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342 results for “GNSS”
BiodivAR comparative user test (GNSS vs. RTK) dataset
<p>This dataset comprises data used to evaluate and compare the BiodivAR open source web location-based AR application in a presentation submitted to the FOSS4G 2023 conference in Pritzen, Kosovo. The application is available at <https://biodivar.heig-vd.ch> and its source code at <https://github.com/MediaComem/biodivar>.</p>
GNSS data IGS and SOAM, GAMIT
<p>GNSS data used to carry out the analysis of the submitted article DOI: <a href="https://doi.org/10.22541/essoar.168319853.39124142/v1">10.22541/essoar.168319853.39124142/v1</a>. POS files are in ITRF 2008 reference frame.</p>
Pre-processed and modeled GNSS time-series after the 2011 Tohoku Earthquake
<p>The raw, pre-processed, and modeled GNSS time-series of the 213 GEONET sites in the Tohoku region, Japan, from Mar. 12, 2011 to Nov. 20, 2021, relative to the Okhotsk plate (Argus et al., 2011, <em><em>Geochemistry, Geophysics, Geosystems</em></em>).</p> <p>The original GNSS time-series are F5 solutions, which are distributed by Geospatial Information Authority of Japan (GSI, https://www.gsi.go.jp/). The details and availability of F5 solutions are written in Takamatsu et al. (2023, Earth, Planets, and Space) https://doi.org/10.1186/s40623-023-01787-7.</p> <p>The GNSS time-series processing was performed by Tomita (submitted), and the following signals were excluded from the raw time-series: seasonal variation, coseismic step, antenna maintenance offset, and common mode errors. Then, the pre-processed time-series were modeled by a trajectory modeling method considering postseismic deformation of the 2011 Tohoku earthquake, the Boso SSEs, and postseismic deformations due to aftershocks and L-ASE (long-term aseismic slip event) since late 2019.<br> <br> "sitelist.txt" - Site information file<br> column 1: Full site ID<br> column 2: 4digits site ID<br> column 3: Longitude [deg]<br> column 4: Latitude [deg]<br> column 5: Height [m] <br> <br> "pre-process/xxxx.txt" - Time-series at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: raw East-West displacement [m]<br> column 3: raw North-South displacement [m]<br> column 4: raw Up-down displacement [m]<br> column 5: pre-processed East-West displacement [m]<br> column 6: pre-processed North-South displacement [m]<br> column 7: pre-processed Up-down displacement [m]</p> <p>"model/xxxx/prediction_yy.txt" - Time-series for yy component (yy=EW, NS, UD) at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: modeled displacement excluding the Boso SSEs [m]<br> column 3: modeled displacement excluding the Boso SSEs and postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. [m]<br> column 4: modeled displacement excluding the Boso SSEs, postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. and the 2019 L-ASE [m]</p> <p><br> The displacement on Mar. 12, 2011 was initially set to be zero before the pre-processing, but the removal of the above factors provided some deviation from zero.</p> <p>The raw time-series excluded outliers from the original F5 solutions, and the raw time-series were transformed into the Okhotsk plate reference.</p> <p>Following the above trajectory modeling, the fully-relaxed postseismic displacement fields due to 2015 Feb. 17 Sanriku-oki earthquake ("Table_displacement1.xlsx"), the 2015 May 13 Miyagi-oki earthquake ("Table_displacement2.xlsx"), and summation of the 2021 Feb. 13 Fukushima-oki, the 2021 Mar. 20 Miyagi-oki, and the 2021 May 1 earthquakes ("Table_displacement2.xlsx") were calculated. Moreover, the cumulative displacement field due to the 2019 L-ASE since Nov. 25, 2019 was also calculated. In those files, the estimation errors are also shown as 1σ standard deviation obtained from diagonal components of the model covariance matrices. </p> <p> </p> <p>The details of these data are introduced in the corresponding paper (Tomita, submitted).</p>
GNSS tomography data for assimilation into the Weather Research and Forecasting model
<p>The data set contains GNSS troposphere tomography estimations of 3D wet refractivity fields for a part of Central Europe (mostly Germany and Czech Republic), for the period of 29 May–14 June 2013 when heavy-precipitation events were observed. The refractivity fields were estimated using two different GNSS tomography models: ATom software package (https://github.com/GregorMoeller/ATom) developed at TU Wien, and the TOMO2 model (Rohm and Bosy, 2011; Rohm et al., 2014; Trzcina and Rohm, 2019) developed at the Wrocław University of Environmental and Life Sciences. Further description of the GNSS tomography processing can be found in the paper by Hanna et al. (2019).</p>
Output data for manuscript "Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland"
<p>We retrieve sea levels in polar regions via GNSS reflectometry (GNSS-R), using signal-to-noise ratio (SNR) observations from eight POLENET GNSS stations. Although geodetic-quality antennas are designed to boost the direct reception from GNSS satellites and to suppress indirect reflections from natural surfaces, the latter can still be used to estimate the sea level in a stable terrestrial reference frame. Here, typical GNSS-R retrieval methodology is improved in two ways, 1) constraining phase-shifts to yield more precise reflector heights and 2) employing an extended dynamic filter to account for the second-order height rate of change (vertical acceleration). We validate retrievals over a 4-year period at Palmer Station (Antarctica), where there is a co-located tide gauge (TG). Because ice contaminates the long-period tidal constituents, we focus on the main tidal species (daily and subdaily), by employing a deseasonalization filter. The difference between sub-hourly GNSS-R retrievals of the ocean surface and TG records has a root-mean-square error (RMSE) of 15.4 cm and a correlation of 0.903, while the tidal prediction has a RMSE of 1.9 cm and a correlation of 0.998. There is excellent millimetric agreement between the two sensors for most eight major tidal constituents, with the exception of luni-solar diurnal (<em>K<sub>1</sub></em>), principal solar (<em>S<sub>2</sub></em>), and luni-solar semidiurnal (<em>K</em><sub>2</sub>) components, which are biased in GNSS-R due to the leakage of the GPS orbital period. We also compare the GNSS-R tidal constituents from seven additional POLENET sites, without co-located TG, to global and local ocean tide models. We find that the root-sum-square-error (RSSE) of eight major constituents varies between 26.0 cm and 56.9 cm for different models. Given that the agreement in tidal constituents between the TG and GNSS-R was better at Palmer Station, we conclude that assimilating the GNSS-R retrievals into tidal models would improve their accuracy in Antarctica and Greenland, provided that care is exercised to avoid the orbital period overtones and also sea ice.</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
RINEX files from low-cost GNSS receivers equipped with few types of low-cost and high-end antennas, Wrocław, Poland; July, 2022
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and few types of low-cost and high-end antennas. Time period: 05.07.2022 - 18.07.2022.</p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </p>
Input GNSS time series data for Tanaka et al. (2024), JGR Solid Earth
<p>Detail explanatios are in the uploaded README file. </p>
Rupture Process of the 2017 Mw 6.3 Earthquake in Jinghe, Northwest China Constrained by GNSS, InSAR and teleseismic waveforms
<p>This dataset include:</p> <p>1. Slip model of 2017 Mw 6.3 Jinghe earthquake invert with GNSS, InSAR and teleseismic waveforms.</p> <p>2. InSAR LOS offsets caused by the mainshock (The file named by sar.static )</p> <p>3. Aftershocks locations relocated with hypoDD</p> <p> </p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Raw GNSS data divided into observation data in CRX, RINEX and mat format and navigation data in SP3 format and both group of the data in mat format
Open the record for dataset details and reuse information.
Dataset: Genasys Inc. (GNSS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Results of GARPOS-MCMC v2.0.0 for GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI"
<p>Contains the results of "<a href="https://doi.org/10.5281/zenodo.13316197">GARPOS-MCMC v2.0.0</a>" for the GNSS-A data publishied by the Japan Coast Guard (<a href="https://doi.org/10.5281/zenodo.6417480">https://doi.org/10.5281/zenodo.6417480</a>). <br>Please refer Watanabe et al. (preprint will be available soon) for the details for methods and figure captions.</p> <p>Contents:</p> <p>- Results of GARPOS-MCMC v2.0.0 for the foure SGO-A sites (FUKU/KUM2/MYGI/TOS2). </p> <ul> <li>File name contains the site name and campaign code <ul> <li>*-chain.csv: Values of MCMC samples </li> <li>*-chain.pdf: Plots of MCMC samples</li> <li>*-hist.pdf: Plots of distributions and histograms for the parameters </li> <li>*-m.p.dat: Model parameters for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>*-obs.csv: Acoustic data including residuals for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>*-percentile.csv: Percentiles of the posterior pdfs for each parameters</li> <li>*-res.dat: Positioning result for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>files in fig/ show the travel time residuals and parameters for the maximum a posteriori sample in the series</li> </ul> </li> <li>Directory "tested models" contains the MCMC series with inverse temperature of 1/(log <em>n</em>). <ul> <li>fig_searchres*png: Plots of relative WBIC values</li> <li>searchres-*csv: WBIC and relevant values for each model</li> <li>Others: same as above</li> </ul> </li> </ul>
High-rate GNSS data collected during shake table experiment
<p>The dataset contains high-rate GNSS data collected during shake table experiment. A displacement-controlled experiment was designed and carried out at the UWM Olsztyn campus. The in-house developed shake table provided artificial dynamic displacements. The device induced forwardbackward uniaxial motion with dedicated speed and range, simulating a sin-wave displacement with constant frequency and amplitude. We have induced a few ~50-s long harmonic motions in the E-W direction of frequency in the 1-4 Hz range and amplitudes of 5-15 mm. A whole data collection period lasted approximately 1 hour, also including initial and inter excitations’ static periods.</p>
GNSS RF Recordings Dataset from Static Antenna
<p>GNSS RF recordings dataset from the static antenna located on the rooftop of the Tampere Wireless Research Center. The recordings were performed using a NI USRP-2953R and an external clock reference Spectracom GSG-6. The files are provided in binary format. A non-selective gain from the USRP has been applied during the recordings.</p> <p>Novatel_20211130_resampled_10MHz_8bit_IQ_gain25</p> <ul> <li>Date: 2021/11/30 - 8:40 (UTC)</li> <li>Centre frequency: 1575.42 MHz</li> <li>Sampling frequency: 40 MHz</li> <li>Intermediate frequency: 0 Hz (Baseband)</li> <li>Quantization: 8 bits integers, I+Q </li> <li>Gain: 25 dB (non-selective)</li> <li>Note: The In-Phase and Quadraphase measurements are recorded in binary as follow: I_1 Q_1 I_2 Q_2, etc.</li> </ul> <p>Novatel_20240731_142746_40MHz_10MHz_8bit_real_gain15.bin</p> <ul> <li>Date: 2024/07/31 - 11:27 (UTC)</li> <li>Centre frequency: 1575.42 MHz</li> <li>Sampling frequency: 40 MHz</li> <li>Intermediate frequency: 10 MHz</li> <li>Quantization: 8 bits integers, real</li> <li>Gain: 15 dB (non-selective)</li> <li>Note: The real measurements are recorded in binary as follow: R_1 R_2 etc.</li> </ul> <p> </p>
GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation
<p>The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.</p>
GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity
<p>This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units.</p> <p>The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached.</p> <p>Reference:</p> <p><a href="https://publications.copernicus.org/">Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.</a></p> <p> </p> <p>POC: Jie.Gong@nasa.gov</p> <p>10/17/2024</p> <p> </p> <p>Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above.</p> <p>--- THE END ---</p>
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