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455 results for “microwave”
Data and code for "Niobium Quantum Interference Microwave Circuits with Monolithic Three-Dimensional (3D) Nanobridge Junctions"
<p>Data and measurements scripts for the paper "Niobium Quantum Interference Microwave Circuits with Monolithic Three-Dimensional (3D) Nanobridge Junctions"</p> <p>The package "stuelab" used in the scripts is also included. <br><br>Funding by the Deutsche Forschungsgemeinschaft (DFG) via Grants No. BO 6068/1-1, No. BO 6068/2-1, and No. KO 1303/13-2, and support from the COST actions NANOCOHYBRI (CA16218) and SUPERQUMAP (CA21144)</p>
Exploring the impact of varied background on quantification of soil carbon content using microwave and millimeter wave signal reflectance
<p>The quantification of soil carbon content is paramount to the advancement of soil carbon management practices, serving as the bedrock for the development and implementation of various carbon-negative and carbon-neutral technologies. These technologies are crucial in the battle against climate change and in enhancing soil health to boost agricultural productivity. Therefore, we employ an innovative sensor technology comprised of a compact array, including 18 pairs of radar transmitters (TX) and receivers (RX) within a microwave radar array sensor, alongside a configuration of 20 TX and 20 RX pairs in a millimeter wave radar array sensor. For this kind of setup to work, the sensors need to be carefully tested to make sure they can capture changes in the soil's carbon content over time and space.</p> <p>This dataset presents an exhaustive series of raw data aimed at evaluating the efficacy of these two sensor technologies in detecting soil carbon content. A distinctive aspect of this study is the examination of sensor performance across four divergent backgrounds—glass, metal, sponge, and wood surfaces—to ascertain the influence of background material on radar wave reflectance and, consequently, measurement accuracy. Furthermore, the investigation extends to the analysis of sensing distance as a critical parameter for classification accuracy, with the microwave radar array sensor tested across distances ranging from 1 inch to 4 inches and the millimeter wave radar sensor evaluated at intervals of 2.5 inches, 5 inches, 7.5 inches, and 10 inches. By providing a comprehensive dataset on the performance evaluation of cutting-edge sensor technologies, this study aims to facilitate further advancements in the field of soil carbon management, ultimately supporting the global effort to combat climate change through innovative and sustainable agricultural practices.</p>
Influence of variable conditions of microwave-assisted hydrotropic pretreatment on the composition of biomass and its susceptibility to enzymatic hydrolysis.
<p>The results of the first stage of research concerning the influence of variable process conditions of microwave-assisted hydrotropic pretreatment with the use of NaCS on the composition of biomass of various origins, the level of extractivity and susceptibility to hydrolysis. The work was supported by the National Science Centre, Poland, grant No. 2020/37/B/NZ9/00372.</p>
Microwave-activated gates between a fluxonium and a transmon qubit
<p>Numerical data and figures generators for the paper A. Ciani, B. Varbanov, N. Jolly, C. Andersen, B. Terhal "Microwave-activated gates between a fluxonium and a transmon qubit" </p>
Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices
<p>Raw data for the manuscript "Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices".</p> <p>In reference to the manuscript, the dataset is organized in three subsets, each related to one of the Figures in the main texts of the article:</p> <ol> <li>The return losses (S11 spectra) from the reconfigurable microwave resonators, in the form of .s1p files, realized with different tuning conjugated polymers;</li> <li>The electrochemical and microwave characterization of an enzymatic reaction cell / microwave resonator assembly;</li> <li>The microwave characterization of a amplitude- and frequency-tunable resonator.</li> </ol>
Report of microwave oven for synthesis of N/TiO2 photocatalysts
<p>This file contains reports created by the microwave oven during the synthesis of N/TiO2 photocatalysts, in which the temperature, inner pressure of the reaction vessel, and energy delivered by the equipment are controlled over time. </p>
Data from: Ultra-low-noise Microwave to Optics Conversion in Gallium Phosphide
<p>Source data for Figures.</p>
Effect Of Pulsed Electromagnetic Field And Microwave Therapy On Pain And Physical Function In Older Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
<p><strong><span>Background and purpose:</span></strong><span> </span><span>Pulsed electromagnetic field (PEMF) therapy and microwaves (MW) are two electrotherapy modalities that have shown to improve pain and function in patients with knee osteoarthritis (KOA). Nevertheless, the effectiveness of these therapies is controversial due to diversity in the application parameters and treatment protocols in these patients. The objective is to compare the effectiveness of active PEMF versus MW, as well as sham PEMF, in addressing pain and improving functionality for treating KOA.</span></p> <p><strong><span>Methods:</span></strong><span> </span><span>Double-blind, placebo-controlled, randomized clinical trial. Participants diagnosed with KOA were assigned to an intervention combining an exercise program with active PEMF, MW, or sham PEMF, delivered in three weekly sessions over four weeks. The main outcomes were pain, reported on a 0–10 cm visual analogue scale (VAS), and functionality, as evaluated with the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire, and the Timed Up and Go test. The outcomes were measured at pre-intervention, immediately post-intervention, and one and four months after the intervention.</span></p>
Data Repository for the article: "Control of multi-modal scattering in a microwave frequency comb"
<p><strong>Abstract:</strong></p> <p>Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system.<br>We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes.<br>The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. <br>Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. <br>The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.</p> <p> </p> <p>Article DOI:</p> <p>ArXiV: <a href="https://doi.org/10.48550/arXiv.2402.09068">arXiv.2402.09068</a></p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains the datased used in the article "Control of multi-modal scattering in a microwave frequency comb". Scripts to generate the figures are also included.</p>
Photon Pressure with an Effective Negative Mass Microwave Mode
<p>Data and measurements scripts for the paper "Photon Pressure with an Effective Negative Mass Microwave Mode".</p>
Infrared-Microwave-Sounding methanol
<p>Monthly daytime methanol (ppbv) for 2008-2018 produced using the Rutherford Appleton Laboratory Infrared-Microwave-Sounding scheme. Further data description can be found in Pope et al. (2021) and the associated supplementary materials. </p> <p>This data has been used in Sands et al. (2024), currently available as a preprint: https://doi.org/10.5194/egusphere-2024-503. </p>
Data and code for figures in "Level attraction in a microwave optomechanical circuit"
<p>Data and code used to produce the figures in "Level attraction in a microwave optomechanical circuit".</p> <p>The code is tested with Python 2.7.14, Matplotlib 2.1.1, Scipy 1.1.0, Numpy 1.14.0.</p>
Supplement to "Microwave and submillimeter wave scattering of oriented ice particles"
<p>This data set contains the simulated brightness temperatures and the corresponding atmospheric states for the radiative transfer simulations for the journal article "<a href="https://doi.org/10.5194/amt-2019-382">Microwave and submillimeter wave scattering of oriented ice particles</a>", which is on Atmospheric Measurement Techniques (AMTD).</p>
University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)
<p><strong>ABSTRACT </strong></p> <p>Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations. This dataset, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID), contains S-parameter measurements from experimental scans of MRI-derived breast phantoms, obtained with a pre-clinical breast microwave sensing system operating over 1-8 GHz. The dataset consists of measurements from over 1250 scans of a diverse array of phantoms. The phantom array consists of phantoms of various sizes and breast densities. The .stl files used to produce the 3D-printed phantoms are also included in the dataset. We hope that this dataset can serve as a resource for researchers in breast microwave sensing to evaluate signal processing, image reconstruction, and tumour detection methods.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Tyson Reimer, Jordan Krenkevich, Stephen Pistorius, June 16, 2021, "University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)", IEEE Dataport, doi: https://dx.doi.org/10.21227/1y0z-8t98.</p> <p>https://ieee-dataport.org/open-access/university-manitoba-breast-microwave-imaging-dataset-um-bmid</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Results of the analysis of the structure of biomass after microwave-assisted hydrotropic pretreatment.
<p>Results of analyzes of the structure of lignocellulosic biomass of various origins. The analyzes include the use of FTIR, SEM, XRD and NMR techniques. The work was supported by the National Science Centre, Poland, grant No. 2020/37/B/NZ9/00372.</p>
Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration
<p>Data and code for the manuscript <em>Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration</em> accepted for publication to<em> </em> <em>Earth and Space Science</em> in October 2021.</p> <p>The dataset contains 7 month of total losses (transmitted - received power levels) and retrieved water vapor density from a 4.87 km long full-duplex E-band commercial microwave link (CML) operating at 73.5 and 83.5 GHz in Prague, CZ. The CML was operated as a part of a mobile phone backhaul. Furthermore, observations of air temperature, and air relative humidity from sites close to the CML end nodes are provided. Finally, theoretical gaseous attenuation calculated from the air temperature and relative humidity is included as a part of the dataset.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. All time series are regular and have 5-min temporal resolution. Metadata are stored in text files.</p> <p>The code is in a form of R Markdown files and html notebooks. Results presented in the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration and in its Supporting information are fully reproducible using this dataset.</p>
Dataset: Tailoring the Magnetic and Structural Properties of Manganese/Zinc Doped Iron Oxide Nanoparticles through Microwaves-Assisted Polyol Synthesis
<p>Data set</p>
A microwave link simulation dataset for rainfall observation applications
<p>A microwave link simulation dataset is developed for theoretical research of rainfall observation applications. The dataset is built based on electromagnetic propagation theory attenuation effects with measured raindrop size distribution data from a PARSIVEL disdrometer and air temperature, air pressure and humidity data from nearby weather stations. Frequencies from 6 to 180 GHz, horizontal or vertical polarization, link lengths from 0.1 km to 20 km, and quantization resolutions from 0 to 1 dB are all fully considered for various existing and future complications. The dataset can be utilised to validate the performance of the microwave link-based rainfall inversion process algorithm in various equipment configurations and to assess the potential of microwave link-based rainfall monitoring technology in future high-frequency communication infrastructure conditions on a theoretical level with a very low cost.</p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Dataset for "Microwave Observations of Ganymede's Sub-Surface Ice Part II: Reflected Radiation"
<p>Dataset for figures in paper "Microwave Observations of Ganymede's Sub-Surface Ice Part II: Reflected Radiation".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.