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Metadata Source: DaRUS

91 to 100 of 2,664 Results

OpenFAST model of the Senvion 5M (REpower) offshore wind turbine (Metadata only) Apr 7, 2025 - OpenFAST Ruck, Nico, 2025, "OpenFAST model of the Senvion 5M (REpower) offshore wind turbine (Metadata only)", https://doi.org/10.18419/DARUS-4977, DaRUS, V1 The data set provided contains the OpenFAST (V.3.5.3) input files for the Senvion 5M offshore wind turbine (OWT) of the alpha ventus wind farm. The OpenFAST model was developed based on a previously implemented Flex5 model of the Senvion 5M and includes the structural and aerodynamic properties of the real blades as well as the fully functional bla...
CSI Dataset espargos-0007: Passive target with four synchronized ESPARGOS antenna arrays in a lab room Apr 7, 2025 - ESPARGOS WiFi Channel State Information Measurements Euchner, Florian; Kellner, David; Stephan, Phillip; ten Brink, Stephan, 2025, "CSI Dataset espargos-0007: Passive target with four synchronized ESPARGOS antenna arrays in a lab room", https://doi.org/10.18419/DARUS-4973, DaRUS, V1 Dataset containing WiFi channel state information (CSI) alongside ground truth data (position tags, timestamps) measured with ESPARGOS antenna arrays. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json). A passive target is moving in a lab room, with four time- and phase-synchronous ESPARGOS...
OpenFAST(Universität Stuttgart) Apr 4, 2025SWE
Figures of "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments" Apr 4, 2025 - Visualisierungsinstitut der Universität Stuttgart Rau, Tobias, 2025, "Figures of "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments"", https://doi.org/10.18419/DARUS-4755, DaRUS, V1 Here we provide access to the images used in our paper "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments". The figures describe the proposed interaction techniques, a simplified transition pipeline, a schematic study setup, and show the results of the technique study...
Supplementary material for ''Ring-Expansion Metathesis Polymerization under Confinement" Apr 3, 2025 - Institute of Thermodynamics and Thermal Process Engineering Högler, Marc; Hansen, Niels, 2025, "Supplementary material for ''Ring-Expansion Metathesis Polymerization under Confinement"", https://doi.org/10.18419/DARUS-4537, DaRUS, V1 This dataset contains simulation input files, topologies and coordinate files in GROMACS format accompanying the mentioned publication. With the provided Jupyter notebooks the data can be visualized and also used for further analysis. We recommend viewing the data by choosing the option "tree".
Replication Data for: GPRat: Gaussian Process Regression with Asynchronous Tasks Apr 2, 2025 - Scientific Computing Helmann, Maksim; Strack, Alexander; Pflüger, Dirk, 2025, "Replication Data for: GPRat: Gaussian Process Regression with Asynchronous Tasks", https://doi.org/10.18419/DARUS-4743, DaRUS, V1 This repository complements the identically titled paper submitted to WAMTA 2025 and allows to reproduce the published results. For a more description please consider the README.md file.
Deep Drawing and Cutting Simulations Dataset Apr 2, 2025 - DFG SPP 2422 Baum, Sebastian; Heinzelmann, Pascal, 2025, "Deep Drawing and Cutting Simulations Dataset", https://doi.org/10.18419/DARUS-4801, DaRUS, V1, UNF:6:kR8BEJgV33K792gINT4dbA== [fileUNF] The benchmark dataset was generated through a comprehensive simulation study of the deep drawing process for DP600 sheet metal, incorporating variations in geometry, material properties, and process parameters. The simulations were based on the deep drawing of modified quadratic cups with a length of 210 mm and a drawing depth of 30 mm. Three disti...
PN 6-8 (II)(Universität Stuttgart) Mar 28, 2025PN 6 Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...
DFG SPP 2422(Universität Stuttgart) Mar 27, 2025Institute of Industrial Automation and Software Engineering
Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix Mar 24, 2025 - Institute of Applied Analysis and Numerical Simulation Zhang, Liwei; Mazzeo, Patrizia; Nottoli, Michele; Cignoni, Edoardo; Cupellini, Lorenzo; Stamm, Benjamin, 2025, "Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix", https://doi.org/10.18419/DARUS-4902, DaRUS, V1 This archive for reproducibility contains the datasets used to train the models, a small script to read the datasets, and the finest model. More information can be found in the README.md.

OpenFAST model of the Senvion 5M (REpower) offshore wind turbine (Metadata only)

Apr 7, 2025 - OpenFAST

Ruck, Nico, 2025, "OpenFAST model of the Senvion 5M (REpower) offshore wind turbine (Metadata only)", https://doi.org/10.18419/DARUS-4977, DaRUS, V1

The data set provided contains the OpenFAST (V.3.5.3) input files for the Senvion 5M offshore wind turbine (OWT) of the alpha ventus wind farm. The OpenFAST model was developed based on a previously implemented Flex5 model of the Senvion 5M and includes the structural and aerodynamic properties of the real blades as well as the fully functional bla...

CSI Dataset espargos-0007: Passive target with four synchronized ESPARGOS antenna arrays in a lab room

Apr 7, 2025 - ESPARGOS WiFi Channel State Information Measurements

Euchner, Florian; Kellner, David; Stephan, Phillip; ten Brink, Stephan, 2025, "CSI Dataset espargos-0007: Passive target with four synchronized ESPARGOS antenna arrays in a lab room", https://doi.org/10.18419/DARUS-4973, DaRUS, V1

Dataset containing WiFi channel state information (CSI) alongside ground truth data (position tags, timestamps) measured with ESPARGOS antenna arrays. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json). A passive target is moving in a lab room, with four time- and phase-synchronous ESPARGOS...

OpenFAST(Universität Stuttgart)

Apr 4, 2025SWE

Figures of "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments"

Apr 4, 2025 - Visualisierungsinstitut der Universität Stuttgart

Rau, Tobias, 2025, "Figures of "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments"", https://doi.org/10.18419/DARUS-4755, DaRUS, V1

Here we provide access to the images used in our paper "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments". The figures describe the proposed interaction techniques, a simplified transition pipeline, a schematic study setup, and show the results of the technique study...

Supplementary material for ''Ring-Expansion Metathesis Polymerization under Confinement"

Apr 3, 2025 - Institute of Thermodynamics and Thermal Process Engineering

Högler, Marc; Hansen, Niels, 2025, "Supplementary material for ''Ring-Expansion Metathesis Polymerization under Confinement"", https://doi.org/10.18419/DARUS-4537, DaRUS, V1

This dataset contains simulation input files, topologies and coordinate files in GROMACS format accompanying the mentioned publication. With the provided Jupyter notebooks the data can be visualized and also used for further analysis. We recommend viewing the data by choosing the option "tree".

Replication Data for: GPRat: Gaussian Process Regression with Asynchronous Tasks

Apr 2, 2025 - Scientific Computing

Helmann, Maksim; Strack, Alexander; Pflüger, Dirk, 2025, "Replication Data for: GPRat: Gaussian Process Regression with Asynchronous Tasks", https://doi.org/10.18419/DARUS-4743, DaRUS, V1

This repository complements the identically titled paper submitted to WAMTA 2025 and allows to reproduce the published results. For a more description please consider the README.md file.

Deep Drawing and Cutting Simulations Dataset

Apr 2, 2025 - DFG SPP 2422

Baum, Sebastian; Heinzelmann, Pascal, 2025, "Deep Drawing and Cutting Simulations Dataset", https://doi.org/10.18419/DARUS-4801, DaRUS, V1, UNF:6:kR8BEJgV33K792gINT4dbA== [fileUNF]

The benchmark dataset was generated through a comprehensive simulation study of the deep drawing process for DP600 sheet metal, incorporating variations in geometry, material properties, and process parameters. The simulations were based on the deep drawing of modified quadratic cups with a length of 210 mm and a drawing depth of 30 mm. Three disti...

PN 6-8 (II)(Universität Stuttgart)

Mar 28, 2025PN 6

Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...

DFG SPP 2422(Universität Stuttgart)

Mar 27, 2025Institute of Industrial Automation and Software Engineering

Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Mar 24, 2025 - Institute of Applied Analysis and Numerical Simulation

Zhang, Liwei; Mazzeo, Patrizia; Nottoli, Michele; Cignoni, Edoardo; Cupellini, Lorenzo; Stamm, Benjamin, 2025, "Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix", https://doi.org/10.18419/DARUS-4902, DaRUS, V1

This archive for reproducibility contains the datasets used to train the models, a small script to read the datasets, and the finest model. More information can be found in the README.md.

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