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77 results for “optical fiber”
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
Atomic clock dataset for 'Coherent Optical-Fiber Link Across Italy and France'
<p>Dataset of the comparison of the atomic clocks at LNE-SYRTE and INRIM via optical fibre link between October 2021 and February 2022. Results discussed in Clivati et al., Coherent Optical-Fiber Link Across Italy and France, <em>Phys. Rev. Applied, American Physical Society, </em><em> 18</em>, 054009, <strong>202<em>2</em></strong>.</p> <p>The involved atomic clocks are the Cs fountains SYRTE-F02Cs, IT-CsF2, the Rb fountain SYRTE-F02Rb and the Yb optical lattice clock IT-Yb1.</p> <p>Data is organized in folders, one for each comparison. In the folders data is separated is one file per day. Data is reported as fractional frequency ratios in bins of 864 s. Timetags are reported in modified Julian date (MJD). A validity flag is given where 0 = invalid, valid otherwise. Each folder includes a yaml file with metadata required for generalized data processing as in [Lodewyck et al., 2020]. The Python package used for data processing can be found on <a href="https://github.com/INRIM/tintervals">github.</a></p> <p> </p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Dataset used in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d<br> </p> <p> </p>
Heat Dissipation Test with single Fiber Optic cable
<p> A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer.</p> <p>The data presented here are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data.</p> <p>Additionally, we included all the raw data obtained from the heated cable installed in the N325 borehole which was calibrated externally. And finally, we added two more files were we included the smooth heating curves and log-derivative resulting from filtering all data obtained from the heat dissipation test.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin
<p>This dataset is associated with "Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin", by Mikko Partanen, Hyeonwoo Lee, and Kyunghwan Oh, Photonics Res. 9, 2016 (2021) [https://doi.org/10.1364/PRJ.433995].</p> <p>It includes data files and Matlab (R2017b) scripts to allow for the replication of the figures. The data files give the oscillator mirror position in the units of nanometers measured at the rate of 200 times per second.</p>
Estimation of groundwater flow rate by an actively heated fiber optics based thermal response test in a grouted borehole
<p>The dataset contains the numerical data and the <em>in-situ</em> measurements in the manuscript titled "Estimation of groundwater flow rate by an actively heated fiber-optics-based thermal response test in a grouted borehole". The data is stored in MAT files, which are Binary MATLAB files. There are a series of codes used in this manuscript to estimate groundwater flow rates. The codes were written in MATLAB Live Script, version 2021b.</p> <ul> <li>The numerical data contains temperatures of the heating stage in different thermal response tests in a numerical model, which considers the borehole effects. The model is set up by COMSOL Multiphysics, and a series of flow rates is set to the model respectively for different thermal response tests.</li> <li>The <em>in-situ</em> measurements include temperatures of the heating stage in an actively heated fiber-optics-based thermal response test, which was performed in July 2021 in the grouted borehole, which is located in the lower section of the Sima bend of the Yangtze River. The temperature for the flow rate estimation was thinned to 120 s records from 10s records for the limited computing resources.</li> <li>The <em>data_process.mlx</em> provides pre-processing for the observational data recorded by Silixa Ultima-M MK2 DTS. The <em>estimation_process.mlx </em>gives a groundwater flow estimation case in a grouted borehole.</li> </ul>
Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks
<p>Research data of <em>Modelling and Simulation in Engineering </em>journal article “Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks”.</p> <p>Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine and raised sine apodizations are studied to achieve the main requirements, which are - maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm), FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.</p> <p> </p>
Insight into the Mechanical Coupling Behavior of Loose Sediment and Embedded Fiber-optic Cable using Discrete Element Method
<p>The dataset contains the simulation codes and generated data in the manuscript titled "Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method". The codes (M files) were written in MatDEM, version 3.0 (free access at <strong>www.matdem.com</strong>), and the data is stored in MAT files.</p> <ul> <li>Test2D_2L1.m - codes for initial compacted elements</li> <li>Test2D_2L1.mat - generated data for initial compacted elements</li> <li>Test2D_2L2.m - codes for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L2.mat - generated data for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L3.m – codes for confining pressure setting</li> <li>Test2D_2L-0MPa3.mat ~ Test2D_2L-1.0MPa3.mat - generated data for confining pressure setting</li> <li>Test2D_2L4.m – codes for fiber-optic cable pullout tests under various confining pressures</li> <li>Test2D_2L-05-26-20mm-0MPa-un-No1-4.mat ~ Test2D_2L-07-21-20mm-1MPa-un-No1-4.mat - generated data for fiber-optic cable pullout tests under various confining pressures</li> </ul>
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>Figure16: <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
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Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor
<p>Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor.</p> <p>Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.</p> <p> </p> <p>A full description can be found in:</p> <p>Schilperoort, B. (2022). <em>Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors</em> [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b</p>
Dataset in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d</p>
High-resolution air temperature observations near the surface using fiber-optic distributed temperature sensing
<p>Time-lapse animation of air temperature observations near the surface, highlighting wave-like motion in opposite direction of the mean wind. </p> <p> </p>
Propagation of optical pulses through a periodic dielectric structure (Bragg Grating) designed as a delay line interferometer. Example of a designed fiber Bragg grating.
<p>Propagation of optical pulses through a periodic dielectric structure (Bragg Grating) designed as a delay line interferometer. <br> <br> A 9 cm fiber Bragg grating is designed (and fabricated) for this purpose.</p> <p>The top video shows the simulated propagation of a single optical pulse.</p> <p>The bottom video shows the simulated propagation of a sequence of optical pulses, with relative pi-phase shifts in the last pulse, showing both constructive and destructive interferences effect</p>
Data for "Heat treatment and fiber drawing effect on the matrix structure and fluorescence lifetime of Er- and Tm-doped silica optical fibers"
<p>Includes data for absorption and attenuation measurements and calculations, profiles of refractive index and concentrations, TEM images, XRD patters, and data for fluorescence decay curves presented in the graphs.</p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Stanford fiber-optic DAS array: Earthquake detection dataset
<p>Repurposing the fiber-optic cables from the existing telecommunication infrastructure makes it possible to record dense continuous seismic data in urban areas at low cost. From 2016 to 2019, we connected a disctributed acoustic sensing (DAS) interrogator unit to the fiber-optic cables in telecommunication conduits under Stanford University campus, recording years of continuous seismic data.</p> <p>This repository contains processed TensorFlow Record data files containing examples of earthquake and background noise signals recorded by the Stanford fiber-optic DAS array. These data were used for training, evaluation, and testing of a convolutional neural network for earthquake detection. </p> <p> </p> <p> </p>
Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free Fiber-optic Raman Spectroscopy
<p>Dataset for the manuscript "<span>Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free </span><span>F</span><span>iber-optic Raman Spectroscopy"</span></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)
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DANDI Archive for NWB datasets
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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.