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490 results for “Propagation”
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Self-propagation of reactive Al/Ni multilayers with textured surface topography
<p>Reactive multilayer systems (RMS) were fabricated by magnetron sputtering deposition of Al and Ni nanolayers on a copper substrate with textured surface topography, the copper substrate was subsequently removed in order to obtain free-standing RMS. The morphology of the substrate surface impacted the microstructure of the produced RMS, therefore the RMS have a textured surface. The samples were papered with a surface texture in different orientations (0°, 45°, and 90°). Besides RMS with the same characteristics were prepared on Si wafer (flat surface) in order to compare the propagation behavior of the RMS with a textured surface and RMS with a relatively flat surface. During the ignition test, the propagation front was recorded by a High-Speed camera with a resolution of 50,000 fps. In the videos, it is possible to observe the effect of the textured surface of the RMS on the propagation front of the reaction.</p>
The conductivity profile of Earth-ionosphere cavity used in the paper "Finite-difference time-domain analysis of ELF radio wave propagation in the spherical Earth-ionosphere waveguide and its validation based on analytical solutions" by Volodymyr Marchenko, Andrzej Kulak, Janusz Mlynarczyk
<p>The file "Marchenko_FDTD_Paper_Conductivity_Profile.dat" contains the conductivity profile of Earth-ionosphere cavity. The first column provides the altitude (in km) and the second column provides the conductivity (in S/m).</p>
Data supporting On the step type of continuous propagating positive leader in laboratory-scale long spark discharges
<p>The data supports the manuscript entitled "On the step type of continuous propagating positive leader in laboratory-scale long spark discharges". The *.txt file contains the applied voltage waveform and discharge current waveform. The*.doc file contains the data for Figure 5. The *.rar file contains the high-speed video frames, and can be decompressed and opened. The data can be used freely for scientific purposes with appropriate citation.</p>
Data for figures of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō
<p>Data for figures in a planned publication of scenario-based study of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō.</p>
Table 1 for Radiocarbon and Stable Carbon Isotope Constraints on the Propagation of Vent CO2 to Fluid in the Acidic Kueishantao Shallow Water Hydrothermal System
<p>This table contains radiocarbon (<sup>14</sup>C) and stable carbon isotope (<sup>13</sup>C) compositions of CO<sub>2</sub> in vent gas, dissolved inorganic carbon and particulates of hydrothermal fluid from Kueishantao shallow water hydrothermal system, offshore northeastern Taiwan.</p>
Dataset of "Particle simulation study of obliquely propagating whistler waves in low-beta plasmas"
<p>This dataset contain all necessary output data to make the plots contained in the article. It also includes parameter files to run simulations.<br><br>Dataset list:</p> <p>parameter files/*.py: Input parameter files<br>rms_amplitude.npz: RMS maximum wave amplitude for each run<br>run_1-distributions/*.npz: Electron distribution function at different moments.<br>run_1-Bz-*.npz: 2D Wave field or wave number spectrum data of run 1<br>run_1-freq_spectrum.npz: spacially averaged wave fequency spectrum of Bz<br>run_1-spectrogram.npz: wave Bz spectrogram recorded at y=0<br>scalars_run*.npz: raw scalar output of each run, containing wave and particle enegy profiles<br>summary_data.npz: a summary data containing info of each run (init paras, maximum wave amplitude, init linear growth rate, etc)<br>temperature_profile_run*.npz: the time evolution of parallel and perpendicular temerature of each run<br><br></p> <p>* Not all raw datas are included in this repository due to the limit of file size. One can directly contact the author for more detailed datas.</p>
Self-propagating wave drives noncanonical antidurotaxis of skull bones in vivo
<p>Cellular motion is a key feature of tissue morphogenesis and is often driven by migration. However, migration, need not explain cell motion in contexts where there is little free space or no obvious substrate such as those found during organogenesis of mesenchymal organs including the embryonic skull. Through <em>ex vivo</em> imaging, biophysical modeling, and perturbation experiments, we find that mechanical feedback between cell fate and stiffness drives bone expansion and controls bone size <em>in vivo</em>. This mechanical feedback system is sufficient to propagate a wave of differentiation that establishes a collagen gradient which we find sufficient to describe patterns of osteoblast motion. Our work provides a mechanism for coordinated motion that may not rely upon cell migration but on emergent properties of the mesenchymal collective. Identification of such alternative mechanisms of mechanochemical coupling between differentiation and morphogenesis will help in understanding how directed cellular motility arises in complex environments with inhomogeneous material properties.</p>
Propagation delays in carry-chains and calculation of estimated min-entropy
<p>Measurements of the propagation delays in the carry-chains of Spartan 6 Xilinx and Cyclone IV Intel FPGAs and calculation of estimated min-entropy. </p> <p>Hardware used: Cyclone IV (EP4CGX150DF31C7) & Spartan 6 (XC6SLX16) FPGAs</p>
Excitation wave propagation on London street network. Oregonator model.
<p>Supplementary material to paper </p> <p>Andrew Adamatzky, Neil Phillips, Roshan Weerasekera, Michail-Antisthenis Tsompanas, and Georgios Ch. Sirakoulis</p> <p>Street map analysis with excitable chemical medium. Phys. Rev. E . Accepted 19 June 2018</p> <p>https://journals.aps.org/pre/accepted/7c072R1eD7e1c71d762165a4977a6cb036ce6e12e</p> <p> </p> <p>Files:</p> <p> </p> <p>London_Eps_0_02_Fi_0_065_long --> $\phi=0.065$</p> <p>London_Eps_0_02_Fi_0_075_long --> $\phi=0.075$</p> <p>Time lapsed snapshots of a single wave-fragment recorded every 150\textsuperscript{th} step of numerical integration.</p> <p>lapse_eps_0.02_fi0.064.jpg lapse_eps_0.02_fi0.063.jpg lapse_eps_0.02_fi0.062.jpg lapse_eps_0.02_fi0.061.jpg lapse_eps_0.02_fi0.06.jpg lapse_eps_0.02_fi0.059.jpg lapse_eps_0.02_fi0.058.jpg lapse_eps_0.02_fi0.056.jpg lapse_eps_0.02_fi0.055.jpg lapse_eps_0.02_fi0.066.jpg lapse_eps_0.02_fi0.054.jpg lapse_eps_0.02_fi0.052.jpg lapse_eps_0.02_fi0.05.jpg lapse_eps_0.02_fi0.067.jpg lapse_eps_0.02_fi0.068.jpg lapse_eps_0.02_fi0.069.jpg lapse_eps_0.02_fi0.07.jpg lapse_eps_0.02_fi0.071.jpg lapse_eps_0.02_fi0.065.jpg lapse_eps_0.02_fi0.074.jpg lapse eps_0.02_fi0.076.jpg lapse_eps_0.02_fi0.073.jpg lapse eps_0.02_fi0.077.jpg lapse_eps_0.02_fi0.075.jpg lapse_eps_0.02_fi0.072.jpg</p> <p>Coverage frequency is visualised in the images below (see details in the paper)</p> <p>coverageFrequency eps_002_fi0076.jpg coverageFrequency eps_002_fi0077.jpg coverageFrequency eps_002_fi0078.jpg coverageFrequency eps_002_fi0079.jpg coverageFrequency eps_002_fi0080.jpg coverageFrequency eps_002_fi0081.jpg coverageFrequency eps_002_fi0082.jpg coverageFrequency eps_002_fi0083.jpg coverageFrequency eps_002_fi0084.jpg coverageFrequency eps_002_fi0085.jpg coverageFrequency_eps_0.02_fi0.07.jpg coverageFrequency_eps_0.02_fi0.052.jpg coverageFrequency_eps_0.02_fi0.054.jpg coverageFrequency_eps_0.02_fi0.055.jpg coverageFrequency_eps_0.02_fi0.056.jpg coverageFrequency_eps_0.02_fi0.058.jpg coverageFrequency_eps_0.02_fi0.059.jpg coverageFrequency_eps_0.02_fi0.061.jpg coverageFrequency_eps_0.02_fi0.062.jpg coverageFrequency_eps_0.02_fi0.063.jpg coverageFrequency_eps_0.02_fi0.064.jpg coverageFrequency_eps_0.02_fi0.065.jpg coverageFrequency_eps_0.02_fi0.066.jpg coverageFrequency_eps_0.02_fi0.067.jpg coverageFrequency_eps_0.02_fi0.068.jpg coverageFrequency_eps_0.02_fi0.069.jpg coverageFrequency_eps_0.02_fi0.071.jpg coverageFrequency_eps_0.02_fi0.072.jpg coverageFrequency_eps_0.02_fi0.073.jpg coverageFrequency_eps_0.02_fi0.074.jpg coverageFrequency_eps_0.02_fi0.075.jpg coverageFrequency_eps_002_fi0050.jpg coverageFrequency_eps_002_fi0060.jpg</p> <p>Dynamics of integral excitation</p> <p>activity eps_0.02_fi0.050.txt activity eps_0.02_fi0.052.txt activity eps_0.02_fi0.054.txt activity eps_0.02_fi0.055.txt activity eps_0.02_fi0.056.txt activity eps_0.02_fi0.057.txt activity eps_0.02_fi0.058.txt activity eps_0.02_fi0.059.txt activity eps_0.02_fi0.060.txt activity eps_0.02_fi0.061.txt activity eps_0.02_fi0.062.txt activity eps_0.02_fi0.063.txt activity eps_0.02_fi0.064.txt activity eps_0.02_fi0.065.txt activity eps_0.02_fi0.066.txt activity eps_0.02_fi0.067.txt activity eps_0.02_fi0.068.txt activity eps_0.02_fi0.069.txt activity eps_0.02_fi0.070.txt activity eps_0.02_fi0.071.txt activity eps_0.02_fi0.072.txt activity eps_0.02_fi0.073.txt activity eps_0.02_fi0.074.txt activity eps_0.02_fi0.075.txt activity eps_0.02_fi0.076.txt activity eps_0.02_fi0.077.txt activity eps_0.02_fi0.078.txt activity eps_0.02_fi0.079.txt activity eps_0.02_fi0.080.txt activity eps_0.02_fi0.081.txt activity eps_0.02_fi0.082.txt activity eps_0.02_fi0.083.txt activity eps_0.02_fi0.084.txt activity eps_0.02_fi0.085.txt</p> <p>Abstract </p> <p>Belousov-Zhabotinsky (BZ) thin layer solution is a fruitful substrate for designing unconventional computing devices. A range of logical circuits, wet electronic devices, and neuromorphic prototypes have been constructed. Information processing in BZ computing devices is based on interaction of oxidation (excitation) wave fronts. Dynamics of the wave fronts propagation is programmed by geometrical constraining and interaction of colliding wave fronts is tuned by illumination. We apply the principles of BZ computing to explore a geometry of street networks. We use two-variable Oregonator equations, the most widely accepted and verified in laboratory experiments model of BZ, to study propagation of excitation wave fronts for a range of excitability parameters, gradual transition from excitable to sub-excitable to non-excitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities and characterising geometry of street networks.</p> <p>Earlier draft (missing some new findings presented in PRE paper) is on arXiv: </p> <p>https://arxiv.org/abs/1803.01632</p> <p> </p> <p> </p>
Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS - Supporting Data
<p>Processed data used in the paper Smittarello et al., JGR 2019</p> <p>Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS</p> <p>e.g. GNSS, seismic and InSAR data</p>
Crack propagation in heterogenous radiated material.
<p>Plot representing equations in string form used to implicitly propagate a crack in radiated material.</p>
Outputs of numerical simulation of irregular waves propagating over an idealized shoal under non-breaking conditions
<p>This dataset contains bathymetry files (.txt) and outputs files (.mat) of numerical simulation of irregular waves propagating over an idealized shoal under non-breaking conditions, conducted with SWASH model. The spatial and temporal resolution are 0.5 m and 0.1 s, respectively.</p>
plant propagation method: plant-propagation-method
plant propagation method for all available taxa
User Generated Content (EOL v2): taxonomic propagation - image ratings
<p></p>https://eol-jira.bibalex.org/browse/DATA-1786 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): taxonomic propagation - exemplar images
<p></p>https://eol-jira.bibalex.org/browse/DATA-1786 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Radial Hydraulic Fracturing Experiment: 1 Cycle of Fracture Propagation, Arrest, and Closure in Molasse de Villarlod Sandstone - Sample M03
<h4><strong>Overview</strong></h4> <p>This dataset encompasses detailed measurements from a lab-scale radial hydraulic fracturing experiment conducted on a cubic sample of Molasse de Villarlod sandstone, designated as Sample M03. The sandstone, sourced from a quarry in Fribourg, Switzerland, is known for its porosity (18.1%) and permeability, making it an ideal material for studying hydraulic fracture processes. The primary focus of the experiment was to observe and analyze the propagation, arrest, and closure of hydraulic fractures under controlled triaxial stress conditions.</p> <h4><strong>Experimental Setup</strong></h4> <p>The experiment was conducted on a cubic sandstone sample with dimensions of 25 × 25 × 25 cm. The sample was placed in a truetriaxial frame that applied confining stresses in all three principal directions:</p> <ul> <li><strong>Vertical Confining Stress:</strong> 7 MPa</li> <li><strong>Horizontal Confining Stress:</strong> 14 MPa</li> </ul> <p>A <strong>viscous glucose fluid containing a UV additive</strong> was used as the fracturing fluid. This fluid was injected through a 1/8'' high-pressure tube cemented with epoxy into a centrally drilled hole within the sample. An axisymmetric notch was created at the injection point to facilitate fracture initiation and promote the planarity of the fracture.</p> <p>The experiment was designed to simulate one cycle of fracture initiation, propagation, arrest, and closure. The closure of the fracture was occured by the leakoff of the fracturing fluid into the surrounding porous medium.</p> <h4><strong>Acoustic Monitoring</strong></h4> <p>To capture the dynamics of fracture propagation and closure, the experiment employed both passive and active acoustic monitoring systems:</p> <ol> <li> <p><strong>Passive Acoustic Monitoring:</strong></p> <ul> <li><strong>Sensors:</strong> 16 Vallen VS150-M passive piezoelectric sensors were used to capture Acoustic Emissions (AEs) within the frequency range of 50 kHz to 600 kHz.</li> <li><strong>Signal Processing:</strong> Continuous signal analysis and denoising were performed on the captured AE data. The STA/LTA algorithm was applied to the denoised signal to identify potential p-wave arrivals, providing insights into the fracture mechanics.</li> </ul> </li> <li> <p><strong>Active Acoustic Monitoring:</strong></p> <ul> <li><strong>Transducers:</strong> A total of 64 piezoelectric transducers were integrated into the loading platens, with 32 acting as sources and 32 as receivers. The array included 10 shear-wave and 54 longitudinal-wave transducers.</li> <li><strong>Signal Generation and Acquisition:</strong> A Ricker excitation signal with a central frequency adjustable between 300 and 750 kHz was generated and amplified using a high-power amplifier. The signal was routed to one of the 32 source transducers via a multiplexer, and the resulting signals were recorded simultaneously by the 32 receiver transducers at a sampling frequency of 50 MHz. Each source was excited 50 times to improve the signal-to-noise ratio, with the complete acquisition sequence taking approximately 2.5 seconds.</li> </ul> </li> </ol> <h4><strong>Additional Measurements</strong></h4> <p>In addition to acoustic monitoring, several other key measurements were recorded during the experiment:</p> <ul> <li><strong>Fluid Injection Parameters:</strong> The pressure and rate of fluid injection were continuously monitored.</li> <li><strong>Flat-Jack and Piston Parameters:</strong> The pressures and volumes exerted by each pair of flat-jacks were recorded at a frequency of 1 Hz.</li> <li><strong>Fracture Opening Measurement:</strong> An eddy current sensor, an electromagnetic inductive device, was placed inside the wellbore at the notch/inlet to directly measure the fracture opening.</li> </ul> <p>All measurements were synchronized using a dedicated LabView application to ensure consistency across the dataset.</p> <h4><strong>Conclusion</strong></h4> <p>This dataset provides a comprehensive view of the hydraulic fracturing behavior of Molasse de Villarlod sandstone under controlled laboratory conditions, with a focus on the processes of fracture propagation, arrest, and closure. The dataset includes raw and processed acoustic data, fluid injection metrics, and direct observations of fracture opening. It is an invaluable resource for researchers and engineers studying hydraulic fracturing, rock mechanics, and related fields. The data is suitable for detailed analysis and modeling of fracture mechanics in porous, permeable sandstones.</p> <h4><strong>Note</strong></h4> <p>Since the fracture did not extend to the boundaries of the sample, the sample was subsequently cut, and a core was extracted. This core was then sent for CT-scan analysis, which was used to reconstruct the residual fracture surfaces and assess their roughness. The dataset from this analysis is available in the Related Work section via the provided URL (Talebkeikhah, M. (2024). CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13358916" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13358916</a>).</p> <h4><strong>Processing code</strong></h4> <p>Follow the <strong>URL repositories</strong> below to access to the codes for processing these dataset.</p> <p><a href="https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX">https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX</a></p> <p><a href="https://github.com/GeoEnergyLab-EPFL/FracLowRate">https://github.com/GeoEnergyLab-EPFL/FracLowRate</a></p> <p><strong>Contact and Support</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Physical controls on the variability of offshore propagation of convection from Sumatra
<p>Supporting data and code for <em>Physical controls on the variability of offshore propagation of convection from Sumatra</em>, submitted to Journal of Geophysical Research: Atmospheres.</p>
Geophysical Signals from Magma Propagation: Experimental and Processed Data
<p>This repository contains the experimental data analyzed and interpreted in the manuscript titled "<em>Geophysical signals induced by magma propagation: Insights from analog experiments</em>" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.</p>
Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET)
<p><strong>Literature review dataset</strong></p> <p>This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1<sup>st</sup>, 2020 to December 15<sup>th</sup>, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.</p> <p>This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.</p> <p>The reference to cite the related paper is the following:</p> <p><strong>Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p>To read the manuscript please follow this link: <strong>https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.