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309 results for “swarm”
Point locations for spatial and morphological analyses of barchans in swarms
<div>This dataset contains the long-lat coordinates of seven points on ~6000 barchans located in six swarms.</div> <div> </div> <div>Four of the locations are on Earth (three in the Tarfaya region of the Western Sahara, one in Mauritania).</div> <div>The other two swarms are from high latitudes of the northern hemisphere of Mars.</div> <div> </div> <div>In each location between 850 and 1112 barchans were measured.</div> <div> </div> <div>The measurements were carried out manually by Dominic T Robson and Andreas CW Baas according to the method described in</div> <div>Robson, D. T., Annibale, A., & Baas, A. C.W. (2022). Reproducing size distributions of swarms of barchan dunes on Mars and Earth using a mean-field model. Physica A: Statistical Mechanics and its Applications, 606, 128042.</div> <div> </div> <div>The included metadata file lists the copyrights and dates (DD/MM/YYYY) for the imagery used, all imagery was accessed through Google Earth. </div> <div> </div> <div>The metadata file also includes descriptions of the format of the data. The data themselves are provided in separate comma delimited files for each location. Only the bedforms identified as barchans are included although other bedforms in the locations were also measured (see Robson et al. Physica A (2022)).</div> <div> </div> <div>Using the seven points recorded for each dune it is possible to calculate:</div> <div>Body length</div> <div>Total length</div> <div>Horn lengths</div> <div>Total width</div> <div>Horn-to-horn width</div> <div>Port flank width</div> <div>Starboard flank width</div> <div>Slipface length</div> <div>Dune orientation</div> <div> </div> <div>The area of the polygons formed by the points also provides an estimate for the basal area of the dune though it is not a perfect match.</div> <div> </div> <div>We hope that these data will be of use to those seeking to study the morphology, size, asymmetry, and spatial distribution of barchans in swarms.</div> <div> </div> <div>Dominic T Robson and Andreas CW Baas.</div>
Barchan Swarm Simulations Using the Two-Flank Agent-Based Model v3
<p>This archive contains several animations of barchan swarms simulated using the Two-Flank Agent-Based model which was introduced in following article: </p> <p> </p> <p>Robson, Dominic T., and Andreas CW Baas. "A Simple Agent‐Based Model That Reproduces All Types of Barchan Interactions." Geophysical Research Letters 50.19 (2023): e2023GL105182. </p> <p> </p> <p>which is Open Access and available at https://doi.org/10.1029/2023GL105182</p> <p> </p> <p>The Two-Flank Agent-Based Model has been developed openly on GitHub by Dominic T Robson and Andreas CW Baas. All the necessary source files together with an example run file can be found at:</p> <p> </p> <p>https://github.com/DTRobson/TwoFlankABModel/releases/tag/TFABM</p> <p> </p> <p>The following model parameters and initial conditions were used for these simulations:</p> <p> </p> <p> simwidth = 9000 or 15000 #(m)</p> <p> simlength = 10000 #(m)</p> <p> fieldwidth = 3000 or 5000 #(m)</p> <p> </p> <p> qsatinit = 79 #(m^2 year^{-1})</p> <p> q0 = 0.25 #(q_sat)</p> <p> dt = 1/8 #(years) </p> <p> collson = True</p> <p> inject = True</p> <p> injectdist = Uniform </p> <p> periodic = False</p> <p> </p> <p> lambda1 = 1.</p> <p> lambda2 = 1.8</p> <p> lambda3 = 1/3</p> <p> alpha = 0.05</p> <p> delta = 4.6 #(m)</p> <p> a = 0.45</p> <p> b = 0.1</p> <p> </p> <p> </p> <p> eqw = 0.5 * delta/(q0 - alpha)</p> <p> injectparams = [2*eqw, 2*eqw]</p> <p> lws = [eqw]</p> <p> rws = [eqw]</p> <p> xs = [fieldwidth*1.5] </p> <p> ys = [simlength - 1]</p> <p> </p> <p> c = 45. </p> <p> </p> <p> w0 = 16.6 #(m)</p> <p> </p> <p> outfluxmode = 'Hersen' or 'Duran' #Hersen for unscaled outflux, Duran for scaled outflux</p> <p> plottinghornflux = False</p> <p> </p> <p> keep_coll_rec = True</p> <p> </p> <p> helpplotting = True</p> <p> </p> <p> </p> <p>The remaining model parameters varied across the different runs and took the values shown in the filenames. Where the filename does not list the angular separation of the secondary wind mode (thetab) simulations were unidirectional. The primary mode was normally distributed with mean 270degrees and standard deviation theta_sd. The list of all values used are shown here:</p> <p> </p> <p> initdensity = 12 or 24 or 37#(km^{-2}) this sets the rate at which dunes are injected into the model</p> <p> qshift = 0. or 0.05 or 0.1 or 0.15#(q_sat)</p> <p> thetab = 22.5 or 45 or 67.5 #(degrees)</p> <p> theta_sd = 3 #(degrees) </p> <p> </p> <p>In bimodal simulations the secondary mode angle was normally distributed with mean 270+thetab and standard deviation theta_sd. Each year (12 iterations) the first 9 iterations had wind direction taken from the primary mode and the final 3 iterations were from the secondary mode i.e. the 3:1 seasons of primary:secondary wind direction. Note that, in the simulations the primary mode 270deg means that dunes migrate in the negative y-direction, to produce the plots and revert to the convention of the primary wind being in the x-direction, the dunes were then rotated.</p> <p>The simulations were performed by Dominic T Robson using a 12th Gen Intel(R) Core(TM) i7-1255U 1.70 GHz processor and 16.0GB of RAM.</p> <p> </p> <p> </p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Distributed Predictive Drone Swarms in Cluttered Environments
<p>This folder contains data, videos, and supplementary material for the article titled "Distributed Predictive Drone Swarms in Cluttered Environments".</p> <p>In the article, we present a Distributed Model Predictive Control (DMPC) algorithm for drone swarm navigation in two types of cluttered environments, i.e., a forest and a funnel-like environment. <br> </p> <p>The material in `zenodo_upload` is organized as follows.<br> 1. a data folder, with the logs of simulation and hardware experiments;<br> 2. an analysis folder, with Matlab scripts that analyze the logs in the data folder;<br> 3. a plotting folder, with Matlab functions used by the analysis scripts;<br> 4. an mp4 video file, on simulation and hardware experiments;<br> 5. a pdf, with supplementary materials.<br> <br> The `qp_swarm` folder contains MATLAB code for simulation experiments.</p>
MODUL4R EU Founded Project Swarm Learning framework dataset
<p>MODUL4R EU Founded Project Swarm Learning framework dataset v1.0.0</p> <p>Contains values for:</p> <ul> <li>Capacitor type </li> <li>Grab Pressure (Pa)</li> <li>Leg Cutting (mm)</li> <li>Polarity</li> <li>Quality Metric</li> </ul>
Dataset: Swarms in Central Utah – event detection lists, arrival picks, relocations & moment tensor solutions
<p>Dataset containing results of moment tensor inversions, relocations and event detections for our study "Petersen & Pankow (2023): Small-magnitude seismic swarms in Central Utah: Interactions of regional tectonics, local structures and hydrothermal systems" (<a href="https://doi.org/10.1029/2023GC010867">https://doi.org/10.1029/2023GC010867</a>).<br>Please read the pdf-README file for more information on the dataset.</p>
Experimental Results for the study "A Modular Hybridization of Particle Swarm Optimization and Differential Evolution"
<p>This repository contains the experiment results and R scripts to analyze the data for the study "A Modular Hybridization of Particle Swarm Optimization andDifferential Evolution", which is accepted in <em>The Genetic and Evolutionary Computation Conference</em> (GECCO) '20 conference: </p> <p>Rick Boks, Hao Wang, and Thomas Bäck. 2020. A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. In <em>Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. </em>ACM, New York, NY, USA, 8 pages. <a href="http://https: //doi.org/10.1145/3377929.3398123">https: //doi.org/10.1145/3377929.3398123</a></p> <p>Bibtex:</p> <pre><code class="language-markdown">@inproceedings{BoksWB20, author = {Rick Boks and Hao Wang and Thomas B\"ack}, title = {{A Modular Hybridization of Particle Swarm Optimization and Differential Evolution}}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2020, Canc\'un, Mexico, July 8-12, 2020}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3321707.3321816}, doi = {doi.org/10.1145/3377929.3398123, }</code></pre> <p><strong>Data description:</strong> we benchmarked <strong>800 </strong>different<strong> </strong>hybridizations of the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on a well-known continuous black-box problem set called <a href="https://coco.gforge.inria.fr/">COCO/BBOB</a>, which consists of 24 test functions. 30 independent runs are conducted for each algorithm on each problem.</p> <ul> <li>'ERT.csv': a data frame with columns DIM (5D or 20D), funcId (F1-24), algId (algorithm names), target (<span class="math-tex">\(10^{\{-8,-7, \ldots, 1\}}\)</span>), ERT (expected running time), and sd (standard deviation).</li> <li>'raw-data.csv': the running time recorded in each independent run. </li> <li>'analysis.R': the R script that generates ERT tables in the paper.</li> <li>'ecdf.R': the R script that renders the ECDF (empirical cumulative distribution function) plots in the paper.</li> </ul>
Figures 17-26. Epigynes, ventral view 17 in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Figures 17-26. Epigynes, ventral view 17 Zelotibia angelica sp. n.; 18 Z. curvifemur sp. n.; 19 Z. fosseyae sp. n., female; 20 Z. johntony sp. n.; 21 Z. kanama sp. n.; 22 Z. kibira sp. n. 23 Z. lejeunei sp. n. 24 Z. major; 25 Z. paucipapillata; 26 Z. subsessa sp. n., (scale bar 0.2 mm).
Figures 12-16 in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Figures 12-16. Zelotibia curvifemur sp. n. 12 Male palp, retrolateral view; 13 Male palp, ventral view; 13b. Male palp, expanded; Zelotibia lejeunei 14 Male palp, retrolateral view; 15 Male palp, dorsal view; 16 male palp, ventral view. CY: cymbium; E: embolus; MA: median apophysis; SD: sperm duct; ST: subtegulum; T: tegulum. (scale bar 0.5 mm).
Figures 1-11 in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Figures 1-11. Habitus of Zelotibia 1 Zelotibia angelica sp. n. female; 2 Z. curvifemur sp. n., female; 3 male; 4 Z. fosseyae sp. n., female; 5 Z. johntony sp. n., female; 6 Z. kanama sp. n., female; 7 Z. kibira sp. n., female; 8 Z. lejeunei sp. n., female; 9 Z. major, female; 10 Z. paucipapillata, female; 11 Z. subsessa sp. n., female.
Tip of epigynal scape forming an obtuse angle, spermathecae located imme- diately posterior to tip of scape (e3) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Tip of epigynal scape forming an obtuse angle, spermathecae located imme- diately posterior to tip of scape (e3)
Tibial apophysis of palp small and semi-translucent in lateral view (M1). Palpal tibia with a group of small dark papillae on a boss behind apophysis (M2) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Tibial apophysis of palp small and semi-translucent in lateral view (M1). Palpal tibia with a group of small dark papillae on a boss behind apophysis (M2)
Tibial apophysis slightly curved outward as seen from below (F1); embolus twisted with deep indentation, appearing bifid (F2) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Tibial apophysis slightly curved outward as seen from below (F1); embolus twisted with deep indentation, appearing bifid (F2)
Male palpal femur clearly curved (A2); retrolateral margin of cymbium with triangular extension (A1) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Male palpal femur clearly curved (A2); retrolateral margin of cymbium with triangular extension (A1)
Waveforms, relocated earthquake and matched filter catalog of seismic swarm preceding the 2017 Mount Agung eruption
<p>Datasets for the manuscript:</p> <p>Sianipar, D., Ulfiana, E., and Sipayung, R. (2020), Seismic swarm preceding the 2017 Mount Agung eruption in Bali (Indonesia) enhanced by the matched filter approach (submitted) (preprint is available at EarthArxiv: <a href="https://eartharxiv.org/a7yx2/">https://eartharxiv.org/a7yx2/</a>)</p> <p>by Dimas Sianipar, Emi Ulfiana, and Renhard Sipayung (STMKG, BMKG, Indonesia).</p> <p>Files including:</p> <p>1) List of continuous waveforms</p> <p>2) HypoDD files: dt.cc, dt.ct, event.dat, hypoDD.reloc, phase.dat</p> <p>3) Processed (filtered) 407 template waveforms</p> <p>4) BMKG catalog</p> <p>5) MFT catalog in ZMAP format</p> <p>6) Table S1: template candidates</p> <p>7) Table S2: MFT catalog</p> <p>The compressed file (*.rar) has been successfully extracted in Ms. Windows OS using WinRAR.</p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Phylogenomics indicates Amazonia as the major source of Neotropical swarm-founding social wasp diversity
The Neotropical realm harbors unparalleled species richness and hence has challenged biologists to explain the cause of its high biotic diversity. Empirical studies to shed light on the processes underlying biological diversification in the Neotropics are focused mainly on vertebrates and plants, with little attention to the hyperdiverse insect fauna. Here, we use phylogenomic data from ultraconserved element (UCE) loci to reconstruct for the first time the evolutionary history of Neotropical swarm-founding social wasps (Hymenoptera, Vespidae, Epiponini). Using maximum likelihood, Bayesian, and species tree approaches we recovered a highly resolved phylogeny for epiponine wasps. Additionally, we estimated divergence dates, diversification rates, and the biogeographic history for these insects in order to test whether the group followed a "museum" (speciation events occurred gradually over many millions of years) or "cradle" (lineages evolved rapidly over a short time period) model of diversification. The origin of many genera and all sampled extant Epiponini species occurred during the Miocene and Plio-Pleistocene. Moreover, we detected no major shifts in the estimated diversification rate during the evolutionary history of Epiponini, suggesting a relatively gradual accumulation of lineages with low extinction rates. Several lines of evidence suggest that the Amazonian region played a major role in the evolution of Epiponini wasps. This spatio-temporal diversification pattern, most likely concurrent with climatic and landscape changes in the Neotropics during the Miocene and Pliocene, establishes the Amazonian region as the major source of Neotropical swarm-founding social wasp diversity.
Predictive Control of Aerial Swarms in Cluttered Environments
<p>This repository contains all data that has been used to produce the results contained in the submission to Nature Machine Intelligence titled "Predictive Control of Aerial Swarms in Cluttered Environments". It also contains the code necessary for plotting simulation and hardware experimental data.</p> <p>For the code used to run simulation and hardware experiments, please visit <a href="http://doi.org/10.5281/zenodo.4379503">doi.org/10.5281/zenodo.4379503</a>.</p>
Seismology dataset of Chianti swarm in Tuscan Italy
<p>Seismology data of Chianti swarm with earthquekes and focal mechanism of 2010-2015 period. Data are download by INGV database.</p>
February 2017 Western Turkey Earthquake Swarm Sentinel-1 TOPS Differential Interferogram (20170131-20170212)
<p>In february 2017 a series of earthquakes affected the Biga Peninsula in Western Turkey. Over 350 buildings sustained extensive damage. The seismic events occurred at the intersection of the Kestanbol Fault and the Edremit Fault Zone. The Sentinel-1 TOPS co-seismic interferogram was generated with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub: S1A_20170131-S1A_20170212 from ASCENDING orbit 131.</p> <p> </p>
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