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11 results for “Speed Profile”
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores
<p>This repository contains the accompanying inlists and run_star_extras files for Buchele et al. (2024): Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores.</p> <p> </p> <p>The MESA version used is r22.05.1. We have modified the /star/private/pulse_fgong.f90 file in the MESA source code to output the Gamma1 derivatives that are necessary to compute u-Y kernels by replacing the file with the version from <a href="https://github.com/MESAHub/mesa/pull/431">https://github.com/MESAHub/mesa/pull/431</a>. Additionally, we use the MESA SDK versions 22.6.1 and the GYRE version 6.0 that is included with the r22.05.1 release of MESA. </p> <p> </p> <p>The directory grid_base contains the inlist which set the parameters that do not change across the tracks of our grid</p> <p> </p> <ul> <li> <p>Inlist_all includes parameters that are constant across all tracks and evolutionary stages </p> </li> <li> <p>Inlist_prems includes parameters for the pre-main sequence evolution of all tracks </p> </li> <li> <p>Inlist_ms includes parameters for the main sequence evolution of all tracks </p> </li> <li> <p>run_star_extras.f90 includes the code which stops the evolution of a track when the central hydrogen abundance reaches the target value </p> </li> <li> <p>gyre.in includes parameters for calculating of frequencies using GYRE</p> </li> </ul> <p> </p> <p>The directory reference_models provides the inlists to generate our reference model for each star in our sample. This directory also includes the FGONG structure files of the computed reference models. Each file is labeled with the identifier used in the text, typically the KIC number. </p> <p> </p> <p>The physics_tweaks directory provides the inlists to generate the models with modified input physics. The suffixes refer to the physics that was changed in each model, as follows: </p> <ul> <li> <p>core_kap: Core opacity</p> </li> <li> <p>pp2: change to the He3+He4->Be7 reaction rate</p> </li> <li> <p>cno: change to the N14 + p -> O15. </p> </li> </ul>
Experimental absorption spectra used in "Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model"
<p>Experimental absorption spectra used in the article entitled <strong>"Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF<sub>6</sub>: testing the β-</strong><strong>correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model", </strong>to be published in the Journal of Quantitative Spectroscopy and Radiative Transfer. Accepted 19 March 2024.</p> <div> <div> <div> <div> <h3><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jqsrt.2024.108977" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jqsrt.2024.108977</a></h3> </div> </div> </div> </div> <p> </p> <p>Comma separated ASCII files with 1 header line.</p> <p>First column is wavenumber in cm-1</p> <p>The rest of the columns contain the napierian absorbance at the total pressure given in the header. </p> <p>Mind the units!: pressure of Ar-mixtures is expressed in Torr, pressure of He- and SF6-mixtures is expressed in mbar</p> <p> </p>
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Speed profiles of freeways in California (I5-S and I210-E)
<p>Speed profiles of freeways in California (I5-S and I210-E). Original data is retrieved from PeMS.</p> <p>Each YEAR_FREEWAY.csv file contains Timestamp and Speed data.</p> <p>freeway_meta.csv file contains meta information for each detector: freeway number, direction, detector ID, absolute milepost, and x y coordinates.</p> <p>Please refer the readme file.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Effect of heterogeneous substrate adhesivity of follower cells on speed and tension profile of leader cells in primary keratocyte collective cell migration
<p><span>In single keratocyte motility, membrane tension is reported to be high at cell-fronts and believed to establish front coherence. To understand role of membrane mechanics in collective cell migration, we study membrane height fluctuations in cell sheets from fish scales using interference reflection microscopy (IRM). We report the monolayer to have cells lacking substrate adhesion and show that such "non-sticky" cells can form bridges between leader cells and far-away follower cells. Do such interactions alter motility and membrane mechanics in such leaders? We find non-significant, but reduced speed for leaders with "non-sticky" followers in comparison to other leaders. Cells show high phenotypic variability in their membrane fluctuation tension profiles. On average, this tension is found to be lower at cell fronts than the mid-section. However, leaders with non-sticky followers are more prone to display higher tension at their front and have a negative correlation between cell speed and front-mid tension difference. We, thus, conclude that intracellular tension gradients are heterogeneous in cell sheets and substrate adhesivity of followers can control the coupling of the gradient to cell speed.</span></p>
Effect of heterogeneous substrate adhesivity of follower cells on speed and tension profile of leader cells in primary keratocyte collective cell migration
Open the record for dataset details and reuse information.
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
Simulated probabilistic speed profiles for selected routes in Prague
<p><strong>Simulated probabilistic speed profiles for 1608 road segments in Prague, Czech Republic</strong></p> <p>This dataset contains simulated speed profiles for selected road segments in Prague, Czech Republic. The profiles describe changes in average driving speed on a particular segment over a given time interval discretised to a time steps of a fixed size. Number of the time steps is stored in the "<strong>IntervalsPerSegment</strong>" attribute of the root group. Size of a single time step in <strong>seconds</strong> is stored in the "<strong>TimeStep</strong>" attribute. Total length of time interval covered by the profiles is computed as IntervalsPerSegment x TimeStep.</p> <p>The file contains two types of profiles.</p> <p>The first type are plain speed profiles which provide just average driving speed assigned to each time step - dataset "<strong>speed_profile</strong>".</p> <p>The second type are probabilistic speed profiles which contain discrete probability distribution of the driving speed for particular segment and given time step. The probability distribution in this case has 4 support points, corresponding to 4 Levels of Service (LoS). The first LoS corresponds to an optimal (freeflow) speed and the last one describes the worst case (accident, heavy traffic, etc.). The discrete distribution is stored in the "<strong>prob_profile</strong>" dataset with the following format:</p> <p>Column 1 - Speed corresponding to the first LoS in <strong>km/h</strong><br> Column 2 - Probability assigned to this speed (float between 0.0 - 1.0)<br> ...</p> <p>Sampling from this distribution is implemented by the <a href="http://www.keithschwarz.com/darts-dice-coins/">Vose's alias method</a>. We provide precomputed tables for implementing this method for each segment in datasets "<strong>alias</strong>" and "<strong>probability</strong>".</p> <p>Further applications of the probabilistic speed profiles are available in:</p> <p>Golasowski, M., Tomis, R., Martinovič, J., Slaninová, K., & Rapant, L. (2016, September). Performance evaluation of probabilistic time-dependent travel time computation. In <em>IFIP International Conference on Computer Information Systems and Industrial Management</em> (pp. 377-388). Springer, Cham.</p> <p><strong>File structure</strong></p> <p>Profiles for the individual segments are stored as subgroups in the root group. Names of the subgroups correspond to unique road segments identifiers corresponding to a subset of road network of Prague.</p> <p>The identifiers correspond to a routing index published here: <a href="https://doi.org/10.5281/zenodo.2275556">10.5281/zenodo.2275556</a></p> <pre><code class="language-javascript">HDF5 "profiles_8runs.h5" { GROUP "/" { ATTRIBUTE "IntervalsPerSegment" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 59 } } ATTRIBUTE "NumberOfProfiles" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 4 } } ATTRIBUTE "TimeStep" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 60 } } ATTRIBUTE "Version" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLTERM; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "1.0" } } GROUP "66678115" { DATASET "alias" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 4 ) / ( 59, 4 ) } } DATASET "prob_profile" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 8 ) / ( 59, 8 ) } } DATASET "probability" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 4 ) / ( 59, 4 ) } } DATASET "speed_profile" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 1, 59 ) / ( 1, 59 ) } } }</code></pre> <p> </p>
Power-speed-endurance Profile (Cycling/Rowing) : Optimize Performance of the French Athletes at the Paris Olympics 2024
ClinicalTrials.gov study NCT05314543. IPD Sharing: NO. Countries: 1. Publications: 0.
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