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278 results for “stereo”
Typewriter indentations in a letter by W. H. Auden - visualized with photometric stereo
<p>This figure shows a section of a letter from W. H. Auden to Stella Musulin.</p> <p>Top: conventional photograph.<br> Middle: raking light photograph. Indentations in the paper become visible, but hardly legible.<br> Bottom: a false-color visualization generated with photometric stereo. The simultaneous display of depth, albedo and curvature gradient allow the discrimination of four text layers.</p>
HiRISE DTMs generated using NASA's Ames Stereo Pipeline
<p>In an effort to better understand the surface roughness of Martian lava flows, we generated over 30 HiRISE DTMs using ISIS3 and ASP and extracted their roughness (Rodriguez Sanchez-Vahamonde and Neish, 2020). We have posted these DTMs for public use here. </p> <p>HiRISE stereo images typically have a spatial sampling of 25 - 50 centimeters, providing us with DTMs of 1 - 2 meters per pixel. We also converted the HiRISE stereo-pair ID for each product into its proper DTM ID using the NASA Planetary Data System product naming convention for HiRISE DTMs (<a href="https://www.uahirise.org/dtm/about.php">https://www.uahirise.org/dtm/about.php</a>; last accessed 18.09.2019).</p>
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Recordings for loudness analysis of the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Mat files of live recordings of the music mixes for the comparison of wave field synthesis, surround, and stereo listening test. The recordings were performed at different levels and were analyzed with a loudness model afterwards for adjusting the levels. For details, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation
<h1>Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation</h1> <h2>Introduction</h2> <p>This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project <a href="https://robotik.dfki-bremen.de/en/research/projects/deepersense/">DeeperSense</a> (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. <a href="../records/7728089">Parts</a> <a href="../records/10220989">of</a> the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the <a href="https://www.go-fair.org/fair-principles/">FAIR</a> principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately.</p> <h3>Locations and sensors</h3> <p>The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The <code>examples/</code> directory holds a typical output image for each sensor at each available location.</p> <h3>Data volume per session</h3> <p>Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session:</p> <table> <tbody> <tr> <th>Session dates</th> <th>Location</th> <th>Number of datasets</th> <th>Total duration of datasets [h]</th> <th>Total logfile size [GB]</th> <th>Number of images</th> <th>Total image size [GB]</th> </tr> <tr> <td>2021-08-09 - 2021-08-12</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>52</td> <td>10.8</td> <td>28.8</td> <td>389’047</td> <td>88.1</td> </tr> <tr> <td>2022-02-07 - 2022-02-08</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>35</td> <td>4.4</td> <td>54.1</td> <td>629’626</td> <td>62.3</td> </tr> <tr> <td>2022-04-26 - 2022-04-28</td> <td>Chalk Lake Hemmoor</td> <td>52</td> <td>8.1</td> <td>133.6</td> <td>1’114’281</td> <td>97.8</td> </tr> <tr> <td>2022-06-28 - 2022-06-29</td> <td>Tank Wash Basin Neu-Ulm</td> <td>42</td> <td>6.7</td> <td>144.2</td> <td>824’969</td> <td>26.9</td> </tr> <tr> <td>2023-04-26 - 2023-04-27</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>55</td> <td>7.4</td> <td>141.9</td> <td>739’613</td> <td>9.6</td> </tr> <tr> <td>2023-09-01 - 2023-09-02</td> <td>Lake Starnberg</td> <td>19</td> <td>2.9</td> <td>40.1</td> <td>217’385</td> <td>2.3</td> </tr> <tr> <th> </th> <th> </th> <th>255</th> <th>40.3</th> <th>542.7</th> <th>3’914’921</th> <th>287.0</th> </tr> </tbody> </table> <h2>Data and metadata structure</h2> <h3>Sensor data corpus</h3> <p>The sensor data corpus comprises two processing stages:</p> <ul> <li>raw data streams stored in ROS bagfiles (aka <strong>logfiles</strong>),</li> <li>camera and sonar images (aka <strong>datafiles</strong>) extracted from the logfiles.</li> </ul> <p>The files are stored in a file tree hierarchy which groups them by session, dataset, and modality:</p> <pre><code>${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}</code></pre> <p>A typical logfile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag</code></pre> <p>A typical datafile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg</code></pre> <p>All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file <code>entities.json</code>.</p> <h3>Metadatabase</h3> <p>The metadatabase is provided in two equivalent forms:</p> <ul> <li>as a standalone <a href="https://www.sqlite.org/index.html">SQLite</a> (https://www.sqlite.org/index.html) database file <code>metadata.sqlite</code> for users familiar with SQLite,</li> <li>as a collection of CSV files in the <code>csv/</code> directory for users who prefer other tools.</li> </ul> <p>The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information.</p> <p>An entity relationship diagram of the metadatabase tables is stored in the file <code>entity_relationship_diagram.png</code>. Each entity, its attributes, and relations are documented in detail in the file <code>entities.json</code></p> <p>Some general design remarks:</p> <ul> <li>For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.</li> <li>In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities <code>stream</code> and <code>logfile</code> are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.</li> <li>A <code>modality</code> is not an attribute of a <code>sensor</code> alone, but of a <code>datafile</code>: Because a <code>sensor</code> is an attribute of a <code>stream</code>, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.</li> </ul> <p>As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query:</p> <div> <pre><code><span><span>SELECT</span></span> <span> PRINTF(</span> <span> <span>'%s - %s'</span>,</span> <span> <span>SUBSTR</span>(session_start, <span>1</span>, <span>10</span>),</span> <span> <span>SUBSTR</span>(session_end, <span>1</span>, <span>10</span>)) <span>AS</span> <span>'Session dates'</span>,</span> <span> location_name_english <span>AS</span> Location,</span> <span> number_of_datasets <span>AS</span> <span>'Number of datasets'</span>,</span> <span> total_duration_of_datasets_h <span>AS</span> <span>'Total duration of datasets [h]'</span>,</span> <span> total_logfile_size_gb <span>AS</span> <span>'Total logfile size [GB]'</span>,</span> <span> number_of_images <span>AS</span> <span>'Number of images'</span>,</span> <span> total_image_size_gb <span>AS</span> <span>'Total image size [GB]'</span></span> <span><span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(dataset_id) <span>AS</span> number_of_datasets,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(dataset_duration) <span>/</span> <span>3600</span>,</span> <span> <span>1</span>) <span>AS</span> total_duration_of_datasets_h,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(total_logfile_size) <span>/</span> <span>10e9</span>,</span> <span> <span>1</span>) <span>AS</span> total_logfile_size_gb</span> <span> <span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> view__dataset_total_logfile_size <span>USING</span> (dataset_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(datafile_id) <span>AS</span> number_of_images,</span> <span> <span>ROUND</span>(<span>SUM</span>(datafile_size) <span>/</span> <span>10e9</span>, <span>1</span>) <span>AS</span> total_image_size_gb</span> <span> <span>FROM</span></span> <span> <span>session</span></span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> stream <span>USING</span> (dataset_id)</span> <span> <span>JOIN</span> <span>datafile</span> <span>USING</span> (stream_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span><span>ORDER</span> <span>BY</span> session_id;</span></code></pre> </div>
EUV-ML solar physics dataset from STEREO + SOHO, 2 solar cycles
<p>A unified ML-ready dataset of all SOHO EIT and STEREO EUVI EUV images, aligned and set to 512x512 pixels, are available via the NASA TOPS ODR in HelioCloud at s3://gov-nasa-hdrl-data1/contrib/euvml/ (inside of AWS, and with free egress via https). We also provide the software used to create it via a public github repository, and a sample Python Jupyter Notebook (in this Zenodo link, and in the HelioCloud sample tutorials) for accessing them via the 'cloudcatalog' Python client.We</p> <p>We created a full set of ML-ready EUV data from 1995 to present, accessible via the cloud, by bringing in historical restoration of the STEREO/SOHO era into a machine-learning (ML) -ready dataset. This work will enable research on events, evolution of solar irradiance, segmentation approaches, 360 degree maps of the sun, and other research topics as well as for use with space weather. The reduced dataset is 6TB in size. The data will be freely available to scientists both within the AWS cloud and for downloading for local use on their laptops. The creation of this dataset had several steps, starting with the mechnical ingest stage (get the raw data), an analysis of best approach to create the ML-ready set, bulk processing, uploading the cloud, and dissemination and promotion. The task also required determination best approaches for cadence matching, interpolation, and effective spans of contiguous data for ML applications.</p> <p>Work was funded under NASA Heliophysics 21-LWSTM21_2-0018, award number 80NSSC22K0643.</p>
plan view TEM stereo pair of microcracks
<p><a name="_Toc162747595"></a><strong><em><span>Figure SI1.</span></em></strong><span><strong><em><span><span>2</span></span></em></strong></span><span><em><span>: The cross-eye stereo pair can be difficult to visualise.<span> </span>This animation of the same images allows the three-dimensional structure to be seen using motion parallax.</span></em></span></p>
The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements
<p>This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.</p>
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
3D Stereo Body Pose Estimation - Evaluation Plots
<p>Evaluation plots for the "3D Stereo Body Pose Estimation" project, aimed at estimating 3d keypoints from humans captured using a OAK-D camera.</p> <p>The project is originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</p>
Photometric stereo data of non-crimp fabric boundaries
<p><strong>DATA DESCRIPTION</strong></p> <p>This data set was acquired in the context of EU project ZAero. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 721362. Project duration: 2016/10/01 - 2019/09/30. This data set contains a set of 68 HDF5 files from two different example surface patches of NCF carbon fiber fabrics.</p> <p>For more information about the HDF5 format, please visit the HDF5 Group website:<br> https://www.hdfgroup.org/solutions/hdf5/</p> <p>Each file contains data from a photometric stereo vision system [1]. The data set is intended for evaluation of methods for carbon fiber fabric edge detection [2].</p> <p>Each file contains a single 3-dimensional (W x H x L) array 'raw' with:<br> H...height of the images<br> W...width of the images<br> L...number of images with different light sources (L = 8)</p> <p>An example for loading and visualizing the data in Python comes with this data set:<br> readDataExample.py</p> <p><br> <strong>REFERENCES</strong></p> <p>[1]<br> @inproceedings{Palfinger2011, <br> author = {Palfinger, Werner and Thumfart, Stefan and Eitzinger, Christian},<br> title = {Photometric stereo on carbon fiber surfaces},<br> booktitle = {35th Workshop of the Austrian Association for Pattern Recognition},<br> year = {2011}<br> }</p> <p>[2]<br> @inproceedings{Zambal2019,<br> author = {Zambal, Sebastian and Heindl, Christoph and Eitzinger, Christian}<br> title = {Probabilistic Modelling combined with a CNN for boundary detection of carbon fiber fabrics},<br> booktitle = {IEEE International Conference on Industrial Informatics (INDIN)}<br> year = {2019}<br> }</p> <p> </p> <p> </p>
Data from: Improved STEREO simulation with a new gamma ray spectrum of excited gadolinium isotopes using FIFRELIN
<p>Supplemental material to the article “Improved STEREO simulation with a new gamma ray spectrum of excited gadolinium isotopes using FIFRELIN”</p> <p>The files available are aimed to simulate the de-excitation cascade following neutron capture on<sup> 155</sup>Gd and <sup>157</sup>Gd. Therefore, the FIFRELIN simulation was done for the <sup>156</sup>Gd and <sup>158</sup>Gd isotopes, with the initial condition of an excitation energy of E* = S<sub>n</sub>, the neutron separation energy.</p> <p>Please cite this publication when using the files provided below:<br> H. Almazán et al., <a href="http://doi.org/10.1140/epja/i2019-12886-y">Eur. Phys. J. A 55 (2019) 183</a>, <a href="http://doi.org/10.48550/arXiv.1905.11967">arXiv:1905.11967 [physics.ins-det]</a></p> <p>Please see <a href="http://doi.org/10.5281/zenodo.6861341">Zenodo 6861341</a> for an improved version of the provided data.</p>
Gummern - Digital elevation model 0.5 m, created from Pléiades Neo tri-stereo satellite imagery
<h2>Abstract</h2> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Gummern_PleiadesNeo_DSM05m & Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>surface model, Pleiades Neo, tri-stereo</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation & GNSS</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>1.10.2023 (27.10.2023 – GNSS)</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>1.12.2023 (6.11.2023 – GNSS)</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster / Vector</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF / CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.30m (0.01m – GNSS)</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
User 1_360 stereo video
<p>Complete 360 stereoscopic version for User 1.</p> <p><br> Video Composition of the full experience:<br> Interrogation room story video and video<br> compositing for conversation between the<br> users.<br> Equirectangular.<br> Stereoscopic.</p>
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Listening preferences for the different reproduction systems Stereo, Surround, and Wave Field Synthesis in the context of popular music
<p>We did a paired comparison preference test where listeners rated their listening preference for four different pop musical pieces presented by WFS, stereo or surround. The musical pieces were all mixed by the same person in order to try to minimize the influence of the mix on the ratings, but still trying to get the best out of every system, see [1] for details. The mixes are available at https://doi.org/10.14279/depositonce-5173.</p> <p>Here, we provide the results of the 22 listeners that participated in the experiment together with an analysis which calculates a Bradley-Terry-Luce model after Wickelmayer et al. [2].</p> <p>[1] Hold, C., Wierstorf, H., Raake, A. (2016), “The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music,” 140th AES Convention, Paper 9533</p> <p>[2] https://cran.r-project.org/web/packages/eba/index.html</p>
SC-XRD diffraction images of stereo-defined piperazine (2/2)
<p>Structures of a piperazine in the publication: Suarez-Pantiga, S.; Colas, K.; Johansson, M. J.; Mendoza, A.* "Scalable synthesis of piperazines enabled by visible light irradiation and aluminum organometallics" <br> Angew. Chem. Int. Ed. 2015, 54, 14094–14098</p> <p>Structure solutions were deposited in the CCDC: 1052437 (3b - PiPy3Me)<br> https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201505608</p>
SC-XRD diffraction images of stereo-defined piperazines (1/2)
<p>Structures of two piperazines in the publication: Suarez-Pantiga, S.; Colas, K.; Johansson, M. J.; Mendoza, A.* "Scalable synthesis of piperazines enabled by visible light irradiation and aluminum organometallics" <br> Angew. Chem. Int. Ed. 2015, 54, 14094–14098</p> <p>Structure solutions were deposited in the CCDC: 1052438 (3g - PiPy5Br) and 1053734 (3ij - PiPyzIm)<br> https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201505608</p>
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