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525 results for “Displace”
Preliminary displacement analyses over Surfside Florida KMZ file pointing to REST URL
<p><em><strong>Preliminary displacement analyses over Surfside Florida. KMZ file pointing to REST URL. The file can be opened in Google Earth Pro and will allow overlaying displacement results with Google's temporal optical imagery catalog. Contains modified Copernicus Sentinel-1 data. Persistent Scatterers Interferometry (PSI) was performed using SARscape v5.6.</strong></em></p>
Global dataset of Sentinel-1 azimuth shift values and derived horizontal displacements from COMET LiCSAR system
<p>For this dataset, we were exploiting spatio-temporal behaviour of side-products of coregistration of Sentinel-1 radar images, that were generated in last couple of years, within the COMET LiCSAR system for computing interferograms. These side-products are sub-pixel (azimuth) offsets, allowing for a very precise match of the images (up to a 0.0005 pixels). We have shown that by a proper approach, these offsets can be used as measurements of large-scale horizontal motion - in our case, using 250x250 km resolution cells, we aimed to measure motion of tectonic plates. Our measurements are fitting well to the latest ITRF2014 plate motion model, although the E component contains an extra overall shift, not explained at the moment of sharing the outputs.</p> <p>The sub-pixel offsets (result of intensity cross correlation and spectral diversity estimation, w.r.t. precise orbit ephemerides) were corrected for solid Earth tides and ionospheric phase advance, using external models. The code shared within the dataset includes the correction functions. The data contains both original and corrected values. Also, the data contains ITRF2014 plate motion values.</p> <p>The dataset contains outputs from both COMET LiCSAR frame units and their decomposition into N, E motion vectors, as well as some metadata on the frames that are necessary for the reprocessing if needed.</p>
Raw measurements data of piezo actuator displacements, in a form of a groove pattern, obtained with a laser interferometer and a roundness instrument
<p><strong>A brief description of the repository content</strong></p> <p>The data presented here were obtained during an experimental calibration of the Taylor Hobson 130 (Leicester, UK) roundness instrument with the Thorlabs LPS710M (Thorlabs, Newton, NJ, USA) piezo actuator driven by Thorlabs PPC001 Piezo Controller and controlled with Kinesis® (Thorlabs) software. Before the calibration, the piezo actuator itself was calibrated with the Renishaw XL-80 interferometer system (Renishaw, Wotton-under-Edge, UK) along with the Renishaw small optics kit (A-8003-3244). These data are included in the repository as well.</p> <p><strong>Description of the dataset</strong></p> <p>Inside the .zip file, the data obtained with the Renishaw XL-80 laser interferometer and the Taylor Hobson 130 roundness instrument are stored in the “Renishaw_XL-80” and “Taylor_Hobson_130” folders, respectively. Each of these folders contains six subfolders: “0.24um”, “0.75um”, “2.4um”, “7.5um”, “24um”, and “75um”, which include the measurements data obtained with these devices (not simultaneously) while Thorlabs LPS710M piezo actuator was performing displacements simulating a groove pattern. The names of subfolders correspond to the groove’s depth.</p> <p>The data from the measurements performed with the Renishaw XL-80 interferometer were exported with the Laser XL system’s software and are saved with the extension “.RTX”. Two data files are available for each depth being considered that correspond to two measurement series. Inside each file, 40 s recording is stored. Eight grooves should be visible (nominal values: 1.5 s groove width, 3 s distance between subsequent grooves). The data were acquired with a 50 kS/s sampling rate.</p> <p>The data from the measurements performed with the Taylor Hobson 130 roundness instrument were exported with the Ultra® (Taylor Hobson) software and are saved with the extension “.SBF”. 30 data files are available for each depth being considered - 15 files for each of two measurement series. Inside each file, 10 s recording is stored. Two grooves should be visible (nominal values: 1 s groove width, 4 s distance between subsequent grooves). The data were acquired with a 360 S/s sampling rate.</p> <p>Caution:</p> <ul> <li>No synchronisation between the piezo actuator and XL-80 interferometer or Taylor Hobson 130 roundness instrument was used. Thus, the first or the last groove within the response to the simulated pattern might be too short to be considered valid.</li> <li>The square excitation of the piezo actuator was used. Thus, ringing oscillations near the grooves’ edges are present.</li> <li>The motion of the piezo actuator might happen to be initiated while the acquisition already had started. Thus, the first response inside each file should be analysed carefully.</li> <li>The piezo actuator did not hold time dependencies properly. The widths of simulated grooves usually differ from their nominal values to some extent.</li> </ul> <p><strong>Acknowledgement</strong></p> <p>This dataset was obtained within the 18RP01 ProbeTrace project. This project (18RP01 – ProbeTrace) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.</p> <p>Project title: Traceability for contact probe and stylus instrument measurements<br> Funder name: European Metrology Programme for Innovation and Research (EMPIR)<br> Funder ID: 10.13039/100014132<br> Grant number: 18RP01 ProbeTrace<br> Link to project homepage: http://probetrace.org/</p>
The effect of shear displacement and wear on fault stability: laboratory constraints
<p>This is the ReadMe file corresponding to the study entitled:<br> "The effect of shear displacement and wear on fault stability: laboratory constraints"<br> By Noël C., Giorgetti c., Scuderi M.M., Collettini C. and Marone C.</p> <p>This study has been submited in JGR: Solid Earth </p> <p>This Read-Me file has been last edited in December 2022</p> <p>This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@uniroma1.it</p>
Data from: Wind turbines in managed forests partially displace common birds
<p><span>Wind turbines are increasingly being installed in forests, which can lead to land use disputes between climate mitigation efforts and nature conservation. Environmental impact assessments precede the construction of wind turbines to ensure that wind turbines are installed only in managed or degraded forests that are of potentially low value for conservation. It is unknown, nevertheless, if animals deemed of minor relevance in environmental impact assessments are affected by wind turbines in managed forests. We investigated the impact of wind turbines on common forest birds, by counting birds </span><span>along an impact-gradient of wind turbines</span><span> in 24 temperate forests in Hesse, Germany. </span><span>During 860 point counts, we counted 2,231 birds from 45 species. Bird communities were strongly related to forest structure, season and the rotor diameter of wind turbines, but were not related to wind turbine distance. For instance, bird abundance decreased in structure-poor (-38%) and monocultural (-41%) forests with wind turbines, and in young (-36%) deciduous forests with larger and more wind turbines (-24%). Overall, our findings suggest that wind turbines in managed forests partially displace common forest birds. If these birds are displaced to harsh environments, wind turbines might indirectly contribute to a decline of their populations. Yet, forest bird communities are locally more sensitive to forest quality than to wind turbine presence. To prevent further displacement of forest animals, forests of lowest quality for wildlife should be preferred in spatial planning for wind turbines, for instance small and structure-poor monocultures along highways.</span></p>
Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745]. </p> <p>Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel)</p> <p><strong>The training set </strong>relies on two python/numpy data files corresponding to <strong>harmonic displacements</strong> for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm : </p> <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> <p><strong>The test </strong>set follows the same architecture and format as the training set, but contains only <strong>random displacements </strong>generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz :</p> <ul> <li>"Displacement_test.npy" : with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> <p>These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations. </p>
CLIMOVE- Climate induced Displacement Data Analysis
<p>CLIMOVE addresses the challenging socio-legal avenues for the European Union (EU) to respond the climate migration from a gender perspective. The objectives of the project will increase the most innovative and diversified social scientific skills to explore, recognize and understand the complex experience of migrant women in the context of climate change and of EU migration crisis: female migration still often clashes with censorship, patriarchal laws or lack of diversity or equality in countries of origin and even destination. As part of this project, a report has been produced analyzing and processing available official data on climate change-induced displacement. A part of this analysis is presented here.</p>
Data from: Displacement experiments provide evidence for path integration in Drosophila
<p>Like many other animals, insects are capable of returning to previously visited locations using path integration, a memory of travelled direction and distance. Recent studies suggest that <em>Drosophila</em> can also use path integration to return to a food reward. However, the existing experimental evidence for path integration in <em>Drosophila</em> has a potential confound: pheromones deposited at the site of reward might enable flies to find previously rewarding locations even without memory. Thus, we designed an experiment to determine if flies can use path integration memory despite potential pheromonal cues by displacing the flies shortly after an optogenetic reward. We found that rewarded flies returned to the location predicted by a memory-based model. Several analyses are consistent with path integration as the mechanism by which flies returned to the reward. We conclude that while pheromones may often be important in fly navigation and must be carefully controlled in future experiments, <em>Drosophila</em> may indeed be capable of performing path integration.</p>
Dataset and fitting methods for Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network
<p>This upload contains raw experimental data and the fitting methods used for the article "Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network".</p>
Geodetic displacement data from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study.
<p>This repository the results produced by Hurtado-Pulido, Amer, Ebinger, and Holcomb “Variations in subsidence patterns in the Gulf of Mexico passive margin from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study”.</p> <p>This repository presents data sets for figures 4, 5, 6, 7 and 8. Processing methods are described in the paper. The READme file contains details about each file. Please address any questions about this dataset to Hurtado-Pulido.</p> <ul> <li>LiDAR data from 1999 is stored and distributed by the Atlas: The Louisiana Statewide GIS (<a href="https://maps.ga.lsu.edu/lidar2000/">https://maps.ga.lsu.edu/lidar2000/</a>). LiDAR data from 2018 is stored and distributed by the USGS Server through The National Map Download Manager (<a href="https://apps.nationalmap.gov/downloader/">https://apps.nationalmap.gov/downloader/</a>).</li> <li>EnviSAT SAR images were retrieved from the Earth Observation Catalogue (<a href="https://eocat.esa.int/sec/#data-services-area">https://eocat.esa.int/sec/#data-services-area</a>). Sentinel-1 SAR images from the Copernicus Open Access Hub (<a href="https://scihub.copernicus.eu/dhus/#/home">https://scihub.copernicus.eu/dhus/#/home</a>). Both property of the European Space Agency.</li> <li>GNSS information was processed by the Nevada Geodetic Laboratory (Blewitt et al., 2018; <a href="http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html">http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html</a>).</li> <li>Data from water, injection, and extraction wells is stored in the Strategic Online Natural Resources Information System property of the Louisiana Department of Natural Resources (<a href="http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181">http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181</a>).</li> </ul>
DrIFT 2 Study: Displacement in Feeding Tubes
ClinicalTrials.gov study NCT06239610. IPD Sharing: YES. Countries: 1. Publications: 12.
Data from: 3D morphology of an outer-hair-cell hair bundle increases its displacement and dynamic range
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Mechanisms of coexistence: Exploring species sorting and character displacement in woody plants to alleviate belowground competition
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Three-dimensional offsets of geomorphic piercing lines displaced by the quaternary-active Beaufort range fault, northern Cascadia forearc, BC, Canada
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Anolis carolinensis character displacement SNP
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Data from: Wind turbines in managed forests partially displace common birds
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Data from: Displacement experiments provide evidence for path integration in Drosophila
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Simulation data for Modelling DNA-strand displacement reactions in the presence of base-pair mismatches
<p>Raw and processed simulation data from the paper Modelling DNA-strand displacement reactions in the presence of base-pair mismatches (J. Am. Chem. Soc.)</p>
Supplementary material for the publication: J. D. Nixon, K. Bhargava and E. Gaura, Analysis of standalone solar streetlights for improved energy access in displaced settlements, 2020.
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed 11 advanced solar streetlights, with additional energy access provided by ground-level AC sockets, at two project sites: Gihembe refugee camp, Rwanda (4 lights) and Uttargaya settlement, Nepal (7 lights), in July 2019. The aim of this study was to (a) identify best practices in the construction, location and security measures for long-lived street lighting (b) understand how communities would use a shared energy resource when available through energy sockets, auxiliary to the main function of the streetlights.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Analysis of standalone solar streetlights for improved energy access in displaced settlements</strong>' (currently under review). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED_Nepal_SL and HEED_Rwanda_SL repositories.</p>
Character displacement in the midst of background evolution in island populations of Anolis lizards: a spatiotemporal perspective
<p>Negative interactions between species can generate divergent selection that causes character displacement. However, other processes cause similar divergence. We use spatial and temporal replication of island populations of <i>Anolis </i>lizards to assess the importance of negative interactions in driving trait shifts. Previous work showed that the establishment of <i>Anolis sagrei </i>drove resident <i>Anolis carolinensis </i>to perch higher and evolve larger toepads. To further test the interaction's causality and predictability, we resurveyed a subset of islands nine years later. <i>Anolis sagrei</i> had established on one island between surveys. We found that <i>A. carolinensis </i>on this island now perch higher and have larger toepads. However, toepad morphology change on this island was not distinct from shifts on six other islands whose <i>Anolis </i>community composition had not changed. Thus, the presence of <i>A. sagrei </i>only partly explains <i>A. carolinensis </i>trait<i> </i>variation across space and time<i>. </i>We also find that <i>A. </i><i>carolinensis</i> on islands with previously established <i>A. sagrei</i> now perch higher than a decade ago, and that current <i>A. carolinensis </i>perch height is correlated with <i>A. sagrei</i> density. Our results suggest that character displacement likely interacts with other evolutionary processes in this system, and that temporal data are key to detecting such interactions.</p>
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