the data files in this repository published together with the article: \nAI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data \nVELOCITY FILES \n    AlpsLocPS_VEL.mod       \n        - VELEST model file of 'AlpsLocPS_VELEST'   (red in Fig. 6 of Braszus et al., 2024)\n    AlpsLocPS_McMC.mod      \n        - McMC model of 'AlpsLocPS_McMC'            (orange in Fig. 6 of Braszus et al., 2024)\n    GAR1D_PS_VEL.mod        \n        - VELEST model file of 'GAR1D_PS_VELEST'    (lime in Fig. 6 of Braszus et al., 2024)\n    GAR1D_PS_McMC.mod       \n        - McMC model of 'GAR1D_PS_McMC'             (purple in Fig. 6 of Braszus et al., 2024)\nSTATION FILES   \n    Station corrections have to be substracted from the synthetic travel times ! \n    Only stations with >= 10 observations per phase are included.\n    AlpsLocPS_sta_cors.csv  \n        - File listing station data and P- & S-phase station correction terms for the \"AlpsLocPS_VELEST\" and \"AlpsLocPS_McMC\" models after relocating all events ( see Table 2 'run2' in Braszus et al., 2024 )\n    GAR1D_sta_cors.csv      \n        - File listing station data and P- & S-phase station correction terms for the final \"GAR1D_PS_VELEST\" and \"GAR1D_PS_McMC\" models \nEVENT FILES\n    events_VELEST.csv\n        - Catalog of relocated events using VELEST\nPICK CATALOG\n    pick_catalog.csv\n        The following describes the header entries of \"pick_catalog.csv\" in some more detail \n        \"network name,station name,station latitude,station longitude, station elevation in m, event origin time, event latitude, event longitude, event depth in km, pick_type, pick_phase, pick_time, res in s\"" , "The recent rapid improvement of machine learning techniques had a large impact on the way seismological data can be processed. During the last years several machine learning algorithms determining seismic onset times have been published facilitating the automatic picking of large data sets. Here we apply the deep neural network PhaseNet to a network of over 900 permanent and temporal broad band stations that were deployed as part of the AlpArray research initiative in the Greater Alpine Region (GAR) during 2016-2020.\nWe selected 384 well distributed earthquakes with M_L >= 2.5 for our study and developed a purely data-driven pre-inversion pick selection method to consistently remove outliers from the automatic pick catalog. This allows us to include observations throughout the crustal triplication zone resulting in 39,599 P and 13,188 S observations. Using the established VELEST and the recently developed McMC codes we invert for the 1D P- and S-wave velocity structure including station correction terms while simultaneously relocating the events. As a result we present two separate models differing in the maximum included observation distance and therefore their suggested usage. The model AlpsLocPS is based on arrivals from <= 130km and therefore should be used to consistently (re)-locate seismicity based on P & S observations. The model GAR1D_PS includes the entire observable distance range of up to 1000km and for the first time provides consistent P- & S-phase synthetic travel times for the entire Alpine orogen. Comparing our relocated seismicity with hypocentral parameters from other studies in the area we quantify the absolute horizontal and vertical accuracy of event locations as ~2.0km and ~6.0km, respectively.";
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[arXiv](https://arxiv.org/) in 2024.\nIf we create further versions of this paper in the future, these experimental data may cover them as well.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery) for the code and instructions to reproduce the experiments.\nWe obtained the experimental results on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 160 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 34 hours.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [0a57bcd529](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/0a57bcd52938dce6285e8113d777360c2b17f30f).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation_arxiv.py`) is [48f2465b4c](https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery/tree/48f2465b4cabf2d6657e4ff4b73c28c240d0b883).\nWe also tagged both commits (`run-2024-05-13` and `evaluation-2024-05-15`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation_arxiv.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., the prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 27 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth file types are CSVs that only contain numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column either corresponds to a feature (in `_X`) or the target (in `_y`).\nThe first line in each `_X` file contains the names of the features as strings; for `_y` files, there is only one column, always named `target`.\nFor the prediction target, we ensured that the minority (i.e., less frequent) class is the positive class (i.e., has the class label `1`), so the labeling may differ from PMLB.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation_arxiv.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 27 datasets, 5 cross-validation folds, and 6 subgroup-discovery methods.\nThus, there are `27 * 5 * 6 = 810` files containing subsets of the results.\nEach row in a result file corresponds to one subgroup.\nOne can identify individual subgroup-discovery runs with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- subgroup-discovery method `sd_name`\n- feature-cardinality threshold `param.k` (missing value if no feature-cardinality constraint)\n- solver timeout `param.timeout` (missing value if not solver-based search)\n- number of alternatives `param.a` (missing value if only original subgroup searched)\n- dissimilarity threshold `param.tau_abs` (missing value if only original subgroup searched)\nFor each value combination of these seven columns, there is either one subgroup (search for original subgroups)\nor six subgroups (search for alternative subgroup descriptions, in which case the column `alt.number` identifies individual subgroups within a search run).\nFurther, note that the last four mentioned columns contain missing values, which should be treated as a category on their own.\nIn particular, if you use `groupby()` from `pandas` for analyzing the results and you want to include any of the last four mentioned columns in the grouping,\nyou should either fill in the missing values with an (arbitrary) placeholder value or use `dropna=False`,\nbecause the grouping (by default) ignores the rows with missing values in the group columns otherwise.\nThe remaining columns represent results and evaluation metrics.\nIn detail, all result files contain the following columns:\n- `objective_value` (float in `[-0.25, 1]` + missing values): Objective value of the subgroup-discovery method on the training set.\n  WRAcc when searching original subgroups and normalized Hamming similarity when searching alternative subgroup descriptions.\n  Missing value for *MORS* as the subgroup-discovery method, since *MORS* does not explicitly compute an objective when searching for subgroups.\n- `optimization_status` (string, 2 different values + missing values): For *SMT*, `sat` if optimal solution found and `unknown` if timeout.\n  Missing value for all other subgroup-discovery methods (which do not use solver timeouts).\n- `optimization_time` (non-negative float): The runtime of optimization in the subgroup-discovery method, i.e., without pre- and post-processing steps.\n- `fitting_time` (non-negative float): The complete runtime of the subgroup-discovery method (as reported in the paper), i.e., including pre- and post-processing steps.\n  Very similar to `optimization_time` except for *SMT* as the subgroup-discovery method, which may spend a considerable amount of time formulating the optimization problem.\n- `train_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the training set.\n- `test_wracc` (float in `[-0.25, 0.25]`): The weighted relative accuracy (WRAcc) of the subgroup description on the test set.\n- `train_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the training set.\n- `test_nwracc` (float in `[-1, 1]`): The normalized weighted relative accuracy (WRAcc divided by its dataset-dependent maximum) of the subgroup description on the test set.\n- `box_lbs` (list of floats, e.g., `[-inf, 0, -inf, -2, 8]`): The lower bounds for each feature in the subgroup description.\n  Negative infinity if a feature's lower bound did not exclude any data objects from the subgroup.\n- `box_ubs` (list of floats, e.g., `[inf, 10, inf, 5, 9]`): The upper bounds for each feature in the subgroup description.\n  Positive infinity if a feature's upper bound did not exclude any data objects from the subgroup.\n- `selected_feature_idxs` (list of non-negative ints, e.g., `[0, 4, 5]`): The indices (starting from 0) of the features selected (= restricted) in the subgroup description.\n  Is an empty list, i.e., `[]`, if no feature was restricted (thus, the subgroup contains all data objects).\n- `dataset_name` (string, 27 different values): The name of the `PMLB` dataset used for subgroup discovery.\n- `split_idx` (int in `[0, 4]`): The index of the cross-validation fold of the dataset used for subgroup discovery.\n- `sd_name` (string, 6 different values): The name of the subgroup-discovery method (`Beam`, `BI`, `MORS`, `PRIM`, `Random`, or `SMT`).\n- `param.k` (int in `[1, 5]` + missing values): The feature-cardinality threshold for subgroup descriptions.\n  Missing value if unconstrained subgroup discovery.\n  Always `3` if alternative subgroup descriptions searched.\n- `param.timeout` (int in `[1, 2048]` + missing values): For *SMT*, solver timeout (in seconds) for optimization (not including formulation of the optimization problem).\n  Missing value for all other subgroup-discovery methods.\n- `alt.hamming` (float in `[0, 1]` + missing values): Normalized Hamming similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.jaccard` (float in `[0, 1]` + missing values): Jaccard similarity between the current subgroup (original or alternative) and the original subgroup if alternative subgroup descriptions searched.\n  Missing value if only original subgroup searched.\n- `alt.number` (int in `[0, 5]` + missing values): The number of the current alternative if alternative 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                "Die Versuche wurden in einer Borosilikatglaskapillare mit einem inneren Querschnitt von 2 mm × 2 mm durchgeführt. Die Taylor-Strömung wurde mit einer Injektionsnadel im co-flow erzeugt. Am Eintritt wurde das Verhältnis der Gas- und Flüssigvolumenströme, die Gesamtleerrohrgeschwindigkeit, sowie das Verhältnis von CO2 zu N2 in der Gasphase variiert. Es wurde mit 1 M und 2 M Natronlauge gearbeitet. Die Konzentrationsprofile wurden nur im Flüssigkeitspfropfen zwischen den Blasen aufgenommen. \nEs handelt sich um zwei Datensätze: erstens die in-situ mit dem Raman-Analyse-System ermittelten Konzentrationsprofile des Karbonats und des Bicarbonats und ergänzend die offline ermittelte Blasengröße.\nKonzentrationsprofile - RamanData.csv:\nDie Ausdehnung des Detektionsvolumen der konfokalen Optik des Laser-Raman-Systems wurde experimentell zu 15 μm × 15 μm × 70 μm bestimmt. Die Messdauer wurde durch die Pulsdauer des Lasers von 10 μs definiert. Die Produkte Karbonat und Bikarbonat konnten mit einer Nachweisgrenze von 0,04 mol/L bzw. 0,05 mol/L detektiert werden. Mit dem Schattenverfahren betrug die Messunsicherheit 3 % für Sluglänge und Blasengeschwindigkeit bzw. 8 % für die Bestimmung der Position des Messpunktes relativ zur Blase. Die Genauigkeit konnte mit dem holographischen Verfahren um Faktor zwei verbessert werden. Bei der reproduzierbaren Taylor-Strömung ermöglicht die Analyse einer Vielzahl an Messpunkten die Rekonstruktion der Konzentrationsfelder im Slug um die Blase. \nDer Datensatz enthält 22880 Messpunkte mit den folgenden Daten:\n1: 'expNo' bezieht sich auf die in der Dissertationsschrift veröffentlichte Nummerierung der verschiedene Versuchsbedingungen\n2: 'distancez(height)' Abstand zur Kontaktierung von Gas und Flüssigkeit / mm\n3: 'distancey(lateral)' Versatz lateral zum Kanalzentrum in mm (siehe Abb. 7.14, S. 182 in der Dissertationsschrift)\n4: 'distancex(frontal)' Versatz frontal zum Kanalzentrum in mm (siehe Abb. 7.14, S. 182 in der Dissertationsschrift)\n5: 'Konz. CO3in mol/l' mit dem Raman-System ermittelte Karbonatkonzentration in mol/L\n6: 'Konz. HCO3mol/L' mit dem Raman-System ermittelte Bikarbonatkonzentration in mol/L\n7: 'distanceto cap' Abstand des Messpunkts zur Blasenkappe der nachfolgenden Blase in μm \n8: 'distanceto tail' Abstand des Messpunkts zum Blasenende der vorangehenden Blase in μm\n9: 'sluglengthin um' Sluglänge in μm\n10: 'velocityin m/s' Geschwindigkeit der Blase in m/s\n11: 'J*_NaOHin ml/min' Volumenstrom der Natronlauge am Kanaleintritt in mL/min\n12: 'NaOH_invalue in mol/l' Konzentration der Natronlauge am Kanaleintritt in mol/L\n13: 'J*_N2in ml/min' Volumenstrom des Stickstoffs am Kanaleintritt in mL/min\n14: 'J*_CO2in ml/min' Volumenstrom des Kohlenstoffdioxids am Kanaleintritt in mL/min\n15: 'Temp.inlet gas' Temperatur des Gasgemisches am Kanaleintritt in °C\n16: 'Temp.inlet liquid' Temperatur der Natronlauge am Kanaleintritt in °C\n17: 'Temp.outlet' Temperatur am Kanalaustritt in °C\n18: 'Datum' Aufnahmedatum des Messpunkts\nBlasenlänge - BubbleLength.csv:\nDie Blasenlänge wurde im Anschluss an die Raman-Messungen mithilfe einer Spiegelreflexkamera (Canon EOS 400D Digital, 3888 × 2592 Pixel, Verschlusszeit 1/200 s, Blitzdauer 80 μs) untersucht. Für jedes Experiment wurde aus mehreren Bildern die Blasenlänge abhängig vom Abstand zur Kontaktierung ermittelt.\nDer Dataframe enthält für jedes Experiment mit expNo X und den Eingangsbedingungen entsprechend des ersten Dataframes den Abstand des Blasenzentrums der betrachteten Blase zur Kontaktierung 'expX_y/mm' in mm und die Blasenlänge 'expX_lB/mm' in mm.\n1: 'exp1_y/mm'\n2: 'exp1_lB/mm'\n3: 'exp2_y/mm'\n4: 'exp2_lB/mm'\n5: 'exp3_y/mm'\n6: 'exp3_lB/mm'\n7: 'exp4_y/mm'\n8: 'exp4_lB/mm'\n9: 'exp5_y/mm'\n10: 'exp5_lB/mm'\n11: 'exp6_y/mm',\n12: 'exp6_lB/mm',\n13: 'exp7_y/mm',\n14: 'exp7_lB/mm'\n15: 'exp8_y/mm'\n16: 'exp8_lB/mm'\n17: 'exp9_y/mm'\n18: 'exp9_lB/mm'\n19: 'exp10_y/mm'\n20: 'exp10_lB/mm'\n21: 'exp11_y/mm'\n22: 'exp11_lB/mm'\n23: 'exp12_y/mm'\n24: 'exp12_lB/mm'\n25: 'exp13_y/mm'\n26: 'exp13_lB/mm'\n27: 'exp14_y/mm'\n28: 'exp14_lB/mm'\n29: 'exp15_y/mm'\n30: 'exp15_lB/mm'\n31: 'exp16_y/mm'\n32: 'exp16_lB/mm'\n33: 'exp17_y/mm'\n34: 'exp17_lB/mm'\n35: 'exp18_y/mm'\n36: 'exp18_lB/mm'\n37: 'exp19_y/mm'\n38: 'exp19_lB/mm'\n39: 'exp20_y/mm'\n40: 'exp20_lB/mm'\n41: 'exp21_y/mm'\n42: 'exp21_lB/mm'\n43: 'exp22_y/mm'\n44: 'exp22_lB/mm'\n45: 'exp23_y/mm'\n46: 'exp23_lB/mm'\n47: 'exp24_y/mm'\n48: 'exp24_lB/mm'\n49: 'exp25_y/mm'\n50: 'exp25_lB/mm'\n51: 'exp26_y/mm'\n52: 'exp26_lB/mm'\nDetails zum Versuchsaufbau, den Auswerteroutinen sowie den Messunsicherheiten sind der zugehörigen Dissertationsschrift und den Veröffentlichungen zu entnehmen." , "This data set was collected as part of the priority program \"Reactive Bubbly Flows\" of the German Research Foundation. The chemical absorption of CO2 in sodium hydroxide solution in a borosilicate glass capillary with a cross-section of 2 mm × 2 mm was considered. The experimental conditions led to the formation of the reactive gas/liquid Taylor flow, a stable and reproducible flow pattern\nUsing a specially adapted laser Raman analysis system, the concentration profiles of the products carbonate and bicarbonate in the plug between the bubbles could be determined. The two techniques, Mach-Zehnder off-axis holography and the direct shadow method, were used equivalently and synchronized to the Raman system to acquire additional information regarding bubble size and velocity as well as the position of the Raman measurement relative to the bubble. \nThe temporally and spatially resolved measurements experimentally collected in this work form the basis for a better understanding of gas/liquid reactions. They will be used to validate existing and new three-dimensional CFD models that consider hydrodynamics, mass transfer and transport as well as reaction and their interaction in the Taylor flow." , "Dieser Datensatz wurde im Rahmen des Schwerpunktprogramms „Reaktive Blasenströmungen“ der Deutschen Forschungsgemeinschaft erhoben. Betrachtet wurde die chemische Absorption von CO2 in Natronlauge in einer Borosilikatglaskapillare mit einem Querschnitt von 2 mm × 2 mm. Die vorliegenden Versuchsbedingungen führten zur Ausbildung der reaktiven Gas/Flüssig-Taylor-Strömung, einer stabilen und reproduzierbaren Strömungsform.\nMithilfe eines speziell adaptierten Laser-Raman-Analysesystems konnten die Konzentrationsprofile der Produkte Karbonat und Bikarbonat im Pfropfen zwischen den Blasen ermittelt werden.\nDie beiden Techniken, Mach-Zehnder-Off-Axis-Holographie und das direktes Schattenverfahren, wurden äquivalent verwendet und auf das Raman-System synchronisiert. So konnten zusätzliche Informationen bezüglich der Blasengröße und -geschwindigkeit sowie der Position der Raman-Messung relativ zur Blase erfasst werden. \nDie im Rahmen dieser Arbeit experimentell erhobenen zeit- und ortsaufgelösten Messungen bilden die Grundlage für ein besseres Verständnis von Gas/Flüssig-Reaktionen. Sie sollen zur Validierung bestehender und neuer dreidimensionaler CFD-Modelle dienen, welche die Hydrodynamik, den Stoffübergang und -transport sowie die Reaktion und deren Zusammenspiel bei der Taylor-Strömung berücksichtigen.";
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                "The simulations were performed with the atmospheric component of the ICON model. The model was run with the physics package used for numerical weather prediction (version 2.1.00) and with R2B04 horizontal resolution (approximately 160km) with 47 levels extending up to 75km. A time step of 720s was used.\nWe used a present-day setup and the cloud-locking method to determine the impact of cloud-radiative changes.\nFor the cloud-locked simulations, the number after T (sea-surface temperature) and C (cloud-radiative properties) are\n- 1 for the control simulation (CTL)\n- 2 for the uniform SST increase (UNI)\n- 3 for the patterned SST increase (PAT)\n# 4d (time-lev-lat-lon) data\nfree clouds\n- ICON-NWP_AMIP_free_3d_mm.nc (control simulation)\n- ICON-NWP_AMIP4K_free_3d_mm.nc (uniform 4K SST increase)\n- ICON-NWP_AMIPFuture_free_3d_mm.nc (patterned SST increase)\nlocked clouds\n- ICON-NWP_AMIP_T?C?_3d_mm.nc\nFiles contain monthly-mean data for 30 years (free clouds) and 29 years (locked clouds), respectively. The first simulated year is excluded from the files (spin-up period).\n# Partial Radiative Perturbation Calculation\n- ICON-NWP_prp_T1C1_vs_T1C2_3d_mm.nc (PRP for uniform SST increase)\n- ICON-NWP_prp_T1C1_vs_T1C3_3d_mm.nc (PRP for patterned SST increase)\nFiles contain monthly-mean data for 29 years. The first simulated year is excluded from the files (spin-up period).\n# storm track data\n- st_ICON-NWP_AMIP_T?C?_geopot500.nc: time-mean storm track (mean over all years)\n- st_T?C?_1980to2008.nc: time series of annual-mean storm track data\n- st_ICON-NWP_AMIP_T?C?_geopot500_DJF.nc: time-mean storm track for DJF season (mean over all years)\n- st_T?C?_1980to2008_DJF.nc: time series of DJF storm track data\n-> analogously for MAM, JJA, SON";
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                "Processed micro-tomographic scan of the ant Strumigenys benten,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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<https://radar.kit.edu/id/phFpBycsAvgACVhC>
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                  <http://schema.org/name>        "Klein, Sarah"
                ];
        <http://schema.org/contributor>
                [ a                               <http://schema.org/Person>;
                  <http://schema.org/familyName>  "Wetzel";
                  <http://schema.org/givenName>   "Thomas";
                  <http://schema.org/name>        "Wetzel, Thomas"
                ];
        <http://schema.org/contributor>
                [ a                               <http://schema.org/Person>;
                  <http://schema.org/familyName>  "Dietrich";
                  <http://schema.org/givenName>   "Benjamin";
                  <http://schema.org/name>        "Dietrich, Benjamin"
                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Weise, Sonja"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Weise";
                                          <http://schema.org/givenName>   "Sonja";
                                          <http://schema.org/name>        "Weise, Sonja"
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        <http://schema.org/datePublished>
                "2023-06-21";
        <http://schema.org/description>
                "***Sponges***\n---\n**10 ppi copper sponge (total length 200 mm, diameter 14 mm) made by replication technique**\n* total porosity: 91%\n* open porosity: between 85% and 88% \n* nominal cell density: approximately 10 pores per inch\n* mean strut diameter: 0.45 mm\n* mean window diameter: 1.6 mm\n**10 ppi plastic sponge (total length 47 mm, diameter 14 mm) made by 3D printing**\n* total and open porosity: 86%\n* nominal cell density: approximately 10 pores per inch\n* mean strut diameter: 0.6 mm\n* mean window diameter: 2.0 mm\n**20 ppi copper sponge (total length 247 mm, diameter 14 mm) made by replication technique**\n* total porosity: 90%\n* open porosity: between 84% and 87%\n* nominal cell size: approximately 20 pores per inch\n* mean strut diameter: 0.28 mm\n* mean window diameter: 1.0 mm\n***Operating conditions***\n---\n**two-phase flow:**\n* pressure: 12 bar and 26.5 bar\n* mass flux: 25 kg m-2 s-1 to 150 kg m-2 s-1\n* vapor quality: 10% to 100%\n  * adiabatic: heat flux: less than 0.9 kW m-2\n  * diabatic: heat flux: 8 kW m-2 to 40 kW m-2\n**single-phase flow:**\n* temperature: -10 °C to -15 °C\n* mass flux: 25 kg m-2 s-1 to 150  kg m-2 s-1\n* heat flux: less than 0.9 kW m-2" , "Experimental data on the adiabatic and diabatic two-phase flow of saturated CO2 in a horizontal cylindrical tube (diameter 14 mm) with integrated metal sponges (open-cell metal foams) are presented. Moreover, the pressure drop of single-phase flow inside the same metal sponges was investigated. The nominal cell density of the sponges is 10 ppi (“pores per inch”) or 20 ppi. The mass flow was varied from 25 kg/(m²s) to 150 kg/(m²s) and the flow vapor quality from 0.1 to 1 in case of two-phase flow. The saturation pressure was either 12 bar or 26.5 bar. The length filled with sponges upstream of the test section varied from 12 mm to 210 mm for 10 ppi sponges and was 109 mm for 20 ppi sponges. In addition, geometric properties of the sponges relevant to pressure loss, such as window diameter, strut diameter, porosity and specific surface area, were determined." , "Experimentelle Daten zur Zweiphasenströmung von gesättigtem CO2 in einem horizontalen zylindrischen Rohr (Durchmesser 14 mm) mit integrierten Metallschwämmen (offenzellige Metallschäume) unter adiabaten sowie diabaten Bedingungen werden vorgestellt. Außerdem wurde der Druckverlust bei einphasiger Durchströmung derselben Schwämme untersucht. Die nominelle Zelldichte der Schwämme beträgt 10 ppi („Poren pro Zoll“) bzw. 20 ppi. Der Massen-strom wurde von 25 kg/(m²s) bis 150 kg/(m²s) variiert und im Falle einer Zweiphasen¬strömung ebenfalls der Strömungsdampfgehalt (von 0,1 bis 1). Der Sättigungsdruck betrug entweder 12 bar oder 26,5 bar. Die mit Schwämmen gefüllte Länge stromaufwärts der Messstrecke variierte zwischen 12 mm und 210 mm bei 10 ppi Schwämmen und betrug 109 mm bei 20 ppi Schwämmen. Zusätzlich wurden die für den Druckverlust relevanten geometrische Eigenschaften der Schwämme, nämlich der Fensterdurchmesser, der Steg¬durchmesser, die Porosität und die spezifische Oberfläche, bestimmt.";
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        <http://schema.org/identifier>  "https://doi.org/10.35097/1126";
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                true;
        <http://schema.org/keywords>    "metal sponges" , "Engineering" , "open-cell foam" , "pressure drop" , "two-phase flow";
        <http://schema.org/name>        "Pressure drop data of two-phase flow in a horizontal tube filled with metal sponge";
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        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/phFpBycsAvgACVhC" .
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                                          <http://schema.org/name>        "Katzke, Julian";
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                                          <http://schema.org/name>        "Wenzel, Wolfgang"
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                                          <http://schema.org/name>        "Chakravadhanula, Venkata Sai Kiran"
                                        ];
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                                          <http://schema.org/affiliation>
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                                          <http://schema.org/familyName>  "Krupke";
                                          <http://schema.org/givenName>   "Ralph";
                                          <http://schema.org/name>        "Krupke, Ralph";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-8427-8592"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
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                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Kumar";
                                          <http://schema.org/givenName>   "C. N. Shyam";
                                          <http://schema.org/name>        "Kumar, C. N. Shyam"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT), Karlsruhe Nano Micro Facility (KNMF), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Wang";
                                          <http://schema.org/givenName>   "Di";
                                          <http://schema.org/name>        "Wang, Di";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0001-9817-7047"
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        <http://schema.org/datePublished>
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                "Polymer pyrolysis has emerged as a simple and versatile method to synthesize graphenoid (graphene like) materials with varying thickness and properties. The morphology of the thin film, especially the thickness, greatly affects the graphitizability (ability to from ordered graphene/ graphite) and thus the properties of the resulting graphenoid material. Using in situ current annealing inside a transmission electron microscope (TEM), we followed the thickness-dependent structural evolution of the polymer film with a special focus on thickness effects. Up to intermediate temperatures, the structural evolution during current annealing is similar to the evolution during thermal annealing of the thin films. At higher temperatures, thin samples form large graphene layers oriented parallel to the substrate, whereas in thick samples multi-walled cage-like structures are formed. MD simulations reveal a critical film thickness of 40 Å below which, the carbonized layers align parallel to the surface. For thicker samples, the orientation of the layers becomes increasingly misoriented starting from the surface to the center. This structural change can be attributed to the formation of bonded multi-layers from the initially unsaturated activated edges. The resulting cage-like structures are stable even during simulated annealing at temperatures as high as 3500 K. An atomistic understanding of the formation of these structures is presented. The results clearly indicate the critical effect of thickness on graphitizability of polymers and provide a new understanding of the structural evolution during pyrolysis." , "Information on the data formats provided in the individual subdirectories";
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        <http://schema.org/isAccessibleForFree>
                true;
        <http://schema.org/keywords>    "In-situ TEM" , "Engineering";
        <http://schema.org/name>        "Graphitizability of polymer thin films: an in situ TEM study of thickness effects on nanocrystalline graphene/glassy carbon formation";
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/cHEVaMLgSvWmPwlp" .
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        a                               <http://schema.org/Dataset>;
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                  <http://schema.org/name>  "Laeng, Alexandra"
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                                          <http://schema.org/familyName>  "Laeng";
                                          <http://schema.org/givenName>   "Alexandra";
                                          <http://schema.org/name>        "Laeng, Alexandra"
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        <http://schema.org/datePublished>
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                "1. Regression coefficients of a parametrisation of the natural atmospheric variability as function of distance and time separation for the atmospheric trace gases: BrO, BrONO2, CH3CL, HNO3, N2O5, CCl4, CFC11, CFC113, CFC12, CH4, ClO, ClONO2, CO, CO2, H2O, HBr, HCl, HF, HNO4, HO2, HOBr, HOCl, N2O, NO, NO2, NO3, O3, OH, and temperature, calculated on BASCOE model data for ESA VACUUM'R Project.\n2. Software for reparametrisation of natural atmospheric variability on latitudinal gradients. The software takes as input the species name (among 30 available), the distance and time mismatch chosen, the latitude band, the value of latitudinal gradients of user's data and the height. As an output the user obtains a value of the natural variability of the gas at altitude and latitude band given, for the chosen distance/time mismatch." , "Regression coefficients archive: The name of the file indicates if the coefficients stored are for natural variability as function of time or distance. The variability is expressed in percents. The coefficients are given from 90S to 90N in 18 latitude bands of 10 degree width, and for 46 altitudes from 15 to 60 km.\nReparametrisation software archive: This is an IDL source code, and auxiliary data are in idlsave format. If a validation study is perfomed in latitude domain larger than 10 degrees, the values in correspoding 10-degree bands should be added quadratically.";
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                                          <http://schema.org/familyName>  "Azuma";
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                                          <http://schema.org/name>        "Azuma, Fumika";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-5275-8775"
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                                          <http://schema.org/name>        "Katzke, Julian";
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                                          <http://schema.org/name>        "van de Kamp, Thomas";
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                "Processed micro-tomographic scan of the ant Strumigenys aff trudifera,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology(KIT)";
                                          <http://schema.org/sameAs>  "https://ror.org/04t3en479"
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                                          <http://schema.org/name>  "Karlsruhe Institute of Technology (KIT)"
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        <http://schema.org/description>
                "Three .xlsx-files are provided. Each file consists of multiple worksheets that contain daily pollutant concentrations, whereby the corresponding date is indicated by the worksheet title in DD-MM-YYYY format. No post-processing of the data has been performed. Note that few individual readings of the UFP-concentration may be affected by measurement artifacts (unplausibly high or negative concentrations) and should be discarded.\n\"UFP-Wind-Data\" contains 15-minute average UFP-concentrations and wind data (wind speed and direction) for the measurement period ranging from Nov. 20, 2024 until Mar. 30, 2025. Note that the wind data is only an indicative measurement close to the ground and may be influenced by the local building geometry.\n\"PM2_5-Data\" contains 15-minute average PM2.5-concentrations for the measurement period ranging from Nov. 20, 2024 until Mar. 30, 2025.\n\"Examples_Aerosol_Dispersion\" contains 1-minute average UFP-concentrations and wind data (wind speed and direction) for two exemplary events with increased concentration levels during the measurement period. Stationary temperature inversions ranging from Feb. 08, 2025 - Feb. 13, 2025 and the effect of New-Year's fireworks.\ntimestamp = Time (CET) respectively UTC+1; \nE.g. 23:15:00 CET corresponds to 00:15:00 AM local time in Germany and contains the 15-minute average data ranging from 00:00:00 AM up 00:15:00 AM.\naerosol_cn = Total particle number concentration (UFP) measured by the AQ Guard Smart 2000; Unit: 1/cm³\nweather_wind_speed = Local wind speed; Unit: km/h\nweather_wind_direction = Local wind direction; Unit: °\npm2_5 = Particle concentration of the PM2.5 fraction measured by the Fidas Smart 100; Unit: µg/m³" , "The concentration of ultrafine particles (UFP) and fine dust (PM2.5) in ambient air was measured in a residential area near Karlsruhe during the time period of Nov. 20, 2024 until Mar. 30, 2025. The used aerosol measurement technology were an AQ Guard Smart 2000 (UFP) and a Fidas Smart 100 (PM2.5) of the manufacturer Palas. In addition to the pollutant concentrations, local wind speed and direction were measured." , "Im Zeitraum vom 20.11.2024 bis zum 30.03.2025 wurde in einem Wohngebiet bei Karlsruhe die Belastung der Atemluft mit ultrafeinen Partikeln (UFP) und Feinstaub der PM2,5 Fraktion gemessen. Die verwendeten Messgeräte waren ein AQ Guard Smart 2000 (UFP) und ein Fidas Smart 100 (PM2,5) der Firma Palas. Zusätzlich zur Konzentration der Luftschadstoffe wurde auch die örtliche Windgeschwindigkeit und Windrichtung gemessen.";
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                                          <http://schema.org/givenName>   "Maike";
                                          <http://schema.org/name>        "Rees, Maike"
                                        ];
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                                          <http://schema.org/affiliation>
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                                                  ];
                                          <http://schema.org/familyName>  "Pilia";
                                          <http://schema.org/givenName>   "Nicolas";
                                          <http://schema.org/name>        "Pilia, Nicolas"
                                        ];
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                                          <http://schema.org/affiliation>
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                                                    <http://schema.org/name>  "Institut für Biomedizinische Technik"
                                                  ];
                                          <http://schema.org/familyName>  "Moik";
                                          <http://schema.org/givenName>   "Gerald";
                                          <http://schema.org/name>        "Moik, Gerald"
                                        ];
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                "1.8 million ECGs derived from multiscale simulations of cardiac electrophysiology of ventricular extrasystoles. 1000 anatomical variants of a bi-ventricular mesh x 600 excitation origins x 3 heart posiitions in the torso." , "This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model.\nIt has been used in [1,1a]. Here, only the noise-free BSPs are provided.\nDue to its size, this is a multi-part dataset.\nPart 1: https://doi.org/10.5445/IR/1000156139\nPart 2: https://doi.org/10.5445/IR/1000156554\nPart 3: https://doi.org/10.5445/IR/1000156555\nPart 4: https://doi.org/10.5445/IR/1000156556\nPart 5: https://doi.org/10.5445/IR/1000156557\nEach archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:\n- heart.vtp:\n  A triangle mesh of the heart including the point data:\n  - ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\n  - class: Boundary regions used as input for the computation of fiber orientations [3].\n  - trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).\n- heart_transform_matrices.mat:\n  A 1 x 3 cell array containing 4 x 4 transformation matrices that\n  describe the pose of the heart within the torso.\n  Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.\n- actTimes.mat:\n  A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5]\n  (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).\n- bsp.mat:\n  - bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with\n         scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].\n  - bspEnd: Time index of the end of depolarization (largest scaled activation time).\nThe archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:\n- torso.vtp:\n  A triangle mesh of the torso including the point data:\n  - electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).\n- heart_meanshape.vtp:\n  A triangle mesh of the mean shape of the statistical shape model [7,8].  \n- heart_shapemodel.mat:\n  - pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).\n  - var: A numModes x 1 vector of variances.\n  - weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.\n- heart_alignment_matrix.mat:\n  A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).\n- heart_transform_params.mat:\n  A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).\n- fiber_angles.mat:\n  - alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.\n  - alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.\n- actTimeScalings.mat:\n  - A numModels x numFoci matrix of factors used to scale the activation times.\n- tmv_template.mat:\n  The transmembrane voltage time course used to compute BSPs.\n- heart_classes.vtp:\n  A coarse triangle mesh of the mean shape used for fuzzy classification.\n- heart_classes_subdiv.vtp:\n  A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates.\n[1] https://doi.org/10.48550/arXiv.2209.08095\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\n[2] https://doi.org/10.1016/j.media.2021.102247\n[3] https://github.com/KIT-IBT/LDRB_Fibers\n[4] https://github.com/KIT-IBT/FIM_Eikonal\n[5] https://doi.org/10.1137/120881956\n[6] https://doi.org/10.1016/j.cmpb.2007.09.004\n[7] https://doi.org/10.5281/zenodo.4506463\n[8] https://doi.org/10.1016/j.media.2015.08.009";
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        <http://schema.org/name>        "In silico electrocardiograms of 1.8 million ventricular extrasystoles and corresponding activation maps (part 2)";
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                                          <http://schema.org/familyName>  "van de Kamp";
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                                          <http://schema.org/name>        "van de Kamp, Thomas";
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                "Processed micro-tomographic scan of the ant Neivamyrmex cf hetschkoi,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/familyName>  "Lyons";
                                          <http://schema.org/givenName>   "K.";
                                          <http://schema.org/name>        "Lyons, K."
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                                          <http://schema.org/familyName>  "Atkinson";
                                          <http://schema.org/givenName>   "G.";
                                          <http://schema.org/name>        "Atkinson, G."
                                        ];
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                                          <http://schema.org/familyName>  "Coldrick";
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                                          <http://schema.org/name>        "Coldrick, S."
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                "This report summarises the ‘rainout’ experiments undertaken by HSE, which was an\nexperimental investigation into the release characteristics and dispersion of elevated spills of\nLH 2 , including the propensity for rainout to form.\nA series of 25 LH 2 releases from a tanker delivery hose at elevated positions were carried out\nthrough 6 mm, 12 mm and 25.4 mm nozzles with an indicated tanker pressure of 1 or 5 barg.\nThe dispersion from these releases was measured using hydrogen concentration and\ntemperature measurements. Other attributes such as pressure, mass flow rate and temperature\nwere measured in the pipework to provide an understanding of the release characteristics. The\nexperiments were carried out at the LH 2 release facility, which was located on a 32 m diameter\nconcrete pad at the Frith Valley site at the HSE Science and Research Centre in Buxton.\nWhile no evidence of rainout was found during these releases, evidence of rainout immediately\nafter a release was observed. Further to this, condensed components of air formed around the\nrelease point and on impingements. Pools were only formed with low, vertically downward\nreleases. These pools potentially comprised of LH 2 , condensed components of air, or a mixture\nof the two. It is possible that with different initial conditions or obstruction geometries, rainout\nand pool formation could still occur.\nThe phase of hydrogen at the release point was indicated by the pipework measurements. For\nthe majority of the trials a two-phase flow developed within the release pipework. This has\nbeen attributed predominantly to the heat transfer into the fluid in the final 3 m of pipework,\nwhich contained the instrumentation and therefore was not vacuum insulated. The mass flow\nrate for each source configuration was measured and is reported including the methodology.\nThe development and dispersion of the gaseous H 2 cloud that forms from a release of LH 2 was\ncaptured by the instrumentation and video footage. The jet is typically momentum dominated\nfor the initial section, which ranges between 1.5 m and 6 m depending on the release pressure\nand nozzle size, but then becomes extremely dependent on the wind, including transient\nlocalised gusts.\nThe video and data output of these experiments will be provided for model validation, which is\ndue to take place in upcoming stages of the PRESLHY project." , "exel file consists of data from following sensors, obtained during experiments:\n    • near‐field thermocouples \n    • far‐field thermocouples \n    • pressure sensor\n    • tank Pressure sensor\n    • mass flow sensor\n    • H2 concentration sensors\n    • H2 PPM \n    • O2 depletion\n    • near‐field weather station PCE\n    • far‐field wind sensor \n    • far‐field humidity sensor\nand videos getted during experiments.\nReport";
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                "This data is the dataset created in the context of the masters' thesis of Richard Rudolph" , "In the past 10 years, an increase in cyberattacks and their professionalisation, both by organised crime and suspected state actors, known as Advanced Persistent Threat (APT) groups, can be observed. A particular rise has been noted in connection with the active war in Ukraine [1,2], just as the Ukrainian power grid had already become victim of sophisticated attacks multiple times before the outbreak of the war with incidents like Industroyer and BlackEnergy [3,4]. A common defence against  such attacks is the use of Intrusion Detection Systems (IDS) and/or Security Incident and Event Monitoring (SIEM) systems.\nHowever, these systems have the drawback that their effective use requires a team of security analysts to review numerous alarms and decide whether they represent a relevant threat. This can lead to the effect known as alert fatigue. Additionally, these systems often rely on signature databases and predefined rules to detect anomalies. This results in a high maintenance effort due to the arms race between attackers and defenders, as malware must first be detected and added to the database, and undetected incidents must be covered by rule updates, which then need to be rolled out. Furthermore, many systems assume that the network was not compromised at the time of setup in order to learn normal behaviour based on system observation over, for example, 1-2 weeks. Additionally, alarms that are related are often not directly correlated, making the detection of so-called \\emph{low and slow} attacks, which are typical for APT groups, very difficult.\nThere are approaches in the scientific literature to tackle the problem of detecting APT attacks. However, a significant effort is required to independently evaluate the effectiveness and functionality of these approaches. Research teams are also faced with the challenge of first setting up an infrastructure to execute and evaluate detection programs, and then bringing the programs to execution on this specific infrastructure.\nThis thesis aims to first reproduce existing approaches to detecting attacks from APT groups. Additionally, existing datasets that have recorded such attacks will be examined. A categorisation of the datasets in terms of the attack steps they contain and the recording methods will be created. It will also be evaluated how different approaches can be applied to these datasets.\n[1] https://services.google.com/fh/files/blogs/google_fog_of_war_research_report.pdf, Whitepaper, (Accessed 2024-07-12)\n[2] https://www.blackhat.com/us-22/briefings/schedule/#real-cyber-war-espionage-ddos-leaks-and-wipers-in-the-russian-invasion-of-ukraine-27206, (Accessed 2024-07-12)\n[3] https://www.blackhat.com/us-17/briefings.html#industroyer-crashoverride-zero-things-cool-about-a-threat-group-targeting-the-power-grid, (Accessed 2024-07-12)\n[4] https://www.blackhat.com/us-22/briefings/schedule/#industroyer-sandworms-cyberwarfare-targets-ukraines-power-grid-again-27832, (Accessed 2024-07-12)" , "In den vergangenen 10 Jahren lässt sich eine Zunahme an Cyberattacken und deren Professionalisierung sowohl von organisierter Kriminalität als auch von mutmaßlichen Staatsakteuren, sogenannten Advanced-Persistent-Thread-Gruppen (APT-Gruppen) beobachten. Einen besonderen Anstieg konnte hierzu im Zusammenhang mit dem aktiven Krieg in der Ukraine beobachtet werden [5,6], genauso wie auch schon im Vorfeld des Krieges mit Industroyer und BlackEnergy das ukrainische Stromnetz mehrfach Opfer von anspruchsvollen Angriffen wurde [7, 8]. Eine übliche Verteidigung im Allgemeinen gegen Angriffe ist der Einsatz von Intrusion Detection Systemen (IDS) und/oder Security Incident and Event Monitoring (SIEM) Systemen.\nDiese Systeme haben allerdings die Nachteile, dass für deren effektiven Einsatz ein Team an Security Analysten benötigt wird, die zahlreichen Alarme sichten und entscheiden, ob es sich dabei um einen relevanten oder echten Alarm handelt. Dies kann zu dem Effekt bekannt als alert fatique führen. Zudem basieren diese Systeme oft auf Signaturdatenbanken und fest definierten Regeln, um Anomalien zu erkennen. Das führt dazu, dass durch ein Wettrüsten zwischen Angreifenden und Verteidigenden diese Systeme einen hohen Wartungsaufwand erfordern, da Malware zunächst entdeckt und zu der Datenbank hinzugefügt, sowie unerkannte Incidents gefunden, mit Regelnvorschriften abgedeckt und die Regeln ausgerollt werden müssen. Zudem basieren viele Systeme darauf, dass zum Einrichtungszeitpunkt das eigene Netzwerk noch nicht kompromittiert wurde, um ein Normalverhaltenbasierend auf der Beobachtung des Systems über beispielsweise 1-2 Wochen hinweg zu lernen. Zudem können oft in Zusammenhang stehende Alarme nicht direkt verknüpft werden, was die Detektion von sogenannten \\emph{low and slow} Angriffen, wie sie für APT-Gruppen typisch sind, sehr schwierig macht. \nFür das Problem der Detektion von APT-Angriffen existieren in der wissenschaftlichen Literatur Ansätze. Allerdings besteht ein signifikanter Aufwand, diese Ansätze unabhängig auf ihre Wirksamkeit und Funktionsfähigkeit zu evaluieren. Forschungsteams stehen zudem vor der Aufgabe, zuvor eine Infrastruktur für die Ausführung und Evaluation von Detektionsprogrammen aufzubauen und zudem die zu evaluierenden Programme auf dieser spezifischen Infrastruktur zur Ausführung zu bringen.\nDiese Thesis hat zum Ziel, zunächst vorhandene Ansätze zur Erkennung von Angriffen von APT-Gruppen zu reproduzieren. Parallel dazu werden vorhandene Datensätze, in denen solche Angriffe aufgezeichnet worden sind, untersuchen. Eine Einordnung der Datensätze hinsichtlich der darin enthaltenen Angriffsschritte sowie Aufzeichnungsverfahren wird erstellt. Es soll auch evaluiert werden, wie unterschiedliche Ansätze auf diesen Datensätzen angewandt werden kann. \n[5] https://services.google.com/fh/files/blogs/google_fog_of_war_research_report.pdf, Whitepaper, (Abgerufen 2024-07-12)\n[6] https://www.blackhat.com/us-22/briefings/schedule/#real-cyber-war-espionage-ddos-leaks-and-wipers-in-the-russian-invasion-of-ukraine-27206, (Abgerufen 2024-07-12)\n[7] https://www.blackhat.com/us-17/briefings.html#industroyer-crashoverride-zero-things-cool-about-a-threat-group-targeting-the-power-grid, (Abgerufen 2024-07-12)\n[8] https://www.blackhat.com/us-22/briefings/schedule/#industroyer-sandworms-cyberwarfare-targets-ukraines-power-grid-again-27832, (Abgerufen 2024-07-12)";
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                                          <http://schema.org/name>        "Prokein, Michael"
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                "Experimental Details: Cyclic voltammetry (CV) experiments were conducted under both ambient conditions (25°C, 101.3 kPa) and elevated pressures (5 bar, 10 bar, and 25 bar). For ambient conditions, a three-electrode glass cell filled with CO2-saturated 0.1 M TBABF4 in acetonitrile electrolyte solution was employed. A polished planar copper disk served as the working electrode. A coiled Pt-wire inserted in a glass tube with a porous glass frit at the bottom acted as the counter electrode and Ag/AgCl as a reference electrode. At elevated pressure, a 316L stainless steel cell was used. Inside the cell, a three-electrode setup consisting of a Cu-foil working electrode, an AgBF4-coated silver wire as quasi-reference, and a platinum gauze counter-electrode was employed. Before the experiment, the cell was filled with the electrolyte (0.1 M TBABF4 in acetonitrile) and purged five times with CO2 (until 6 atm) under constant stirring with a magnet-operated stirrer. Afterward, the cell was filled with CO2 until the desired absolute pressure. The reference potential was adjusted to account for the different Ag/Ag+ reference systems employed in the ambient and high-pressure setups. Cyclic voltammograms were collected for five consecutive cycles for each scan rate between –0.8 V and –2.4 V. Data of the third cycle is provided. Post-iR correction was applied to all CV data to eliminate solution resistance effects.\nComputational Details: The model was implemented and simulated with the MATLAB 2022b software. The set of differential equations was solved using the ode15 ODE solver function. A finite volume discretization scheme with central difference quotients was applied to discretize the spatial domain into 100 segments with logarithmically increasing cell width. The total thickness of the simulation domain was set to 6 mm. The initial concentrations for each species were set to their bulk concentrations. The *CO2 surface coverage was initially set to equilibrium with the liquid phase.\nFor further details regarding the experimental and computational procedures, please refer to the Supporting Information.";
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                "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only." , "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d.";
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                "# Dataset for Attacker Propagation\n## Content\nThere are 4 folders:\n* **binary** for an excutable version\n* **effort_reduction** data-sheet for the effort calculation\n* **evaluationmodel** PCM models of the 3 case studies and expected results\n* **source** source for the analysis and the metamodel\n## Executing with the binary\nWe bundled a eclipse product, which can be used to start our analysis and view the models. It should be configured that it automatically opens a workspace with the necessary projects loaded. In case that won't happen, the projects can be manually loaded over the source folder and there *Palladio-Addons-ContextConfidentiality-Analysis/tests/org.palladiosimulator.pcm.confidentiality.context.analysis.testmodels/* or for the automatic test *Palladio-Addons-ContextConfidentiality-Analysis/tests/edu.kit.ipd.sdq.kamp4attack.tests/*\nHere we describe the step to execute the binary:\n*  Unzip the version of your Operating System\n    * **Attention:** The MAC-Version might not work, because of MACOS security features. In that case [this](https://sdqweb.ipd.kit.edu/wiki/PCM_Installation#Mac_OS_X) might help. If not, you can still use the update site or manually install the tooling, but you are required to solve the dependencies manually.\n    * We also provide an vm image for the tooling in the binary folder and there the vm folder\n    * In the VM the Procuct is in the home directory under AttackerPropagation\n    * The credentials for the vm are:\n        * User: icsa\n        * Password: icsa\n        * Root-Password: icsa\n* Start the Application by executing the *PalladioBench* binary (not the eclipse one!)\n* After the load screen you should see 3 Projects in the Modelviewer on the left side:\n    * edu.kit.ipd.sdq.kamp4attack.tests\n    * org.palladiosimulator.pcm.confidentiality.context.analysis.testframework\n    * org.palladiosimulator.pcm.confidentiality.context.analysis.testmodels\n* The models are stored in *org.palladiosimulator.pcm.confidentiality.context.analysis.testmodels*.\n    * By clicking on the arrow before the project you can see the content.\n    * The evaluation models are stored in the following folders:\n        * models/powerGrid\n        * models/targetBreach\n        * models/travelplanner\n    * Each folder contains the pcm models (allocation, reposity, resourceenvironment, system, usagemodel), the attackermodel (*.attacker), the access control model (*.context), the result model (*.kamp4attackmodificationmarks) and eclipse launchconfig (*.launch)\n        * with the launch config the scenario can be executed, by opening the context menu (normally right clock) and clicking \"Run as\"\n    * for a description of the models see Model Description\n* Additionally the accuracy tests can be executed automatically as Junit-Plugin-Test (only in the Linux binary):\n    * Open edu.kit.ipd.sdq.kamp4attack.tests project\n    * Navigate in the src folder to *edu.kit.ipd.sdq.kamp4attack.tests.casestudies* and *edu.kit.ipd.sdq.kamp4attack.tests.casestudies.travelplanner*.\n    * By opening the context menu (right click usually) and \"Run as\" Junit-**Plugin**-Test\n    * It is important to execute the tests as Plugin Tests since otherwise the dependencies can't be solved\n    * For Windows and Mac-User they can run the test by executing `mvn clean verify` in *source/Palladio-Addons-ContextConfidentiality-Analysis/* or use the vm image in case maven does not work\n## Executing without the binaries\n* Build each source project with maven\n* Projects generates an updatesite usually in the releng folder in an folder ending with .updatesite\n* These updatesites need to be installed in an eclipse installation. An README can be find in the source analysis source folder for the necessary dependencies\n## Model Descripton\n* Target Breach\n    * in folder targetBreach\n* Ukrainian Power Grid\n    * in folder powerGrid\n* TravelPlanner\n    * in folder travelplanner\n    * Scenarios in folder *Attacker_Propagation_Accuracy*: \n         1. An Empty Attacker model. The analysis has no attacker, therefore no propagation should happen\n         2. The attacker has no attack therefore only the initial component is affected\n         3. The attacker has no specific attack but has some stolen credential. Therefore, only the credentials are allowed for the propagation\n         4. Propagations based on vulnerabilites. The attacker has attacks for mainly one attack step. To verify that each propagation types work\n            1. A Component to a Seff Propagation\n            2. A Component to Component Propagtion\n            3. The component compromises the resource it is deployed on\n            4. The component compromises a remote resource (not the one it is deployed on)\n            5. A linking Resource compromises a connected Resource container\n            6. A linking Resource compromises a connected component\n            7. A resource compromises a connected component\n            8. A resource compromises another connected resource\n        5. The attacker gains a new credential based on an attack, but can't take full control of the Linking Resource\n        6. Tests whether the AttackVector option is considered in the analysis\n        7.  Tests whether the Privilege option is considered in the analysis\n## Resultsmodel\nThe ids of the non pcm elements (ServiceRestrictions and CompromisedData) might change for every run, since they are dynamically calculated for each analysis step. However, this is not problematic since they can identified by their other properties. \n## Further Information\nAdditional information and the current source code can be found at our Github repositories:\n* [Metamodel]https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Metamodel)\n* [Analysis](https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Analysis)\n* [Bench-Product](https://github.com/FluidTrust/Palladio-Bench-Product-AttackerPropagation)";
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                "Various multi-mass oscillator models were parameterized as part of a publication on modeling the rotational mechanical impedance around the xh- and zh-axes of the human coordinate system according to ISO 5349-1:2001, The publication will appear shortly in the conference proceedings of the “5th VDI Symposium on Vibrations 2025: Solutions for Vibrations.” The underlying studies used for the parametrization are published as data sets under DOI 10.35097/1r2qfkquz7mzar0s and DOI 10.35097/vfrrc2hbxkujanz8. Based on this data sets, the three-, four-, and five-mass oscillators are parametrized for four different combinations of grip and push forces, as well as the mean rotational mechanical impedance of the study. The parametrization was achieved by using an optimization algorithm of the Matlab (2023b, The Math Works Inc., Natick, USA) internal functions GlobalSearch and fmincon. Furthermore, all multi-mass oscillators were parameterized without weighting and with a weighting function for emphasizing frequencies between 10 and 150Hz. The resulting parameters of each force combination and multi-mass oscillator are published here. The error metrics between the measured and modeled rotational mechanical impedance can also be found individually for each model." , "The csv data contains different sheets. \n Sheet \"Overview\":\n- shows all multi-mass oscillators and their abbrevations\n- the weighting function\n- the objective function\n- the combinations of grip and push forces\n- the used formulas for calculating the error metrics\nSheet \"Summary xh-axis\":\n- error metrics of the different multi-mass oscillators for evaluating between 10-500Hz and 10-125Hz, already summarized across the grip and push force levels \n- broken down into directly comparable error metrics for comparing the different models for modeling the measured rotational mechanical impedance around the xh-axis\nSheet \"Summary zh-axis\":\n- error metrics of the different multi-mass oscillators for evaluating between 10-500Hz and 10-125Hz, already summarized across the grip and push force levels\n- broken down into directly comparable error metrics for comparing the different models for modeling the measured rotational mechanical impedance around the zh-axis\nSheet \"xh-axis Parameters, Errors\"\n- lists all model parameters for the different mutli-mass oscillators for each grip and push force combination, parametrized both weighted and unweighted, resulting in 40 rows\n- the error metrics between the measured and modeled rotational mechanical impedance around the xh-axis for each parametrized model is specified, for both evaluation ranges (10-125Hz and 10-500Hz)\nSheet \"zh-axis Parameters, Errors\"\n- lists all model parameters for the different mutli-mass oscillators for each grip and push force combination, parametrized both weighted and unweighted, resulting in 40 rows\n- the error metrics between the measured and modeled rotational mechanical impedance around the zh-axis for each parametrized model is specified, for both evaluation ranges (10-125Hz and 10-500Hz)";
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                "Included are 1H MRI data recorded on a 500 MHz Avance NEO spectrometer (Bruker), using thermal equilibrium polarization, SABRE hyperpolarization and RASER excitation. Data is provided in PV360-3.2 format, BIDS and as PNG images. For more information, see the linked publication" , "Conventional Magnetic Resonance Imaging (MRI) relies on high‐power Radio‐Frequency (RF) pulses to excite nuclear spins and in turn generate NMR signals. These pulses require large high‐power RF‐amplifiers and cause heat deposition in the tissue, which must be minimized for safety, presenting a growing problem when moving toward ever‐higher field MRI. An alternative to RF‐pulse excitation is self‐excitation of nuclear spins using Radiofrequency Amplification by Stimulated Emission of Radiation (RASER), where the nuclear spins undergo spontaneous transition, without RF excitation, from an over‐populated state to a ground state. Here, the feasibility of recording rapid proton RASER MRI images of pyrazine at low concentration (120 mM) with large matrix (128x128 pixels) in as little as 78 ms is demonstrated at 500 MHz (11.7 T). We also recorded a time‐series of images using a single bolus hyperpolarized pyrazine highlighting the feasibility of dynamic tracking. The demonstrated approach allows recording MRI scans without transmit‐receive electronics of the MRI scanner, which is highly desirable for portable MRI as well as the emerging field of hyperpolarized MRI using, e.g., HP protons, 129Xe gas or HP 13C labeled biomolecules as molecular tracers and imaging agents.";
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                "Raw data for operando XAS characterization\nThe overview file \"overview_data.pdf\" provides information on which data was used in each figure and where the data can be found.\nIn agreement with FAIR data principles, the XAS spectra of reference materials were additionally provided for upload in the RefXAS database (http://xafsdb.ddns.net/). More information on RefXAS can be found in the work of Paripsa and Gaur et al. (DOI: 10.1107/S1600577524006751)\nComplete raw dataset is published after embargo period (12/2026) at ESRF data portal under CC-BY-4.0 licence:\nhttps://doi.esrf.fr/10.15151/ESRF-ES-1357822885\nAdditional funding, to the below mentioned KIT-related funding, can be found in the acknowledgements of the publication and in the overview file.\nAdditional affiliation for A. Nicolai, D. A. Montalvo Berltran, F. J. Summ and C. Kiener:\nFoundational Technologies, Research & Predevelopment, Advanced\nManufacturing & Circularity\nSiemens AG\nOtto-Hahn-Ring 6\n81739 Munich\nGermany";
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                "BVOCs vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modelled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction–mass spectrometer combined with automatically-moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.";
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                "Über den Ablauf einer Gefriertrocknung auf Mycrometerebene gibt es bislang nur modellhafte Vorstellungen. Über einen speziellen Versuchsaufbau ist es nun möglich, Mycro-CT-Messungen während der Gefriertrocknung durchzuführen. In den gezeigten Daten wurde eine gefrorene Karottenscheibe in einen vakuumfesten, runden Plexiglascontainer gelegt, mittels einer drehbaren Absaugung evakuiert und an der Topo/Tomo-Beamline des IPS mit Synchrotronstrahlung gescannt. Dadurch konnte in einem Abstand von 5 Minuten die Porenstruktur der Karottenscheibe visualisiert werden. Auch die Unterschiede zwischen mit Eis gefüllten Poren und schon leeren, getrockneten Poren konnten aufgelöst werden. Dies macht es möglich, den Verlauf einer Gefriertrocknung zu verfolgen und nachzuvollziehen." , "The development of a freeze-drying process on a micrometer scale hast not yet been visualized. With a new experimental setup, micro-CT measurements can be performed during a freeze-drying process. For the shown data, a frozen carrot disc was placed inside a round perplex container, which was evacuated through a rotatable hose. The whole container was then positioned in synchrotron radiation in the Topo/Tomo-beamline of the IPS, which enabled a full scan of the carrots pore structure each 5 minutes. Hereby, the differences between ice-filled pores and already dried, empty pores could be visualized. This enabled a visualization of the progress of a freeze-drying process." , "The data is given as a .tif stack and can be viewed with Fiji/ImageJ. Simply drag and drop the file and you can scroll between the different times during drying. The time of the process is given in the form hh:mm in the top left corner. Bright structures are dense (cell walls, ice), while darker structures are less dense (vacuum, air). Notice that X and Y describe the horizontal dimensions of the carrot disc, while Z describes the height. As a result, the scan in the X-Y-layer shows a horizontal cut through the carrot, while the scan in the X-Z-layer shows a vertical cut.";
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        <http://schema.org/description>
                "# Experimental Data for the Dissertation \"Leveraging Constraints for User-Centric Feature Selection\"\nThese are the experimental data for the dissertation> Bach, Jakob. \"Leveraging Constraints for User-Centric Feature Selection\"\nat the [Department of Informatics](https://www.informatik.kit.edu/english/index.php) of the [Karlsruhe Institute of Technology](https://www.kit.edu/english/).\nThe subfolders correspond to individual chapters of the dissertation:\n- `chap4-syn`: Chapter 4 - \"Evaluating the Impact of Constraints on Feature-Selection Results\"\n- `chap5-ms`: Chapter 5 - \"Formulating Scientific Hypotheses as Constraints - A Case Study\"\n- `chap6-afs`: Chapter 6 - \"Finding Alternative Feature Sets\"\n- `chap7-csd`: Chapter 7 - \"Discovering Sparse and Alternative Subgroup Descriptions\"\nSee the corresponding `README` files in the subfolders for more information.\nWe already published prior versions of the experimental data, as the dissertation bases on prior papers:\n- Chapters 4 and 5: [Data](https://doi.org/10.35097/1345) for the [paper](https://doi.org/10.1007/s42979-022-01338-z) \"An Empirical Evaluation of Constrained Feature Selection\"\n- Chapter 6: [Data](https://doi.org/10.35097/1920) for the [paper](https://doi.org/10.48550/arXiv.2307.11607) \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\" (Version 2)\n- Chapter 7: [Data](https://doi.org/10.35097/caKKJCtoKqgxyvqG) for the [paper](https://doi.org/10.48550/arXiv.2406.01411) \"Using Constraints to Discover Sparse and Alternative Subgroup Descriptions\" (Version 1)\nFor Chapters 4, 5, and 7, we mainly consolidate the existing data.\nIn particular, all `*.csv` files (datasets and results) remain unchanged compared to the data linked above.\nFor Chapter 6, we reran the experimental pipeline to integrate a change for the feature-selection method \"Greedy Wrapper\".\nThe other feature-selection methods have not changed, but experimental data may slightly differ regarding runtimes and for results affected by solver timeouts.\nFor all four chapters, the following files (in each subfolder) differ from prior versions:\n- `Evaluation_console_output.txt`: The dissertation's evaluation partly differs from the papers' evaluations (e.g., some analyses added, adapted, or removed).\n- `README.md`: We adapted these files to the context of the dissertation, added some explanations, and proofread them." , "These are the experimental data for the dissertation> Bach, Jakob. \"Leveraging Constraints for User-Centric Feature Selection\"\nat the [Department of Informatics](https://www.informatik.kit.edu/english/index.php) of the [Karlsruhe Institute of Technology](https://www.kit.edu/english/).\nYou can find the dissertation [here](https://doi.org/10.5445/IR/1000178649).\nSee the `README` for details.\nMany input datasets (which we also provide here) either\n- originate from [OpenML](https://www.openml.org) and are CC-BY-licensed or\n- originate from [PMLB](https://epistasislab.github.io/pmlb/) and are MIT-licensed.\nPlease see the `LICENSE` files in the corresponding `datasets/` subfolders for details.";
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                "The data set consists of five types.\n1) In table AnthroDemoMaxForcesTable.mat the anthropometric data, the gender and the maximum grip and push forces of the subjects are listed.\n2) In Matrix resultsMatrix.mat the mechanical impedance and the phase angle of the measurements of the subjects study are summarized. The resulting mechanical impedance and the phase angle are already calibrated in each direction, and can be used directly from the matrix. The matrix consists of two dimensions, the first is the mechanical impedance and the phase angle around the direction of excitation (zh), the second dimension is the cross-axial mechanical impedance and the phase angle around xh-direction. The directions are named according to the coordinate system of DIN 5349:2001. Each row of the matrix represents one measurement. The following data information and results are in the corresponding columns:\n    1: number of the subect\n    2: is 0 if the grip and push forces during the measurements are within the range of 5N and 14%, otherwise a 1 is at this position. This can be used for data exclusion.\n    3: predefined push force value\n    4: predefined grip force value\n    5: experiment number according to the resultsMatrix for each subject\n    6: sequence of the taken measurements of each subject, due to a randomized test plan\n    7: mean measured grip force\n    8: median measured grip force\n    9: standard deviation of the measured grip force\n    10: mean measured push force\n    11: median measured push force\n    12: standard deviation of the measured push force\n    13: mechanical impedance at 10 Hz\n    14: phase angle at 10 Hz\n    15: mechanical impedance at 15 Hz\n    16: phase angle at 15 Hz\n    13:2:209 mechanical impedance between 10-500 Hz in 5 Hz steps\n    14:2:210 phase angle of the mechanical impedance between 10-500 Hz in 5 Hz steps\n    3) The VPAmatrix.mat is according to resultsMatrix.mat, instead of the mechanical impedance in positions 13:2:209 the vibration power absorption values are listed. Here, only the direction of excitation is evaluated.\n    4) Raw data, the data name consists of the time stamp and is named afterwards in the same order as the matrix: Px, where x is the number of the subject, followed by the push and then the grip force and the position in the matrix. In each struct the metadata, the time and the signals are saved. The excitation in the subject study was applied around the forearm axis (zh according to DIN5349:2001). Therefore, the angular_acceleration_z is already calculated from the recorded sensor signals az_top and az_bottom (the acceleration at the top and bottom of the measuring handle) in excitation direction. Torque_around_z is the torque resulting from the force sensor signals Fz_top and Fz_bottom around the direction of excitation. Torque_around_z and the angular_acceleration_z are used to calculate the mechanical impedance and vibration power absorption around the excitation direction (dimension 1 in the resultsMatrix.mat file). The signals used for the second dimension of the resultsMatrix are Torque_around_x (resulting from Fx_top, Fx_bottom) and angular_acceleration_z. The raw data includes the measurements of 16 levels of grip and push force combinations with three repetitions for each subject, as well as additionally measured validation data at random intermediate force levels. \n    5) emptyMeasurement.mat is a measurement of the unloaded handle while excited with the same signal as in the subject study. This file can be used for the calibration of the handle." , "The data set contains the raw as well as the evaluated data from a subject study with 6 subjects to measure the mechanical impedance and the vibration power absorbtion while varying the grip and push forces in four levels between 25-135N and 30-100N respectively. For each subject 10 additional measurements at random grip and push force combinations were recorded for validation purposes. The subjects were randomised to hold the force levels while the measurement handle was excitated around the lower arm axis (zh-axis according to DIN5349:2001) to their hand-arm system via a shaker system, with the posture of the subjects lying within the ranges specified in ISO 10819:2022. The measuring handle on which the study was conducted complies with the specifications in ISO10068:2012 for translatory excitations. To calibrate the handle for rotational excitation, a calibration function was calculated by exciting the handle with different weights. The excitation was a multisine with frequencies between 10 to 500 Hz in 5 Hz increments. Between 10 to 30Hz the amplitude of the multisines angular acceleration in the frequency domain are linearly increased from 20 to 100rad/s². From 30 to 500Hz to amplitude of the angular acceleration is at each frequency of the multisine 100rad/s². The acquisition time of each measurement is 16.5 sec, for the evaluation the data is cutted to exclude the first 2 sec and the last 3.5 sec.\nThe data set for translatory excitation in zh-direction is published as: DOI: 10.35097/uwz0qdyzh8h8uekw KITopen-ID: 1000181216";
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                "Messung/Test" , "Im Schuljahr 2021/22 wurde im elften Jahr in Folge im Rahmen von BERLIN HAT TALENT die physische Fitness von Berliner Drittklässlerinnen und Drittklässlern mit Hilfe des Deutschen Motorik-Tests (Bös et al., 2016) untersucht. Der Fokus lag dabei auf entwicklungsbezogenen und zeitlichen Trends der motorischen Leistungsfähigkeit sowie deren Auswirkungen auf die körperliche Gesundheit im Kindesalter in Berlin. \nIn jedem der 11 Jahre erhielten ausgewählte ö?entliche Grundschulen aus den 12 Berliner Stadtbezirken von der Berliner Schulverwaltung ein schriftliches Angebot für die Teilnahme. Die Auswahl der zu untersuchenden DrittklässlerInnen erfolgte schulweise durch eine vom Landessportbund Berlin (LSB) und der Berliner Schulverwaltung beauftragte Projektgruppe, wobei der Umfang dieser Stichprobe von Jahr zu Jahr ausgebaut wurde. Insgesamt 75.945 Berliner DrittklässlerInnen (mehr als 300 Schulen) absolvierten in ? mit schriftlicher Einverständniserklärung ihrer Eltern ? die sportmotorischen Tests. Die Erhebung der sportmotorischen Leistungen wurde einer auf die Erhebung von Sportdaten spezialisierten Firma übertragen, die diese mit nahezu unverändertem Personal über die Jahre realisiert hat.";
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        <http://schema.org/description>
                "# Experimental Data for the Paper \"An Empirical Evaluation of Constrained Feature Selection\"\nThese are the experimental data for the paper> Bach, Jakob, et al. \"An Empirical Evaluation of Constrained Feature Selection\"\naccepted at the journal [*SN Computer Science*](https://www.springer.com/journal/42979).\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` [CPU](https://www.amd.com/en/products/cpu/amd-epyc-7551) (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe Python version was `3.8`.\nOur paper contains two studies, and we provide data for both of them.\nRunning the experimental pipeline for the study with synthetic constraints (`syn_pipeline.py`) took several hours.\nThe commit hash for the last run of this pipeline is [`acc34cf5d2`](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection/tree/acc34cf5d22b0a8427852a01288bb8b34f5d8c98).\nThe commit hash for the last run of the corresponding evaluation (`syn_evaluation.py`) is [`c1a7e7e99e`](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection/tree/c1a7e7e99e56c1a178a602596c13641d7771df0a).\nRunning the experimental pipeline for the case study in materials science (`ms_pipeline.py`) took less than one hour.\nThe commit hash for the last run of this pipeline is [`ba30bf9f11`](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection/tree/ba30bf9f11703e2a8a942425e2cd4b9f36ead513).\nThe commit hash for the last run of the corresponding evaluation (`ms_evaluation.py`) is [`c1a7e7e99e`](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection/tree/c1a7e7e99e56c1a178a602596c13641d7771df0a).\nAll these commits are also tagged.\nIn the following, we describe the structure/content of each data file.\nAll files are plain CSVs, so you can read them with `pandas.read_csv()`.\n## `ms/`\nThe input data for the case study in materials science (`ms_pipeline.py`).\nOutput of the script `prepare_ms_dataset.py`.\nAs the raw simulation dataset is quite large, we only provide a pre-processed version of it\n(we do not provide the input to `prepare_ms_dataset.py`).\nIn this pre-processed version, the feature and target parts of the data are already separated into two files: `voxel_data_predict_glissile_X.csv` and `voxel_data_predict_glissile_y.csv`.\nIn `voxel_data_predict_glissile_X.csv`, each column is a numeric feature.\n`voxel_data_predict_glissile_y.csv` only contains one column, the numeric prediction target (reaction density of glissile reactions).\n## `ms-results/`\nOnly contains one result file (`results.csv`) for the case study in materials science.\nOutput of the script `ms_pipeline.py`, input to the script `ms_evaluation.py`.\nThe columns of the file mostly correspond to evaluation metrics used in the paper;\nsee Appendix A.1 for definitions of the latter.\n- `objective_value` (float): Objective `Q(s, X, y)`, the sum of the qualities of the selected features.\n- `num_selected` (int): `n_{se}`, the number of selected features.\n- `selected` (string, but actually a list of strings): Names of the selected features.\n- `num_variables` (int): `n`, the total number of features in the dataset.\n- `num_constrained_variables` (int): `n_{cf}`, the number of features involved in constraints.\n- `num_unique_constrained_variables` (int): `n_{ucf}`, the number of unique features involved in constraints.\n- `num_constraints` (int): `n_{co}`, the number of constraints.\n- `frac_solutions` (float): `n_{so}^{norm}`, the number of valid (regarding constraints) feature sets relative to the total number of feature sets.\n- `linear-regression_train_r2` (float): `R^2` (coefficient of determination) for linear-regression models, trained with the selected features, predicting on the training set.\n- `linear-regression_test_r2` (float): `R^2` (coefficient of determination) for linear-regression models, trained with the selected features, predicting on the test set.\n- `regression-tree_train_r2` (float): `R^2` (coefficient of determination) for regression-tree models, trained with the selected features, predicting on the training set.\n- `regression-tree_test_r2` (float): `R^2` (coefficient of determination) for regression-tree models, trained with the selected features, predicting on the test set.\n- `xgb-linear_train_r2` (float): `R^2` (coefficient of determination) for linear XGBoost models, trained with the selected features, predicting on the training set.\n- `xgb-linear_test_r2` (float): `R^2` (coefficient of determination) for linear XGBoost models, trained with the selected features, predicting on the test set.\n- `xgb-tree_train_r2` (float): `R^2` (coefficient of determination) for tree-based XGBoost models, trained with the selected features, predicting on the training set.\n- `xgb-tree_test_r2` (float): `R^2` (coefficient of determination) for tree-based XGBoost models, trained with the selected features, predicting on the test set.\n- `evaluation_time` (float): Runtime (in s) for evaluating one set of constraints.\n- `split_idx` (int): Index of the cross-validation fold.\n- `quality_name` (string): Measure for feature quality (absolute correlation or mutual information).\n- `constraint_name` (string): Name of the constraint type (see paper).\n- `dataset_name` (string): Name of the dataset.\n## `openml/`\nThe input data for the study with synthetic constraints (`syn_pipeline.py`).\nOutput of the script `prepare_openml_datasets.py`.\nWe downloaded 35 datasets from [OpenML](https://www.openml.org) and removed non-numeric columns.\nAlso, we separated the feature part (`*_X.csv`) and the target part (`*_y.csv`) of each dataset.\n`_data_overview.csv` contains meta-data for the datasets, including dataset id, dataset version, and uploader.\n**Licensing**\nPlease consult each dataset's website on [OpenML](https://www.openml.org) for licensing information and citation requests.\nAccording to OpenML's [terms](https://www.openml.org/terms), OpenML datasets fall under the [CC-BY](https://creativecommons.org/licenses/by/4.0/) license.\nThe datasets used in our study were uploaded by:\n- Jan van Rijn (user id: 1)\n- Joaquin Vanschoren (user id: 2)\n- Rafael Gomes Mantovani (user id: 64)\n- Tobias Kuehn (user id: 94)\n- Richard Ooms (user id: 8684)\n- R P (user id: 15317)\nSee `_data_overview.csv` to match each dataset to its uploader.\n## `openml-results/`\nResult files for the study with synthetic constraints.\nOutput of the script `syn_pipeline.py`, input to the script `syn_evaluation.py`.\nOne result file for each combination of the 10 constraint generators and the 35 datasets, plus one overall (merged) file, `results.csv`.\nThe columns of the result files are the those of `ms-results/results.csv`, minus `selected` and `evaluation_time`;\nsee above for detailed descriptions." , "These are the experimental data for the paper> Bach, Jakob, et al. \"An Empirical Evaluation of Constrained Feature Selection\"\npublished at the journal [*SN Computer Science*](https://www.springer.com/journal/42979).\nYou can find the paper [here](https://doi.org/10.1007/s42979-022-01338-z) and the code [here](https://github.com/Jakob-Bach/Constrained-Filter-Feature-Selection).\nSee the `README` for details.\nSome of the datasets used in our study (which we also provide here) originate from [OpenML](https://www.openml.org) and are CC-BY-licensed.\nPlease see the paragraph `Licensing` in the `README` for details, e.g., on the authors of these datasets.";
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                "This dataset provides pixel-wise annotations of photovoltaic (PV) systems at a spatial resolution of 0.2 m, distinguishing between rooftop PV (RTPV) and ground-mounted PV (GMPV). Two types of labels are included: (1) automatic annotations, available only for the state of Hesse, derived from geospatial sources; and (2) manually curated annotations for selected areas in Hesse and North Rhine-Westphalia (NRW), produced using high-resolution orthoimagery from Hesse and openNRW. The label data are provided without imagery, enabling integration with arbitrary remote sensing sources for applications such as PV detection and segmentation.\n\nThe automatic annotations were generated using geospatial sources including OpenStreetMap (OSM), the Official Topographic-Cartographic Information System (ATKIS), and data from the Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden. After merging these sources, GMPV installations were supplemented with data from Manske, D. (2025), “Geo-locations and System Data of Renewable Energy Installations in Germany,” Zenodo. doi:10.5281/zenodo.14627853." , "The dataset is distributed as GeoTIFF files, each named according to its corresponding orthoimage tile. For manually annotated labels, the final segment of the file name indicates the reference year of the source imagery. All data are projected in EPSG:25832 (ETRS89 / UTM zone 32N). Raster pixel values are encoded as follows: 0 for background, 1 for RTPV, and 2 for GMPV.\n\nThe corresponding aerial imagery can be downloaded from the following sources:\nfor Hesse: Digitales Orthophoto © Hessisches Landesamt für Bodenmanagement und Geoinformation (HVBG), 2021/22. (https://gds.hessen.de)\nfor NRW: Geobasis NRW - Open Data © Digitale Geobasisdaten NRW, Ministerium für Heimat, Kommunales, Bau und Digitalisierung des Landes Nordrhein-Westfalen, 2021/22/23/24  (https://open.nrw/dataset/)";
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                "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d." , "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only.";
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                "Images including *_ in the name are rotated by 90°. This originates from the process of image acquisition and does not harm the quality of the dataset but can be seen as a data augmentation technique. This can be reversed.\nImages with \"N\" in the file name are images without defect whereas images with \"P\" in the file name are images showing Pittings." , "The dataset contains of 21835 150x150 Pixel RGB images of the surface of Ball Screw Drives. 11075 of these images are images without surface defects whereas the rest shows images with surface defects in form of so called pittings. So the dataset is evenly split over the classes. Pittings result from surface disruption and can ultimately lead to the breakdown of the component. To keep the availability of machines high it is important to find surface defects in time. The here presented dataset gives researchers and practitioners the possibility to train and test models for the classification of surface defects on machine tool elements.";
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                "Supplementary material for \"Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models\" (DOI 10.1145/3524059.3532391)\n# Step 1: System analysis\nWe provide the processed JSON output of Perf-Taint for the Pace3D and Kripke case studies, as well as the bitcode of Kripke used as input of Perf-Taint. Because Pace3D is closed source, we cannot provide the source code to reproduce the analysis results. \n# Step 2: Experiment design\nWe provide scripts that: \n* check the output of Perf-Taint for parameters not interacting with each other,\n* detect iteration parameters potentially linearly influencing the runtime of the calculation\n* check the coefficient of variation of single iterations to confirm the linear influence\n# Step 3: Instrumented experiments\nWe provide the profiles of all measurements conducted for the training points. \nFor Pace3D, we had to add one function to the instrumentation filter manually because of a bug in Score-P: Functions declared as static inline inside a header file which are then called from different translation units are not instrumented correctly by Score-P. Thus, the resulting profiles are incorrect. However, we can work around this bug by including the function calling the static inline function. \n## Training Data Pace3D\n### Training Data Performance-Detective\nPerformance-Detective derived a minimal experiment design with 25 configurations to be measured once, resulting in 25 measurements. \nThe data for Extra-P is splitted into two folders that contain the same measurements that are labeled differently. As we did not implement modeling for the optimized subset, we later create two models (one using procs and vol, one using procs and cubes) and extract the model of each function from the respective model (depending on whether the functions relies on vol OR on cubes). \nThe data for PIM is the same as in the Extra-P folder, just labeled with all the values measured.\n### Training Data Full-Factorial\n625 measurements for all possible combinations of the five values of procs, vol, and cubes. Each measurement was repeated 5 times. \n### Training Data Plackett-Burman\n49 samples selected using a random seed and 5 levels -- effectively a subset of the full-factorial measurements. Each measurement was repeated 5 times.\n## Training Data Kripke\n### Training Data Performance-Detective\nPerformance-Detective derived a minimal experiment design with 5 configurations to be measured once, resulting in 5 measurements. \nThe data for Extra-P is splitted into two folders that contain the same measurements that are labeled differently. As we did not implement modeling for the optimized subset, we later create two models (one for procs, one for dirsets) and extract the model of each function from the respective model (depending on whether the functions relies on procs OR on dirsets). However, for the single-parameter modeler, modeling based on dependencies from Perf-Taint is not yet implemented. Therefore, the measurements are labeled as two parameter-experiments following the policy detailed in the respective folder. \nThe data for PIM is the same as in the Extra-P folder, just labeled with all the values measured.\n### Training Data Full-Factorial\n125 measurements for all possible combinations of the five values of procs and dirsets. Each measurements was repeated 5 times. \n### Training Data Plackett-Burman\n10 samples selected using a random seed and 5 levels -- effectively a subset of the full-factorial measurements. Each measurement was repeated 5 times. \n# Step 4: Modeling and Evaluation\nData of each case study is in the respective folder. \n## Evaluation Measurements\nExtra- and interpolated measurement data in the respective folder. \n## Extra-P\nContains script for calculating the model errors regarding extra- and interpolated evaluation measurements. \n### Models\nContains Extra-P models as well as a converter from Extra-P format to text (creates PerformanceModel.py), that is used in `calculate_model_errors.py`. It requires an installation of Extra-P to run. \n## Performance-Influence Models\nWe use the scripts by [Weber et al.](https://github.com/AI-4-SE/White-Box-Performance-Influence-Models/tree/main/supplementary-website/code/modeling), partly modified to account for the configurations of the case studies and modeling based on known dependencies to functions.\n### Data\nContains the data of the cubex files in csv format. Data is parsed with `create_csv_from_cubex.py`. \n### Model errors\nContains the csv files with the model errors of the respective models. Files are generated by `learn_method_level_model_with_deps.py` and `learn_method_level_without_deps.py`, respectively.\n# License\nThe files `calculate_model_errors.py`, `extrap_to_text.py`, and `create_csv_from_cubex.py` are modified parts of the Extra-P Software (cf. [cube_file_reader2.py](https://github.com/extra-p/extrap/blob/master/extrap/fileio/cube_file_reader2.py) and the license file `LICENSE-BSD3-EXTRAP`). \nThe files `learn_method_level_model_with_deps.py` and `learn_method_level_model_without_deps.py` are modified parts of the supplementary material of [Weber et al.](https://github.com/AI-4-SE/White-Box-Performance-Influence-Models/tree/main/supplementary-website/code/modeling). The license file `LICENSE-GPL-PIM` applies to these files.";
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                                          <http://schema.org/givenName>   "Xingchen";
                                          <http://schema.org/name>        "Shen, Xingchen"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Paolosini";
                                          <http://schema.org/givenName>   "Luigi";
                                          <http://schema.org/name>        "Paolosini, Luigi"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Diekman";
                                          <http://schema.org/givenName>   "Florian K.";
                                          <http://schema.org/name>        "Diekman, Florian K."
                                        ];
        <http://schema.org/datePublished>
                "2025-01-15";
        <http://schema.org/description>
                "Suppressing of an ordered state that competes with superconductivity is one route to enhance superconducting transition temperatures. Whereas the effect of suppressing magnetic states is still not fully understood, materials featuring charge-density waves and superconductivity offer a clearer scenario as both states can be associated with electron-phonon coupling. Metallic transition-metal dichalcogenides are prime examples for such intertwined electron-phonon-driven phases, yet, various compounds do not show the expected interrelation or feature additional mechanisms which make an unambiguous interpretation difficult. Here, we report high-pressure X-ray diffraction and inelastic X-ray scattering measurements of the prototypical transition-metal dichalcogenide 2H-TaSe2 and determine the evolution of the charge-density-wave state and its lattice dynamics up to and beyond its suppression at the critical pressure pc = 19.9(1) GPa and at low temperatures. The high quality of our data allows the full refinement of the commensurate CDW superstructure at low pressure and we find the quantum critical point of the CDW to be in close vicinity to the reported maximum superconducting transition temperature Tsc = 8.2 K. Ab-initio calculations corroborate that 2H-TaSe2 is a reference example of order-suppressed enhanced superconductivity and can serve as a textbook case to investigate superconductivity near a charge-density-wave quantum critical point." , "Raw data from the x-ray diffraction experiment performed at the beamline ID15b at the European Synchrotron Radiation Facility (ESRF), France (DOI: 10.15151/ESRF-ES-902987807); selected cif files; data used to create Figures in publication; table with structural parameters for refinements";
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        <http://schema.org/keywords>    "Physics";
        <http://schema.org/name>        "CDW quantum critical point under pressure in 2$H$-TaSe$_2$";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/crad209kjcxvvnkc" .
<https://radar.kit.edu/id/3cz74r3x124nj7cu>
        a                               <http://schema.org/Dataset>;
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Said, Ayman H."
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Guguschev, Christo"
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                  <http://schema.org/name>    "Weber, F.";
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                [ a                         <http://schema.org/Thing>;
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Habicht, Klaus"
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Merz, Michael"
                ];
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Maity, Avishek"
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                [ a                         <http://schema.org/Thing>;
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                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Dittmar, Andrea"
                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
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                ];
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                                          <http://schema.org/familyName>  "Merz";
                                          <http://schema.org/givenName>   "Michael";
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                                          <http://schema.org/name>        "Kojda, Danny"
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                                          <http://schema.org/familyName>  "Ryll";
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                                          <http://schema.org/name>        "Ryll, Britta"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Dittmar";
                                          <http://schema.org/givenName>   "Andrea";
                                          <http://schema.org/name>        "Dittmar, Andrea"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Guguschev";
                                          <http://schema.org/givenName>   "Christo";
                                          <http://schema.org/name>        "Guguschev, Christo"
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                                          <http://schema.org/familyName>  "Maity";
                                          <http://schema.org/givenName>   "Avishek";
                                          <http://schema.org/name>        "Maity, Avishek"
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                                          <http://schema.org/familyName>  "Keller";
                                          <http://schema.org/givenName>   "Thomas";
                                          <http://schema.org/name>        "Keller, Thomas"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Hoffmann";
                                          <http://schema.org/givenName>   "Jan-Ekkehard";
                                          <http://schema.org/name>        "Hoffmann, Jan-Ekkehard"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für QuantenMaterialien und Technologien (IQMT), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Weber";
                                          <http://schema.org/givenName>   "F.";
                                          <http://schema.org/name>        "Weber, F.";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0002-4256-1354"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Said";
                                          <http://schema.org/givenName>   "Ayman H.";
                                          <http://schema.org/name>        "Said, Ayman H."
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Habicht";
                                          <http://schema.org/givenName>   "Klaus";
                                          <http://schema.org/name>        "Habicht, Klaus"
                                        ];
        <http://schema.org/datePublished>
                "2025-02-24";
        <http://schema.org/description>
                "data taken in an investigation of SrTiO3 using inelastic x-ray scattering" , "The continuous displacive phase transition in SrTiO$_3$ near $𝑇_c\\approx105\\,\\rm{K}$ features a central elastic peak in neutron scattering investigations at temperatures above $𝑇_c$, i.e., before the corresponding soft phonon mode is overdamped upon cooling. The origin of this central peak is still not understood. Here, we report an inelastic x-ray scattering investigation of the cubic-to-tetragonal phase transition in SrTiO$_3$. We compare quantitatively measurements of the soft phonon mode on two differently grown samples and discuss the findings regarding results from thermodynamic probes such as specific heat and thermal conductivity. Furthermore, we use inelastic x-ray scattering to perform elastic scans with both high momentum- and milli-electronvolt energy-resolution and, thus, be able to separate elastic intensities of the central peak from low-energy quasielastic phonon scattering. Our results indicate that the evolution of the soft mode is similar in both samples though the intensities of the central peak differ by a factor of four. Measurements revealing anisotropic correlation lengths on cooling towards $T_c$, indicate that local properties of the crystals to which collective lattice excitations are insensitive are likely at the origin of the central elastic line in SrTiO$_3$.";
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                true;
        <http://schema.org/keywords>    "032345" , "Physics";
        <http://schema.org/name>        "Soft phonon and the central peak at the cubic-to-tetragonal phase transition in SrTiO$_3$";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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                          ];
                  <http://schema.org/familyName>  "Dsoke";
                  <http://schema.org/givenName>   "Sonia";
                  <http://schema.org/name>        "Dsoke, Sonia";
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                ];
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                          ];
                  <http://schema.org/familyName>  "Schwarz";
                  <http://schema.org/givenName>   "Björn";
                  <http://schema.org/name>        "Schwarz, Björn";
                  <http://schema.org/sameAs>      "http://orcid.org/0000-0002-9461-1448"
                ];
        <http://schema.org/copyrightHolder>
                [ a                         <http://schema.org/Thing>;
                  <http://schema.org/name>  "Zemlyanushin, Eugen"
                ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Angewandte Materialien – Energiespeichersysteme (IAM-ESS), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Zemlyanushin";
                                          <http://schema.org/givenName>   "Eugen";
                                          <http://schema.org/name>        "Zemlyanushin, Eugen"
                                        ];
        <http://schema.org/datePublished>
                "2025-02-17";
        <http://schema.org/description>
                "Electrochemical data: GCPL and CV measurements of Mo-foil, powder and Co3O4 coated on Mo-foil.\nICP-OES: Measurement of dissolved Mo in AlCl3:EMImCl (1.5:1) ionic liquid electrolyte.\nNMR: 1H- and 13C-NMRs of pure ionic liquid electrolyte and after soaking of Mo.\nUV-VIS: Pure ionic liquid electrolyte and after soaking Mo, diluted with 1,2-Difluorobenzene.\nXPS: Survey and Mo spectra on Al-foil stopped at 0.3V.\nPPMS: Magnetic measurements of Mo-foil/powder, pure ionic liquid and after soaking Mo, which results in a change of color of the electrolyte." , "Rechargeable aluminum batteries (RABs) using Lewis acidic aluminum chloride–1-ethyl-3-methylimidazolium chloride (AlCl3-EMImCl) ionic liquid electrolytes are promising alternative energy storage systems. Molybdenum (Mo), often used as a current collector, is typically considered stable with negligible redox activity in such electrolytes. However, this study shows that Mo reacts with AlCl3-EMImCl (1.5:1) electrolyte. When Mo-foil or powder is im-mersed, the initially colorless/yellowish ionic liquid turns red, indicating Mo dissolution. Magnetometry confirms the presence of Mo species with localized unpaired electrons in the red liquid, not found in metallic Mo. UV-VIS spec-troscopy reveals Mo3+ and Mo4+ species formation. ICP-OES shows 1.99±0.06 mass-% of Mo dissolves in the electro-lyte. Due to Mo’s instability, cyclic voltammetry (CV) and galvanostatic cycling with potential limitation (GCPL) show increasing redox activity over cycles, similar to unstable Platinum (Pt), with a discharge capacity of ~136 mAh.g-1 at 20 mA.g−1 after 100 cycles. X-ray photoelectron spectroscopy (XPS) indicates three oxidation states of Mo (Mo4+/5+/6+) on the aluminum negative electrode, due to Mo-cation migration and adsorption. Covering the Mo current collector with electrochemically inactive Co3O4 suppresses Mo reactivity by reducing active Mo surface area. These findings demonstrate Mo’s significant impact on AlCl3-based RABs’ electrochemical performance, which is not negligible.";
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        <http://schema.org/name>        "Dissolution of Molybdenum Current Collector as Crucial and Undesired Process in Aluminum Batteries";
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                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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        a                               <http://schema.org/Dataset>;
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                                                  ];
                                          <http://schema.org/familyName>  "Kramer";
                                          <http://schema.org/givenName>   "Dominik";
                                          <http://schema.org/name>        "Kramer, Dominik"
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                                                  ];
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                                                  ];
                                          <http://schema.org/familyName>  "Kirchlechner";
                                          <http://schema.org/givenName>   "Christoph";
                                          <http://schema.org/name>        "Kirchlechner, Christoph"
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                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Angewandte Materialien – Werkstoff- und Grenzflächenmechanik (IAM-MMI), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Lang";
                                          <http://schema.org/givenName>   "Sabrina";
                                          <http://schema.org/name>        "Lang, Sabrina"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Angewandte Materialien – Werkstoff- und Grenzflächenmechanik (IAM-MMI), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Müller";
                                          <http://schema.org/givenName>   "Sonja";
                                          <http://schema.org/name>        "Müller, Sonja"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Institut für Angewandte Materialien – Werkstoff- und Grenzflächenmechanik (IAM-MMI), Karlsruher Institut für Technologie (KIT)"
                                                  ];
                                          <http://schema.org/familyName>  "Avadanii";
                                          <http://schema.org/givenName>   "Diana";
                                          <http://schema.org/name>        "Avadanii, Diana";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-4534-1485"
                                        ];
        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Ganschow";
                                          <http://schema.org/givenName>   "Steffen";
                                          <http://schema.org/name>        "Ganschow, Steffen"
                                        ];
        <http://schema.org/datePublished>
                "2025-05-08";
        <http://schema.org/description>
                "Solid-state batteries with a lithium–metal anode are energy-storage devices that promise increased energy density and improved safety compared with liquid systems. Despite significant developments, the chemomechanical degradation of solid-state batteries represents a significant challenge to their widespread adoption. Specifically, Li-filled cracks (called “dendrites”) and electronically isolated Li inclusions (“dead” Li) are key defects resulting from coupled electrochemical and mechanical degradation during cycling. In this study, we use a symmetrical Li|LLZO|Li cell with a single-crystal electrolyte and demonstrate that an electronically isolated Li-metal inclusion exhibits bipolarity under an external electrical field, which leads to further crack expansion. We suggest that this process of “dead” metal activation accelerates chemomechanical degradation in solid-state batteries with alkali anodes." , "These data correspond to the publication with the DOI: https://doi.org/10.1021/acsenergylett.5c00101. The data represent .txt files corresponding to electrochemical measurements and images of cell assembly and disassembly as stated in the Methods section. The data also comprise operando measurements corresponding to the Supplementary Materials submission.";
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        <http://schema.org/keywords>    "LLZO" , "Li metal dendrites" , "operando" , "dead Li" , "Materials Science";
        <http://schema.org/name>        "Disconnected Lithium Metal Damages Solid-State Electrolytes-Data Repository";
        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Karlsruhe Institute of Technology";
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                  <http://schema.org/name>    "Hamel, Adrian";
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                "This dataset contains the relevant data of the submission \"A ductile chromium-molybdenum alloy\nresistant to high temperature oxidation\". All files highlighted by \"MC\" denote data on Cr-37.2Mo and \"MSC\" on Cr-36.1Mo-3Si (both alloy compositions given in at.%).\nFig1 = Scanning electron grayscale micrographs using backscattered electron contrast as *.tif images\nFig2a = Area-specific mass changes (average and uncertainty in mg/cm²) as a function of oxidation time (in h); \"800C\" denotes 800°C and \"1100C\" denotes 1100°C; the dataset highlighted by \"single_sample\" is not averaged but determined on a single specimen\nFig2b = Engineering stress-strain curves (in MPa and 1, respectively) for room temperature (= \"RT\") and 900°C (= \"900C\")\nFig2c = 1% offset yield strength (in MPa) as a function of testing temperature (in °C)\nFig2d = True work hardening (in MPa) as a function of true stress (in MPa) for room temperature (= \"RT\") and 900°C (= \"900C\")\nFig3a and b = Energy-dispersive X-ray spectroscopy maps as fully annotated comma-separated matrix file; the characteristic lines a highlights in the file name \"CrK\" = Cr-K, \"MoL\" = Mo-L, \"SiK\" = Si-K, \"OK\" = O-K\nFig3c = Atom probe tomography data in the form of Cameca proprietary data file format\nFig4 = Electron backscatter diffraction maps as fully annotated *.ang file\nFigE1 = X-ray diffraction patterns of the as cast condition as background-subtracted intensity (in 1) as a function of 2Theta diffraction angle (in °)\nFigE3 = Engineering stress-strain curves (in MPa and 1, respectively) for room temperature\nFigE4 = X-ray diffraction patterns of the oxidized material (100 h at 800°C = \"800C\" and 1100°C = \"1100C\") as background-subtracted intensity (in 1) as a function of 2Theta diffraction angle (in °)\nFigE5 = Energy-dispersive X-ray spectroscopy maps as fully annotated comma-separated matrix file; the characteristic lines a highlights in the file name \"CrK\" = Cr-K, \"MoL\" = Mo-L, \"OK\" = O-K\nFigE6 = Time-dependent (in h) scale thicknesses determined form scanning electron micrographs of oxide scales (in µm)\nFigE7 = Electron backscatter diffraction maps as fully annotated *.ang file";
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                "In deep geological repositories for nuclear waste, iron container-bentonite interactions may impact the long-term performance of engineered clay barrier. Anaerobic canister corrosion, leads to Fe(II) release. Reactions of Fe(II) with bentonite, may modify its properties over time. Accurate performance predictions for high-level radioactive waste (HLW) storage systems require detailed insights into these interactions under relevant thermal conditions. This study explores the secondary phase formation during the reaction of iron powder with GMZ bentonite, MX-80 bentonite, or KGa-2 kaolinite under anoxic conditions. Two series of batch experiments are presented: a continuous high-temperature experiment with mixed bentonite iron suspensions stored at 90°C for up to seven months, and a high-plus-low temperature experiment, with mixed iron clay suspensions stored for four months at 90°C and five months at ambient temperature.\nIn the presented experiments, montmorillonite - the main component of bentonite - undergoes complete delamination within one week. Iron is taken up by the clay matrix, reaching Fe-contents of ~ 30 atom %, which then remain constant. We suggest that the Fe uptake is driven by the formation of Fe(II/III) containing hydroxide sheets near/at delaminated montmorillonite sheets, inducing the formation of a serpentine like 1:1 phyllosilicate. Crystalline iron oxides (i.e. magnetite) form only at later stages, suggesting the kinetic preference of silicate-bound iron retention. At ambient temperature, the crystallinity of the Fe-phyllosilicate improves. All clay educts, bentonites as well as kaolinite are transformed into chemically and structurally similar phyllosilicates, emphasizing the relative stability and preferential formation of the resulting serpentine phases.\nIn this data publication we publish the spectroscopic and diffraction data as well as geochemical modelling files, which are explained in detail in the associated manuscript." , "In this data publication we publish the spectroscopic and diffraction data as well as geochemical modelling files, which are explained in detail in the associated manuscript with the same title, authors and publication year.";
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                "This dataset contains the relevant raw data of the submission \"Overcoming barriers for high temperature alloys:\noxidation and ductility\". All files highlighted by \"MC\" denote data on Cr-37.2Mo and \"MSC\" on Cr-36.1Mo-3Si (both alloy compositions given in at.%).\nFig1 = Scanning electron grayscale micrographs using backscattered electron contrast as *.tif images\nFig2a = area-specific mass changes (average and uncertainty in mg/cm²) as a function of oxidation time (in h); \"800C\" denotes 800°C and \"1100C\" denotes 1100°C; the dataset highlighted by \"single_sample\" is not averaged but determined on a single specimen\nFig2b = engineering stress-strain curves (in MPa and 1, respectively) for room temperature (= \"RT\") and 900°C (= \"900C\")\nFig2c = 1% offset yield strength (in MPa) as a function of testing temperature (in °C)\nFig2d = true work hardening (in MPa) as a function of true stress (in MPa) for room temperature (= \"RT\") and 900°C (= \"900C\")\nFig3 = Energy-dispersive X-ray spectroscopy maps as fully annotated comma-separated matrix file; the characteristic lines a highlights in the file name \"CrK\" = Cr-K, \"MoL\" = Mo-L, \"SiK\" = Si-K, \"OK\" = O-K\nFig4 = Electron backscatter diffraction maps as fully annotated *.ang file\nFigS1 = X-ray diffraction patterns of the as cast condition as background-subtracted intensity (in 1) as a function of 2Theta diffraction angle (in °)\nFigS2 = X-ray diffraction patterns of the oxidized material (100 h at 800°C = \"800C\" and 1100°C = \"1100C\") as background-subtracted intensity (in 1) as a function of 2Theta diffraction angle (in °)\nFigS3 = Energy-dispersive X-ray spectroscopy maps as fully annotated comma-separated matrix file; the characteristic lines a highlights in the file name \"CrK\" = Cr-K, \"MoL\" = Mo-L, \"OK\" = O-K\nFigS4 = time-dependent (in h) scale thicknesses determined form scanning electron micrographs of oxide scales (in µm); \"internal_corrosion_zone\" denotes the thickness of the internally oxdized regions; \"800C\" denotes 800°C and \"1100C\" denotes 1100°C\nFigS5 = Electron backscatter diffraction maps as fully annotated *.ang file";
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                "Die nachlassende körperliche Leistungsfähigkeit von Kindern führt zu gesundheitlichen Risiken. Während die Einflussfaktoren auf eine reduzierte motorische Leistungsfähigkeit bisher größtenteils in Querschnittsstudien untersucht wurden, war das Ziel dieser Studie, die Entwicklung dieser Faktoren und ihrer Bedeutsamkeit für die motorische Leistungsfähigkeit über die Grundschulzeit zu analysieren. Dazu wurden von 83 Augsburger Grundschülern in der 2., 3. und 4. Klasse Daten zu Migrationshintergrund, Sozialstatus, Medienausstattung, Body-Mass-Index (BMI), Sportaktivität und motorischer Leistungsfähigkeit erhoben. Mehr als 60% der Kinder waren durchschnittlich mindestens einmal pro Woche im organisierten Sport aktiv. Die Medienausstattung im Kinderzimmer stieg von der 2. bis zur 4. Klasse deutlich an. In der 4. Klasse waren 21,6% der Kinder übergewichtig. Je niedriger der Sozialstatus war, desto höher war die Medienausstattung der Kinder. Der BMI war für Kinder mit Migrationshintergrund und niedrigem Sozialstatus bedeutsam erhöht. Über den gesamten Messzeitraum stellte der BMI den wichtigsten Prädiktor für die motorische Leistungsfähigkeit dar. Bis zur 4. Klasse wurden regelmäßige Sportaktivitäten und eine geringere Medienausstattung ebenfalls bedeutsam. Die Studie legt somit nahe, bereits im Grundschulalter viel dafür zu investieren, das Gesundheitsrisiko durch einen erhöhten BMI zu reduzieren, das sowohl unmittelbar, als auch durch eine verminderte Fitness offenkundig wird. Paus";
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                                          <http://schema.org/givenName>   "M.";
                                          <http://schema.org/name>        "Schnaiter, M.";
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                "The data is in a NetCDF format. The file contains the following variables:\ntime_sec_af_midn: time as seconds after midnight\nT: temperature in degree Celsius\naltitude: flight altitude \nbin_midpoints: bin midpoint diameters in µm - droplets\nbin_ice_midpoints: bin midpoint diameters in µm - ice\nbin_endpoints: bin endpoint diameters in µm - droplets\nbin_ice_endpoints: bin endpoint diameters in µm - ice\nNtot_drop: Total concentration in particles per cubic centimeter – droplets\ndNtot_drop_upper: Clopper-Pearson confidence upper limit – droplets\ndNtot_drop_lower: Clopper-Pearson confidence lower limit – droplets\nNtot_ice: Total concentration in particles per cubic centimeter – ice\ndNtot_ice_upper: Clopper-Pearson confidence upper limit – ice\ndNtot_ice_lower: Clopper-Pearson confidence lower limit – ice\nPSD_all: Particle size distribution – ALL particles\nPSD_ice: Particle size distribution – ice\nPSD_ice_upper: Clopper-Pearson confidence upper limit - Particle size distribution – ice\nPSD_ice_lower: Clopper-Pearson confidence lower limit - Particle size distribution – ice \nPSD_drop: Particle size distribution – Droplets\nPSD_drop_upper: Clopper-Pearson confidence upper limit - Particle size distribution – Droplets\nPSD_drop_lower: Clopper-Pearson confidence lower limit - Particle size distribution – Droplets\nNDropImag: Number of droplet images used to construct particle size distribution\nNIceImag: Number of ice images used to construct particle size distribution\nd_eff_ice: Effective diameter – ice\nd_eff_drop: Effective diameter – droplets\nIWC: Ice water content assuming a sphere\nLWC: Liquid water content\nLWC_upper: Liquid water content upper limit – sphere. Calculated using Clopper-Pearson confidence lower limit.\nLWC_lower: Liquid water content lower limit – sphere. Calculated using Clopper-Pearson confidence lower limit. \nb_ext_drop: Extinction coefficient - droplets\nb_ext_ice: Extinction coefficient - ice" , "This data set contains particle size distributions of spherical and aspherical cloud particles between 5 and 45 microns with a 10-second time resolution calculated from the Small Ice Detector Mark 3 (SID-3). The SID-3 detects individual cloud particles passing a 532 nm laser beam using two nested trigger detectors. The trigger signal is recorded as a histogram with a maximum rate of 11 kHz that can be used to derive particle size distributions by using the procedure described in Vochezer et al. (2016), doi:10.5194/amt-9-159-2016. For a sub-set of triggered particles a two-dimensional (2-D) scattering pattern is recorded that can be analysed for particle sphericity by a specifically developed image analysis software. Occasionally, coincidence sampling in the camera field of view causes optical distortions of the 2-D scattering patterns of liquid droplets and, consequently, a misclassification of such scattering patterns to be aspherical by the classification software. For the subsequent identification and re-classification of coincidence scattering patterns a machine learning (ML) algorithm was developed.\nFrom the numbers of observed spherical and aspherical 2-D scattering patterns the fractions of spherical and aspherical particles are derived. Multiplication of those number-based fractions with the total particle size distribution yields phase-specific particle size distribution. The uncertainty due to the fact that the imaged particles are a subset of all sampled particles can be estimated from the Clopper–Pearson confidence limits.\nThe data was collected during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign, which was conducted northwest of Svalbard (Norway) between May 23 and June 6, 2017. The measurement area comprises an area north of Svalbard, approximately between 78 and 82°N. The SID-3 instrument was installed in the Polar-6 aircraft during the ACLOUD campaign.\nThe data is in NetCDF format and contains the total number concentration, the total particle size distribution, the total number of aspherical particles, the aspherical particle size distribution, the total number of spherical particles, the spherical particle size distribution and the upper and lower limits for the total aspherical and spherical particles and aspherical and spherical particle size distributions calculated using Clopper–Pearson confidence limits.";
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                "Data provided from experimental results presented in the publication and supporting information of HT-OEMS, XRD and OEMS analysis." , "Thermal events remain a safety challenge for lithium-ion batteries due to self-reinforcing exothermic reactions that occur at elevated temperatures. Higher states of charge have been shown to exacerbate the onset and severity of these events. This has been attributed to the release of lattice oxygen in cells containing Ni-rich layered oxide electrodes. The degradation reactions on the electrode/electrolyte interface triggered by this oxygen remain insufficiently understood. In this study, we investigate high-temperature degradation pathways of ethylene carbonate (EC)-based electrolytes in contact with Ni-rich positive electrode active materials up to 130 ◦C. By combining in-situ high-temperature online electrochemical mass spectrometry with post-mortem analyses, we identify and validate key degradation intermediates and products. Two distinct EC oxidation pathways are revealed: one activated at high voltages, and one initiated by trace water impurities. Theoretical calculations reveal the reactions are thermodynamically favorable and quantify their heat release. Both pathways produce significant heat and lead to gassing of CO$_2$ and H$_2$. These findings suggest a significant contribution of EC to thermal gas evolution and exothermicity under abuse conditions, thereby establishing a mechanistic link between electrolyte chemistry and thermal events. This integrated experimental-computational approach provides critical insights to guide improved electrolyte formulations and predictive thermal models.";
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                                          <http://schema.org/familyName>  "Diouf";
                                          <http://schema.org/givenName>   "Abdoul Aziz";
                                          <http://schema.org/name>        "Diouf, Abdoul Aziz"
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                                          <http://schema.org/affiliation>
                                                  [ a                         <http://schema.org/Organization>;
                                                    <http://schema.org/name>  "Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany"
                                                  ];
                                          <http://schema.org/familyName>  "Haas";
                                          <http://schema.org/givenName>   "Edwin";
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                "West African Sahelian and Sudanian ecosystems are providing essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing and it remains uncertain how these changes will affect the potential of these regions for providing food and fodder resources or the biosphere-atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. We tested the model for various sites with different proportions of trees and grasses, as well as for the most typical arable cropping systems of the region. In order to describe the phenological development with a common parameterization across all ecosystem types, we introduced soil-water availability in addition to temperature as a driver as seasonal soil water-shortage is a common feature for all these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations and leaf area index data from satellite images (MODIS). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed a good performance regarding simulated biomass development. Overall, the comparison of simulated and observed biomass at sites with a dominating land cover of crops, grass or trees yielded correlation coefficients of 0.82, 0.94, and 0.77 and the Root Mean Square Error of 0.15, 0.22, and 0.12 kg m−2, respectively. In absolute terms, the model results indicate above-ground carbon stocks up to 1733, 3291, and 5377 kg C ha−1 yr−1 for agricultural, savanna grasslands, and savanna mixed tree-grassland sites. Carbon stocks as well as exchange rates correlated in particular with the abundance of trees. The simulations indicate higher grass biomass and crop yields under more humid climatic conditions as can be found in the Sudanian savanna region. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semi-arid West Africa under a wide range of conditions, so that it might be used to assess the impact of land-use and climate change on the regional biomass productivity.";
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                "Processed micro-tomographic scan of the ant Cephalotes duckei,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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        <http://schema.org/publisher>   [ a                           <http://schema.org/Organization>;
                                          <http://schema.org/name>    "Okinawa Institute of Science and Technology Graduate University(OIST)";
                                          <http://schema.org/sameAs>  "https://ror.org/02qg15b79"
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        <http://schema.org/url>         "https://www.radar-service.eu/radar/en/dataset/6mpskk8fkp55rq1j" .
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                "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only." , "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d.";
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                "These are the experimental data for the paper> Bach, Jakob, Markus Iser, and Klemens Böhm. \"A Comprehensive Study of k-Portfolios of Recent SAT Solvers\"\npublished at the conference [*SAT 2022*](http://satisfiability.org/SAT22/).\nYou can find the paper [here](https://www.doi.org/10.4230/LIPIcs.SAT.2022.2) and the code [here](https://github.com/Jakob-Bach/Small-Portfolios).\nSee the `README` for details." , "# Experimental Data for the Paper \"A Comprehensive Study of k-Portfolios of Recent SAT Solvers\"\nThese are the experimental data for the paper> Bach, Jakob, Markus Iser, and Klemens Böhm. \"A Comprehensive Study of k-Portfolios of Recent SAT Solvers\"\naccepted at the conference [*SAT 2022*](http://satisfiability.org/SAT22/).\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Small-Portfolios) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` [CPU](https://www.amd.com/en/products/cpu/amd-epyc-7551) (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe Python version was `3.8.10`.\nWith this configuration, the experimental pipeline (`run_experiments.py`) took about 25 h.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [`d402353e7f`](https://github.com/Jakob-Bach/Small-Portfolios/tree/d402353e7f5804d3b693c3057f203a2c719c4098).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation.py`) is [`5ba0468088`](https://github.com/Jakob-Bach/Small-Portfolios/tree/5ba0468088fc0ce0ce3d2f04229946c1df83fa9d).\nBoth commits are also tagged.\nIn the following, we describe:\n- how to use the experimental data\n- the structure/content of each data file\n## Using the Data\nMost of the experimental pipeline's input and output files are plain CSVs.\nYou can easily read in any of the CSVs with `pandas` if you are using Python:\n```python\nimport pandas as pd\nresults = pd.read_csv('<path>/search_results.csv')\n```\nAll CSVs were written with `<DataFrame>.to_csv(<path>, index=False)`, i.e., they follow `pandas`' defaults for CSVs,\nso it is unnecessary to pass further parameters when reading them in with `pandas`.\nThe CSVs mostly contain plain numbers and strings;\nthe latter are only quoted if necessary, e.g., if they contain commas.\nHowever, the column `solvers` in `search_results.csv` contains lists of solver names.\nYou can convert this column from string to proper Python lists as follows:\n```python\nimport ast\nsearch_results['solvers'] = search_results['solvers'].apply(ast.literal_eval)\n```\n## `*.db` files and corresponding `*.csv` files\nRaw instance-feature databases and solver-runtime databases from [GBD](https://gbd.iti.kit.edu),\nas well as CSV exports of them.\nOutputs of the script `prepare_dataset.py`.\n- `meta`:\n  Meta-data of SAT instances, e.g., in which competition(s) the instances were used.\n  We only use this information to filter instances for the experimental datasets but not as features for predictions.\n- `satzilla`:\n  Instance features obtained with the feature extractor of [*SATzilla 2012*](https://www.cs.ubc.ca/labs/algorithms/Projects/SATzilla/Report_SAT_features.pdf).\n  Numeric matrix, apart from the `hash` column that identifies instances.\n  Each row represents a SAT instance; each column represents a feature (column names are feature names).\n  The value `timeout` represents missing values (feature extractor exceeded time or memory limits).\n- `sc2020`:\n  Solver runtimes from the [*SAT Competition 2020*](https://satcompetition.github.io/2020/).\n  Numeric matrix, apart from the `hash` column that identifies instances.\n  Each row represents a SAT instance (not all of them are actually from this SAT competition, so we filter instances later);\n  each column represents a solver (column names are solver names).\n  The values `failed`, `unknown`, and `timeout` represent missing values (solver did not solve instance within cut-off time).\n- `sc2021`:\n  Solver runtimes from the [*SAT Competition 2021*](https://satcompetition.github.io/2021/).\n  Numeric matrix, apart from the `hash` column that identifies instances.\n  Each row represents a SAT instance (not all of them are actually from this SAT competition, so we filter instances later);\n  each column represents a solver (column names are solver names).\n  The value `timeout` represents missing values (solver did not solve instance within cut-off time).\n## `sc(2020|2021)_features.csv`\nPre-processed instance-feature data for the *Main Track* of the SAT Competitions 2020 and 2021.\nOutput of the script `prepare_dataset.py`; input to the script `run_experiments.py`.\nNumeric matrix, apart from the `hash` column that identifies instances\n(we do not have any categorical features; they would need to be encoded beforehand).\nEach row represents a SAT instance; each column represents a feature (column names are feature names).\nHas the same number of rows as the corresponding runtime file.\nThe empty string represents missing values caused by the feature extractor exceeding time or memory limits;\nthese missing values are handled (imputed) in the prediction pipeline.\n## `sc(2020|2021)_runtimes.csv`\nPre-processed solver-runtime data from the *Main Track* of the SAT Competitions 2020 and 2021.\nOutput of the script `prepare_dataset.py`; input to the script `run_experiments.py`.\nNumeric matrix, apart from the `hash` column that identifies instances.\nEach row represents a SAT instance; each column represents a solver (column names are solver names).\nHas the same number of rows as the corresponding feature file.\nMissing values were replaced with the double cut-off time according to PAR-2 scoring (= 10000).\n## `search_results.csv`\nResults of portfolio search, e.g., portfolios, train/test objective values, and search times.\nOutput of the script `run_experiments.py`; input to the script `run_evaluation.py`.\n- `solvers` (string, but actually a list of strings):\n  List of the names of the solvers forming the portfolio.\n  The solver names are column names in `sc(2020|2021)_runtimes.csv`.\n- `train_objective` (float):\n  Objective value of a solution (= portfolio) to the `K-Portfolio-Problem`,\n  using the SAT instances from the training data.\n  The objective is defined as the PAR-2 score of the portfolio's virtual best solver (VBS).\n- `test_objective` (float):\n  Objective value of the `K-Portfolio-Problem` for the SAT instances from the test data,\n  i.e., take the solvers of the portfolio determined on the training data,\n  and compute their VBS on the test instances (without running portfolio search again).\n- `(train|test)_portfolio_vws` (float):\n  PAR-2 score of the virtual worst solver (VWS) formed from the portfolio,\n  i.e., select the worst (in terms of PAR-2 score) solver from the portfolio for each instance.\n  Might be used for comparison purposes; we do not use it in our evaluation.\n  Bounds the objective value for portfolios with instance-specific solver selection.\n- `(train|test)_portfolio_sbs` (float):\n  PAR-2 score of the single best solver (SBS) from the portfolio,\n  i.e., the best individual solver contained in the portfolio.\n  Might be used for comparison purposes; we use it in Figures 4 and 5 as a baseline.\n- `(train|test)_portfolio_sws` (float):\n  PAR-2 score of the single worst solver (SWS) from the portfolio,\n  i.e., the worst individual solver contained in the portfolio.\n  Might be used for comparison purposes; we do not use it in our evaluation.\n- `(train|test)_global_sws` (float):\n  PAR-2 score of the single worst solver (SWS) from all solvers (independent from current portfolio),\n  i.e., the globally worst individual solver.\n  Might be used for comparison purposes; we use it in Figures 1 and 2 for the submodularity-based upper bound.\n- `search_time` (float):\n  Runtime (in seconds) of the portfolio search (on the particular dataset and cross-validation fold,\n  with the particular portfolio-search approach).\n- `search_id` (int):\n  Identifier denoting combinations of dataset, cross-validation fold, and portfolio-search approach.\n  The experimental pipeline parallelizes these tasks.\n  Each `search_id` might be associated with multiple portfolios;\n  combining `search_id` and `solution_id` allows joining `search_results` and `prediction_results`.\n- `solution_id` (int):\n  Identifier to distinguish between multiple portfolios found with\n  a particular portfolio-search approach on a particular dataset and cross-validation fold.\n  Apart from `mip_search` (the *optimal solution*), all search approaches yield multiple portfolios.\n- `fold_id` (int in `{0, 1, 2, 3, 4}`):\n  Index of the cross-validation fold.\n- `problem` (string, 2 different values):\n  Dataset name (in our experiments: `SC2020` or `SC2021`).\n- `algorithm` (string, 4 different values):\n  Search approach used to determine portfolios\n  (in our experiments: `beam_search`, `kbest_search`, `mip_search`, or `random_search`).\n- `k` (int in `[1, 48]`):\n  Desired number of solvers in the portfolio, an input parameter to portfolio search.\n  The actual number of solvers in column `solvers` might differ,\n  as *beam search* and *k-best* are only run with the maximal `k` for each dataset,\n  but also yield all smaller portfolios (intermediate results).\n- `w` (int in `[1, 100]`, but stored as float):\n  Beam width if *beam search* was used, or number of random samples if *random search* was used.\n  Missing value (represented as an empty string) for the other two search approaches.\n  Input parameter to portfolio search.\n## `prediction_results.csv`\nResults of predictions with portfolios, e.g., train/test prediction performance, train/test objective values, and feature importance.\nOutput of the script `run_experiments.py`; input to the script `run_evaluation.py`.\n- `model` (string, 2 different values):\n  Name of the prediction model (in our experiments: `Random Forest` and `XGBoost`).\n  Each prediction model is trained for each portfolio from the search\n  (thus, `prediction_results` has twice the number of rows as `search_results`).\n- `pred_time` (float):\n  Runtime (in seconds) for training the prediction model (for one portfolio) on the training data,\n  and making predictions on training data as well as test data.\n- `(train|test)_pred_mcc` (float in `[-1, 1]`):\n  Prediction performance in terms of Matthews Correlation Coefficient for predicting\n  the best solver from the portfolio for each instance.\n- `(train|test)_pred_objective` (float):\n  PAR-2 score of the (instance-specific) solver recommendations made by the prediction model.\n- `imp.<feature_name>` (float in `[0, 1]`):\n  Feature importances extracted from the prediction model after training.\n  Importances are normalized and should sum up to one for each row. \n  Missing values (represented as empty strings) occur if no prediction model was trained\n  since the prediction target only had one class, e.g.,\n  portfolio had size one or always the same solver (out of multiple solvers) was best.\n- `search_id` (int), `solution_id` (int):\n  Same as in `search_results.csv`, can be used for joining the results.\n## `Evaluation_console_output.txt`\nOutput of the script `run_evaluation.py`, manually copied from the console to a file.";
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The excitation was a multisine with frequencies between 10 to 500 Hz in 5 Hz increments. Between 10 to 30Hz the amplitude of the multisines angular acceleration in the frequency domain are linearly increased from 20 to 100rad/s². From 30 to 500Hz to amplitude of the angular acceleration is at each frequency of the multisine 100rad/s². The acquisition time of each measurement is 16.5 sec, for the evaluation the data is cutted to exclude the first 2 sec and the last 3.5 sec.\nThe data set for translatory excitation in zh-direction is published under: DOI: 10.35097/uwz0qdyzh8h8uekw KITopen-ID: 1000181216\nThe data set for rotatory excitation around the zh-axis is published under: DOI: 10.35097/vfrrc2hbxkujanz8 KITopen-ID: 1000185118" , "The data set consists of five types.\n1) In table AnthroDemoMaxForcesTable.mat the anthropometric data, the gender and the maximum grip and push forces of the subjects are listed.\n2) In Matrix resultsMatrix.mat the mechanical impedance and the phase angle of the measurements of the subjects study are summarized. The resulting mechanical impedance and the phase angle are already calibrated in each direction and can be used directly from the matrix. The matrix consists of two dimensions, the first is the mechanical impedance and the phase angle around the direction of excitation (xh), the second dimension is the cross-axial mechanical impedance and the phase angle around the zh-axis. The directions are named according to the coordinate system of DIN 5349:2001. Each row of the matrix represents one measurement. The following data information and results are in the corresponding columns:\n1: number of the subect\n2: is 0 if the grip and push forces during the measurements are within the range of 5N and 14%, otherwise a 1 is at this position. This can be used for data exclusion.\n3: predefined push force value\n4: predefined grip force value\n5: experiment number according to the resultsMatrix for each subject\n6: sequence of the taken measurements of each subject, due to a randomized test plan\n7: mean measured grip force\n8: median measured grip force\n9: standard deviation of the measured grip force\n10: mean measured push force\n11: median measured push force\n12: standard deviation of the measured push force\n13: mechanical impedance at 10 Hz\n14: phase angle at 10 Hz\n15: mechanical impedance at 15 Hz\n16: phase angle at 15 Hz\n13:2:209 mechanical impedance between 10-500 Hz in 5 Hz steps\n14:2:210 phase angle of the mechanical impedance between 10-500 Hz in 5 Hz steps\n3) The VPAmatrix.mat is according to resultsMatrix.mat, instead of the mechanical impedance in positions 13:2:209 the vibration power absorption values are listed. Here, only the direction of excitation is evaluated.\n4) Raw data, the data name consists of the time stamp and is named afterwards in the same order as the matrix: Px, where x is the number of the subject, followed by the push and then the grip force and the position in the matrix. In each struct the metadata, the time and the signals are saved. The excitation in the subject study was applied around the palm (xh according to DIN5349:2001). Therefore, the angular_acceleration_x is already calculated from the recorded accelerometer signals ax_top and ax_bottom (the acceleration at the top and bottom of the measuring handle) in excitation direction. Torque_around_x is the torque resulting from the force sensor signals Fx_top and Fx_bottom around the direction of excitation. Torque_around_x and the angular_acceleration_x are used to calculate the mechanical impedance and vibration power absorption around the excitation direction (dimension 1 in the resultsMatrix.mat file). The signals used for the second dimension of the resultsMatrix are Torque_around_z (resulting from Fz_top, Fz_bottom) and angular_acceleration_x. The exact calculation of the cross axis mechanical imepdance can be found under DOI 10.51202/9783181024508-83. The raw data includes the measurements of 16 levels of grip and push force combinations with three repetitions for each subject, as well as additionally measured validation data at random intermediate force levels.\n5) emptyMeasurement.mat is a measurement of the unloaded handle while excited with the same signal as in the subject study. This file can be used for the calibration of the handle.";
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These were included with explicit permission from the respective authors or rights holders.\n   The following table lists the included files and the corresponding source information:\n| File Name | Title | Authors | Link |\n|-----------|-------|---------|------|\n| `10200-Klimaleitplanken_2.0` | * Klimazukunft Baden-Württemberg - Was uns ohne effektiven Klimaschutz erwartet! * | Dr. Kai Höpker, Dr. Sabrina Plegnière (LUBW)  | [Link]( https://pd.lubw.de/10200) |\n| `10506-Landesweite_Brutbestandserfassung_2022_im_Auftrag_der_LUBW_-_Landesanstalt_für_Umwelt_Baden-Württemb.tml` | * Der Kormoran (Phalacrocorax carbo) in Baden-Württemberg 2022 * | Ornithologische Gesellschaft Baden-Württemberg  | [Link]( https://pd.lubw.de/10506) |\n| `10274-Sondermessungen_2020_Abschlussbericht.tml` | * Verkehrsnahe Sondermessungen 2020 in Baden-Württemberg * | LUBW Refarat 33  | [Link]( https://pd.lubw.de/10274) |\n| `10199-Immissionsbelastung_für_Baden-Württemberg_2016_-_2025.tml` | * Flächendeckende Ermittlung der Immissionsbelastung für Baden-Württemberg 2016 und Prognose für 2025 * | LUBW Refarat 33  | [Link]( https://pd.lubw.de/10199) |\n| `Grundwasser-Überwachungsprogramm - 2022.tml` | * Grundwasser-Überwachungsprogramm 2022 * | LUBW Referat 42  | [Link]( https://pd.lubw.de/10597) |\n| `10582-Zahlen_und_Fakten_zum_Stand_der_Altlastenbearbeitung_in_Baden-Württemberg.tml` | * Altlastenstatistik 2022 * | Jochen Stark, Alicia Graf  | [Link]( https://pd.lubw.de/10582) |\n| `82479-Untersuchungen_unter_Verwendung_des_Programms_ARTM_(Atmosphärisches_Radionuklid-Transport-Modell).tml` | * ABR-ARTM - Untersuchung zur Langzeitausbreitung von radioaktiven Schadstoffen an den Standorten von Atomkraftwerken in Baden-Württemberg * | Walter Scheuermann, Andreas Piater, Christoph Krass, Herbert Pohl  | [Link]( https://pd.lubw.de/82479) |\n| `95109-Leitfaden` | *Überwachung von industriellen Abwasseranlagen und -einleitungen* | LUBW | [Link]( https://pd.lubw.de/95109 ) |\n| `Anlage 4 der V813-2023_Abschlussbereicht.tml` | * V813/2023 ANLAGE 4: ABSCHLUSSBERICHT * | Stadt Mannheim, MVV Regioplan  | [Link](https://www.mannheim.de/sites/default/files/2024-03/Anlage%204%20der%20V813-2023_Abschlussbereicht.pdf) |\n| `Kommunale_Waermeplanung_Beschlussvorlage.tml` | *Kommunale Wärmeplanung für München * | Referat für Klima- und Umweltschutz und Referat für Arbeit und Wirtschaft | [Link]( Beschluss der Vollversammlung des Münchner Stadtrats vom 15.5.2024, München) |\n| `01_2024_uib_energieeffiziente_mobilitaet.tml` | * Beschäftigung durch energieeffiziente Mobilität - Methodik der Ermittlung und Ergebnisse für das Jahr 2019 * | Luisa Sievers, Anna Grimm  | [Link]( https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/01_2024_uib_energieeffiziente_mobilitaet.pdf) |\n| `333_LF_Urbansky.tml` | * Untersuchung der Verteilnetzbelastung bei Proaktivem Energy-Sharing nach Österreichischem Vorbild * | Julian Urbansky, Lennart Schürmann  | [Link]( https://www.researchgate.net/publication/380376111_Untersuchung_der_Verteilnetzbelastung_bei_Proaktivem_Energy-Sharing_nach_Osterreichischem_Vorbild ) |\n| `Innovative_Technologien_Gebäudebereich_EWB.tml` | *Innovative Technologien im Gebäudebereich der Forschungsinitiative ENERGIEWENDEBAUEN* | Jessica Preuss, Heike Erhorn-Kluttig | [Link](https://publica.fraunhofer.de/entities/publication/cbca8246-0337-4700-90d1-8ce7fb1259c2) |\n| `05_2024_cc_dekarblnd_tb1.tml` | *Dekarbonisierung der industriellen Produktion (DekarbInd) AP 1: Ganzheitliches Bewertungsschema für Technologien* | Peter Viebahn, Dietmar Schüwer, Georg Holtz, Andreas Pastowski, Jacqueline Klingen, Sören Steger, Ali Aydemir | [Link](https://www.umweltbundesamt.de/publikationen/dekarbonisierung-der-industriellen-produktion) |\n| `1436-4980-2024-01-02-11.tml` | *Effizienzsteigerung durch Energiemanagementsysteme* | Laura Jung, Kerim Torolsan, Artur Gergert, Felix Förster, Alexander Sauer | [Link](https://publica.fraunhofer.de/entities/publication/93f2de91-6881-4ab2-bae6-6e1a9246f8af) |\n| `Wärmeplanung_Augsburg_Infoblatt_Jan2024` | *Hinweise zur Wärmeplanung und zum Fernwärmeausbau im Stadtgebiet Augsburg (Stand Januar 2024)* | Stadt Augsburg, Umweltamt | [Link](https://www.augsburg.de/fileadmin/user_upload/umwelt_soziales/umwelt/klima%20und%20energie/W%C3%A4rmeplanung_Augsburg_Infoblatt_Jan2024.pdf) |\n| `2024-04-05_Endbericht_kommunale_Wärmeplanung_Stadt_Pforzheim_nach__7_Klimaschutzgesetz_Baden-Württemberg.tml` | *Kommunale Wärmeplanung der Stadt Pforzheim* | Stadt Pforzheim, Amt für Umweltschutz | [Link](https://www.klimaschutz-pforzheim.de/fileadmin/user_upload/umwelt/klimaschutz/waermeplanung/2024-04-05_Endbericht_kommunale_W%C3%A4rmeplanung_Stadt_Pforzheim_nach__7_Klimaschutzgesetz_Baden-W%C3%BCrttemberg.pdf) |\n| `Bericht_zur_kommunalen_Waermeplanung.tml` | *Kommunale Wärmeplanung Für die Stadt Offenburg* | Stadt Offenburg | [Link](https://www.offenburg-klimaschutz.de/fileadmin/redakteur/Waermeplanung/Bericht_kommunale_Waermeplanung.pdf) |\n| `10598-Archewiesen_-_Saatgutgewinnung.tml` | *„Archewiesen“ Ein Projekt zur Erhaltung des artenreichen Grünlands in Baden-Württemberg* | LUBW Referat 56 | [Link](https://pd.lubw.de/10598) |\n| `Auf zu neuen Wegen – gemeinschaftlich und nachhaltig wirtschaften!_2022.tml` | *Auf zu neuen Wegen – gemeinschaftlich und nachhaltig wirtschaften!* | Binta Bah, Gerd Oelsner | [Link](https://pd.lubw.de/10442) |\n⚠️ **The individuals and institutions listed here are the copyright holders of the corresponding documents.**\nThe text content is used with their permission and **may only be used for non-commercial, scientific purposes**.\n2. **Annotations**  \n   Metadata, structural, or linguistic annotations created manually or semi-automatically, intended to support various forms of scientific analysis.\n## License\n### Annotations\nAll annotations and the dataset structure are licensed under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.  \nYou are free to use, modify, and redistribute the annotations — including for commercial purposes — provided that appropriate credit is given.🔗 [CC BY 4.0 Summary](https://creativecommons.org/licenses/by/4.0/)\n### Text Content\nThe original text content is **copyrighted** and included **with permission** from the respective authors or publishers.  \nIt is made available **exclusively for non-commercial, scientific use**.\n- ✅ Permitted: Academic research, teaching, citation in scientific publications.\n- ❌ Not permitted: Commercial use, redistribution of raw text content outside of research settings, or adaptation beyond scholarly work.\nFor more details, refer to the `LICENSE.txt` file.\n## Author and Contact Information:\n- Institution: Karlsruhe Institute of Technology (KIT), Institute for Automation and Applied Informatics (IAI)\n- Main Author: Nicolas Doms, ndoms716@gmail.com \n- Authors: Toni Golian, Ruidie Hu\n- Principle Investigator: Thorsten Schlachter, thorsten.schlachter@kit.edu \n## Version and Date of Publication\n- Version: 1.0\n- Date: 2025-04-08\n## Disclaimer\nThis dataset is provided \"as is\" without warranty. 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                                          <http://schema.org/familyName>  "Pantenburg";
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                                          <http://schema.org/name>        "Pantenburg, Isabel"
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                "Lithium-ion batteries (LIBs) are the dominating energy storage technology for electric vehicles and portable electronic devices. Since the resources of raw materials for LIBs are limited and recycling technologies for LIBs are still under development, improvements in the long-term stability of LIBs are of paramount importance, and, in addition, would lead to a reduction in the levelized cost of storage (LCOS). A crucial limiting factor is the ageing of the solid electrolyte interphase (SEI) on the active material particles in the anode. Here, we demonstrate the potential of atom probe tomography for elucidating the complex mosaic-type structure of the SEI in a graphite composite anode. Our 3D reconstruction shows unseen details and reveals the existence of an apolar organic microphase pervading the SEI in its entire thickness. This finding is in stark contrast to the prevalent two-layer SEI model, in which organic compounds are the dominating species only in the outer SEI layer being in contact to the liquid electrolyte. The observed spatial arrangement of the apolar organic microphase promises a better understanding of the passivation capability of the SEI, which is necessary to expand battery lifetime." , "List of used files\n*.csv\ncomposition_profiles.csv\nThe file  contains the underlying data for the atomic percentage plots in Figure S5. \nListed are distance in z-direction and atomic percentages of Li, C, F, and O.\nmass_spectrum.csv\ncontains the presented mass spectrum in Figure S1 \ndetection_rate.csv\ndetection rate observed during the APT run used for Figure S2\nvoltage_history.csv\nvoltage observed during the APT run used for Figure S2\n*.pos \nfile contains the reconstructed data for the APT run\n*.rrng \ncontains the mass identification used in the reconstruction\n*.rhit \ncontains the raw data for the APT run";
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        <http://schema.org/creator>     [ a                               <http://schema.org/Person>;
                                          <http://schema.org/familyName>  "Freese";
                                          <http://schema.org/givenName>   "Jan Philip";
                                          <http://schema.org/name>        "Freese, Jan Philip"
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                "2023-06-21";
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                "The measurement mode was full-field and the data available here contains the recorded 2D projections, consisting of absorption contrast images. The data is saved in TIFF files each corresponding to one specific energy point and rotation angle. For a description of exact energy points and rotational angles used, see the corresponding publication.\nThis dataset is measured under SCR conditions (1000 ppm NO, 1000 ppm NH3, 7.8% O2 in He as balance gas, flow rate 50 ml/min) at 350°C." , "Die Daten wurden an der microXAS-Beamline an der Swiss Light Source (SLS, Villigen, Schweiz) im Juni 2019 aufgezeichnet. Gegenstand des Experiments war ein Zeolith-Katalysator auf Kupferbasis, der als beschichteter Monolith hergestellt wurde. Eine detaillierte Beschreibung der Daten ist in der Readme-Datei dieses Datensatzes und in der entsprechenden Publikation zu finden." , "The data was recorded at the microXAS beamline at the Swiss Light Source (SLS, Villigen, Switzerland) in June 2019. Subject of the experiment was a copper-based zeolite catalyst prepared as a washcoated monolith. A detailed description about the data can be found in the readme of this data set and in the corresponding publication.";
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                "# Secure logging from fault-tolerant aggregate signatures\n## Information\nThis is a simple implementation of the logging system described in \"Practical and Robust Secure Logging from Fault-Tolerant Sequential Aggregate Signatures\" (International Conference on Provable Security, 2017, [also available from the IACR E-Print-Server](https://eprint.iacr.org/2017/949)).\n## Disclaimer\nThis is an academic implementation used for benchmarking in the paper. Since this is the only purpose, this impelmentation is not well tested and reviewed for security and therefore should be used with caution.\n## Used libraries\n- Boost unit test framework\n- [libpbc](https://crypto.stanford.edu/pbc/) with [pbcwrapper](https://crysp.uwaterloo.ca/software/PBCWrapper/)\n- [NTL](http://www.shoup.net/ntl/)\n## Build instructions\nMake sure that boost, libpbc and NTL are installed. (The versions of libpbc and NTL used for the benchmarks reported in the paper can be found in the lib folder.) Then run:\n```\nmkdir build\ncd build\ncmake ..\nmake\n```\npbcwrapper will be built automatically during this process.";
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                "### **DATA SET DESCRIPTION**\n### **1. Spatial coverage**\nThe measuring data were collected during intensive observation periods (IOP) in Stuttgart, Germany, under the BMBF Programme ‘Urban Climate Under Change’ [UC]2.\n### **2. Temporal coverage**\n01.02.2017 - 31.08.2018\n### **3. Data file structure and variables**\nThe parent UC2_IMK_TRO_KIT data folder contains archived directories, corresponding to a particular measuring device (***WINDCUBE8, RPG_HATPRO_G4*** and ***VIRTUAL_TOWERS*** ).\n***WINDCUBE8*** folder contains files of wind vectors measurements at different heights retrieved from Windlidar WINDCUBE8.\n***RPG_HATPRO_G4*** folder contains data files from microwave radiometer measurements. The stored variables are cloud liquid water content, atmosphere mass content of water vapor, infrared radiometer brightness temperature, air pressure, as well as vertical temperature profiles and vertical absolute humidity profiles.\n***VIRTUAL_TOWERS*** folder contains profiles of horizontal wind retrieved with the dual-Doppler Lidar technique ‘Virtual towers’.\n### **4. Devices and Locations**\nThe Doppler wind lidar WINDCUBE8 (WLS8-3 manufactured by Leosphere) was located on a rooftop of the city hall in the city centre (48.774667°N / 9.178287°E). The name of this location is cityhall. This abbreviation is the part of file name; see paragraph 6.\nThe microwave radiometer (RPG HATPRO G4) manufactured by RPG-Radiometer Physics GmbH was located on a rooftop of the city hall tower in the city centre (48.775017°N / 9.177928°E). The name of this location is cityhalltow. This abbreviation is the part of file name; see paragraph 6.\nThe six virtual towers were located in the Stuttgart city basin and in the Neckar valley. The names and coordinates of the six virtual towers are: wolfram (48.792912°N / 9.180950°E), citycannstat (48.788833°N / 9.190096°E), citywera (48.787575°N / 9.194673°E), naehter (48.780160°N / 9.226197°E), bcmercedes (48.787883°N / 9.233923°E), uthbherzogen (48.794566°N / 9.245064°E). These abbreviations are the parts of file names; see paragraph 6.\n### **5. Type of files**\nWINDCUBE8, RPG_HATPRO_G4, VIRTUAL_TOWERS folders contain NetCDF-files created using Matlab R2016b regarding CF-1.7 convention. \n### **6. The file name**\nThe name of the NetCDF-file consists of some abbreviations:\n***campaign-location-site-acronym-data_content-origin_date-version.nc***\nExample: ***IOP02-S-cityhall-KITimktro-meteo-20170815-1.nc***\n***campaign***: IOP0X , where X is the number of an IOP. X=1: February-March 2017. X=2: June-August 2018. X=3: January-March 2018. X=4: June-August 2018.\n***location***: S indicates Stuttgart.\n***site***: i.e cityhall stands for location of WINDCUBE8.  See paragraph 4.\n***acronym***: KITimktro, stands for  Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Department Troposphere Research.\n***origin_date***: measuring date in the format YYYYMMDD (year, month, day) YYYYMMDD. \n***version***: 1\n***data_content***: see paragraph 7.\n### **7. Data content**\n***WINDCUBE8*** folder contains data files with two different data contents: ***meteo*** and ***w***. The data file with the content ***meteo*** includes time series profiles of eastward (***ua***) wind and northward wind (***va***) vectors averaged over 10 minutes periods. The data file with the content ***w*** includes time series profile of vertical wind velocity (time resolution of 7 sec).\n***RPG_HATPRO_G4*** folder contains data files with three different data types: ***meteo***, ***ta***, ***haa***. ***meteo*** includes time series of atmosphere cloud liquid water content (***lwp***), atmosphere mass content of water vapor (***prw***), infrared radiometer brightness temperature (***tb***), and air pressure (***plev***) from microwave radiometer measurement (time resolution of 4 sec). ta includes vertical temperature profiles (time resolution of 15 min) and ***haa*** contains vertical absolute humidity profiles (time resolution of 4 sec).\n***VIRTUAL_TOWERS***   includes time series profiles of eastward (***ua***) wind and northward wind (***va***) vectors (time resolution of ca. 15 min).\nInformation about processing of variables is given in the global attributes and in the attributes for the individual variables in each NetCDF-file." , "Die meteorologischen Daten zu den Temperatur-, Feuchte- und Windfeldern wurden während vier Messkampagnen in Stuttgart im Sommer 2017, Winter 2017, Sommer 2018 und Winter 2018 erhoben. Doppler Lidarsysteme und ein Mikrowellenradiometer wurden zur Erfassung und zur Analyse der horizontalen Struktur des innerstädtischen Windfeldes und der vertikalen Struktur der urbanen Grenzschicht eingesetzt. Die Messungen wurden im Rahmen des Verbundprojekts „Dreidimensionale Observierung atmosphärischer Prozesse in Städten (3DO)“ durchgeführt. 3DO gehört zu einem der drei Module der BMBF-Fördermaßnahme Stadtklima im Wandel (UC2).\nMehr Information über Ziele der UC2 Fördermaßnahme ist auf http://www.uc2-program.org/ und in den aufgelisteten Übersichtspublikationen zu finden.\nDieter Scherer, Felix Ament, Stefan Emeis, Ute Fehrenbach, Bernd Leitl, Katharina Scherber, Christoph Schneider, and Ulrich Vogt (2019): Three-Dimensional Observation of Atmospheric Processes in Cities. Meteorologische Zeitschrift 2, 2019. doi: 10.1127/metz/2019/0909.\nDieter Scherer, Florian Antretter, Steffen Bender, Jörg Cortekar, Stefan Emeis, Ute Fehrenbach, Günter Gross, Guido Halbig, Jens Hasse, Björn Maronga, Siegfried Raasch, und Katharina Scherber (2019): Urban Climate Under Change [UC]2 – A National Research Programme for Developing a Building-Resolving Atmospheric Model for Entire City Regions. Meteorologische Zeitschrift 2, 2019. doi:10.1127/metz/2019/0913." , "This dataset of meteorological observations (temperature, humidity, wind) was collected during four field campaigns in Stuttgart, Germany, in summer 2017, winter 2017, summer 2018, and winter 2018. Doppler lidars and a microwave radiometer were deployed to investigate horizontal structures of intra-urban horizontal wind field patterns and vertical structures of the urban boundary layer. The measurements were conducted within the project ‘Three-dimensional Observation of Atmospheric Processes in Cities’ (3DO). 3DO is one of three modules of the programme ‘Urban Climate Under Change’, [UC]2, funded by the German Federal Ministry of Education and Research (BMBF).\nMore information on the goals of the UC]2 programme can be found on http://www.uc2-program.org/ and in the overview papers listed below.\nDieter Scherer, Felix Ament, Stefan Emeis, Ute Fehrenbach, Bernd Leitl, Katharina Scherber, Christoph Schneider, and Ulrich Vogt (2019): Three-Dimensional Observation of Atmospheric Processes in Cities. 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                "Unter Verwendung der im ZIP-File bereitgestellten Informationen bzw. Anwendungen, lassen sich die im Forschungsverbund zur Selbstregulation im Alltag von Studierenden (SriAS 2 Practice, gefördert vom BMBF) produzierten Kurzvideos miteinander verknüpfen.  Sofern die vier Videos der Serie (siehe Verweise zur Relation) gemeinsam präsentiert werden sollen, lässt sich die Verknüpfung unter Verwendung der hier bereitgestellten Sekundärinformation leicht umsetzen." , "Um Zugriff auf das ZIP-File mit allen notwendigen Daten und Informationen zu erhalten, rufen Sie eine korrigierte Version dieses Nachweises unter folgendem Link auf:\nhttp://dx.doi.org/10.5445/IR/1000126678";
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                "The interplay of electronic correlations, multi-orbital excitations, and strong spin-orbit coupling is a fertile ground for new states of matter in quantum materials. Here, we report on a confocal Raman scattering study of momentum-resolved charge dynamics from a thin film of semimetallic perovskite $\\mathbf{SrIrO_3}$. We demonstrate that the charge dynamics, characterized by a broad continuum, is well described in terms of the marginal Fermi liquid phenomenology. In addition, over a wide temperature regime,  the inverse scattering time is for all momenta  close to the Planckian limit $\\mathbf{\\tau^{-1}_{\\hbar}=k_{\\rm B} T/\\hbar}$. Thus,  $\\mathbf{SrIrO_3}$ is a semimetallic multi-band system that is as correlated as, for example, the cuprate superconductors.\nThe usual challenge to resolve the charge dynamics in multi-band systems with very different mobilities is circumvented by taking advantage of the momentum space selectivity of polarized electronic Raman scattering.\nThe Raman responses of both hole- and electron-pockets display an electronic continuum extending far beyond 1000\\icm ($\\sim$125 meV), much larger than allowed by the phase space for creating particle-hole pairs in a regular Fermi liquid. Analyzing this response in the framework of a memory function formalism, we are able to extract the frequency dependent scattering rate and mass enhancement factor of both types of charge carriers, which in turn allows us to determine the carrier-dependent mobilities and electrical resistivities. The results are well consistent with transport measurement and demonstrate the potential of this approach to investigate the charge dynamics in multi-band systems." , "Excel file containing all the data of the manuscript (Fig. 2 to 5)";
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                                          <http://schema.org/familyName>  "Frohnapfel";
                                          <http://schema.org/givenName>   "Bettina";
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                "Direct numerical simulation (DNS) is used to systematically investigate the applicability of minimal channel approach for the characterization of double-averaged velocity and temperature profiles in irregular rough surfaces. Roughness is generated mathematically using a random algorithm, in which the power spectrum (PS) and probability density function (PDF) of the surface height function can be prescribed. 12 different combinations of PS and PDF are examined at $Re_\\tau$=500." , "These files contain the data used in the publication:\n“Study of irregular roughness in minimal channels”\nJ. Yang, A. Stroh, S. Jakirlic, B. Frohnapfel and P. Forooghi\npublished in proceeding ETMM13\ndoi: 10.5445/IR/1000140429\nNumerical Details:\nThe carried out DNS is based on a pseudo-spectral solver for incompressible boundary layer flows developed at KTH/Stockholm. The Navier-Stokes equations are numerically integrated using the velocity-vorticity formulation by a spectral method with Fourier decomposition in the horizontal directions and Chebyshev discretization in the wall-normal direction. For temporal advancement, the convection and viscous terms are discretized using the 3rd order Runge-Kutta and Crank-Nicolson methods, respectively. The simulation domain represents an turbulent channel flow with periodic boundary conditions applied in streamwise and spanwise directions, while the wall-normal extension of the domain is bounded by no-slip and zero-temperature boundary conditions at the upper and lower domain wall. The flow is driven by a prescribed constant pressure gradient (CPG), a heat source term is added to the flow. The friction Reynolds number for the present case is fixed to Re_τ = 500. The structured surface is introduced through an immersed boundary method (IBM) based on the method proposed by Goldstein et al. (1993) and is essentially a proportional controller which imposes zero velocity in the solid region of the numerical domain.\nData Files:\nThe data files are saved and labeled in *.mat files. Each file contains MATLAB data consisting of the roughness height distribution and corresponding coordinates. The roughness structures are non-dimensionalized with the channel half height δ. The naming convention of the data in this database follows the JFM publication \"DNS-based characterization of pseudo-random roughness in minimal channels\" DOI: 10.1017/jfm.2022.33.\nReference:\nPlease provide a reference to the article above when using this data.\nPlease direct questions regarding numerical setup/data to Jiasheng Yang (jiasheng.yang@kit.edu)";
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                "This repository contains the datasets shown in the study 'High Homogeneous Freezing Onsets of Sulfuric Acid Aerosol at Cirrus Temperatures'. Where needed, detailed information on the data can be found in there.";
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                                          <http://schema.org/givenName>   "Patrick";
                                          <http://schema.org/name>        "Philipp, Patrick"
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                                          <http://schema.org/affiliation>
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                                          <http://schema.org/givenName>   "Felix";
                                          <http://schema.org/name>        "Hertlein, Felix"
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                "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only." , "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d.";
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                "This archive contains A) the data and B) the analysis scripts corresponding to the manuscript “Ice-free tropical waterbelt during Snowball Earth events questioned by uncertain clouds” by Christoph Braun [1], Johannes Hörner [2], Aiko Voigt [2], and Joaquim G. Pinto [1]\n[1] IMK-TRO, Karlsruhe Institute of Technology \n[2] IMG, University of Vienna\nPlease contact Christoph Braun (christoph.braun@kit.edu) or Aiko Voigt (aiko.voigt@univie.ac.at) if you have questions regarding the content of this archive.\nA) Data\nThe dataset contains postprocessed model output data produced with the general circulation models “Community Atmosphere Model v3.1” (CAM) from the National Center for Atmospheric Research, Boulder, Colorado, and “ICOsahedral Nonhydrostatic” model with the climate physics package (version AES 1.3.00) from the Max-Planck Institute of Meteorology, Hamburg, Germany.\nThe dataset contains\n- 2d data for all simulations and 3d data for selected simulations; zonal-mean and global-mean monthly-mean and climatological data (format: netcdf)\n- initial and boundary conditions (format: netcdf)\n- runscripts (located in the subdirectories corresponding to each model)\n- analysis code\nThe data is organized in 5 subdirectories, each corresponding to one of the following model configurations.\n- CAM\n- CAM pCOOKIE\n- CAM pCOOKIE subtropical columns\n- ICON\n- ICON WBF\nWe describe how we obtained and modified CAM and ICON in the files “CAM_description” and “ICON_description”. We also provide the runscripts for the conducted simulations. We further describe the postprocessing steps performed with CDO in the file “Postprocessing_CDO”.\nB) Analysis scripts\nThe analysis scripts are located in the subdirectory “analysis_scripts”.\nThe directory \"analysis\" will contain intermediate results generated by the analysis scripts and the directory \"figures\" will contain the figures generated by the analysis scripts.\nTo run the analysis scripts we applied the following libraries as provided by the German Climate Computing Center (DKRZ) using the Kernel „ESMValTool“.\n* xarray (0.17.0)\n* numpy (1.20.1)\n* matplotlib (3.3.4)\n* scipy (1.5.3)\n* cftime (1.2.1)\n* pandas (1.2.2)\n* statsmodels (0.12.2)";
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                "# Error analysis of DGTD for linear Maxwell equations with inhomogeneous interface conditions \nThis code was used for the numerical experiments in the paper> ERROR ANALYSIS OF DGTD FOR LINEAR MAXWELL EQUATIONS WITH INHOMOGENEOUS INTERFACE CONDITIONS>> By B. Dörich, J. Dörner, M. Hochbruck\nThis software is published in accordance with the guidelines for safeguarding good research practice and thus serves to reproduce the experiments in the above-mentioned publication. \n## Requirements\nThe code is written in C++17 and uses the software packages\n*   [deal.II](https://www.dealii.org/), Version 9.5.0\n*   [CMake](https://cmake.org/), Version 3.22.1\n*   [TiMaxdG](https://gitlab.kit.edu/kit/ianm/ag-numerik/projects/dg-maxwell/timaxdg), Commit #2002f893\n*   [fftw](https://www.fftw.org/) Version 3.x.x (with quadmath)\nNote that the authors of this paper develope the software package TiMaxdG.\nTherefore, the source code is provided within this repository and uses of the main branch with the hash #2002f893.\nFor plot generation, Python 3 is used with the usual scientific software stack (numpy, scipy, matplotlib ...).\nAny reasonable new distribution works. \nFor convenience, we provide a reproduction environment via [docker](https://www.docker.com/) and [enroot](https://github.com/NVIDIA/enroot).\nFor that, please consider the installation instructions for the tools and the files:\n*   .devcontainer/Dockerfile\n*   .devcontainer/README.md\n*   .devcontainer/ENROOT.md\n## Reproduction\n### Figure 2:\nThe experiment is build with the commands\n```bash\n    mkdir build\n    cd build\n    cmake -DCMAKE_BUILD_TYPE=Release ..\n    cd src\n    make CavitySolution_Prototype\n```\nThe experiment is executed with the commands\n```bash\n    ./CavitySolution_Prototype\n```\nThe experiment produces a tabular file `error.txt` with the results and a folder structure with additional run artifacts.\nThe plot is generated with\n```bash\n    python3 ../../python/CavityPlots.py error.txt\n```\nThe output file `cavity_error_plot.pdf` is produced.\n### Figure 4:\nThe experiment is build with the commands\n```bash\n    mkdir build\n    cd build\n    cmake -DCMAKE_BUILD_TYPE=Release ..\n    cd src\n    make ReferenceSolution_Prototype \n```\nThe experiment is executed with the commands\n```\n    mkdir artifacts\n    cd artifacts\n    ../ReferenceSolution_Prototype ../../../src/config.json\n    cd ..\n    ../../python/postprocess.py artifacts/\n```\nThe experiment produces a tabular file `error.txt` with the results.\nThe plots are generated with\n```bash\n    python3 ../../python/ReferenceErrorPlots.py error.txt\n```\nThe output files `full_error_deg_1pdf` and `full_error_deg_2.pdf` are produced.\n**Note that this experiment is computationally very costly and the calculations should be distributed on several high performance computers!**\n### Figure 5\nThe experiment is build with the commands\n```bash\n    mkdir build\n    cd build\n    cmake -DCMAKE_BUILD_TYPE=Release ..\n    cd src\n    make PolynomialSolution_Prototype\n```\nThe experiment is executed with the commands\n```bash\n    ./PolynomialSolution_Prototype\n```\nThe experiment produces a tabular file `output/error.txt` with the results and a folder structure `output/` with additional run artifacts.\nThe plot is generated with\n```bash\n    python3 ../../python/PolynomialErrorPlot.py output/error.txt\n```\nThe output file `polynomial_error_plot.pdf` is produced.";
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                                          <http://schema.org/familyName>  "Emmendörfer";
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                                          <http://schema.org/name>        "Emmendörfer, Franziska"
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                                          <http://schema.org/familyName>  "Dietrich";
                                          <http://schema.org/givenName>   "Benjamin";
                                          <http://schema.org/name>        "Dietrich, Benjamin"
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                                          <http://schema.org/familyName>  "Wetzel";
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                                          <http://schema.org/name>        "Wetzel, Thomas"
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                                          <http://schema.org/name>        "Marocco, Luca"
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                "Flüssigmetalle können als effiziente Wärmeträgerfluide für Anwendungen mit hohen Wärmelasten and mittleren bis hohen Temperaturen eingesetzt werden. Aufgrund ihres niedrigen Schmelzpunktes sind einige Gallium-basierte Legierungen flüssig bei Raumtemperatur und bieten daher vielfältige Einsatzmöglichkeiten in technischen Anwendungen. Für derartige Anwendungen ist die Kenntnis der thermophysikalischen Eigenschaften unerlässlich. In dieser Veröffentlichung werden die spezifischen thermophysikalischen Eigenschaften der nah-eutektischen flüssigen Ga-In-Sn Legierung vorgestellt. Die temperaturabhängige Dichte, die spezifische Wärmekapazität bei konstantem Druck, sowie die Wärmeleitfähigkeit werden experimentell bestimmt." , "Thermophysical properties of the near eutectic liquid Ga-In-Sn (gallium (65.9 mass%), indium (20.3 mass%), tin (13.8 mass%)) alloy:\n- density (293.15 K <= T <= 411.15 K)\n- specific heat capacity at constant pressure (307.15 K <= T <= 408.15 K)\n- thermal conductivity (298.15 K <= T <= 403.15 K)" , "Liquid metals can be used as efficient heat transfer fluids for applications with high heat loads  and medium to high temperatures. Due to their low melting points, some Ga-based alloys are  liquid at room temperature and offer therefore various usages in technical applications. For designing  such  equipment,  knowledge  of  thermophysical  properties  is  essential.  In  this publication, specific thermophysical properties of the near eutectic liquid Ga-In-Sn alloy are \npresented. The temperature dependent density, the specific heat capacity at constant pressure as well as the thermal conductivity are determined experimentally.";
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                "The data sets found here were collected with the KITcube measurement platform in the Inn Valley, Austria, from 1 Aug 2019 to 13 Oct 2019, within the CROSSINN field campaign (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements). The goal of the campaign was to study the multi-scale three-dimensional flow structure and the impact of the flow on the mountain atmospheric boundary layer (MoBL) structure and evolution across the Inn Valley. Further details of the campaign can be found in Adler et al (2020).\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1" , "### **DATA SET DESCRIPTION**\n### **1. Spatial coverage**\nMeasurements were collected during the CROSSINN field campaign in the Inn Valley, Austria. The focus of the campaign was located at a mild valley curvature approximately 20 km northeast of Innsbruck. To optimize deployment requirements (power, permits, etc.) as well as to reduce demand for additional KITcube flux towers, all instruments were deployed as close as possible to three pre-existing i-Box flux towers (CS-VF0, CS-SF1, CS-NF27).\n### **2. Temporal coverage**\n01.08.2019 - 13.10.2019\n### **3. Instrument details and data file structure**\nAvailable here are archived directories, corresponding to a particular device (***CHM15k***, ***GRAW***, ***HATPRO2***, ***Mobotix***, and ***Photos***).\n***CHM15k*** .zip file contains daily NetCDF files organized into monthly folders. Measurements were collected with the CHM 15k ceilometer (Lufft GmbH) deployed at Kolsass. The ceilometer sampled uncalibrated backscatter profiles in zenith mode.\n***GRAW*** .zip file contains final L2 files for each sounding performed during the ten CROSSINN IOPs (Intensive Observation Period), organized into their respective IOP folders. Further details of the setup can be found in two PDFs:\n- CROSSINN_GRAW_postprocessing_procedures.pdf contains detailed explanations on the steps involved in generating the final L2 files.\n- CROSSINN_IOP_overview.pdf represents a visualisation of all soundings launched during CROSSINN.\n***HATPRO2*** .zip file contains data files from microwave radiometer HATPRO-G4 (Radiometer Physics). Available are data files at various post-processing levels (raw, levels 0, 1, 2), processed with algorithms\nprovided by the University of Cologne (more details can be found here: ftp://gop.meteo.uni-koeln.de/pub/loehnert/mwr_data_flow/). Temperature profiles before 16 August 2019 are not usable due to issues with temperature channels. The stored variables are cloud liquid water content, atmosphere mass content of water vapor, infrared radiometer brightness temperature, air pressure, as well as vertical temperature profiles and vertical absolute humidity profiles.\n***Mobotix*** .zip file contains image files created by the MX-S15D visible and infrared camera system (Mobotix AG) deployed at Kolsass. Images are organized into hourly, daily, and monthly folders, respectively. The camera operated in four modes (there is one .zip file for each of the four modes): \nD10Sky - zenith fish-eye view.\nD22Land - view towards the north, capturing the i-Box CS-VF0 flux tower, HATPRO2, CHM15k (foreground) and the A12 Autobahn (background).\nD43Sky - zenith view of the atmosphere (visible)\nL43SkyThermal - zenith view of the atmosphere (infrared)\n***Photos*** .zip file contains two compressed files with an assortment of photos taken by various individuals throughout the campaign, during IOPs, as well as during the deployment/teardown phases.\n### **4. Locations**\nWith the exception of the WLS200s-115 (@ Hochhaeuser) and WLS200s-125 (@ Mairbach) Windcube Doppler lidars, all other KITcube instruments were deployed at Kolsass, the headquarter site for the field campaign. All sondes were also launched and tracked from Kolsass. \nKolsass - 47.305290°N / 11.622231°E\nHochhaeuser - 47.286182°N / 11.631232°E\nMairbach - 47.326664°N / 11.608736°E\n### **5. Contact**\nContact nevio.babic@kit.edu for any questions regarding the data sets.\n### **6. References**\nHaid, M., 2019: marenha/doppler_wind_lidar_toolbox: First release (v1.0.0). Zenodo, doi:10.5281/zenodo.3583083" , "Die hier zu findenden Datensätze wurden mit der KITcube-Messplattform im Inntal, Österreich, vom 1.8.2019 bis 13.10.2019, im Rahmen der CROSSINN-Feldkampagne (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) erhoben. Das Ziel der Kampagne war es, die multiskalige dreidimensionale Strömungsstruktur und den Einfluss der Strömung auf die Struktur und Entwicklung der atmosphärischen Grenzschicht (MoBL) über dem Inntal zu untersuchen. Weitere Details der Kampagne sind in Adler et al. (2020) zu finden.\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. 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                "# LinearShimDB: A subset of the NMR magnet shimming database ShimDB\nLinearShimDB is a subset of the NMR magnet shimming database [ShimDB](https://github.com/mobecks/ShimDB) and contains over 9000 instances. Data is acquired on a Spinsolve 80 Carbon spectrometer (Magritek GmbH, Aachen, Germany, www.magritek.com) using a water solution with CuSO4 (5mmol/L). \nLinearShimDB is part of \"Deep Regression with Ensembles enables Fast, First-Order Shimming in low-field NMR\" by M. Becker et al. [1].\nThe acquisition procedure was as follows. The manufacturer's automated shimming technique, based on the downhill simplex method, was used to obtain a reference spectrum of decent quality. Then, all shim values except the three linear shims X, Y and Z were set to zero. The resulting spectrum and corresponding shim settings were used as the reference values. The database parameters were obtained by relative offsets from the reference shim values in a range R with stepsize s, in a grid-like manner. For each combination, the raw FID, acquisition parameters, and the shim values were stored. \n| Topic                  | Parameter        | Value     |\n|------------------------|------------------|-----------|\n| Characteristics        | Nr. Spectra      | 9261      |\n|                        | Shim range R     | +/- 10000 |\n|                        | Step size s      | 1000      |\n|                        | Shims            | X,Y,Z     |\n| Acquisition parameters | Nucleus          | 1H        |\n|                        | Bandwidth        | 20kHz     |\n|                        | Points           | 32768     |\n|                        | Dwell time       | 50us      |\n|                        | Repetition time  | 2000ms    |\n|                        | Filter           | -        |\n|                        | Phase correction | phi0 |      \n**We strongly encourage researchers to extend ShimDB with their own subsets to stimulate developments. We offer to include raw data or links to your publications into ShimDB.**\n## Files format\nEach folder in LinearShimDB contains the following files: \n- data.1d -> the raw FID.\n- shims.par -> Shim values, where only linear shims are non-zero.\n- acqu.par -> Acquisition parameters.\n- proc.par -> Processing parameters.\nThe LinearShimDB root folder also contains the reference starting shims (ReferenceShims.par).\n## Data loading\nWe deliver a python script ```utils_IO.py``` alongside [ShimDB](https://github.com/mobecks/ShimDB) to easily load the database into numpy array structure using the [nmrglue packages](https://github.com/jjhelmus/nmrglue)[2].\nThe following python libraries and packages are required: os, numpy, glob, nmrglue (>= v0.9.dev0)\n# References\n[1] M. Becker, M. Jouda, A. Kolchinskaya, J. G. Korvink, Deep regression with ensembles enables fast, first-order shimming in low-field NMR, Journal of Magnetic Resonance 2022, 107151, ISSN 1090-7807, https://doi.org/10.1016/j.jmr.2022.107151\n[2] J.J. Helmus, C.P. Jaroniec, Nmrglue: An open source Python package for the analysis of multidimensional NMR data, J. Biomol. NMR 2013, 55, 355-367, http://dx.doi.org/10.1007/s10858-013-9718-x";
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                "The Reynolds number dependent flow resistance of heterogeneous rough surfaces is largely unknown at present. The present work provides novel reference data for spanwise-alternating sandpaper strips as one idealized case of a heterogeneous rough surface. Experimental data are presented and analysed in direct comparison with drag measurements of homogeneous sandpaper surfaces and numerical simulations. Based on the homogeneous roughness data the related challenges and sensitivities for the evaluation of roughness functions from experiments and simulations are discussed. A hydraulic channel height is suggested as an alternative measure for the drag impact of rough surfaces in internal flows. \nFor the investigated heterogeneous roughness it is found that turbulent flow does not exhibit a fully-rough flow behaviour, indicating that the assignment of an equivalent sand grain height as commonly applied for homogeneous roughness is not possible. A prediction of the drag behaviour of rough strips based on an average between rough and smooth drag curves appears promising, but requires further refinement in order to capture the impact of turbulent secondary flows and spatial transients linking smooth and rough surface parts.\nWhile turbulent secondary flow induced by the roughness strips yield significant spanwise variation of the mean velocity profile for the investigated rough strips we show that the spanwise averaged velocity profiles collapse reasonably well with a smooth or homogeneous rough wall flow. This allows to extract a global roughness function from the spanwise averaged flow field in good agreement with the one deduced from global pressure drop measurements." , "Database: Flow resistance over heterogeneous roughness made of spanwise-alternating sandpaper strips\nThis database provides the data presented in the manuscript \"Flow resistance over heterogeneous roughness made of spanwise-alternating sandpaper strips\". \nThe database encompasses both experimental and numerical data pertaining to homogeneous and heterogeneous irregular roughness.\nTo better understand the naming convention used in this database, we kindly direct readers to the associated publication for further details.";
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                "### **DATA SET DESCRIPTION**\n### **1. Spatial coverage**\nMeasurements were collected during the CROSSINN field campaign in the Inn Valley, Austria. The focus of the campaign was located at a mild valley curvature approximately 20 km northeast of Innsbruck. This data set contains aircraft measurements conducted during five IOPs.\n### **2. Temporal coverage**\n*IOP1*    - 05.08.2019\n*IOP2b* - 09.08.2019\n*IOP4*    - 14.08.2019\n*IOP6*    - 22.08.2019\n*IOP7b* - 27.08.2019\n### **3. Instrument details and data file structure**\nAvailable here is a single file, FDLR.zip, containing the following folder structure:\n***10 Hz*** folder contains quality-controlled (following Mallaun et al, 2015) raw measurements (temporal resolution 10 Hz) collected with the METPOD meteorological sensor package mounted on the Cessna Grand Caravan 208B of the German Aerospace Center (DLR). \n***100 Hz*** folder contains quality-controlled (following Mallaun et al, 2015) raw measurements (temporal resolution 100 Hz) collected with the METPOD meteorological sensor package mounted on the Cessna Grand Caravan 208B of the German Aerospace Center (DLR). \n***overview*** folder contains .kml, .kmz, and .pdf overviews of all ten flights, performed during five selected CROSSINN Intensive Observation Periods (IOP). Additionally, the file 'extracted_straight_leg_segments.txt' contains starting and ending indices of the straight leg segments, obtained for the 10 Hz data set by Nevio Babić. The image 'google_earth_flightpattern_sites.jpg' contains a sample of a combined set of along-valley and cross-valley legs.\n### **4. Locations**\nThe along-valley and cross-valley legs were flown in the vicinity of the CROSSINN investigation area, centered at the Kolsass headquarter site.\nKolsass - 47.305290°N / 11.622231°E\n### **5. Contact**\nContact nevio.babic@kit.edu for any questions regarding the data sets.\n### **6. References**\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1\nMallaun C., A. Giez, and R. Baumann. \"Calibration of 3-D wind measurements on a single-engine research aircraft.\" Atmospheric Measurement Techniques 8.8 (2015): 3177-3196." , "The data sets found here were collected with the Cessna Grand Caravan aircraft (German Aerospace Center, DLR) in the Inn Valley, Austria, from 1 Aug 2019 to 13 Oct 2019, within the CROSSINN field campaign (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements). The goal of the campaign was to study the multi-scale three-dimensional flow structure and the impact of the flow on the mountain atmospheric boundary layer (MoBL) structure and evolution across the Inn Valley. Further details of the campaign can be found in Adler et al (2020).\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1" , "Die hier zu findenden Datensätze wurden mit dem Cessna Grand Caravan Flugzeug (Das Deutsche Zentrum für Luft- und Raumfahrt, DLR) im Inntal, Österreich, vom 1.8.2019 bis 13.10.2019, im Rahmen der CROSSINN-Feldkampagne (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) erhoben. Das Ziel der Kampagne war es, die multiskalige dreidimensionale Strömungsstruktur und den Einfluss der Strömung auf die Struktur und Entwicklung der atmosphärischen Grenzschicht (MoBL) über dem Inntal zu untersuchen. Weitere Details der Kampagne sind in Adler et al. (2020) zu finden.\nAdler B., A. Gohm, N. Kalthoff, N. Babić, U. Corsmeier, M. Lehner, M. W. Rotach, M. Haid, P. Markmann, E. Gast, G. Tsaknakis, and G. Georgoussis. \" CROSSINN - a field experiment to study the three-dimensional flow structure in the Inn Valley, Austria\", Bulletin of the American Meteorological Society (2020): 1-55, accessed Dec 11, 2020, https://doi.org/10.1175/BAMS-D-19-0283.1";
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                "The dataset contains the raw oscilloscope time-domain data of the voltage across and current through a coil used to excite an inductively coupled plasma. Excitation is carried out by means of two superimposed frequencies in the current, the first being about 2.5 MHz and the second about 7.5 MHz. The current and voltage curves during the plasma ignition were recorded. At different amplitude ratios between 0 and 1, 50 to 60 ignitions were recorded in each case. Likewise, the point of minimum power maintaining the discharge is set for these amplitude ratios. At this point, the voltage over and current through the coil were also recorded over time." , "Der Datensatz enthält die Oszilloskoprohdaten über den zeitlichen Verlauf der Spannung über und des Stroms durch eine Spule, die zum Anregen eines induktiv gekoppelten Plasmas verwendet wird. Anregung erfolgt mittels zweier überlagerter Frequenzen im Strom, wobei die erste bei ca. 2,5 MHz und die zweite bei ca. 7,5 MHz liegt. Der Strom- und Spannungsverlauf während der Zündung des Plasmas wurde aufgezeichnet. Bei verschiedenen Amplitudenverhältnissen zwischen 0 und 1 wurden jeweils 50 bis 60 Zündungen aufgenommen. Ebenso wird für diese Amplitudenverhältnisse der Punkt der minimalen Leistungseinkopplung eingestellt, der gerade noch die Aufrechterhaltung der induktiven Entladung gewährleistet. An diesem Punkt wurden die Spannung über und der Strom durch die Spule ebenfalls über die Zeit aufgezeichnet." , "The data is organized in to folders according to the set amplitude ratio of the two excitation currents. Where for example \"A01\" corresponds to an amplitude ratio of 10%. For most of the tuningpoints there exists a MSP measurement in an additional folder with the \"_msp\" suffix for the corresponding folder name. The data in those folder contains all the different channels of the ozilloscope. Regular ignition captures follow the naming:\n\"SaveOnEvent_chX_TIMESTAMP.wfm\"\nWhere X is the number of the oszilloscope channel, and TIMESTAMP is the date and time the data was measured in the format: year month day hour minute second milliseconds.\nChannel 1 contains the voltage across the coil.\nChannel 2 contains the current through the coil.\nChannel 3 contains the drain source voltage across low voltage side of the driving inverter.\nChannel 4 contains the dc supply voltage.\nChannel 5 contains the voltage signal of the photo diode used to trigger the measurements.\nAdditionally, each folder also contains a measurement labled \"leer_chX.wfm\". Those contain the no load (plasma off) measurement to reference the signals for a impedance analysis.\nAll the data is in the \"wfm\" format of a MSO5 Tektronics oszilloscope, to read the data use the added matlab fuction \"simp_wfm_read.m\". \nThe csv file cotains the result of an impedance measurement of the excitation coil in the setup. This file in conjuction with the \"leer_chX.wfm\" files in each folder allows for amplitude and phase correction of the measurement signals.";
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                "Processed micro-tomographic scan of the ant Camponotus sp ,             scanned at KIT Light Source. This dataset is part of the                 open-source digitization project ‘Antscan’, with a database                     consisting of thousands of scanned ethanol-preserved ant                         samples from museums and personal collections worldwide.";
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                                          <http://schema.org/familyName>  "Blarr";
                                          <http://schema.org/givenName>   "Juliane";
                                          <http://schema.org/name>        "Blarr, Juliane";
                                          <http://schema.org/sameAs>      "http://orcid.org/0000-0003-0419-0780"
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                "This dataset includes 3D µCT images of nine different specimen of 10 mm \\times 10 mm of a carbon fiber reinforced polyamide 6 plaque produced in the long fiber reinforced thermoplastic direct (LFT-D) process. The position of the specimen in the plaque can be learned from the referenced publication (Blarr et al., Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images, Computational Materials Science, 2022). After small pre-processing steps, the fiber orientation tensor of each of the image stacks is determined with the help of the structure tensor based implementation of Pinter et al. The code can be found here: https://sourceforge.net/p/composight/code/HEAD/tree/trunk/SiOTo/StructureTensorOrientation/FibreOrientation/StructureTensorOrientationFilter.cxx#l186. Hence, nine .dat-files containing the fiber orientation tensor of second order are also included in this dataset.\nMost importantly, this dataset contains three different Python codes. The author implemented a different interpolation method in each of those codes; two algebraic and one machine learning based one. The component averaging method is the simplest; the decomposition method is mathematically more difficult. It works with the decomposition of the tensor into shape and orientation and subsequent separate invariant and quaternion weighting, before reassembling the then interpolated tensor. The deep learning based method is the only Jupyter notebook in this dataset, where an ANN is implemented for the same interpolation task. Please consider the reference paper mentioned before for details.\nFor the visualization of the tensor glyphs, a Matlab function by Barmpoutis is used, which can be found here: https://de.mathworks.com/matlabcentral/fileexchange/27462-diffusion-tensor-field-dti-visualization." , "In the folder \"code\" there are three Python scripts. The \"component_averaging_method.py\" and the \"decomposition_method.py\" work the same: The script needs an input .txt-file with coordinates and the corresponding fiber orientation tensors (the example used in the publication is given (file \"Input_file_FOT.txt\")). After running the code you are asked in the console for the name of the output file and for lower and upper x and y limit, which are 1 and 13, respectively, in the given case. The scripts then calculate the fiber orientation tensors at all missing positions with the respective method, which are then written into a MATLAB file (which is named the way you input in the console). This MATLAB file is structured in a way that the fiber orientation tensors can be plotted directly with the tensor glyph visualization function of Barmpoutis (\"plotDTI\") given in the abstract.\nThe Jupyter Notebook \"ANN_method.ipynb\" works a bit differently as it is an artificial neural network. There, .csv-files are needed as input data. The components of the tensors are given to the network in separate files and the coordinates of the positions in another separate .csv-file. This is all documented in the paper as well. The output again is a .csv-file that has to be transferred into MATLAB if users want to use the same visualization function. \nThe folder \"scans_and_FOT\" includes all nine scans and respective fiber orientation tensors used for the publication. The scans are given as .mhd- and .raw-files, the orientation tensors are given in the .dat-files. To generate the fiber orientation tensors from the images, the code by Pinter et al., which is given in the abstract, was used. This C++ code writes out a vector valued image with the orientations per voxel. From this, again with another MATLAB file, which composes the orientation tensor from the vector-valued image, these .dat files can be generated. As this is not the main focus of the publication, and the functionality of the python scripts can be verified with the given orientation tensors, this MATLAB script is not part of this dataset.\nPlease consider the paper or contact the author Juliane Blarr for further questions.";
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                                          <http://schema.org/name>        "Beygelzimer, Yan"
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                                          <http://schema.org/name>        "Davydenko, Oleksandr"
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                "TEM data as dm3 files" , "The mechanism of structural evolution of a three-layer Cu-Mo-Cu laminate under high-pressure torsion (HPT) was studied using scanning and transmission electron microscopy, atom probe tomography, and nanoindentation, complemented with finite element calculations. The results demonstrate a gradual refinement of Mo phases; a greater degree of refinement is observed in the peripheral part of the disk-shaped HPT specimen, although some heterogeneity of the structure remains even at a gigantic degree of shear deformation accumulated therein. The elemental distribution calculated from STEM EDX mapping as well as 3D reconstruction of atom probe tomography results shows a significant degree of mixing of the sample components at the atomic level, such that the concentration of copper in Mo reaches ~4.3 at. %; the corresponding concentration of molybdenum in Cu is ~6 at. %. These observations correlate with nanoindentation results showing an increase in the hardness of both phases due to strain hardening and solid solution strengthening, as well as grain refinement. Numerical simulations made it possible to provide a detailed description of the stages of the structure fragmentation, including its self-organizing nature, to show the formation of rupture forerunners in the hard Mo layer and the deformation of harder fragments in a softer matrix. The proposed model of the process assumes the development of a fractal self-organizing self-similar structure under the influence of such factors as the distribution of the elemental constituents of the laminate, their mechanical properties, and the conditions of HPT processing.";
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                "# SriAS2 Practice H5P Standalone\nZur Nutzung der SriAS 2 Practice Videos gibt es mehrere Möglichkeiten:\n1. Hosten in einem Web-Server\n2. Lokales Öffnen ohne Web-Server\n3. Einbinden in eigene Webseite\n## Hosten in einem Web-Server\nDie zip-Datei kann direkt in den Hosting-Ordner des Web-Servers entpackt werden und ist mit der enthaltenen `index.html` ohne weitere Konfiguration lauffähig. Besondere Anforderungen an den Web-Server existieren nicht. Ein einfaches Aufsetzen eines lokalen Web-Servers ist z.B. mit folgenden Programmen möglich:\n* Node.js, Modul [http-server](https://www.npmjs.com/package/http-server)\n* Python 2, Modul [SimpleHttpServer](https://docs.python.org/2/library/simplehttpserver.html)\n* Python 3, Modul [http.server](https://docs.python.org/3/library/http.server.html)\n* [XAMPP](https://www.apachefriends.org/de/index.html)\n## Lokales Öffnen ohne Web-Server\nModerne Browser verhindern aus Sicherheitsgründen das Ausführen der Videos, wenn diese nicht von einem Web-Server gehostet werden. Um sich die Videos trotzdem durch ein einfaches Öffnen der Datei `index.html` angucken zu können, müssen diese Sicherheitseinstellungen geändert werden. [Mozilla Firefox](https://www.mozilla.org) erlaubt ein einfaches Umstellen der entsprechenden Einstellung.\n1. Öffnen der Webseite [about:config](about:config)\n2. Die Warnmeldung bestätigen\n3. Nach `privacy.file_unique_origin` suchen\n4. Durch einen Doppelklick den Wert von `true` auf `false` ändern\nDiese Einstellung sollte nach dem Angucken der Videos wieder zurück auf `true` geändert werden.\n## Einbinden in eigene Webseite\nDas Einbinden in eine HTML-Datei ist mit folgendem Code möglich, die Datei `index.html` dient hier als praktisches Beispiel. Die Pfade müssen entsprechend auf den tatsächlichen Ort der gehosteten Dateien angepasst werden.\n```html<!-- Javascript Code zum Einbinden im Kopf oder am Anfang der Seite --><script src=\"/PFAD/ANPASSEN/main.bundle.js\" type=\"text/javascript\"></script><div id='h5p-container'></div><!-- Hier erscheint später der H5P-Inhalt --><link rel=\"stylesheet\" href=\"/PFAD/ANPASSEN/styles/h5p.css\"><!-- Fullscreen fix, siehe https://github.com/tunapanda/h5p-standalone/issues/51 --><!-- Javascript Code nach dem div zum Aktivieren des Inhalts --><script type=\"text/javascript\">\n    const el = document.getElementById('h5p-container');\n    const h5pLocation = '/PFAD/ANPASSEN/workspace';\n    const options = {\n        frameJs: '/PFAD/ANPASSEN/frame.bundle.js',\n        frameCss: '/PFAD/ANPASSEN/styles/h5p.css'\n    };\n    const displayOptions = { // Aussehen von H5P anpassen\n        frame: false,\n        copyright: true,\n        embed: false,\n        download: false,\n        icon: false,\n        export: false\n    };\n    const h5p = new H5PStandalone.H5P(el, h5pLocation,options, displayOptions);</script>\n```" , "Unter Verwendung der im ZIP-File bereitgestellten Informationen bzw. Anwendungen, lassen sich die im Forschungsverbund zur Selbstregulation im Alltag von Studierenden (SriAS 2 Practice, gefördert vom BMBF) produzierten Kurzvideos miteinander verknüpfen.  Sofern die vier Videos der Serie (siehe Verweise zur Relation) gemeinsam präsentiert werden sollen, lässt sich die Verknüpfung unter Verwendung der hier bereitgestellten Sekundärinformation umsetzen.";
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                "Figure 1a: X-ray diffractograms for as-prepared, and post-calcined (at 400 °C, 500 °C, 600 °C, and 700°C) Ir-oxide nanoparticles (2theta vs. intensity /a.u.).\nFigure 1b: X-ray diffractograms for the reference spectrum for the tetragonal rutile IrO2 (JPCD card no. 15-0870), and Ir fcc (COD, 1512514), (2theta vs. intensity /a.u.).\nFigure 2a: TEM image for the Ir-oxide nanoparticles calcined at 400 °C.\nFigure 2b: TEM image for the Ir-oxide nanoparticles calcined at 500 °C.\nFigure 2c: TEM image for the Ir-oxide nanoparticles calcined at 600 °C.\nFigure 2d: TEM image for the Ir-oxide nanoparticles calcined at 700 °C.\nFigure 3a: Comparison of the high-resolution Ir 4f spectra obtained from the XPS measurements on Ir-oxide nanoparticles (as-prepared, calcined at 400°C and calcined at 700°C), (binding energy / eV vs. intensity /a.u.).\nFigure 3b: Comparison of the high-resolution O 1s spectra obtained from the XPS measurements on Ir-oxide nanoparticles (as-prepared, calcined at 400°C and calcined at 700°C), (binding energy / eV vs. intensity /a.u.).\nFigure 3c: The hydroxide-to-oxide ratio for the post-calcined Ir-oxide nanoparticles.\nFigure 4: Cyclic voltammograms of the post-calcined Ir-oxide nanoparticles (potential, E vs current density, j).\nFigure 5a: Measured current densities at E = 1.53 V at 25 mV·s-1, j@1.53V, for all post-calcined Ir-oxide samples (potential, E vs current density, j).\nFigure 5b: Change in the calculated total surface area of the particles present in the bulk vs. change in j@1.53V when calcination temperature increased (change / % vs calcination change interval).\nFigure 6: Experimental and simulated CV data (at 200 mV·s-1) for the Ir-oxide nanoparticles calcined at 400 °C, 500 °C, 600 °C, and 700 °C (potential, E vs current density, j).\nFigure 7a: Experimental  and simulated  CV curves recorded at the scan rates of 200, 100, 50, and 25 mV·s-1 for the Ir-oxide nanoparticles calcined at 400 °C.\nFigure 7b: Experimental  and simulated  CV curves recorded at the scan rates of 200, 100, 50, and 25 mV·s-1 for the Ir-oxide nanoparticles calcined at 500 °C.\nFigure 7c: Experimental  and simulated  CV curves recorded at the scan rates of 200, 100, 50, and 25 mV·s-1 for the Ir-oxide nanoparticles calcined at 600 °C.\nFigure 7d: Experimental  and simulated  CV curves recorded at the scan rates of 200, 100, 50, and 25 mV·s-1 for the Ir-oxide nanoparticles calcined at 700 °C.\nFigure 8a: Current density simulated at E=1.53V, j@1.53V-sim, double layer capacitance, Cdl, density of active CUS sites, ρCUS, versus calcination temperatures; values extracted from the simulations for post-calcined Ir-oxide particles. \nFigure 8b: Current density simulated at E=1.53V, j@1.53V-sim, double layer capacitance, Cdl, versus density of active CUS sites, ρCUS; values extracted from the simulations for the post-calcined Ir-oxide particles.\nFigure 9a: Comparison of the free reaction energies of all the elementary steps (Step 1−7) of OER on CUS on the post-calcined Ir-oxide nanoparticles (reaction coordinate vs free reaction energy / eV).\nFigure 9b: Comparison of the activation energies of all the elementary steps (Step 1−7) of OER on CUS on the post-calcined Ir-oxide nanoparticles (reaction coordinate vs activation energy / eV).\nFigure 9c: Comparison of the interaction energies of all the elementary steps (Step 1−7) of OER on CUS on the post-calcined Ir-oxide nanoparticles (reaction coordinate vs interaction energy / eV).\nFigure 9d: Comparison of the free reaction energies for the one-electron redox transition on BRI for the post-calcined Ir-oxide nanoparticles (reaction coordinate vs free reaction energy / eV).\nFigure 9e: Comparison of the activation energies for the one-electron redox transition on BRI for the post-calcined Ir-oxide nanoparticles (reaction coordinate vs activation / eV).\nFigure 9f: Comparison of the interaction energies for the one-electron redox transition on BRI for the post-calcined Ir-oxide nanoparticles (reaction coordinate vs interaction energy / eV).\nFigure 9g: Comparison of the bridge site fraction for the post-calcined Ir-oxide nanoparticles.\nFigure 10a_left: Effective reaction rates of the elementary steps of the OER mechanism on CUS for Ir-oxide nanoparticles calcined at 400 °C (potential, E vs reaction rate, r).\nFigure 10a_right: Surface coverage of the adsorbed species on CUS for Ir-oxide nanoparticles calcined 400 °C (potential, E vs surface coverage). \nFigure 10b_left: Effective reaction rates of the elementary steps of the OER mechanism on CUS for Ir-oxide nanoparticles calcined at 700 °C (potential, E vs reaction rate, r).\nFigure 10b_right: Surface coverage of the adsorbed species on CUS for Ir-oxide nanoparticles calcined 700 °C (potential, E vs surface coverage). \nFigure_S1a: XRD diffractograms of as-prepared Ir oxide nanoparticles (2theta vs. intensity /a.u.).\nFigure_S1b: XRD diffractograms of Ir-oxide nanoparticles calcined at 400 °C (2theta vs. intensity /a.u.).\nFigure_S1c: XRD diffractograms of Ir-oxide nanoparticles calcined at 500 °C (2theta vs. intensity /a.u.).\nFigure_S1d: XRD diffractograms of Ir-oxide nanoparticles calcined at 600 °C (2theta vs. intensity /a.u.).\nFigure_S1e: XRD diffractograms of Ir-oxide nanoparticles calcined at 700 °C (2theta vs. intensity /a.u.).\nFigure_S1f: XRD diffractograms of tabulated reference for tetragonal rutile IrO2 and fcc Ir metal (2theta vs. intensity /a.u.).\nFigure S2a-d: Electron diffraction results of Ir-oxide samples calcined at a) 400 °C, b) 500 °C, c) 600 °C, and d) 700 °C.\nFigure S3a: SEM images of Ir-oxide samples calcined 400 °C.\nFigure S3b: SEM images of Ir-oxide samples calcined 500 °C.\nFigure S3c: SEM images of Ir-oxide samples calcined 600 °C.\nFigure S3d: SEM images of Ir-oxide samples calcined 700 °C.\nFigure S4: a) High-resolution O 1s spectrum of the Ir oxide nanoparticles calcined at 500 °C and 600 °C; b) High resolution C 1s spectrum of the Ir oxide nanoparticles as-prepared and calcined at 400 °C, 500 °C, 600 °C and 700 °C.\nFigure S5: XPS survey scan of the Ir oxide nanoparticles calcined at a) 700 °C, b) 600 °C, c) 500 °C, d) 400 °C and e) as-prepared.\nFigure S6a: Experimental CV curves recorded at the scan rates of 200, 100, 50, 25, and 200 mV/s on the iridium oxide nanoparticles calcined 400 °C (potential, E vs current density, j).\nFigure S6b: ExperimentalCV curves recorded at the scan rates of 200, 100, 50, 25, and 200 mV/s on the iridium oxide nanoparticles calcined 500 °C (potential, E vs current density, j).\nFigure S6c: ExperimentalCV curves recorded at the scan rates of 200, 100, 50, 25, and 200 mV/s on the iridium oxide nanoparticles calcined 600 °C (potential, E vs current density, j).\nFigure S6d: ExperimentalCV curves recorded at the scan rates of 200, 100, 50, 25, and 200 mV/s on the iridium oxide nanoparticles calcined 700 °C (potential, E vs current density, j).\nFigure S7a: Scan rate–normalized CVs for the Ir-oxide samples calcined at 400 °C.\nFigure S7b: Scan rate–normalized CVs for the Ir-oxide samples calcined at 500 °C.\nFigure S7c: Scan rate–normalized CVs for the Ir-oxide samples calcined at 600 °C.\nFigure S7d: Scan rate–normalized CVs for the Ir-oxide samples calcined at 700 °C.\nFigure S8: Comparison of average current density j versus scan rate for all post-calcined Ir-oxide samples.\nFigure S9: Surface area–normalized cyclic voltammograms recorded at 200mV/s for the Ir-oxide catalysts calcined at 400 °C, 500 °C, 600 °C, and 700 °C. \nFigure S10a: Comparison of the experimental and simulated CV curves at the scan rate of 200 mV/s, for the Ir-oxide nanoparticles calcined at 400 °C (potential, E vs current density, j).\nFigure S10b: Comparison of the experimental and simulated CV curves across all scan rates for the Ir-oxide nanoparticles calcined at 400 °C (potential, E vs current density, j).\nFigure S10c: Free reaction energy diagram for all elementary steps of the oxygen evolution reaction (OER) at E=0V, E=1.23V, and E=1.6V for the Ir-oxide nanoparticles calcined at 400 °C (reaction coordinate vs free reaction energy / eV).\nFigure S10d: Identified free reaction energy parameters for steps 1−6 at the electrochemical standard conditions in comparison to DFT-calculated values for the Ir-oxide nanoparticles calcined at 400 °C (reaction step vs free reaction energy / eV).\nFigure S11a: Comparison of the experimental and simulated CV curves at the scan rate of 200 mV/s, for the Ir-oxide nanoparticles calcined at 500 °C (potential, E vs current density, j).\nFigure S11b: Comparison of the experimental and simulated CV curves across all scan rates for the Ir-oxide nanoparticles calcined at 500 °C (potential, E vs current density, j).\nFigure S11c: Free reaction energy diagram for all elementary steps of the oxygen evolution reaction (OER) at E=0V, E=1.23V, and E=1.64V for the Ir-oxide nanoparticles calcined at 500 °C (reaction coordinate vs free reaction energy / eV).\nFigure S11d: Identified free reaction energy parameters for steps 1−6 at the electrochemical standard conditions in comparison to DFT-calculated values for the Ir-oxide nanoparticles calcined at 500 °C (reaction step vs free reaction energy / eV).\nFigure S12a: Comparison of the experimental and simulated CV curves at the scan rate of 200 mV/s, for the Ir-oxide nanoparticles calcined at 600 °C (potential, E vs current density, j).\nFigure S12b: Comparison of the experimental and simulated CV curves across all scan rates for the Ir-oxide nanoparticles calcined at 600 °C (potential, E vs current density, j).\nFigure S12c: Free reaction energy diagram for all elementary steps of the oxygen evolution reaction (OER) at E=0V, E=1.23V, and E=1.66V for the Ir-oxide nanoparticles calcined at 600 °C (reaction coordinate vs free reaction energy / eV).\nFigure S12d: Identified free reaction energy parameters for steps 1−6 at the electrochemical standard conditions in comparison to DFT-calculated values for the Ir-oxide nanoparticles calcined at 600 °C (reaction step vs free reaction energy / eV).\nFigure S13a: Comparison of the experimental and simulated CV curves at the scan rate of 200 mV/s, for the Ir-oxide nanoparticles calcined at 700 °C (potential, E vs current density, j).\nFigure S13b: Comparison of the experimental and simulated CV curves across all scan rates for the Ir-oxide nanoparticles calcined at 700 °C (potential, E vs current density, j).\nFigure S13c: Free reaction energy diagram for all elementary steps of the oxygen evolution reaction (OER) at E=0V, E=1.23V, and E=1.68V for the Ir-oxide nanoparticles calcined at 700 °C (reaction coordinate vs free reaction energy / eV).\nFigure S13d: Identified free reaction energy parameters for steps 1−6 at the electrochemical standard conditions in comparison to DFT-calculated values for the Ir-oxide nanoparticles calcined at 700 °C (reaction step vs free reaction energy / eV).\nFigure S14a_left: Effective reaction rates of the elementary steps of the OER mechanism for Ir-oxide nanoparticles calcined at 400 °C (potential, E vs reaction rate, r).\nFigure S14a_right: Surface coverage of the adsorbed species for Ir-oxide nanoparticles calcined 400 °C (potential, E vs surface coverage).\nFigure S14b_left: Effective reaction rates of the elementary steps of the OER mechanism for Ir-oxide nanoparticles calcined at 500 °C (potential, E vs reaction rate, r).\nFigure S14b_right: Surface coverage of the adsorbed species for Ir-oxide nanoparticles calcined 500 °C (potential, E vs surface coverage).\nFigure S14c_left: Effective reaction rates of the elementary steps of the OER mechanism for Ir-oxide nanoparticles calcined at 600 °C (potential, E vs reaction rate, r).\nFigure S14c_right: Surface coverage of the adsorbed species for Ir-oxide nanoparticles calcined 600 °C (potential, E vs surface coverage).\nFigure S14d_left: Effective reaction rates of the elementary steps of the OER mechanism for Ir-oxide nanoparticles calcined at 700 °C (potential, E vs reaction rate, r).\nFigure S14d_right: Surface coverage of the adsorbed species for Ir-oxide nanoparticles calcined 700 °C (potential, E vs surface coverage).\nFigure S15a: Surface coverage of the bridging hydroxyl (BRI-OH), θ_BRI-OH, for Ir-oxide nanoparticles calcined at 400 °C.\nFigure S15b: Surface coverage of the bridging hydroxyl (BRI-OH), θ_BRI-OH, for Ir-oxide nanoparticles calcined at 500 °C.\nFigure S15c: Surface coverage of the bridging hydroxyl (BRI-OH), θ_BRI-OH, for Ir-oxide nanoparticles calcined at 600 °C.\nFigure S15d: Surface coverage of the bridging hydroxyl (BRI-OH), θ_BRI-OH, for Ir-oxide nanoparticles calcined at 700 °C." , "Proton-exchange membrane (PEM) water electrolysis is a critical technology for hydrogen production. The oxygen evolution reaction (OER) kinetics at the anode significantly determines the electrolysis performance, requiring the development of active and stable catalysts for high conversion rates. Despite extensive experimental studies, it is still difficult to fully understand how the catalyst state, i.e., the structure, morphology and oxidation state - which vary by synthesis conditions - affect the OER kinetics and free energies. In this study, we delve into the influence of catalyst calcination on catalyst state and its relationship with OER kinetics by a combination of experimental analysis and microkinetic modeling. Our results show that the increasing degree of crystallinity upon calcination, and thus, the reduced number of active sites is the main reason for the decreasing performance of Ir-oxide nanoparticles. Additionally, water adsorption step becomes thermodynamically more favorable, CUS-mediated PCET and O₂ release are modestly hindered, and the bridge-site redox contribution declines with increasing crystallinity. These subtle, systematic shifts help explain the non-linear structure–activity relationships reported in the literature. This understanding of the interplay between catalyst synthesis conditions and OER performance facilitates the tailored design and optimization of high-performance catalysts for more efficient electrocatalytic water electrolysis.";
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                "# ACIIE – Asynchronous Collaboration in Immersive Environments\n**ACIIE** is a research framework for enabling **asynchronous collaboration** in XR (VR, AR, MR) environments. Originally developed by [Anjela Mayer](https://codeberg.org/anjela-mayer) as part of her PhD research, it leverages the built-in sensors of XR devices to capture users’ motion and voice. These recordings are then reconstructed through virtual characters, synchronizing voice and movement to replay explanations and actions during collaborative scenarios.> ACIIE is the second iteration of the ACIE framework and is now maintained and extended collaboratively by the [Immersive Creators](https://codeberg.org/ImmersiveCreators) for non-profit research purposes.\n---\n## 🧩 Purpose\nACIIE is designed for **experimental XR applications** involving collaborative learning, training, and asynchronous communication. It enables the creation of immersive scenarios where users can interact across time via avatar-based motion and voice playback.\nIt can also be used to create **NPC character animations** by recording natural user movement and speech with an XR device — requiring no traditional animation pipeline.\n---\n## 🔧 Technologies and Plugins\n- Built with **Unreal Engine** – currently targeting version **5.5**\n- Uses the [**Runtime Audio Importer**](https://github.com/gtreshchev/RuntimeAudioImporter) for microphone input, .wav file import/export, and audio playback\n- Integrated with **Metahuman 5.3+** for realistic full-body virtual characters\n- Employs **MetaXR Plugin v77** for localization, full-body motion tracking, and hand tracking> ⚠️ Cross-platform support via OpenXR is **in progress**. Currently, the system is tested primarily with **Meta XR devices** (e.g., Quest series).\n---\n## 🧪 Use Cases\nACIIE has been applied in multiple XR research and training contexts:\n- 🎥 **PumpStationXR** – [Watch demo](https://youtu.be/vlojVAeedRc?si=DLnT5jbijSXH3uin)\n- 🎥 **XIRCON** – [Watch demo](https://youtu.be/ckKz_hrTQ2g?si=5ScCm23MI3ByaB_l)\n### 🏅 Recognitions\n- Selected for the **ICM Early Ride Program 2024**\n- Winner of the **DIVR Best Concept Award 2024**\n- **4th Place** at the **KIT Neuland Innovation Contest 2025**\n---\n## 👩‍💻 Authorship\nACIIE was created by **Anjela Mayer** during her PhD research. It is now maintained and extended by the **Immersive Creators** collective for academic and non-profit research.> Please cite this work appropriately if used in research. Git commit history reflects original authorship and contributions.\n---\n## 📄 Citation\nIf you use ACIIE in your work, please cite:\n---\n## 📜 License\nThis project is licensed under the **GNU General Public License v3.0 (GPLv3)**.\n© 2025 Anjela Mayer. Maintained by Immersive Creators.\nDual licensing is available for non-GPL use cases.\nPlease contact the maintainers for closed-source or commercial licensing terms.\n---\n## 🧾 Third-Party Dependencies\nThis project includes third-party components under their respective licenses:\n- **Runtime Audio Importer**\nDeveloped by Georgy Treshchev\nSource: [gtreshchev/RuntimeAudioImporter](https://github.com/gtreshchev/RuntimeAudioImporter)\nLicense: [MIT License](https://github.com/gtreshchev/RuntimeAudioImporter/blob/main/LICENSE)\n- For details on third-party components, see [THIRD_PARTY_LICENSES.md](./THIRD_PARTY_LICENSES.md).\n---\n## 📫 Contact\nFor collaboration or contributions, feel free to reach out: anjela.mayer@kit.edu\n---" , "ACIIE is a research framework for enabling asynchronous collaboration in XR (VR, AR, MR) environments. Originally developed by Anjela Mayer as part of her PhD research, it leverages the built-in sensors of XR devices to capture users’ motion and voice. These recordings are then reconstructed through virtual characters, synchronizing voice and movement to replay explanations and actions during collaborative scenarios. This project is licensed under the GNU General Public License v3.0 (GPLv3).";
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                "Instructions:\nThe scripts inside the subfolder are intended to reproduce the figures from the\npreprint\n    Dynamical low-rank integrators for second-order matrix differential equations\nby Marlis Hochbruck, Markus Neher, and Stefan Schrammer\nRequirements\nThe codes are tested with\nUbuntu 20.04.2 LTS and Python 3.8.5 and the following version of its modules:\n    numpy       1.19.2\n    scipy       1.5.2\n    numba       0.51.2\n    colorama    0.4.4\n    h5py        2.10.0\n    matplotlib  3.3.2\n    tikzplotlib 0.9.6\nGeneration of figures (tikz files containing the data are also created)\nIn the folder homwave open a console and run the commands\n    to create the data for Figure (1)\n    python3 hom_planar.py\n    to create Figure (1)\n    python3 hom_planar_plots.py\nIn the folder homwav open a console and run the commands\n    to create the data for Figure (2)\n    python3 hom_semi.py\n    to create Figure (2)\n    python3 hom_semi_plots.py";
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                "##   README   \n# ___________________\n### VoiLA-P19: A 3-D P-wave velocity model of the upper mantle beneath the Caribbean derived from teleseismic travel time tomography \n-------------\nThe files layer_XX.xyz (with XX indicating the number of the layer from 01-08) contain P-wave velocity anomalies (3rd column) in % with reference to the AK135 model at given longitudes (1st column) ranging from -73.5° to -57.0° and latitudes (2nd column) ranging from 6.5° to 23.0° with increments of 0.05° in both longitude and latitude. \nThe files layer_XX.grd contain the same model as .grd-files. \nThe 8 layers of the model correspond to the following depth ranges:\n  0- 40 km : layer01\n 40-100 km : layer02\n100-160 km : layer03\n160-240 km : layer04\n230-320 km : layer05\n320-410 km : layer06\n410-530 km : layer07\n530-660 km : layer08\nA GMT script 'plot_layers.gmt' is provided to plot horizontal slices of the velocity anomalies. \nMore information on the final model and a suggested set of interpretations can be found in:\n        Braszus, B., et al. (2021), Subduction history of the Caribbean from upper-mantle seismic imaging and plate reconstruction, Manuscript in revision for Nature Communications.\nFurther details of the method and tests for an earlier version of the model are in:\n        Braszus, B. (2019), 3D teleseismic travel time tomography along the Lesser Antilles subduction zone, 83 pp, Karlsruhe Institute of Technology, Karlsruhe, Germany, doi:10.5445/IR/1000124003.";
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                "Dieser Anhang gibt einen Überblick über Kompetenzen für Nachhaltigkeit (Key Competencies in Sustainability Transformation) und deren Definitionen. Darüber hinaus werden die einzelnen Kompetenzen für den Kontext der Nachhaltigkeit-Selbstexperimente (Sustainability Self-Experiments, SSE) operationalisiert. SSE stellen ein transformatives Lehrformat dar, das am Karlsruher Transformationszentrum (KAT) des Karlsruher Instituts für Technologie (KIT) entwickelt wurde. \nDiese Kompetenzen bilden die analytische Grundlage der qualitativen Inhaltsanalyse, mit der im Beitrag „Assessing Competency Development in Transformative Teaching – A Real-World Lab Case Study on Sustainability Self-Experiments“ die Kompetenzentwicklung von Studierenden untersucht wird." , "Descriptions of Key competencies for sustainability transformations (KSC), which served as the analytical basis for the qualitative content analysis conducted in the paper 'Assessing Competency Development in Transformative Teaching: A Real-World Lab Case Study on Sustainability Self-Experiments' to. For each competency, a definition is first provided from the literature, and then the competency is contextualised for practical application in the analysis of student journals (operationalisation). Two visualisations are also provided to facilitate comprehension. \nIllustrative examples and empirical quotations are provided in the related Supplement 2, “Sustainability Self-Experiments”. \nRelated publication: Trenks & Beecroft (2026). Assessing competency development in transformative teaching – A real-world lab case study on Sustainability Self-Experiments. International Journal of Sustainability in Higher Education." , "This appendix provides an overview of the key competencies in sustainability transformation, along with their definitions. Additionally, the individual competencies are operationalised for the context of Sustainability Self-Experiments (SSE). SSEs are a transformative teaching format developed at the Karlsruhe Transformation Centre (KAT) of the Karlsruhe Institute of Technology (KIT). The competencies form the analytical basis of the qualitative content analysis employed in the article 'Assessing Competency Development in Transformative Teaching: A Real-World Lab Case Study on Sustainability Self-Experiments' to analyse student competency development.";
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                "Falls Sie hierzu Anmerkungen haben, eine überarbeitbare Fassung der Tabellen wünschen oder weitere Informationen integrieren möchten, wenden Sie sich bitte an: denise.boehnke@kit.edu" , "Cities and municipalities are faced with the challenging task of adapting their communities to the expected consequences of climate change. Significantly higher summer temperatures, a climatic change from the previously very frequent and regular land rainfall in summer to weeks of drought, and an increase in individual heavy rainfall events whose water masses cannot be compensated by the existing structure - these are very likely consequences that today's planning must address. Local authorities are therefore faced with the challenging task of integrating climate adaptation concerns into their planning in the best possible way. A large number of German guidelines on municipal climate adaptation are available for this purpose. The disadvantage of this diversity, however, is the amount of time required to gain an overview of the various topics. Without relating to specific projects, the tables presented here are an attempt to summarise the existing variety of specific measures for municipal climate adaptation and the available knowledge on the individual measures. (publication in german language)" , "Städte und Gemeinden stehen vor der herausfordernden Aufgabe ihre Kommune an die zu erwartenden Folgen des Klimawandels anzupassen. Deutlich höhere Sommertemperaturen und Hitzeextremereignisse, ein klimatischer Wechsel von dem früher sehr häufigen und regelmäßigen Landregen im Sommer hin zu wochenlangen Dürrephasen und vermehrt Starkregenextremereignisse, deren Wassermassen die vorhandene Infrastruktur nicht abfangen kann - sind sehr wahrscheinliche Folgen, denen die heutige Planung gerecht werden muss. Die Kommunen stehen daher vor der herausfordernden Aufgabe, die Belange der Klimaanpassung bestmöglich in ihre Planungen zu integrieren. \nZur Unterstützung wurde bereits eine große Vielzahl an Leitfäden unterschiedlicher Schwerpunktsetzungen zur kommunalen Klimaanpassung entwickelt und veröffentlicht. Der Nachteil dieser Vielfalt jedoch ist der hohe Zeitaufwand der Fachangestellten und Planenden, sich in die Thematik fundiert einzuarbeiten. Die hier vorliegenden Tabellen sind ein Versuch, außerhalb von projektspezifischen Rahmenbedingungen die vorhandene Vielfalt an konkreten Maßnahmen zur kommunalen Klimaanpassung und das vorhandene Wissen zu den Einzelmaßnahmen zusammenzutragen. \nDie Maßnahmen im besiedelten Raum sind zunächst nach ihrer Anpassungsthematik gegliedert, wobei einzelne Maßnahmen auch verschiedenen Anpassungsthemen dienen können. Die Maßnahmen sind zur besseren Übersicht in Kategorien gruppiert, die der gleichen Art angehören oder den gleichen Zweck verfolgen. \nZu jeder Maßnahme sind diverse Zusatzinformationen gegeben, die für den Planenden von Relevanz sein können. Jede Maßnahme wird als erstes über eine Kurzbeschreibung definiert bzw. beschrieben. Es folgen Wirkungsbeschreibungen, die den Planenden bei der Argumentation der Maßnahme (bspw. im Begründungsteil des Bebauungsplans) behilflich sein können. Wenn vorhanden, sind konkrete Zahlen oder Berechnungsansätze zur Wirkungsabschätzung gegeben. Es folgen Informationen u.a. zum Planbezug, die Einschätzung der Eignung der Maßnahmen in bestimmten Stadtstrukturtypen, Einschätzungen zu Kosten und Flächenbedarf, der Verwendung im Bestand versus Neubau, sowie Hinweise zu weiterführender Literatur. Alle Maßnahmen sowie Zusatzinformationen sind bereits veröffentlichten Leitfäden und Berichten entnommen, es handelt sich hier also um ein Reviewbeitrag ohne spezifischen Projektbezug. Die Kategorisierung der Maßnahmen, sowie die Einschätzung ihrer Eignung für den Bestand versus Neubau erfolgte durch die Autorinnen. Ihre Eignung für ein Monitoring mittels Fernerkundung erfolgte durch das IPF des KIT. \nDies ist die erste Auflage dieser Maßnahmensammlung. Einige Felder haben noch keine Informationen, oder können ggf. durch die Praxis anders eingeschätzt werden. Sobald genügend neue Informationen vorliegen bzw. der jetzige Stand umfassend überarbeitet wurde, wird die Tabelle als neue Auflage erneut veröffentlicht werden. \nFalls Sie hierzu Anmerkungen haben, eine überarbeitbare Fassung der Tabellen wünschen oder weitere Informationen integrieren möchten, wenden Sie sich bitte an: denise.boehnke@kit.edu";
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