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32 results for “reflectometry”
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Reflector heights in the Arctic permafrost areas measured by GNSS interferometric reflectometry
<p>This dataset is about the measurements of reflector height, i.e., the vertical distance between receiver antenna and groud surface, at the GNSS sites in the Arctic permafrost areas. Each data file has four columns. The 1st and 2nd show the time as year and doy, respectively. The 3rd and 4th are the reflector height and its uncertainty, respectively.</p>
Reflectometry curves (XRR and NR) and corresponding fits for machine learning
<p>This is a compiled dataset of raw X-ray reflectivity (XRR, reflectometry) measurements together with corresponding fit parameters, intentionally published to use as training or test data for machine learning models. (The authors aim to include NR data in further versions of this dataset and plan to include other substrates and materials for XRR. Contributions welcome!)</p> <p><br> <strong>An interactive documentation can be found in <em>"README.html"</em> or at <a href="https://schreiber-lab.github.io/reflectometry-dataset">https://schreiber-lab.github.io/reflectometry-dataset</a>.</strong></p> <ul> <li>Data structure</li> </ul> <p>All data is provided in an hdf5 file, following <a href="https://www.nexusformat.org/">NeXus</a> convention with respect to the provided metadata in the hdf5 attributes. Some datesets have been measured in-situ and therefore there are stacks of curves that correspond to the different layer thicknesses of the same material on top of SiOx. The measured data is provided under experimental and the corresponding fit parameters under fit. Additional information is collected in metadata.</p> <ul> <li>Where to find the dataset and how to contribute</li> </ul> <p>Have a look at <a href="https://github.com/schreiber-lab/reflectometry-dataset">github</a> and <a href="https://doi.org/10.5281/zenodo.6497438">zenodo</a>. In case you wish to contribute further curves to this dataset or have ideas how to improve the dataset or where else to deposit it, please contact the authors at softmatter AT ifap.uni-tuebingen.de.</p>
Cryogenic hyperabrupt strontium titanate varactors for sensitive reflectometry of quantum dots
<p>Supplementary Data for:</p> <p>"Cryogenic hyperabrupt strontium titanate varactors for sensitive reflectometry of quantum dots"</p> <p>Rafael S. Eggli, Simon Svab,Taras Patlatiuk, Dominique Trüssel, Miguel J. Carballido, Pierre Chevalier Kwon, Simon Geyer, Ang Li,<br> Erik P. A. M. Bakkers, Andreas V. Kuhlmann, and Dominik M. Zumbühl</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>
Supporting information for "Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p><p> </p><p>All data and code (notebooks) required to reproduce the analysis can be found within the "supporting_data_analysis.zip" archive. This archive contains two sub-directories:</p><ul><li>insituAnalysis<ul><li>Jupyter notebook outlining how to perform the `on-the-fly' analysis.</li><li>Data directory containing all temporally sliced neutron reflectometry data.</li></ul></li><li>MaxEnt<ul><li>Jupyter notebook outlining how to perform the maximum entropy modelling approach for polymer volume fraction profiles.</li><li>Data directory containing relevant neutron reflectometry data and PCHIP spline modelling by Gresham et al. (<a href="www.doi.org/10.1107/S160057672100251X">10.1107/S160057672100251X</a>).</li><li>Code available on the <a href="https://github.com/refnx/refnx-models/tree/master/MaxEntVFP">refnx-models GitHub repo</a>.</li></ul></li></ul><p> </p>
Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"
<p>This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks".</p> <p>In particular, the following data is provided:</p> <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>
Jupiter Notebook and example data for "Prospects for a camera-based detector for Neutron Reflectometry"
<p>We report the outcome of a proof-of-principle (IPTS-29165) neutron reflectivity measurement obtained using a neutron scintillator and a Photonis (brand) camera. We were motivated to test this technology because it provides much better spatial resolution and count rate capability than the BL4A <sup>3</sup>He position sensitive detector (Table 1). The report describes the detector setup, challenges encountered, a reflectivity measurement and next steps.</p> <p>Two example measurements are provided and a Jupyter Notebook to create a NumPy binary file consisting of event positions and times.</p>
Reflectometry with Galileo Signals: Ocean and Amazon Basin events from CYGNSS
<p>This dataset contains the results of the work described in "Reflectometry with Galileo Signals: Ocean and<br>Amazon Basin events from CYGNSS" paper, presented in </p> <p>In that work, we have processed Galileo reflectometry events, based on the CYGNSS mission database for 'ocean' and 'Amazon Basin' events.<br><br>For further detail, see the paper.</p>
Time domain reflectometry soil moisture data for Niwot Ridge, 1992 - 2002.
Soil moisture was estimated at several locations on Niwot Ridge using a portable PC in conjunction with time-domain reflectometry (TDR) technology. The system used consisted of a Tektronics 1502B reflectometer, a Tektronics SP232 interface module, a Compaq LTE/232 PC, coaxial cable, and a probe that was left semi-permanently in the soil. The reflectometer generated an electrical step voltage pulse which was propagated down the cable to the probe. The reflected voltage waveform was measured, stored, and displayed by the reflectometer and the Tektronics SP232 interface module. The waveform is represented as a 2-dimensional data array, with the x-axis representing time (which is converted to distance using the dielectric constant of the cable) and the y-axis representing voltage for the given time: in this system this value is represented in millirho units (Tektronics 1991). The length of the waveform is determined by measuring the difference between the soil air interface (SAI) and the end of the voltage step reflection (RE). The SAI value is equivalent to the apex of the first peak in the wave and the RE value is equivalent to the intersection of tangents "drawn" at (a) the bottom of the wave and (b) the point of the steepest rise (see Taylor and Seastedt 1992). The waveform length is then used to estimate the volumetric and gravimetric moisture of the surrounding soil.
Time Domain Reflectometry at the Sevilleta National Wildlife Refuge, New Mexico (1996-2005)
This file contains hourly time-domain reflectometry (TDR) soil moisture data for 1996-2005. A key factor in a spatially explicit water-balance model is a measure of moisture in the soils over time. This metric is crucial for both calibration and validation of such a model. One of the best methods of measuring soil moisture on a continuous basis is TDR. Therefore, a network of TDR soil moisture sensors was installed at all meteorological stations on the Sevilleta National Wildlife Refuge. At two of the sites the sensors were measured on an hourly basis in conjunction with the meteorological variables. At the four other sites the sensors were measured on a much less frequent basis - about every two weeks. Sensors were installed in pits in sets of five. One sensor was installed vertically adjacent to the pit to measure the top 30 cm of soil. The other four were installed horizontally in the face of the pit at 5, 10, 20, and 40 cm. Pits were then backfilled.
Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"
<p>Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"</p>
GNSS interferometric reflectometry data for Silveira et al. 2024
<p>Supporting data for </p> <p>L. Silveira, V.H. Almeida Jr., M.K. Yamawaki, E. Puhl, R. Manica, E. Toldo Jr., T. Silva, F. Geremia-Nievinski (2024), Wide-swath satellite altimetry reveals the 2024 Porto Alegre extreme flood was intensified by backwater effect across choked river section, submitted.</p>
Sensing Reflector Height Variation on Seconds-Scale Using Low Earth Orbit Interferometric Reflectometry (LEO-IR)
<p><span>The simulated data (SNR, elevation and azimuth angles etc.) used in this study.</span></p> <p><span>File name format: arc number for GNSS (or LEO) <span>constellation</span> '_' PRN '_' 'seg'+arc number for a GNSS (or LEO) satellite</span></p> <p><span>File content format: 1[year] 2[month] 3[day] 4[hour] 5[minute] 6[seconds] 7[elevation angle] 8[azimuth angle] 9[chang rate of elevation angle] 10[SNR] 11[rh_ref] 12[rhdot_ref] 13[t_seconds]</span></p> <p> </p>
Compact RINEX dataset of: Optimizing simultaneous water level and wave measurements from multi-GNSS interferometric reflectometry over one year at an exposed coastal site
<p>Compact-RINEX files (Hatanaka-compressed format) for each day of the deployment. Sampling frequency is 1 Hz.</p>
Correlation of Anal Acoustic Reflectometry Parameters With Degree of Rectal Intussusception and Prolapse
ClinicalTrials.gov study NCT02774798. IPD Sharing: NO. Countries: 1. Publications: 5.
Evaluation of the Rectal Reflex Using Anal Acoustic Reflectometry
ClinicalTrials.gov study NCT02782377. IPD Sharing: NO. Countries: 1. Publications: 9.
Evaluation of the Fast Fill Technique for Anal Acoustic Reflectometry (AAR) in the Incontinent Anal Sphincter
ClinicalTrials.gov study NCT02782364. IPD Sharing: NO. Countries: 1. Publications: 6.
Linear reciprocating tribometer for in situ neutron reflectometry of soft matter
Open the record for dataset details and reuse information.
Doppler Reflectometry Graphical Demonstration Movie with IPF-FD3D
<p>Doppler reflectometry is a microwave scattering diagnostic for measuring wavenumber resolved density fluctuation strengths in plasmas. Especially in fusion plasmas, turbulence is an ever-present phenomenon.</p> <p>This movie illustrates the scattering processes involved in building a wavenumber spectrum from scattering at different wavenumbers. For artistic purposes, the wave packets are much shorter than in a real application, where is would be quasi-continuous. The probing wave is emitted at different angles, which satisfies a different wavenumber matching condition each time.</p> <p>The finite difference time domain code IPF-FD3D was used to calculate the wave field inside the plasma.</p> <p>Reference: Lechte C, Conway G D, Görler T, Tröster C and the ASDEX Upgrade Team 2017 Plasma Phys. Contr. Fusion 59 075006</p> <p> </p>
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