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342 results for “gnss”
Strong-motion seismogeodesy by deeply coupling GNSS receivers and inertial measurement units
<p>Understanding the origin and mechanism of destructive earthquakes is predicated on the faithful recordings of their induced ground displacements near the clearly deformed epicentral regions. Global Navigation Satellite System (GNSS) receivers, such as the GPS sort, have been recognized as the best, if not the only reliable, tool to measure such large displacements. This is achieved within the GNSS receivers by continuously tracking the satellite carrier-phase signals. However, GNSS receivers are likely to lose their fidelity in recording the complete and accurate displacement waveforms in case of fierce ground motions, since their carrier-phase tracking becomes instable when strained by such persistent and high dynamic stress. We hence developed an advanced GNSS receiver architecture where the dynamic stress suffered by the carrier-phase tracking components is compensated for by an embedded inertial measurement unit consisting of one accelerometer and one gyroscope. In this case, the carrier-phase signals can be tracked steadily by GNSS receivers, and the displacement accuracy can be improved from sub-centimeter to millimeter level by about 70% when the ground accelerations reach twice the gravitational acceleration. We believe that this advanced GNSS receiver will be an excellent strong-motion seismometer recording displacements directly at a few millimeter accuracy without missing any earthquake signals, even in case of fierce ground motions.</p>
Performance of 6 Different GNSS Receivers at Low Latitude under Moderate and Strong Scintillation
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S<sub>4</sub> and <span class="math-tex">\(\mathbf{\sigma_\phi}\)</span> indexes) from 5 different GNSS receivers.</p> <p>The data are from two nights, 20/21 February and 27/28 November of 2013, and stored in different files. The files are in the HDF5 format using zlib compression and were generated by a Python script using the Pandas library.</p> <p>Each file contain the following folders:</p> <ul> <li> <p>/csm_maker_calc</p> </li> <li> <p>/csm_own_calc</p> </li> <li> <p>/csm_raw</p> </li> <li> <p>/gpstation6_maker_calc</p> </li> <li> <p>/gpstation6_own_calc</p> </li> <li> <p>/gpstation6_raw</p> </li> <li> <p>/gsv_4004b_lisn_calc</p> </li> <li> <p>/gsv_4004b_maker_calc</p> </li> <li> <p>/septentrio_maker_calc</p> </li> <li> <p>/septentrio_own_calc</p> </li> <li> <p>/septentrio_raw</p> </li> <li> <p>/su_iono_sdr_own_calc</p> </li> <li> <p>/su_iono_sdr_raw</p> </li> </ul> <p>The suffix <strong>maker_calc</strong> means that the indices were calculated in real time or post-processed using receiver manufacturer's tools. The suffix <strong>own_calc</strong> means that the indices were calculated using our own methodology. The suffix <strong>raw</strong> corresponds to the raw data (power and phase cycles). And finally the suffix <strong>lisn_calc</strong> corresponds to the S<sub>4</sub> index obtained from the LISN network website.</p>
Comparative analysis of positioning accuracy of Garmin Forerunner wearable GNSS receivers in dynamic testing
<p>Required dataset for the manuscript.</p>
GPS 50 Hz data set for the case study entitled "Comparison of TEC calculations based on Trimble, Javad, Leica and Septentrio GNSS receiver data". Part 1
<p>This dataset contains 50 Hz GPS data to explore JAVAD and Leica receivers quality to reconstruct slant TEC.</p> <p>GNSS receiver is to some extent a “black box” when its data is used for ionospheric studies. The results based on Javad, Septentrio, Trimble and Leica GNSS receivers (which is placed in this data set) proved that the accuracy of the slant Total Electron Content (TEC) calculation can differ significantly if data used for its calculation is from different GNSS receiver make/type. This is because TEC measurements depend on the carrier phase tracking technique applied in a receiver. Based on this data it was found that:</p> <p>1) Correlation coefficient between carrier phase noises in L1 and L2 channel outputs may be considered as an indicator showing whether the L1-aided tracking technique or independent tracking is applied.</p> <p>2) The empirical model of TEC noise component was provided to determine TEC noise value in GNSS receivers of different make/type.</p>
Data from: How a GNSS receiver is held may affect static horizontal position accuracy
The static horizontal position accuracy of a mapping-grade GNSS receiver was tested in two forest types over two seasons, and subsequently was tested in one forest type against open sky conditions in the winter season. The main objective was to determine whether the holding position during data collection would result in significantly different static horizontal position accuracy. Additionally, we wanted to determine whether the time of year (season), forest type, or environmental variables had an influence on accuracy. In general, the F4Devices Flint GNSS receiver was found to have mean static horizontal position accuracy levels within the ranges typically expected for this general type of receiver (3 to 5 m) when differential correction was not employed. When used under forest cover, in some cases the GNSS receiver provided a higher level of static horizontal position accuracy when held vertically, as opposed to held at an angle or horizontally (the more natural positions), perhaps due to the orientation of the antenna within the receiver, or in part due to multipath or the inability to use certain satellite signals. Therefore, due to the fact that numerous variables may affect static horizontal position accuracy, we only conclude that there is weak to moderate evidence that the results of holding position are significant. Statistical test results also suggest that the season of data collection had no significant effect on static horizontal position accuracy, and results suggest that atmospheric variables had weak correlation with horizontal position accuracy. Forest type was found to have a significant effect on static horizontal position accuracy in one aspect of one test, yet otherwise there was little evidence that forest type affected horizontal position accuracy. Since the holding position was found in some cases to be significant with regard to the static horizontal position accuracy of positions collected in forests, it may be beneficial to have an understanding of antenna positioning within the receiver to achieve the greatest accuracy during data collection.
GNSS uplift time series and ice surface elevation changes
<p class="MsoNoSpacing">We use Global Navigation Satellite System (GNSS) stations attached to bedrock to measure elastic displacements of the solid Earth caused by dynamic thinning near the glacier terminus. When we compare our results with discharge, we find a time lag between glacier speedup/slowdown and onset of dynamic thinning/thickening. Our results show that dynamic thinning/thickening on Jakobshavn Isbræ occurs 0.87 ± 0.07 years before speedup/slowdown. This implies that using GNSS time series we are able to predict speedup/slowdown of Jakobshavn Isbræ by up to 10.4 months. For Kangerlussuaq Glacier the lag between thinning/thickening and speedup/slowdown is 0.37 ± 0.17 years (4.4 months).</p>
The incorporated GNSS velocity solution of the southeastern Tibetan Plateau by Rui Xu from Sichuan University
<p>This repository saves the latest incorporated GNSS velocity solution (following GAMIT/GLOBK format) of the southeastern Tibetan Plateau by Rui Xu from Sichuan University. </p> <p>The velocity sources are from,</p> <p><span>Wang, M., & Shen, Z.</span><span>‐</span><span>K. (2020). Present</span><span>‐</span><span>day crustal deformation of continental China derived from GPS and its tectonic implications. Journal of Geophysical Research: Solid Earth, 125, e2019JB018774. </span><span><a href="https://doi.org/10.1029/2019JB018774"><span>https://doi.org/10.1029/2019JB018774</span></a></span></p> <p><span>Dai, C., Gan, W., Li, Z., Liang, S., Xiao, G., Zhang, K., & Zhang, L. (2023). Characteristics of Regional GPS Crustal Deformation before the 2021 Yunnan Yangbi Ms 6.4 Earthquake and Its Implications for Determining Potential Areas of Future Strong Earthquakes. Remote Sensing, 15(12), 3195.</span></p> <p><span>Huang, Y., Meng, G.J., Cheng, X., Wu, W.W., Yang, Y.X. Present–day crustal deformation in the southeastern Tibetan Plateau: Insights from three–dimensional finite–element modeling. Tectonophysics, 2023, 863, 229983.</span></p> <p><span>Li, Y., Song, S., Hao, M., Zhuang, W., Cui, D., Yang, F., & Wang, Q., 2023.Present-day crustal deformation across the Daliang Shan,southeastern Tibetan Plateau constrained by a dense GPS network. Geophys. J. Int., 232, 1619–1638. doi: 10.1093/gji/ggac412.</span></p> <p><span>Li, Z., Wang, Y., Gan, W., Fang, L., Zhou, R., Seagren, E.G., et al.Diffuse deformation in the SE Tibetan Plateau: New insights from geodetic observations. Journal of Geophysical Research: Solid Earth, 2020, 125, e2020JB019383.</span></p> <p><span>Rui, X., Lindsey, E. O., & Wu, J. P. (2024). Geodetic slip rate partitioning in the central Anninghe and Daliangshan faults: Effects of fault geometry, elastic heterogeneity, and viscoelastic rheology. Tectonophysics, 230174.</span></p> <p><span>Rui, X., & Stamps, D. S. (2019). Strain accommodation in the Daliangshan Mountain area, southeastern margin of the Tibetan Plateau. Journal of Geophysical Research: Solid Earth, 124. </span><span><a href="https://doi.org/10.1029/2019JB017614"><span>https://doi.org/10.1029/2019JB017614</span></a></span></p> <p><span>Zhang, L., Liang, S.M., Yang, X.P., Gan, W.J., Dai C.L. Geometric and kinematic evolution of the Jiali fault, eastern Himalayan Syntaxis. Journal of Asian Earth Sciences, 2021, 212, 104722.</span></p> <p><span>Zhou, Y.; Xu, L.; Pan, Z.; Hao, M.; Li, C. A Potential Earthquake with Magnitude Mw 7.2 on the Northern Xiaojiang Fault Revealed by GNSS Measurement. Remote Sens. 2023, 15, 944. </span><span><a href="https://doi.org/10.3390/rs15040944"><span>https://doi.org/10.3390/rs15040944</span></a></span></p>
Storm-Time GNSS Propagation at Mid-Latitude Magnetic Conjugate Points
<p>This data set was used to investigate the responses of Total Electron Content (TEC) values at magnetic conjugate points during geomagnetically active conditions. It was performed for three magnetic conjugate pairs of Beijing-Learmonth, Petropavlovsk-Canberra and Bar Harbor-Palmer Station located in the mid-latitude region.</p>
Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation"
<p>Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". And This database contains GNSS velocity and strain rate field data for mainland China obtained by the spherical spline method. Also, we include the sub-graph data for each graph of the manuscript "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". We have detailed the meaning and format of each data in the "readme.txt", and a separate "readme.txt" is included in each zip.</p>
Dataset for Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera). Tectonics
<p>Dataset for "Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera)" Tectonics<br><br>File "hypoDD_10_300.reloc.txt" compile all the relocated seismicity in the study area.<br>Files "810_.NEU, 811_.NEU, 812_.NEU, 813_.NEU, 814_.NEU, 815_.NEU, and 816_.NEU" presents the time series data of GNSS sites of the Zafarraya network.</p>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
GNSS derived Total Electron Content variation of the Ionosphere along the annularity path during the Annular Solar Eclipse of 21 June 2020
<p>GNSS data set used in the <strong><span>GNSS derived Total Electron Content variation of the Ionosphere along the annularity path during the Annular Solar Eclipse of 21 June 2020 </span></strong><span>research article</span></p>
Data products from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence"
<p>GNSS, GNSS-IR, and mass balance data from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence". Contains the following folders and files</p> <ul> <li>GNSS <ul> <li><em>Precise point positioning solution (.pos, etc) using the CSRS-PPP tool for each GNSS system</em></li> </ul> </li> <li>GNSS_basefix <ul> <li><em>Precise point positioning solution (.pos) with base station observations using the Emlid Studio desktop application for GNSS systems AB floating and AB fixed</em></li> </ul> </li> <li>GNSSIR<br> <ul> <li><em>Reflector height solutions for site AB floating, AB fixed, and D floating (see Fig. 7). The filename is the day of year 2023.</em></li> </ul> </li> <li>Monitored Ablation Stake<br> <ul> <li><em>Processed daily and seasonal climatic mass balance height changes (see Fig. 5)</em></li> </ul> </li> </ul>
GNSS TEC Files CMN format
<p>Total electron content for 4 stations in Africa in .cmn format (text) 30 second sampling for 7 days.</p>
Ross Ice Shelf GNSS data reported in EGUSphere-2023-2793, Baldacchino et al., 2024
<p>GNSS data in rinex format for 4 stations on the Ross Ice Shelf reported in Baldacchino et al, EGUSphere-2023-2793. </p>
The Muon Space GNSS-R Surface Soil Moisture Product
<p>Muon Space is building a constellation of small satellites, many of which will carry global navigation satellite system (GNSS) reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R soil moisture (SM) retrievals using data from NASA’s Cyclone GNSS (CYGNSS) mission. This record includes the complete set of these retrievals from August 1, 2018 until September 26, 2024 in the form of individual "tracks" aggregated into daily, per receiver, files (Level 2 data), along with hourly and daily gridded files (Level 3 data) from the entire constellation. The Muon Space CYGNSS SM product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm3 cm-3 for in situ SM observations from SMAP core validation sites has been demonstrated, though performance is lower than SMAP in forests and/or mountainous terrain. The Muon Space product outperforms the official v1.0 CYGNSS SM product in many measures. This initial release serves as the foundation of the Muon Space operational SM product, which soon will additionally include data from Muon Space satellites.</p> <p>A manuscript describing both the processes involved in generating these SM retrievals, as well as the product files in detail, is available <a title="The Muon Space GNSS-R Surface Soil Moisture Product " href="https://arxiv.org/abs/2412.00072" target="_blank" rel="noopener">here</a>. A code repository demonstrating simple visualization of these data files is available <a title="muon-gnssr-soil-moisture-product-intro git" href="https://github.com/Muon-Space/muon-gnssr-soil-moisture-product-intro" target="_blank" rel="noopener">here</a>. Access to an operational version of the SM datasets will be available from this landing page in Q1 of 2025.</p>
Scintillation event list database produced for "Multiyear Detection, Classification and Hypothesis of Ionospheric Layer Causing GNSS Scintillation"
<table> <tbody> <tr> <td>Database produced for and associated with journal article https://doi.org/10.1029/2021RS007328</td> </tr> <tr> <td>This database contains scintillation events identified by the "Detect, Classify, Hypothesize" method as described in the journal article. </td> </tr> <tr> <td>Created by Datta-Barua, Seebany.</td> </tr> <tr> <td>Open Access License Creative Commons Attribution 4.0 International</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Sheets labeled "S*_yyyy_freq" contain a list of scintillation events based on data from the SAGA array.</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>For S* the abbreviations are:</td> <td>"SP": sigma phi index, i.e., phase scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"S4": S4 index, i.e., amplitude scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"SPS4": sigma phi and S4, i.e., both-phase-and-amplitude events</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>yyyy indicates the four-digit year, from 2014 through 2019</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>freq:</td> <td>"L1CA": refers to GPS L1 frequency 1575 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"L2CL": refers to GPS L2C frequency 1227 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>In each sheet "S*_yyyy_freq" the columns are:</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column A:</td> <td>Year (2014-2019)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column B:</td> <td>day of year (1-366)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column C:</td> <td>frequency (1 for L1, 2 for L2C)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column D:</td> <td>GPS satellite PRN (1-32)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column E:</td> <td>Scintillation start UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column F:</td> <td>Scintillation start UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column G:</td> <td>Scintillation end UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column H:</td> <td>Scintillation end UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column I:</td> <td>number of receivers operational (4-6)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column J:</td> <td>PFISR-based hypothesized layer (1-7, as described below)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>1:</td> <td>E layer (majority peak height < 150 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>2:</td> <td>F layer (majority peak height > 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>3:</td> <td>T layer (150 < majority peak height < 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4:</td> <td>I layer (no majority layer)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>5:</td> <td>"N"- no Poker Flat Incoherent Scatter Radar (PFISR) long pulse data and no alternating code data</td> </tr> <tr> <td> </td> <td> </td> <td>6:</td> <td>"NLP" - no PFISR long pulse</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>7:</td> <td>"NAC" - no PFISR alternating code</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>A blank sheet indicates that there were no events of that type and at that frequency found in that year.</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Interseismic coupling along the Mexican subduction seen by InSAR and GNSS
<p>Data associated with 'Interseismic coupling along the Mexican subduction seen by InSAR and GNSS' submitted in EPSL.</p>
GNSS Data for Tonga 2022 Eruption Analysis
<p>RAR archive of GNSS data used for studying the 2022 Tonga Volcanic Eruption in the study "Global propagation of ionospheric disturbances assocaited with the 2022 Tonga Volcanic Eruption"</p>
The Potential of GNSS-R for Freeze/Thaw Surface State Monitoring in the New Space Era
<p><em><strong>GNSS-R based F/T surface state maps over the Tibet Plateau, Andes Mountains, and Rocky Mountains covering two full F/T cycles 2019-2020:</strong> </em></p> <p><em><strong>(a) </strong>Number of frozen months during 2019</em></p> <p><em><strong>(b)</strong> number of thawed months during 2019</em></p> <p><em><strong>(c) </strong>number of frozen months during 2020</em></p> <p><em><strong>(d)</strong> number of thawed months during 2020</em></p> <p><em><strong>(e) </strong>difference of the number of frozen months between 2019 and 2020</em></p> <p><em><strong>(f) </strong>difference of the number of thawed months between 2019 and 2020</em></p> <p><em><strong>(g) </strong>number of frozen months during the 2019 freezing period (September to December)</em></p> <p><em><strong>(h) </strong>number of thawed months during the 2019 thawing period (January to June)</em></p> <p><em><strong>(i) </strong>number of frozen months during the 2020 freezing period (September to December)</em></p> <p><em><strong>(j)</strong> number of thawed months during the 2020 thawing period (January to June)</em></p> <p><em><strong>(k) </strong>difference of the number of frozen months between 2019 and 2020 during the freezing period (September to December)</em></p> <p><em><strong>(l) </strong>difference of the number of thawed months between 2019 and 2020 during the thawing period (January to June)</em></p>
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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.