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733 results for “passive”
Geometric Distortion Distribution of Sentinel-1 in Southeast Tibet (Including Active and Passive Distortions)
<p>SETPAscending_Distor.tif and SETPDescending_Distor.tif are the geometric distortion images, where the pixel values correspond to the types of geometric distortion as detailed in the table below. Here, L stands for Near layover, R for Far layover, S for Shadow, and F for Forshortening.</p> <table> <tbody> <tr> <td>L</td> <td>R</td> <td>S</td> <td>F</td> <td>LR</td> <td>LS</td> <td>LF</td> <td>RS</td> <td>RF</td> <td>SF</td> <td>LRS</td> <td>RSF</td> <td>LSF</td> <td>LRF</td> <td>LRSF</td> </tr> <tr> <td>1</td> <td>5</td> <td>7</td> <td>9</td> <td>6</td> <td>8</td> <td>10</td> <td>12</td> <td>14</td> <td>16</td> <td>13</td> <td>21</td> <td>17</td> <td>15</td> <td>22</td> </tr> </tbody> </table> <p>SETPAscending_Forshortening_Grading.tif and SETPDescending_Forshortening_Grading.tif are used for the grading of Forshortening, with the following recommendations for the grading scale: Grade 1: 0-0.4, Grade 2: 0.4-0.6, Grade 3: 0.6-0.8, Grade 4: 0.8-1.0, and Grade 5: 1.0 to Infinity (Inf).</p>
Fire promotes passive directed dispersal by birds
Open the record for dataset details and reuse information.
On the impact of the antenna radiation patterns in passive radio sensing: dataset
<p>The measurement sessions took place in a hall with size 6.15 m × 14.45 m and floor-ceiling height equal to 3.35 m. TX and RX nodes are spaced d=4.00 m apart, while the LOS is horizontally placed at h=0.99 m from the floor.</p> <p>The received power P is measured using a real-time spectrum analyzer (SA) with a built-in tracking generator. The SA tracks N=401 frequency points equally spaced with Δf=1.25 MHz. Further settings are detailed in the paper:</p> <p><a title="Deposited paper " href="https://arxiv.org/abs/2405.09352" target="_blank" rel="noopener">https://arxiv.org/abs/2405.09352</a></p> <p>1) dir_dir_measurements.csv: [401x75]: received power measurements [dBm] using antenna with a directional pattern (described in the paper) and target on each of the 75 positions. These are ordered following the enumeration of the scenario scheme (see Figure 2 in the paper).</p> <p>2) dir_dir_measurements.csv: [401x2]: free space received power measurements (before and after the experiment) [dBm]</p> <p>3) frequency_points.csv [401x1]: observed 401 frequencies from 2.149 GHz to 2.649 GHz (resolution BW 400kHz, 1.2MHz freq. spacing)</p> <p>4) omni_omni_measurements.csv: [401x75]: received power measurements [dBm] with omnidirectional antenna and target on each of the 75 positions. These are ordered following the enumeration of the scenario scheme (see Figure 2 in the paper).</p> <p>5) omni_omni_measurements.csv: [401x2]: free space received power measurements (before and after the experiment) [dBm]</p> <p>6) average_radio_map_generation_example.m: matlab script example (generate radio maps with omnidirectional and directional antennas)</p>
Passive Head-Mounted Display Music-Listening EEG dataset
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. The electroencephalographic headset consisted of 16 electrodes. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02085118">https://hal.archives-ouvertes.fr/hal-02085118</a>. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (Cattan and al, 2018). Python code for manipulating the data is downloadable at <a href="https://github.com/plcrodrigues/py.PHMDML.EEG.2017-GIPSA">https://github.com/plcrodrigues/py.PHMDML.EEG.2017-GIPSA</a>. The ID of this dataset is <em>PHMDML.EEG.2017-GIPSA.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02085118">https://hal.archives-ouvertes.fr/hal-02085118</a></p> <p> </p> <p><strong><em>Principal Investigator</em>:</strong> Eng. Grégoire Cattan</p> <p> </p> <p><strong><em>Technical Supervisors</em>:</strong> Eng. Pedro L. C. Rodrigues</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Dr. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>PHMDML.EEG.2017-GIPSA</em></p>
Contributions to the contact resistivity in fired tunnel-oxide passivating contacts for crystalline silicon solar cells
<p>Data underlying the diagrams shown in the manuscript</p>
Data from: Incompressible fluid plays a mechanical role in the development of passive muscle tension
Over short time scales, muscle fibres maintain a nearly constant volume of intracellular fluid. This fluid is essential to normal biochemical function, but its role in determining the mechanical properties of muscle has been considered in only a few theoretical analyses. Here we investigate the mechanical role of fluid in a fundamental property of muscle, its development of passive tension in response to stretch. We test a model of muscle structure in which incompressible fluid directly influences passive tension by constraining the geometry of intramuscular connective tissues. This interaction is demonstrated using a simple physical model of muscle morphology comprising a fluid-filled bladder wrapped by helical fibres. The behaviour of the model is compared with that of isolated bullfrog muscle subjected to an osmotic perturbation of intracellular fluid volume. Increasing muscle volume by 40% resulted in 69% increased passive tension, occurring in a manner consistent with the behaviour of the model. These observations support the notion that the interaction of connective tissues with the muscle fibres they surround influences the mechanical behaviour of whole muscles, and highlight the role of fluid as a mechanical component of muscle.
Targeted and passive environmental DNA approaches outperform established methods for detection of quagga mussels, Dreissena rostriformis bugensis in flowing water
<ol> <li>The early detection of invasive non-native species (INNS) <span><span>is important for informing management actions</span></span>. Established monitoring methods require the collection or observation of specimens, which is unlikely at the beginning of an invasion when densities are likely to be low. Environmental DNA (eDNA) analysis is a highly promising technique for the detection of INNS – particularly during the early stages of an invasion. </li> <li>Here, we compared the use of traditional kick-net sampling with two eDNA approaches (targeted detection using both conventional and quantitative PCR, and passive detection via metabarcoding with conserved primers) for detection of quagga mussel, <i>Dreissena rostriformis bugensis;</i> a high priority INNS, along a density gradient on the River Wraysbury, UK. </li> <li>All three molecular tools outperformed traditional sampling in terms of detection. Conventional PCR and qPCR both had 100% detection rate in all samples, and outperformed metabarcoding when the target species was at low densities. Additionally, quagga mussel DNA copy number (qPCR) and relative read count (metabarcoding) were significantly influenced by both mussel density and distance from source population, with distance being the most significant predictor. </li> <li> <i>Synthesis and application.</i> All three molecular approaches were more sensitive than traditional kick-net sampling for the detection of the quagga mussel in flowing water, and both qPCR and metabarcoding enabled estimates of relative abundance. Targeted approaches were more sensitive than metabarcoding, but metabarcoding has the advantage of providing information on the wider community, and consequently impacts of INNS. </li> </ol>
Neural correlates of temporal recalibration to delayed auditory feedback of active and passive movements
<p>Data related to the study: "Neural correlates of temporal recalibration to delayed auditory feedback of active and passive movements"</p>
Effects of Stress Ball and Hand Holdig on Pain and Mobility Levels During Continuous Passive Motion In Patients With Total Knee Prosthesis: A Randomized Controlled Trial
ClinicalTrials.gov study NCT06608264. IPD Sharing: NO. Countries: 1. Publications: 0.
Safety and Performance Study of the RELIANCE 4-Front Passive Fixation Lead
ClinicalTrials.gov study NCT01856491. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Citric Acid Passivation on Implant Abutments for the Prevention of Peri-implant Disease
ClinicalTrials.gov study NCT05592327. IPD Sharing: NO. Countries: 0. Publications: 1.
Effect of Active vs. Passive Voiding Trials on Time to Discharge, Urinary Tract Infection, and Urinary Retention
ClinicalTrials.gov study NCT02886143. IPD Sharing: NO. Countries: 0. Publications: 1.
Real-Life Passive Exposure Assessment of IQOS
ClinicalTrials.gov study NCT03550989. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Impact of Passive Heat on Metabolic, Inflammatory and Vascular Health in Persons With Spinal Cord Injury
ClinicalTrials.gov study NCT04971408. IPD Sharing: NO. Countries: 1. Publications: 0.
Passive Disinfection Cap Compliance Study
ClinicalTrials.gov study NCT03391960. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of Arch Expansion Changes Between Passive Self-ligating and Conventional Brackets
ClinicalTrials.gov study NCT03363620. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Passive Vertebral Mobilization and Propriocemptive Neuromuscular Techniques in Mechanical Neck Pain
ClinicalTrials.gov study NCT03813680. IPD Sharing: NO. Countries: 0. Publications: 1.
Shoulder Passive Range of Motion and Positioning Exercise on Hemiplegic Stroke Patients
ClinicalTrials.gov study NCT03045432. IPD Sharing: NO. Countries: 0. Publications: 24.
Clinical Outcomes With Passive MPKs vs. Powered Prosthetic Knees
ClinicalTrials.gov study NCT05267639. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cerebral Oxygen Challenge of Passive Leg Raising Test in Sepsis
ClinicalTrials.gov study NCT03523169. IPD Sharing: NO. Countries: 0. Publications: 13.
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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.