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2037 research outputs found
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CSI Dataset espargos-0007: Passive target with four synchronized ESPARGOS antenna arrays in a lab room
No full textDataset 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 arrays in the corner of the room and four transmitters on the ceiling. Great for bistatic / passive Radar experiments.</p
Simulation Results from an SPH Approach to Model the Influence of Assist Gas Forces in Laser Cutting
No full textLaser Cutting
Laser cutting is a non-contact thermal-based material removal method that offers various advantages over conventional machining processes. The workpiece is melted by a high-powered laser beam and the resulting melt is blown away by a jet of gas. The laser assist gas is crucial as it influences the cutting speed, edge quality, and thermal effects, ultimately affecting the overall efficiency and accuracy of the process. However, modeling the laser assist gas domain is highly challenging due to the immense computational costs associated when dealing with shock waves, turbulence, and high speed jet dynamics.
SPH Model of Assist Gas Forces in Laser Cutting
The corresponding publication presents an approach to model the formation of the cutting front and the dynamics of the melt film, influenced by the cutting assist gas forces acting upon it, using the Smoothed Particle Hydrodynamics (SPH) method. SPH is able to deal with large material displacements and moving boundaries occurring in the laser cutting process.
The forces applied to the melt film and cutting kerf are modeled using the Continuum Surface Force approach and boundary particle detection. Consequently, the effects of the assist gas are calculated without modelling the assist gas phase domain, saving a significant amount of computational cost and reducing the complexity of the model. The SPH model is coupled with a ray-tracing scheme and a Fresnel absorption model to determine the laser material interaction.
The presented approach is compared against experimental data from literature, showing good agreement with the experimentally observed cutting front formation, melt rejection, phase transition, dross formation, and striation patterns. The results show that the SPH method can be effectively applied in applications related to laser cutting, showing its potential to design precise and accurate numerical models. Moreover, the proposed parameterized model can help to optimize process parameters to maximize material utilization and reduce energy consumption, operational cost, and processing time during laser cutting.
The videos provided show the simulation results for different boundary conditions and material properties in laser cutting applying the presented SPH model of the assist gas forces
Data for "Decoding the Projective Transverse Field Ising Model"
No full textGnuplot files and data sets to reproduce the plots in "Decoding the Projective Transverse Field Ising Model". The different folders correspond to the figures and panels of this paper. The Gnuplot files were run with gnuplot 5.4. The data files contain all simulation results included in the publication. Furthermore the file "fig9/plotWithAnalytical.py" contains a Python code (run on python3.9) to reproduce the analytical results in fig9 of the publication
Data from the MetSpec Experiments
No full textData from ground testing of meteorites. It comprises video, still frames and spectroscopic data from 31 experiments examining 28 meteorite samples of various origins. The three experimental campaigns took place between 2020 and 2022
Data for: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study
No full textData for the Publication Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study
The dataset contains the DFT data (VASP OUTCARs) that can reproduce the results.
The following systems are included:
N2 molecule
CrMnFeCoNi without interstitial N
CrMnFeCoNi with N at the octahedral and the tetrahedral interstitial sites
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QoI - Tracer particles
No full text
This dataset contains the database of image snippets of dense clusters of PIV tracer particles (i.e. Mie scattering) marking the reactants within sooting flame measurements in an RQL-type combustor.
The database is one subclass utilised in the generation of synthetic training data via domain randomisation. The resulting dataset is subsequently used for training of DL-based image segmentation models.
For further details and citation, please refer to the submitted paper:
B. Jose, K. P. Geigle, F. Hampp, Domain-Randomised Instance-Segmentation Benchmark for Soot in PIV Images, submitted to Machine Learning: Science and Technology (2025)
Data Samples:
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Background - Tracer particle regions
No full text
This dataset contains the database of image snippets of PIV tracer particles (i.e. Mie scattering) in the background, extracted from sooting flame measurements within an RQL-type combustor.
The database is one subclass utilised in the generation of synthetic training data via domain randomisation. The resulting dataset is subsequently used for training of DL-based image segmentation models.
For further details and citation, please refer to the submitted paper:
B. Jose, K. P. Geigle, F. Hampp, Domain-Randomised Instance-Segmentation Benchmark for Soot in PIV Images, submitted to Machine Learning: Science and Technology (2025)
Data Samples:
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Code for training and using the soot (instance) segmentation models
No full textThis dataset contains the necessary code for using our soot (instance) segmentation model used for segmenting soot filaments from PIV (Mie scattering) images. In the corresponding paper, an ablation study is conducted to delineate the effects of domain randomisation parameters of synthetically generated training data on the segmentation accuracy. The best model is used to extract high-level statistics from soot filaments in an RQL-type model combustor to enhance the fundamental understanding soot formation, transport and oxidation. B. Jose, K. P. Geigle, F. Hampp, Domain-Randomised Instance-Segmentation Benchmark for Soot in PIV Images, submitted to Machine Learning: Science and Technology (2025
DuMuX code for modelling solute redistribution below evaporating surfaces
No full textThis dataset contains the source code to reproduce the numerical simulations presented in Chaudhry et al. Non-invasive imaging of solute redistribution below evaporating surfaces using 23-Na-MRI (submitted), TODO ADD PAPER, **TODO: add doi after acceptance**.
The application presents two test cases: the first case contains a setup where salt precipitation occurs, and the second case simulates a convective downward flow due to density-driven instabilities. Both test cases are based on the following experiments: "Experimental data for solute redistribution below evaporating surfaces", https://doi.org/10.18419/DARUS-5094, . </p
Replication Data for: Parallel FFTW on RISC-V: A Comparative Study including OpenMP, MPI, and HPX
No full textThis repository complements the identically titled paper submitted to the International workshop on RISC-V for HPC at ISC 2025. It allows to reproduce the published results. For a more description please consider the README.md file. (2025-03-16