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274 results for “collision”
Collisional damping in debris discs: Only significant if collision velocities are low
<p>This dataset contains results of numerical simulations of debris discs. We used a numerical model of collisional evolution of debris discs to explore the effect of collisional damping on the average eccentricity and inclination of debris particles with collisional outcomes that include fragmentation and cratering processes (Jankovic, Wyatt & Lohne, A&A, 691 (2024) A302, DOI: https://doi.org/10.1051/0004-6361/202451080, available with open access as of 2024 and available as a pre-print on arxiv). Here we provide the key results of our simulations in machine readable form.</p> <p>The ZIP archive contains directories for each of the models presented in the main text of our paper. Model IDs consist of a number (2, 3, 4 or 5) and a letter (a or b). Letters correspond to initial conditions of the simulation with respect to assumed level of excitation in the disc. Letter 'a' is for high-excitation discs with initial maximum eccentricity of 0.2 and letter 'b' is for low-excitation discs with initial maximum eccentricity of 0.01. Numbers in the model IDs correspond to the assumed collisional properties of colliders. Number 2 is for models in which particles are indestructible (inelastic bouncing only), while the other models include fragmentation and cratering with different specific critical energy prescriptions (Q_D^*). Numbers 3, 4 and 5 in model IDs correspond to Q_D^* prescriptions 1, 2 and 3 in Table 1 in the paper, respectively.</p> <p>Each of those directories contains 3 text files: postproc_int_grid.txt (basic data on time points and size grid used in the kinetic model), postproc_int_avgecc.txt (average eccentricity as function of size (columns) and time (rows)) and postproc_int_avginc.txt (average inclination as function of size (columns) and time (rows)). Also supplied is a python script (plot.py) which reads the files and produces a figure for each model. The script can be executed from the command line by providing the name of the model directory, e.g.:<br>python plot.py model_2_a</p>
[Dataset] The impact of particle precipitation on the ion-neutral collision frequency analyzed with EISCAT measurements
<p>Datasets for "The impact of particle precipitation on the ion-neutral collision frequency analyzed with EISCAT measurements" (submitted to Annales Geophysicae)</p>
Landau collision operator in the CUDA programming model applied to thermal quench plasmas
<p>Reproducability data for IPDPS paper rebuttal.</p>
Data of Control of reactive collisions by quantum interference
<p>One can find:</p> <p>(1) The tabulated data for every plot in the main article and the supplemental material of 'Control of reactive collisions by quantum interference.'</p> <p>(2) The jupyter notebook file (Python2) and raw data to reproduce the plots. Open 'ProcessData_FINAL.ipynb' and excute the cell sequentially, after excuting the three cells at the end first to properly define functions.</p>
DeepCollision: Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions
<p>With the aim to test autonomous driving systems, we propose a novel reinforcement learning (RL)-based approach named <strong>DeepCollision </strong>to learn operating environment configurations of autonomous vehicles, including formalizing environment configuration learning as an MDP and adopting DQN algorithm as the RL solution; <strong>DeepCollision</strong> learns environment configurations to maximize collisions of an Autonomous Vehicle Under Test (AVUT).</p> <p>This dataset contains:</p> <ol> <li><strong>algorithms</strong> - The algorithm of DeepCollision, which includes the network architecture and the DQN hyperparameter settings;</li> <li><strong>pilot-study</strong> - All the raw data and plots for the pilot study;</li> <li><strong>formal-experiment</strong> - A dataset contains all the raw data for analysis and the scenarios with detailed demand values;</li> <li><strong>rest-api</strong> - The REST API endpoints for environment configuration and one <strong>example </strong>to show the usage of the APIs.</li> </ol>
Data and code from: A large-scale experiment demonstrates line marking reduces power line collision mortality for large terrestrial birds, but not bustards, in the Karoo, South Africa
<p>Line markers are widely used to mitigate bird collisions with power lines, but few studies have robustly tested their efficacy. Power line collisions are an escalating problem for several threatened bird species endemic to southern Africa, so it is critical to know whether or not marking works to adequately manage this problem. Over 8 years, a large-scale experiment was set up on 72 of 117 km of monitored transmission power lines in the eastern Karoo, South Africa, to assess whether line markers reduce bird collision mortality, particularly for Blue Cranes <i>Grus paradisea</i> and Ludwig's Bustards <i>Neotis ludwigii</i>. We tested the two marking devices commonly used in South Africa: bird flappers and static bird flight diverters. Using a before-after-control-impact design, we show that line marking reduced collision rates for Blue Cranes by 92% (95% CI 77-97%) and all large birds by 51% (95% CI 23-68%), but had no effect on bustards. Both marker types appeared similarly effective. Given that monitoring at this site also confirmed high levels of mortality of a range of species of conservation concern, we recommend that marking be widely installed on new power lines. However, other options need to be explored urgently to reduce collision mortality of bustards. Five bustard species were in the top ten list of most frequently found carcasses, and high collision rates of Ludwig's Bustards (0.68 birds·km<sup>-1</sup>·year<sup>-1</sup> uncorrected for survey biases) add to wider concerns about population level effects for this range-restricted and Endangered species. </p> <p>This dataset includes the data and R code for this journal paper.</p>
Pedestrian movement trajectories and collision avoidance strategies in interweaving pedestrian flow experiment
<p>The mechanisms of Collision Avoidance(CA) behaviors in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. A series of controlled interweaving pedestrian flow experiments with different pedestrian flow densities are carried out to investigate the CA behaviors, especially CA strategy choices. This dataset consists the movement trajectory and the CA strategy choices information of the participants in the experiment and was provided as a supplementary material of a journal paper submitted to "Royal Society Open Science".</p> <p>All these experiments were conducted in an outdoor public square in the campus of Wuhan University of Technology. A total of 40 students aged 18-23 years participated these experiments. The experiment includes three "groups", representing low, medium and high density levels of interweaving pedestrian flow. Each group of experiment was repeated three times to increase the reliability of the observations. Therefore, there are totally 9 data files in this dataset, each file contains the data of one experiment.</p> <p>After the experiment, the PeTrack software was used to extract pedestrians' trajectories by identifying and tracking the coordinates of participants' heads in the video records. This dataset provides the extracted trajectories of all the pedestrians in each experiment. Trajectory of each RP is marked as Collision Avoidance Segment (CAS) and Normal Walking Segment (NWS). During the CAS, pedestrians have shown collision avoidance strategies. Four types of CA strategies, including "deceleration", "acceleration", "detour", and "stop" are manually identified in these experiments and marked in the dataset. More details of the data and the results of the study could be found in the associated journal paper. This dataset could be useful for researchers in this field.</p>
Wildlife-vehicle collisions at 42 Florida State Parks, 2005-2015
<p class="western"><span>Peninsular Florida, USA: 9,254 WVC survey records collected by Florida State Parks personnel at 42 parks during a decade (2005-2015). Data represent observations of wildlife-vehicle collisions for birds (138 species), mammals (35 species), and reptiles (64 species). The 42 state parks encompassed a variety of Florida's ecosystems on the peninsula and keys (and North, but not the Florida panhandle) from barrier islands and the Everglades to pine flatwoods, sandhills, and urban areas. Climate ranged from subtropical in South Florida to warm temperate in North Florida. </span></p>
Large Radius Tracking Events in pp collisions with a Generic detector
<p>This dataset is generated with the <a href="https://github.com/acts-project/acts">ACTS framework</a>, using a generic detector description (similar to <a href="https://www.kaggle.com/c/trackml-particle-identification">the one in the TrackML challenge</a>) and a constant magnetic field of 2 Tesla in the beam direction (i.e. z-axis). The event simulation was done via the Fastras and the digitization was done via smearing functions, which is configured with the <a href="https://github.com/acts-project/acts/blob/main/Examples/Algorithms/Digitization/share/default-smearing-config-generic.json">default configuration file in ACTS</a>. We did not use an exact release when producing the dataset, but we believe release v15.0.0 should reproduce the dataset.</p> <p>We processed the original outputs from ACTS so that the dataset is machine-learning friendly.</p> <p>Four different physics processes are generated with the Pythia8 generator at 13 TeV of proton-proton collisions without the presence of addition collision (i.e. no pileup events).</p> <ul> <li>Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 100 mm</li> <li>Heavy Neural Lepton with the neutral lepton mass of 10 GeV and a lifetime of 100 mm.</li> <li>Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 200 mm.</li> <li>ttbar events</li> </ul> <p>The heavy neural lepton generated along with a neutrino through a virtual W boson is forced to decay to two muons and a muon neutrino.</p> <p>The dataset is saved as the torch dataset. One can access it via the following code snippet.</p> <pre><code class="language-python">>>> import numpy as np >>> arrays = np.load("ttbar_PileUp000_10K/100.npz") >>> [print(key,) for key in arrays.keys()] hid layerless_true_edges layers pid x particles</code></pre> <ul> <li><em>hid</em> is the spacepoint id</li> <li><em>layerless_true_edges</em> is a table of the true-level edges where edges connect spacepoints coming from the same track.</li> <li><em>layers</em> is the layer id where the spacepoint is recorded</li> <li><em>pid</em> is a table of particle IDs each spacepoint associated with. hid is a table of spacepoint IDs,</li> <li><em>x</em> is a table of spacepoints with dimension of [number of spacepoints, number of spacepoint features], the spacepoint features are their 3D global positions.</li> <li><em>particles</em> is a table for particle information. Columns are: ['particle_id', 'particle_type', 'process', 'vx', 'vy', 'vz', 'vt', 'px', 'py', 'pz', 'm', 'q', 'parent_pid', 'pt', 'radius', 'eta']</li> </ul> <p>The first Version 1.0 saved the dataset in torch DataSet format which requires torch 1.7.1 and PyG 1.7.1 in order to read those files. The version 1.1 saved the dataset in numpy file format. The version 2.0 adds particle information.</p>
Plasma electron contribution to beam emittance growth from Coulomb collisions in plasma-based accelerators
<p>This dataset contains some supplementary material for the paper published in the journal Physics of Plasmas. There are two folders in the compressed file. One includes two input files that are used for the open-source particle-in-cell code WarpX(https://github.com/ECP-WarpX/WarpX/). The other includes figure files, and the plotting scripts.</p>
FIGURE 2 in Chub movement is attracted by the collision sounds associated with spawning activities
FIGURE 2. Sound profiles and field experiments. Waveform (rel. amplitude [kUnit] = proportional to the sound pressure of the recording), spectrogram (frequency [kHz]) and power spectral density (PSD, [dB]) of (a) the 3-minutes collision sound, (b) ambient noise and (c) music noise. The right two diagrams highlight the three sounds that fish may sense, including the frequency lower than 8 kHz. (d) Fixed playing order of sound profiles as example for each time of experiment, 5 minutes of music noise; 6 minutes of ambient noise; 3 minutes of collision sound (mating). The bar regions represent 3 minutes of sound for further statistics. Two field experiments with underwater sound player on a putative redd and home-make jerkbaits (e) based on the mature chub's coloration-- (f) Beker without chub-like jerkbaits; (g) Beker with chub-like jerkbaits.
FIGURE 5 in Chub movement is attracted by the collision sounds associated with spawning activities
FIGURE 5. Similar power spectral density (PSD, [dB]) of 3 minutes of collision sound and music noise, compared to ambient noise in FIGURE 2. The PSD demonstrates the energy in each frequency band. If two curves have a similar pattern, it means that those curves have a similar sound frequency composition.
FIGURE 4 in Chub movement is attracted by the collision sounds associated with spawning activities
FIGURE 4. Average numbers of three movement behaviors counted in three sounds, with radom playing order per trial. See FIGURE 1b for the description of three movement behavior types.
FIGURE 3 in Chub movement is attracted by the collision sounds associated with spawning activities
FIGURE 3. Average individual numbers of cohabitated chubs attracted by three sounds with/without playing order and jerkbaits.
FIGURE 1 in Chub movement is attracted by the collision sounds associated with spawning activities
FIGURE 1. Observational and experimental design of this study. (a) Collision sounds associated with spawning activities may attract egg eaters and/or male satellites and (b) description of three movement behaviors. A fish was considered as being attracted if it performed any of the following three movement behaviors: (A) swimming close to the Beker at a distance shorter than the length of the Beker; (B) swimming straight toward the Beker and suddenly stopping and returning, staying nearby, or circling around the Beker; and (C) swimming straight toward the Beker and biting and/or performing spawning-like actions.
Supplementary material 5 from: Brisque T, Campos-Silva LA, Piratelli AJ (2017) Relationship between bird-of-prey decals and bird-window collisions on a Brazilian university campus. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e13729
Table S1: Total numbers of bird fatalities resulting from bird-window collisions on the campus of the Federal University of São Carlos, Sorocaba, Brazil, from August 2014 to March 2016. See Figures S1 and S2 for details. :
Supplementary material 4 from: Brisque T, Campos-Silva LA, Piratelli AJ (2017) Relationship between bird-of-prey decals and bird-window collisions on a Brazilian university campus. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e13729
Figure S4. Birds fatalities after collision with windows in buildings on the campus of the Federal University of São Carlos, in Sorocaba, São Paulo, Brazil. A: Volatinia jacarina; B: Geotrygon montana; C: Pachyramphus validus; D: Zonotrichia capensis. :
Supplementary material 3 from: Brisque T, Campos-Silva LA, Piratelli AJ (2017) Relationship between bird-of-prey decals and bird-window collisions on a Brazilian university campus. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e13729
Figure S3. Bird-of-prey decals (20 cm × 40 cm) applied to buildings on the campus of the Federal University of São Carlos, Sorocaba, São Paulo, Brazil. See text for details. :
Supplementary material 2 from: Brisque T, Campos-Silva LA, Piratelli AJ (2017) Relationship between bird-of-prey decals and bird-window collisions on a Brazilian university campus. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e13729
Figure S2. Views from the buildings on the campus of the Federal University of São Carlos, in Sorocaba, São Paulo, Brazil. A: administrative building (GAD); B: clinic; C: university restaurant (RU); D: library; E: academic management (G.A); F: laboratories; G: lectures building (AT); H: classroom and laboratories (ATLAB); I: departments building (CCTS). :
Supplementary material 1 from: Brisque T, Campos-Silva LA, Piratelli AJ (2017) Relationship between bird-of-prey decals and bird-window collisions on a Brazilian university campus. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e13729
Figure S1. Campus of the Federal University of São Carlos, Sorocaba, São Paulo, Brazil. A: administrative building (GAD); B: clinic; C: university restaurant; D: library; E: academic management; F: laboratories; G: lectures building; H: classroom and laboratories (ATLAB). Source: Google Earth. :
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.