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1,342 results for “rotation”
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
Dataset supporting the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020)"
<p>Dataset corresponding to figure 3 of the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020), DOI: <a href="https://doi.org/10.1021/acs.jpclett.0c01747">10.1021/acs.jpclett.0c01747</a>"</p> <p>List of files:<br> There are two folders corresponding to brominated and debrominated structures:</p> <ul> <li>.siesta files: STM simulated images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> </ul>
Datasets for submitted paper "Color appearance in rotational material jetting"
<p>This folder contains data for the submitted paper "Color appearance in rotational material jetting"<br> For more information, please contact Ali Payami Golhin (payami.ag@gmail.com)</p>
Rotation of electron beams in the presence of localised, longitudinal magnetic fields
<p>Electron Bessel beams have been generated by inserting an annular aperture in the illumination system of a TEM.</p> <p>These beams have passed through a localised magnetic field.</p> <p>As a result a low amount of image rotation (which is expected to be proportional to the longitudinal component of the magnetic field) is observed in the far field.</p> <p>A measure of this rotation should give access to the magneti field.</p> <p>The two datasets have been acquired in a FEI Titan<sup>3</sup> microscope, operated at 300kV.</p> <p>The file focal_series.tif contains a series of images acquired varying the magnetic field through the objective lens.</p> <p>The file line_profile.ser contains a series of images acquired by scanning the beam over a sample with several magnetised nanopillars.</p> <p>For reference, check the associated publication:<br> <em>Giulio Guzzinati, Armand Béché, Damien McGrouther and Jo Verbeeck</em>, <strong>Prospects for out-of-plane magnetic field measurements through interference of electron vortex modes in the TEM, </strong><a href="https://doi.org/10.1088/2040-8986/ab51fc">Journal of Optics 21 124002 (2019)</a></p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Experimental data for the manuscript titled Rotating quantum wave turbulence
<p>This submission contains the minimal dataset required to reproduce the experimental findings related to the manuscript titled <em>Rotating quantum wave turbulence</em>, associated with the DOI 10.1038/s41567-023-01966-z.</p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Datasets for "Mass-stream trajectories with non-synchronously rotating donors"
<p>Datasets to accompany the publication of the paper "Mass-stream trajectories with non-synchronously rotating donors" by David Hendriks and Rob Izzard (<a href="https://doi.org/10.1093/mnras/stad2077">https://doi.org/10.1093/mnras/stad2077</a>).</p> <p>Below follows an explanation of the contents of this repository:</p> <ul> <li>Hendriks2023_ballistic_stream_datafile.csv: Data file containing the ballistic trajectory data for a ranges of initial stream velocity, donor synchronicity and mass ratio. This file contains a header with extra information.</li> <li>Hendriks2023_ballistic_stream_metadata.json: Settings file containing the configuration for the ballistic stream integration simulations and other meta data.</li> <li>Hendriks2023_binary_populations_exploration_data_Z0.02.csv: Data file containing the binary population data at Z=0.02. This file contains a header with extra information.</li> <li>Hendriks2023_binary_populations_exploration_metadata.json: Settings file containing the configuration for the binary population synthesis simulations, including binary_c-python settings, binary_c information and other meta data.</li> <li>RLOF_Hendriks2023_roche_lobe_interpolation_table.csv: Data file containing Roche-lobe radius data for a range of donor synchronicity and mass ratio (q=M_acc/M_don).</li> <li> readme.md: readme file.</li> </ul>
PBG02 Aboveground primary productivity within permanent and rotating grazing exclosures in the Patch-Burn Grazing Experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set includes measurement of aboveground net primary productivity based upon end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation within permanent (“ungrazed”) and rotating (“grazed) sections of grazing exclosures to determine long term effects of bison grazing on aboveground primary production. The permanent section of the exclosure is fixed, while the movable section is rotated every 6 years among the four possible compass coordinates relative to the fixed se
3D motion of flexible ferromagnetic filaments under rotating magnetic field
<p>This repository contains experimental data and numerical results related to the publication: A. Zaben, G. Kitenbergs, A. Cēbers (2020), 3D motion of flexible ferromagnetic filaments under rotating magnetic field. Soft Matter, <a href="https://doi.org/10.1039/D0SM00403K">https://doi.org/10.1039/D0SM00403K</a> / <a href="https://arxiv.org/abs/2003.03737">https://arxiv.org/abs/2003.03737</a>.</p> <p>Figs_data.xlsx contains the data presented in the figures. Experimental_Data.rar contains experimental images used to obtain the results for Fig. 3 and 9. The files are named with the operating frequency, field strength and filament length. Numerical.rar contains numerical results used in Fig.6, 8 and 9. The files are named with Cm values. The results are in .dat files named with Cm values followed by wt (wend_cm_wt). The first column is for time(t) followed by x,y,z values of filament tips. </p> <p> </p>
Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset
<p>Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models:<br> * `search_results.dat` contains the parameters and properties of the successful results of the study.<br> * `model_*.log` are the logs with settings, parameters, and properties of the selected models.<br> * `model_*.out` are the profiles of the selected models in binary format.<br> * `XNS_reader.py` is a python script to read the binary format, convert it to text, and compute some derived and global quantities. EDIT 2022-05-30: the output file in binary format does contain the profiles of temperature and entropy per baryon, but those are not outputted in the converted text file. You can manually modify the python script in order to output these profiles too.<br> * `properties.csv` is a summary of the parameters and properties of the selected models.<br> <br> This dataset has been obtained with the stationary code XNS in General Relativity with the Conformal Flatness Approximation [Bucciantini and Del Zanna 2011; Pili et al. 2014; Camelio et al. 2018 and 2019].<br> The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law.<br> The models have been selected between those obtained in the parameter space exploration.<br> For details see the companion paper [Camelio et al. 2021, PRD 103:063014].<br> <br> If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014].</p> <p>EDIT 2022-05-30: an updated version of the code that has been used to produce this dataset is now on Zenodo (https://doi.org/10.5281/zenodo.6594069).<br> This updated version is called ASWNS code, and it does not contain the model of binary neutron star merger remnant used for this dataset, but an older version of the model of nonbarotropic neutron star (from Camelio et al. 2019).<br> You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.</p>
Lipschitz quaternions in the range [−10, 10]^4, which induce bijective 3D digitized rotations
<p>The file contains Lipschitz quaternions in the range [−10, 10]^4, such that they induce bijective 3D digitized rotations. It is a comma-separated values file format such that each line contains a different quaternion. This is an updated version which contains 576 more quaternions with respect to the previous version. These 576 quaternions where previously certified as ones which do not lead to bijective digitized rotations due to a bug in the used implementation of the algorithm described in:</p> <p>Pluta K., Romon P., Kenmochi Y., Passat N. (2016) Bijectivity Certification of 3D Digitized Rotations. In: Bac A., Mari JL. (eds) Computational Topology in Image Context. CTIC 2016. Lecture Notes in Computer Science, vol 9667. Springer, pp 30-41, doi:10.1007/978-3-319-39441-1_4</p> <p> </p> <p><strong>Acknowledgements:</strong><br> Special thanks for Victor Ostromoukhov and David Cœurjolly of University of Lyon 1, LIRIS, France, for finding the bug.</p>
Raw experimental data for `Tailoring the Rotational Memory Effect in Multimode Fibers`
<p>Raw data for the article [**Tailoring the Rotational Memory Effect in Multimode Fibers**](https://arxiv.org/abs/2310.19337)</p><p>Measurement of transmission matrices and rotational memory effect for 4 segments of 50 micron core graded index multimode fibers with a numerical aperture of 0.2.</p>
Rotating Shallow Water Verification Suite Output
<p>The Rotating_Shallow_Water_Verification_Suite_Output directory contains the output obtained by running a verification suite of shallow water test cases for the barotropic solver of ocean models using two types of spatial discretizations: a mimetic finite volume method based on the TRiSK scheme and a discontinuous Galerkin spectral element method (DGSEM); and a variety of time-stepping methods. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. The output, which is organized into the two directories MPAS_Ocean_Shallow_Water_Output and DGSEM_Rotating_Shallow_Water_Output corresponding to the two spatial discretizations, includes:</p><ul><li>text files containing the exact solution and the numerical error of the various test cases at specific instants of time along with their plots; and </li><li>text files containing the L2 error norm with respect to the exact solution for refinement in both space and time, and the L2 norm of the difference in the numerical solution for refinement only in space and only in time, along with the convergence plots.</li></ul>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
RMTable Consolidated Catalog of Faraday Rotation Measures of Astronomical Radio Sources
<p>This is a catalog of Faraday rotation measures (and other related properties) of astronomical radio sources, consolidated from many published catalogs in the astronomical literature from 1980 to the present day. These catalogs have been converted to the RMTable standard and stored in 3 formats: FITS binary table, tab-seperated-value ASCII, and VOTable XML.</p> <p>These catalog files can be read by any suitable reader, but we have created a Python module, RMTable (https://github.com/CIRADA-Tools/RMTable), which streamlines the process of interacting with and creating new RMTables.</p>
Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"
<p>Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"</p> <p>This repository contains raw experimental data for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates" manuscript. Please refer to the manuscript for the data interpretation.</p> <p>The repository structure:</p> <ul> <li><code>CuNbARB-sample-photo.jpg</code> shows a photograph of as-received Cu(63nm)/Nb(63nm) accumulative roll bonded (ARB) nanolaminate sample</li> <li><code>ARB_63nm_DRX</code> contains X-ray diffraction measurements</li> <li><code>RD/TDXFIBmilling</code> folders contain focused ion beam images captured during pillar milling <ul> <li>In <code>RDX/TDX</code>, <code>X</code> refers to pillar number (see Supplementary information.org for the full pillar list). The numbers in the file names inside refer to the corresponding pillars.</li> </ul> </li> <li><code>RD/TDXSEMbefore</code> folders contain scanning electron (SEM) images of the as-fabricated pillars</li> <li><code>RD/TDX-compression</code> folders contain in situ pillar compression data, including some of the SEM images captured before/after the compression, raw load-displacement data (in <code>.hys</code> native Hysitron piconindenter format), load-displacement data exported to raw text (see Supplementary information for examples how to plot load-displacement using the raw text files), SEM videos, SEM videos combined with the load-displacement data, and accelerated videos</li> <li><code>RD/TDX-SEMafter</code> folders contain SEM images of the compressed pillars</li> <li>Supplementary-info folder contains supplementary information</li> </ul> <p>Author: I. Radchenko, W. Zhu, L. Qing, E. Navarro, R. Sahay, P.S. Lee, N. Raghavan, O. Thomas, A.S. Budiman, K. Chen</p>
video_rotating_blowpipe_left-hand
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_rotating_blowpipe_slowmotion
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
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OpenNeuro
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