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2,649 results for “Optical”
Data for "Comparing ultrastable lasers at 7×10-17 fractional frequency instability through a 2220 km optical fibre network"
<p>Here we share the relevant data of the manuscript “Comparing ultrastable lasers at 7×10<sup>-17</sup> fractional frequency instability through a 2,220 km optical fibre network”.</p> <p>Raw data was acquired using multiple synchronised, dead-time free frequency counters in Lambda-mode [1]. The integration time for each data point was 1 s. The data provided here have been processed to reflect the fractional frequency difference between the ultrastable lasers at NPL and PTB, scaled to 1542 nm. Specifically,<br> <span class="math-tex">\(y=(\nu_{\text{NPL(ULE)}}\frac{777327}{1126090}-\frac{767233}{767235}\nu_{\text{PTB(Si)}})/194.4 \ \text{THz}\)</span></p> <p>where <span class="math-tex">\(y\)</span> is the value recorded in the data files, <span class="math-tex">\(\nu_{\text{NPL(ULE)}}\)</span> and <span class="math-tex">\(\nu_{\text{PTB(Si)}}\)</span> are the optical frequencies of the ultrastable lasers at NPL (referenced to a ULE cavity) and PTB (referenced to Si cavity), respectively. The numerators and the denominators of the scaling factors correspond to mode numbers of the optical frequency comb at NPL and PTB, respectively. The expression for <span class="math-tex">\(y\)</span> corresponds to the fractional transfer beat [2] between the NPL and PTB ultrastable lasers.</p> <p>The file</p> <ul> <li>“833000_s_874000_s_data_for_fig_2.txt”</li> </ul> <p> contains the timeseries data used to compute the modified Allan deviation reported in <strong>Fig. 2a</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The file</p> <ul> <li>“432000_s_912077_s_data_for_fig_3.txt”</li> </ul> <p>contains the timeseries data used in <strong>Fig. 3.</strong> The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. These data have additionally been high pass filtered with a cut off frequency of 1 mHz to decouple the short-term instability of the optical fibre link from the drift of the ultrastable lasers (with a characteristic time >1000 s), as described in the manuscript.</p> <p>The files</p> <ul> <li>“222000_s_232000_s_data_for_supp_fig_1.txt”,</li> <li>“270000_s_288000_s_data_for_supp_fig_1.txt”,</li> <li>“754000_s_765000_s_data_for_supp_fig_1.txt”,</li> <li>“832000_s_890000_s_data_for_supp_fig_1.txt”,</li> </ul> <p>contain the timeseries data used to compute the modified Allan deviation reported in <strong>Supplementary Fig. 1</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The temporal starting point is displayed in seconds in the title of the files relative to 00:00 UTC of 2019/07/06.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Dawkins, S. T., McFerran, J. J. & Luiten, A. N. Considerations on the Measurement of the Stability of Oscillators with Frequency Counters. <em>IEEE Transactions on ultrasonics, ferroelectrics, and frequency control</em> <strong>54</strong>, 918-925 (2007).</p> <p>[2] Telle, H.R., Lipphardt, B. & Stenger, J. Kerr-lens, mode-locked lasers as transfer oscillators for optical frequency measurements. <em>Appl. Phys. B</em> <strong>74</strong>, 1-6 (2002).</p> <p> </p>
Retrieval results for optically thin clouds in the trades
<p>ASTER satellite observations at 15 m pixel resolution are used to extract the signal of optically thin clouds during the EUREC4A field campaign (https://doi.org/10.5194/essd-2021-18). The signal of optically thin clouds is derived as a residual from the all-sky minus the simulated clear-sky (https://doi.org/10.5281/zenodo.4842675) and minus the known cloudy signal according to following a common cloud masking scheme. The paper describing the method, dataset, and results is intended for publication in the journal of Atmospheric Chemistry and Physics (ACP) under Mieslinger et al., 2021.</p> <p>The dataset includes basic information of the relevant input variables to clear-sky radiative transfer simulations as well as the resulting probability density function over reflectance values and for discrete flag values (clear-sky, optically thin clouds, clouds) given an ASTER observation. This data builds the basis for any derived quantities such as the area fraction or the expected reflectance corresponding to a certain flag value.</p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data
<p>Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.</p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Microwave-to-optical conversion with a gallium phosphide photonic crystal cavity
<p>Electrically actuated optomechanical resonators provide a route to quantum-coherent, bidirectional conversion of microwave and optical photons. Such devices could enable optical interconnection of quantum computers based on qubits operating at microwave frequencies. Here we present a platform for microwave-to-optical conversion comprising a photonic crystal cavity made of single-crystal, piezoelectric gallium phosphide integrated on prefabricated niobium circuits on an intrinsic silicon substrate. The devices exploit spatially extended, sideband-resolved mechanical breathing modes at ~3.2 GHz, with vacuum optomechanical coupling rates of up to g<sub>0</sub>/2π ≈ 300 kHz. The mechanical modes are driven by integrated microwave electrodes via the inverse piezoelectric effect. We estimate that the system could achieve an electromechanical coupling rate to a superconducting transmon qubit of ~200 kHz. Our work represents a decisive step towards integration of piezoelectro-optomechanical interfaces with superconducting quantum processors.</p>
Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'
<p>Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process. </p>
Data of Relative errors in derived multi-wavelength intensive aerosol optical proberties
<p>Measurement Data of "Relative errors in derived multi-wavelength intensive aerosol optical<br> properties using cavity attenuated phase shift single-scattering<br> albedo monitors, a nephelometer, and tricolour<br> absorption photometer measurements"</p> <p> </p>
Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels
<p>These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied.</p> <p>[1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in <em>Journal of Lightwave Technology</em>, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI: 10.1109/JLT.2013.2295208.</p> <p>[2] P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.</p>
Experimental testing of 2D Optical Phased Array (OPA) performance
<p>Radiation pattern of single element antenna in PolyBoard platform (with 0.1- and 0.5-degrees resolution)</p>
Optical Cluster set definitions associated with CERTO project deliverable 4.2
<p>This set of files consists of pickle and csv files that describe the optical water class sets computed as part of the CERTO project ( https://certo-project.org ). A written description and discussion of these clusters is provided in Deliverable 4.2 from the CERTO project.</p>
Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC -- Downsampled Version
<p>This repository holds downsampled example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. For full-resolution testing data, please refer to Zenodo repository at DOI: <a href="https://zenodo.org/record/6334810#.YvD6zHZBxD8">10.5281/zenodo.6334810</a>.</p>
Radiance and Cloud Optical Thickness from Large Eddy Simulations over the Sulu Sea
<p>This repository contains the data files to accompany the paper "Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network". Please cite the paper as follows:</p> <p>Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-45, in review, 2022.</p> <p>The 6 HDF5 files were generated using a tool called EaR<sup>3</sup>T developed by Hong Chen using Large Eddy Simulations over the Sulu Sea (Yamaguchi et al., 2019). Each hdf5 file contains 6 fields: </p> <p>cot_inp_3d: COT Input: column integrated COT directly from LES data;</p> <p>rad_mca_1d: MCARaTS 1D Radiance: radiance calculated from COT Input using MCARaTS in IPA mode;</p> <p>rad_mca_3d: MCARaTS 3D Radiance: radiance calculated from COT Input using MCARaTS in 3D mode;</p> <p>rad_ret_1d: Radiance from Input COT: radiance calculated from COT Input using a pre-calculated COT vs Radiance relationship;</p> <p>cot_ret_1d: COT from MCARaTS 1D Radiance: COT obtained from MCARaTS 1D Radiance using a pre-calculated COT vs Radiance relationship;</p> <p>cot_ret_3d: COT from MCARaTS 3D Radiance: COT obtained from MCARaTS 3D Radiance using a pre-calculated COT vs Radiance relationship.</p> <p> </p>
Optical tweezer platform for the characterization of pH-triggered colloidal transformations in the oleic acid/water system
<p>Hypothesis: Soft colloidal particles that respond to their environment have innovative potential for many fields ranging from food and health to biotechnology and oil recovery. The in situ characterisation of colloidal transformations that triggers the functional response remain a challenge.</p> <p>Experiments: This study demonstrates the combination of an optical <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/micromanipulation">micromanipulation</a> platform, polarized optical video microscopy and <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/microfluidics">microfluidics</a> in a comprehensive approach for the analysis of pH-driven structural transformations in emulsions. The new platform, together with <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/synchrotron">synchrotron</a> small angle X-ray scattering, was then applied to research the food-relevant, pH-responsive, <a href="https://www.sciencedirect.com/topics/chemistry/oleic-acid">oleic acid</a> in water system.</p> <p>Findings: The experiments demonstrate structural transformations in individual oleic acid particles from micron-sized onion-type multilamellar oleic acid vesicles at pH 8.6, to nanostructured emulsions at pH < 8.0, and eventually oil droplets at pH < 6.5. The smooth particle-water interface of the onion-type vesicles at pH 8.6 was transformed into a rough particle surface at pH below 7.5. The pH-triggered changes of the interfacial tension at the droplet-water interface together with mass transport owing to structural transformations induced a self-propelled motion of the particle. The results of this study contribute to the fundamental understanding of the structure–property relationship in pH-responsive emulsions for nutrient and drug delivery applications.</p>
Optical polarimetric observations of the black hole binary star Cyg X-1 with RoboPol
<p>The dataset contains raw FITS images of the black hole X-ray binary star <a href="https://simbad.cds.unistra.fr/simbad/sim-id?Ident=%402905066&Name=HD%20226868&submit=submit">Cyg X-1</a>, raw FITS images of a nearby field star used for the interstellar polarization correction and processed measurements of polarimetric standards used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 13 May and 1 June 2022.<br> </p>
Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"
<p>Here we share the relevant data of the manuscript “Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel”.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>
Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints - Dataset
<p>Dataset for the Letter "Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints", in Optics Letters</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>
Optical tomography measurements and reconstructions of a multiple-scattering 3d-printed microphantom
<p>This dataset contains 2 sets of measurements of a 3d-printed microphantom, carried out with optical diffraction tomography system at Warsaw University of Technology. The measurements are conducted for 2 different wavelengths: 633nm and 835nm. Also, tomographic reconstructions of these datasets are shown. The reconstructions were computed with 3 algorithms: GPSC [1], MSBP-I [2] and MSBP-E [3]. Additionally, model of the 3D-printed microphantom is given.</p> <p>All files are *.mat files.</p> <p>In the reconstruction files there are 4 variables:</p> <ul> <li>REC - reconstruction matrix with information about 3D refractive index values in the microphantom</li> <li>dx - sample size in the reconstruction in x-y direction</li> <li>dz - sample size in the reconstruction in z direction (if not given, dz=dx)</li> <li>niter - number of iterations that were computed to generate the reconstruction</li> </ul> <p>The variables in the sinogram files are:</p> <ul> <li>dx - sample size in tomographic projections</li> <li>lambda - wavelength</li> <li>M - magnification in the optical system</li> <li>n_immersion - refractive index of the immersion medium</li> <li>NA - numerical aperture of the optical system</li> <li>rayXY - x-y coordinates of vectors representing illumination directions from which tomographic projections were acquired</li> <li>SINOamp - amplitude distribution of tomographic projections</li> <li>SINOph - phase distributions of tomographic projections</li> </ul> <p>The variables in the phantom model files are:</p> <ul> <li>dx - sample size</li> <li>n_immersion - refractive index of simulated immersion</li> <li>n_phantom - refractive index of the phantom model</li> </ul> <p>[1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> [2] S. Chowdhury, M. Chen, R. Eckert, D. Ren, F. Wu, N. Repina, and L. Waller, “High-resolution 3D refractive index292<br> microscopy of multiple-scattering samples from intensity images,” Optica 6, 1211 (2019).<br> [3] U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach288<br> to optical tomography,” Optica 2, 517 (2015).</p>
The Standardized Vegetation Optical Depth Index SVODI
<p><strong>Related paper with detailed description: </strong><a href="https://doi.org/10.5194/bg-19-5107-2022">https://doi.org/10.5194/bg-19-5107-2022</a></p> <p><strong>Short summary:</strong> The Standardized Vegetation Optical Depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage and spans the past three decades and is derived from multiple space-borne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data, even if the data is not gap-free.</p> <p><strong>Files: </strong></p> <ul> <li>"SVODI_v01.zip" <ul> <li>Contains the bulk SVODI data, globally, from 1987-07-10 to 2019-12-31. Files are daily global netcdf images, with a 0.25 degree spatial resolution. Is unzipped roughly the same size.</li> </ul> </li> <li>"svodi_v01_0_2006-06-26.nc" <ul> <li>An arbitrary file from SVODI_V01.zip. For your convenience in case you want to see an example first without downloading the whole thing.</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Grid of SVODI, Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Data fields:</strong></p> <ul> <li>"svodi" <ul> <li>The standardized vegetation optical depth index</li> <li>unitless</li> <li>range: -inf, inf</li> </ul> </li> <li>"flag" <ul> <li>Bit-flag indicating which <sensor>-<band> combination contributed to each observation <ul> <li>1: AMSRE-C</li> <li>2: AMSR2-C</li> <li>3: WindSat-C</li> <li>4: AMSRE-X</li> <li>5: AMSR2-X</li> <li>6: WindSat-X</li> <li>7: TMI-X</li> <li>8: AMSRE-Ku</li> <li>9: AMSR2-Ku</li> <li>10: WindSat-Ku</li> <li>11: TMI-Ku</li> <li>12: SSMI-Ku</li> </ul> </li> </ul> </li> </ul>
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OpenNeuro
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