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274 results for “collisions”
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
S50 | CCSCOMPEND | The Unified Collision Cross Section (CCS) Compendium
<p>This is the collection associated with list S50 CCSCOMPEND on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S50 CCSCOMPEND <strong>The Unified Collision Cross Section (CCS) Compendium</strong></p> <p>>3800 experimental collision cross section values (drift tube MS), provided by Jackie Picache and John McLean, Vanderbilt. Further details available here: <a href="https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/">https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/</a></p> <p>v0.1.1: removed char errors in InChIKey file. v0.1.2 (17 July 2022): added SMILES and separate substance deposition file, updated InChIKeys. SMILES were added via InChIKey in the CCS records (PubChem ID Exchange) then filling in gaps using PubChem Search to find the preferred tautomer; the substance deposition created from unique CIDs (via webchem), then the InChIKey file was created from this. v0.1.3 (19 July 2022): mapped to parents; substance deposition, SMILES, CIDs and annotations now based on parent form (not original salt form).</p>
S79 | UACCSCEC | Collision Cross Section (CCS) Library from UAntwerp
<p>This is the collection associated with list S79 UACCSCEC Collision Cross Section (CCS) Library from UAntwerp on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A library containing the collision cross section (CCS) values of 311 adducts of 148 contaminants of emerging concern (CECs) and their metabolites measured with drift tube ion mobility high resolution mass spectrometry (in positive and negative ionization modes with N2 as drift gas) as described in Belova <em>et al.</em> (2021) DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.1c00142">10.1021/acs.analchem.1c00142</a>.</p> <p>Changes: 27/04/2021 added new CIDs from deposition. 10/5/2021: added transformations table. 30/8/2022: corrected [M+H]+ for BDCIPP (CID <a href="https://pubchem.ncbi.nlm.nih.gov/compound/188119#section=Collision-Cross-Section">188119</a>) to 157.35 A^2 (from 178.72) upon request of the authors (see Belova <em>et al</em>. (2022) DOI: <a href="https://doi.org/10.1016/j.aca.2022.340361">10.1016/j.aca.2022.340361</a>).</p>
A tectonic model reconciling evidence for the collisions between India, Eurasia and intra-oceanic arcs of the central-eastern Tethys
<p>2 February 2014<br>Version 1.2</p> <p>Supplement and plate model accompanying: <br>Gibbons, A., Zahirovic, S., Müller, R., Whittaker, J., and Yatheesh, V., 2015, A tectonic model reconciling evidence for the collisions between India, Eurasia and intra-oceanic arcs of the central-eastern Tethys: Gondwana Research FOCUS.</p> <p><strong>Gibbons_etal_2015_GR_PlateModel.zip</strong></p> <p>This directory contains four files:</p> <p>TPW_CK95G94_Rigid_Gibbons.rot - the Gibbons et al. global rotation model TPW_CK95G94_PP_Rigid_Gibbons.gpml - the Gibbons et al. global evolving topologies <br>CK95G94_Coastlines.gpmlz - Present-day coastlines with Plate ID assignments<br>CK95G94_StaticPolygons_Gibbons.gpmlz - Present-day block outlines </p> <p>To load these datasets in GPlates do the following:</p> <p>1. Open GPlates<br>2. Pull down the GPlates File menu and select the operation Open Feature Collection<br>3. Click all the files while holding down the shift key to select all files. All the files should be highlighted.<br>4. Click Open </p> <p>Alternatively, drag and drop the files onto the globe.<br> <br>Play around with the GPlates buttons to make an animation, select features, draw features, etc. For more information, read the GPlates manual which can be downloaded from <a href="https://www.gplates.org">www.gplates.org</a></p> <p><strong>Zahirovic_etal_2014_SE</strong></p> <p>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Zahirovic, S., Seton, M., & Müller, R. D. (2014). The Cretaceous and Cenozoic tectonic evolution of Southeast Asia. Solid Earth, 5(1), 227-273. doi:<a href="https://doi.org/10.5194/se-5-227-2014" target="_blank" rel="noopener">10.5194/se-5-227-2014</a></p> <p>Any questions, please email: <br>Ana Gibbons <angi@statoil.com><br>Sabin Zahirovic <sabin.zahirovic@sydney.edu.au></p>
Synthetic Collision Dataset for Spacecraft Collision Avoidance
<p>This dataset is intended to be used as a banchmark for testing collision avoidance strategies.</p> <p>It is made of 21000000 relative geometries between LEO space objects, 1000 of which are true collision (miss-distance smaller than combined hard body radius).<br>These relative geometries are expressed as target and chaser 6-dimensional state vectors (cartesian coordinates) at time of closest approach.</p> <p>The relative geometry of the encounters are statistically matched to the ESA's Kelvins dataset for the collision avoidance challenge through statistical fitting methods.</p> <p>The collision proportion is tuned to reflect a 1year mission in LEO orbit with an a-priori collision probability of 1e-3 (yearly) and a 21 collision warnings per year.</p> <p>*<em><strong> Implementation Description *</strong></em></p> <p>The dataset is made of a series of .mat files storing the following variables:</p> <div> <ul> <li>'rv_t', target's cartesian state at TCA (km, km/s, in ECI) 6xN vector</li> <li>'rv_c', chaser's cartesian state at TCA (km, km/s, in ECI) 6xN vector </li> <li>'Ct', target's position covariance matrix at TCA (km^2, in target's RTN at TCA) 3x3xN </li> <li>'Cc', chaser's position covariance matrix at TCA (km^2, in chaser's RTN at TCA) 3x3xN </li> <li>'Rc', combined hard body radius (m) 1xN</li> <li>'CollFlag', logic value of collision 1xN (0: no-collision, 1: collision)</li> <li>'missDistance', miss distance at TCA (km) Nx1</li> </ul> <p>The name of the .mat file is formatted as:</p> <p>batch_<batch start index>.mat</p> <p>Each batch file has a maximum dimension of N = 1e5.</p> </div>
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
<p>DFEI dataset</p> <p><em>The full description can also be found in README.md.</em></p> <p>The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle.</p> <p>The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy.</p> <p>Generated events</p> <p>The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table.</p> <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> ∼ 5</td> <td> ∼ 150</td> <td> ≪ 1</td> <td> ∼ 1</td> </tr> </tbody> </table> <p>Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets.</p> <p>Datasets</p> <p>The datasets are divided in three categories</p> <p>Training and testing</p> <p>The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays.</p> <p>Evaluation</p> <p>The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000).</p> <p>Exclusive decays</p> <p>In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region.</p> <p>The datasets contained are:</p> <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> <p>More information on them can be found in the paper.</p> <p>Loading the data</p> <p>The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar.</p> <p>An example snippet is given here:</p> <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> <p>The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves.</p> <p>Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles.</p> <p>Variables</p> <p>The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the <em>z</em> axis pointing along the beamline, the <em>x</em> axis beinng parallel to the horizontal and the <em>y</em> axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction.</p> <ul> <li> <p>General:</p> <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> <p>Node variables:</p> <ul> <li> <p>ParticleKey: unique number to identify each particle in a given event.</p> </li> <li> <p>Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> <li> <p>FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not.</p> </li> <li> <p>Transverse momentum (<em>p</em><sub><em>T</em></sub>): component of the three-momentum transverse to the beamline, i.e. the <em>x</em> and <em>y</em> component combined.</p> </li> <li> <p>Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event.</p> </li> <li> <p>Pseudorapidity (<em>η</em>): spatial coordinate describing the angle of a particle relative to the beam axis, computed as <em>η</em> = arctanh(<em>p</em><sub><em>z</em></sub>/∥<em>p⃗</em>∥).</p> </li> <li> <p>Charge (<em>q</em>): for the stable particles under consideration, the charge can take the value 1 or -1.</p> </li> <li> <p><em>O</em><sub><em>x</em></sub>, <em>O</em><sub><em>y</em></sub>, <em>O</em><sub><em>z</em></sub>: cartesian coordinates of the origin point of the particle.</p> </li> <li> <p><em>p</em><sub><em>x</em></sub>, <em>p</em><sub><em>y</em></sub>, <em>p</em><sub><em>z</em></sub>: cartesian coordinates of the three-momentum.</p> </li> <li> <p><em>P</em><em>V</em><sub><em>x</em></sub>, <em>P</em><em>V</em><sub><em>y</em></sub>, <em>P</em><em>V</em><sub><em>z</em></sub>: cartesian coordinates of the position of the associated primary vertex.</p> </li> </ul> </li> <li> <p>Edge variables:</p> <ul> <li> <p>FirstParticleKey: ParticleKey of one of the two particles connected by the edge.</p> </li> <li> <p>SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey.</p> </li> <li> <p>FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay.</p> </li> <li> <p>Opening angle (<em>θ</em>): angle between the three-momentum directions of the two particles.</p> </li> <li> <p>Momentum-transverse distance (<em>d</em><sub> ⊥ <em>P⃗</em></sub>): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles.</p> </li> <li> <p>Distance along the beam axis (<em>Δ</em><sub><em>z</em></sub>): difference between the <em>z</em>-coordinate of the origin points of the two particles.</p> </li> <li> <p><em>F</em><em>r</em><em>o</em><em>m</em><em>S</em><em>a</em><em>m</em><em>e</em><em>P</em><em>V</em>: boolean variable indicating whether the two particles share the same associated primary vertex.</p> </li> <li> <p>Order of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>O</em><em>r</em><em>d</em><em>e</em><em>r</em>): variable that can take the values 0, 1, 2 or 3, as explained in the paper.</p> </li> <li> <p>Identity of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>I</em><em>D</em>): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> </ul> </li> </ul>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></p>
Paths to equilibrium in non-conformal collisions
<p>Non-conformal planar shockwave collisions with the typical dataset from the study <strong>arXiv:1703.09681 </strong>published in JHEP.</p>
Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions
<p>Data release associated with the preprint "<em>Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions</em>'' (2021; <a href="https://arxiv.org/abs/2107.06229">arxiv:2107.06229[nucl-th]</a>)</p> <p>Data includes:</p> <p>EOS files:</p> <ol> <li>chiral effective field theory (CEFT) up to 1nsat and extended with speed-of-sound extension (cse)</li> <li>CEFT up to 1.5 nsat and cse</li> <li>CEFT up to 1.5 nsat and extended with piecewise-polytrope</li> <li>CEFT up to 1.0 nsat, cse and enforced a uniform distribution on a radius for 1.4 solar mass neutron star (R14)</li> <li>CEFT up to 1.5 nsat, cse and enforced a uniform distribution on R14</li> </ol> <p>Posterior probability files: details to be found in README.txt<br> <br> Data used in Fig.1 and Fig.2 are included</p>
Proton collision producing top pair, decaying hadronically via bottom quarks and W bosons
<p>This dataset contains the matrix element calculations for 10,000 events of `p p > t t~ , (t > b W+) , (t~ > b~ W-)`, as produced by MadGraph, without showering or hadronisation, and applying no cuts.</p>
The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts
<p>This dataset contains the joint toque measurements of a robot manipulator (<a href="https://blog.robotiq.com/bid/64944/Collaborative-Robot-Series-KUKA-s-Light-Weight-Robot-4">KUKA LWR4+</a>) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at <a href="https://www.ce.cit.tum.de/en/lsr/home/">Chair of Automatic Control Engineering</a>, <a href="https://www.tum.de/en/">Technical University of Munich</a>, Munich, Germany, by <a href="https://sites.google.com/view/zengjie-zhang/home">Dr. Zengjie Zhang</a>, under the supervision of <a href="https://www.ce.cit.tum.de/lsr/team/dozenten/dirk-wollherr/">Dr. Dirk Wollherr</a>, in 2017. Its detailed recording procedure is explained in the following work:</p> <p>[1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156.</p> <p>The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm.</p> <p>The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB.</p> <p>This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.</p>
Safety impact of DoS attacks on V2X-based collision warning
<p>The dataset represents Straight Crossing Path (SCP) intersection scenarios, where a Host Vehicle (HV) and a Remote Vehicle (RV) approach a right-angled intersection at different velocities and cross each other's paths simultaneously. By manipulating the starting positions, the driving scenarios were defined in such a way that the two vehicles collide in all cases.</p> <p>Scenarios were implemented with the following speed levels:</p> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>RV speed [km/h]</strong></p> </td> <td> <p><strong>HV speed [km/h]</strong></p> </td> </tr> <tr> <td> <p>S1</p> </td> <td> <p>20</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>S2</p> </td> <td> <p>50</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S3</p> </td> <td> <p>20</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S4</p> </td> <td> <p>50</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S5</p> </td> <td> <p>20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S6</p> </td> <td> <p>50</p> </td> <td> <p>130</p> </td> </tr> </tbody> </table> <p> </p> <p>We quantified the <a href="https://www.sciencedirect.com/science/article/pii/S2214209622000614" target="_blank" rel="noopener"><strong>safety risk (Safety Risk Index - SRI)</strong> </a>related to the specific V2X scenarios based on network performance metrics (End-to-End latency – E2E; Packet Delivery Ratio – PDR).</p> <p>In our dataset, we differentiated the strength of the attack based on the primary wireless communication parameters:</p> <p>· the attacker's data transmission rate (AR),</p> <p>· the attack packet length (APL).</p> <p>Based on the six driving scenarios (S1-S6) and the attack parameters (attack packet length, attack rate), 780 scenarios were simulated for a total of 15,600 unique test points (20 static spatial measurement point / scenario).</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
Supplementary Materials for "Depolarization of MgH Solar Lines by Collisions with Hydrogen Atoms"
<p>The files "MgHH_Potential_Singlet" and "MgHH_Potential_Triplet" respectively hold results of ab initio calculation of the potential energy surfaces (PESs), V(R,θ) in units of cm^-1, for the singlet (1A') and triplet (3A') states of the MgH-H system. Here R represents the distance (in atomic units) from the center of mass of MgH molecule to the H atom, and θ is the rotation angle (in degrees) of the H atom around the MgH. All the PESs are obtained using the MOLPRO package (e.g. Werner et al. 2010).</p> <p><br> The files "MgHH_Sigma_0toL_Singlet.csv" and "MgHH_Sigma_0toL_Triplet.csv" respectively contain results of the infinite-order sudden (IOS) approximation calculation of cross sections, σ(0->L) in units of Angstrom^2, as functions of energy in units of cm^-1 for the 1A' and 3A' states of the MgH-H system. The IOS cross sections are calculated using MOLSCAT code (Hutson & Green 1994). The depolarization and transfer of polarization cross sections can be calculated from the IOS cross sections via Eqs. (1) & (2) of Qutub et al. 2020.</p>
Wildlife–vehicle collisions (WVC) on interurban roads in Spain (2016-2021)
<p>CSV that contains 1.000 records of wildlife–vehicle collisions (WVC) on interurban roads in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to <strong>contact me and I will forward it to you</strong>. </p> <p>Data source of each WVC record is the Spanish General Directorate of Traffic (DGT), but the dataset has been enhanced by the integration of other sources: OpenStreetMap (OSM), Global Biodiversity Information Facility (GBIF), the National Geographic Institute of Spain (IGN), State Meteorological Agency (AEMET).Therefore, each record describes an accident by the following fields:</p> <p>• <strong>id_num </strong>(int8): the unique identifier for an accident.<br> • <strong>ind_accda </strong>(int8): a binary variable for property damages involved or not (encoded).<br> • <strong>nombre_ind_accd </strong>(str): a statement for property damages involved or not (decoded).<br> • <strong>ind_acciv </strong>(int8): a binary variable for personal damages involved or not (encoded).<br> • <strong>nombre_ind_acciv </strong>(str): a statement for personal damages involved or not (decoded).<br> • <strong>total_mu30df </strong>(int8): the total number of deaths from the accident.<br> • <strong>total_hg30df </strong>(int8): the total number of injured with hospitalization from the accident.<br> • <strong>total_hl30df </strong>(int8): the total number of injured without hospitalization from the accident.<br> • <strong>fecha_accidente </strong>(date): the reported date of the collision, following ISO 8601 date-time standard. <br> • <strong>hora_accidente </strong>(str): the reported hour of the collision in 24-hour notation. <br> • <strong>mes_1f </strong>(int8): the month as integer of the event date (encoded).<br> • <strong>nombre_mes </strong>(str): the month name of the event date (decoded).<br> • <strong>anyo </strong>(int8): the four-digit year of the event date.<br> • <strong>ccaa_1f </strong>(int8): the autonomous region code from INE where accident is registered (encoded).<br> • <strong>nombre_ccaa </strong>(str): the name of the autonomous region where accident is registered (decoded).<br> • <strong>provincia_1f </strong>(int8): the province code from INE where the accident is registered (encoded).<br> • <strong>nombre_provincia </strong>(str): the province name where the accident is registered (decoded).<br> • <strong>cod_municipio </strong>(int8): the municipality code from INE where the accident is registered (encoded).<br> • <strong>nombre_municipio </strong>(str): the municipality name where the accident is registered (decoded).<br> • <strong>carretera </strong>(str): the road attending to the national road numbering system in Spain where the accident is located.<br> • <strong>km </strong>(float): the kilometre point of the road where the accident is located.<br> • <strong>sentido_1f </strong>(int8): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded).<br> • <strong>nombre_sentido </strong>(str): the vehicle’s direction of traffic reported when the accident occurred (decoded).<br> • <strong>tipo_via_3f </strong>(int8): the type of road as integer attending to the project road classification (encoded).<br> • <strong>nombre_tipo_via </strong>(str): the type of road description attending to the project road classification (decoded).<br> • <strong>titularidad_via_2f </strong>(int8): the road ownership type as integer (encoded).<br> • <strong>nombre_titularidad_via </strong>(str): the road ownership type description (decoded).<br> • <strong>tipo_animal_1f </strong>(int8): the animal species involved in the accident as integer (encoded).<br> • <strong>nombre_tipo_animal_1f </strong>(str): the animal species name involved in the accident (decoded).<br> • <strong>tipo_animal_2f </strong>(int8): the reported animal type of breeding as integer (encoded).<br> • <strong>nombre_tipo_animal_2f </strong>(str): the type of animal breeding description (decoded).<br> • <strong>longitud </strong>(float): the length of the accident location coordinate in decimal degrees.<br> • <strong>latitud </strong>(float): the latitude of the accident location coordinate in decimal degrees.<br> • <strong>geom </strong>(geometry): geometry from latitude and longitude position. Developed for this project.<br> • <strong>dia_semana </strong>(int8): the integer day of the week when the accident occurred (encoded).<br> • <strong>nombre_dia_semana </strong>(str): the name of the day when the accident occurred (decoded).<br> • <strong>tipo_dia </strong>(str): the category name of the day type to separate weekday from weekend (decoded).<br> • <strong>parte_dia </strong>(str): the part name of the day when the accident is registered including day, night and the transitions.<br> • <strong>luna </strong>(int8): the portion of illuminated moon surface represented as an integer value from 0 to 100.<br> • <strong>prec </strong>(float): the daily rainfall measurement of the event day based on pluviometric days.<br> • <strong>tmin </strong>(float): the minimum temperature in Celsius of the event day.<br> • <strong>tmed </strong>(float): the average temperature in Celsius of the event day.<br> • <strong>tmax</strong> (float): the maximum temperature in Celsius of the event day.<br> • <strong>sol </strong>(float): the accumulated sun hours of the event day.<br> • <strong>uso_suelo </strong>(str): the main land usage of the accident area.<br> • <strong>altitud </strong>(float): the altitude in meters above sea level.<br> • <strong>pendiente </strong>(float): the slope median value of a 30 meters buffer around the accident location.<br> • <strong>taxonkey</strong> (str): a taxon key from the GBIF backbone.<br> • <strong>imd_total </strong>(float): the average daily traffic intensity of the accident year.<br> • <strong>maxspeed </strong>(int): the maximum speed of the road section where the reported collision.</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the wildlife–vehicle collision analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model: Supplementary Model Files
<p>Supplementary kinematic model input for the JGR: Solid Earth publication "New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model".</p>
Data and results in "Relationship between crustal structure and plate convergence around the Izu collision zone in central Japan"
<p>“allrfstationlist.dat” contains the list of the used seismic stations. The four columns indicate the name, latitude, longitude, and altitude (m) of each station, respectively.</p> <p>“allrfevent.dat” contains the list of the used teleseismic events. From left to right, the 10 columns indicate the year, month (in number), day, hour, minute, and second of the origin time (Japan Standard Time), and the latitude (from –90 to 90), longitude (from –180 to 180), depth of the hypocenter, and magnitude of each event, respectively.</p> <p>“RFmoho.dat” contains the depth distribution of the Moho determined by our RF analysis. The third column indicates the depth (km) of the Moho at the given latitude (the second column) and longitude (the first column)</p> <p>“Tomo_depth_limited.txt” contains the depth distribution of the lower boundary of a layer with a P-wave velocity of 7.5–7.7 km/s in the model by Ishise et al. (2021), which was assumed as the Moho. The third column indicates its depth (km) at the given latitude (the first column) and longitude (the second column)</p> <p>“crustthickness_tomorf.dat” contains the thickness distribution of the crust of the Philippine Sea Plate determined from the geometry of its upper surface estimated by Hirose et al. (2008a, b) and Nakajima et al. (2009) and the Moho depth distribution shown in RFmoho.dat and Tomo_depth_limited.txt. The third column indicates the thickness (km) of the crust at the given latitude (the second column) and longitude (the first column). “RF” and “tomo” in the fourth column indicate the corresponding thickness determined based on the RF analysis and the model by Ishise et al. (2021), respectively.</p>
TCV-X21-GENEX: influence of collisions on the validation of global gyrokinetic simulations
<p>This repository contains the data that supports the findings of the study that is published in <a href="http://doi.org/10.1063/5.0144688"><em>P. Ulbl et al., Phys. Plasmas 30, </em>052507 <em>(2023)</em></a>. Three global electromagnetic gyrokinetic simulations of the <a href="https://doi.org/10.5281/zenodo.5776286">TCV-X21</a> case have been performed. A collisionless (No Coll) simulation, one with a Bhatnagar-Gross-Krook (BGK) collision operator and one with a Fokker-Planck type Lenard-Bernstein/Dougherty (LBD) collision operator.</p> <p>For getting started, please consider the README file provided in this dataset. The Jupyter notebook herein provides a low level entry on how to access the data from the netCDF file. The zip files contain sets of input parameters that are given for future reference.</p>
iCub Joint Space Self-Collision Avoidance [Data & Code]
<p>These data files containg code sources for dataset creation & model learning (Joint-Space-SCA.zip) and collected synthetic dataset of free & collided postures for humanoid robot iCub (raw_binary_data.zip). Follow the Readme.MD files to launch the code if needed.</p><p>Corresponding Git repo: https://github.com/epfl-lasa/Joint-Space-SCA</p><p> </p>
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