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30 results for “Telecommunications”
A New Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications
<p>This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process.</p> <p>You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below:</p> <blockquote> <p>Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. <em>The 16th International Conference on Quality of Multimedia Experience (QoMEX)</em>. IEEE. DOI: <a href="https://ieeexplore.ieee.org/abstract/document/10598264">10.1109/QoMEX61742.2024.10598264</a></p> </blockquote> <p>BibTeX:</p> <blockquote> <p>@inproceedings{zerman2024spherical,<br> title = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},<br> author = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},<br> booktitle = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},<br> year = {2024},<br> organization = {IEEE},<br> doi = {10.1109/QoMEX61742.2024.10598264}<br>}</p> </blockquote> <p>This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included.</p> <p>N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09.</p>
Behavior of Telecommunication Lattice Towers to Thunderstorm Winds
<h1>Dataset Description</h1> <p>This work aims at closing the knowledge gap between the wind field monitoring of real structures and wind tunnel testing by simulating real atmospheric boundary layer (ABL) and thunderstorm events in the Wind Energy, Environment, Engineering (WindEEE) research facility. The real events were acquired by a wind and structural monitoring system installed on a 50 m telecommunication lattice tower located in Sânnicolau Mare, Romania. The study reproduces complex downburst wind systems, in a controlled laboratory environment, like those observed in the field monitoring. The wind-induced response of two typical telecommunication lattice towers of different heights, i.e. 50 m and 90 m is investigated by means of both aerodynamic and aeroelastic tests. The acquired data will allow to compare and calibrate wind tunnel test results with field monitoring structural data measured during intense ABL and thunderstorm winds by the Sânnicolau Mare monitoring system. This extends the wind field and aerodynamic database which can be further utilized for codification purposes and for validating numerical and analytical models. The proposed work aims to advance code-based design of telecom lattice towers to thunderstorm winds.</p> <p>This work involved three areas of testing – wind field characterization to determine the best settings to match full scale / realistic wind loads, aeroelastic tests of both a 90m and 50m full towers (1:100 scale) using strain gauges as well as force balances, and a 1:10 sectional model of the top of the 50m tower to study the aerodynamics of the tower both with and without ancillaries added</p> <p> </p> <h2>S0. Documentation</h2> <p>Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.</p> <p> </p> <h2>S1. Wind Profile Stand</h2> <p>A vertical stand of 11 TFI cobra probes measured wind field data at heights of 50, 100, 150, 200, 300, 400, 500, 600, 700, 800, and 900 mm from the ground surface. For each experiment described below, the Cobra Probe stand was located in select locations to capture the near-surface flow.</p> <h3>E1. ABL Profile Development</h3> <p>The 60-fan wall located on one side of the hexagonal shaped WindEEE test chamber was used to generate the various ABL flows for this experiment. Each fan on this wall is individually controlled allowing a versatile range of ABL flow conditions. In addition, the ABL flow turbulence and boundary layer gradient were fine-tuned using roughness elements and spires.</p> <h3>E2. Downburst Profile Development</h3> <p>An impinging-jet style downburst is generated at the WindEEE dome through the release of pressure from a plenum above the testing chamber. The plenum is pressurized with six large fans for an adjustable amount of time or until a certain pressure is achieved. The built pressure then releases through a bell mouth with variable orifice sizes, D, to achieve a rapid downdraft of air. Given WindEEE’s unique 3-D test chamber, measurements were taken at various angles, theta, and radius, r, from the centre of the bell mouth. Commonly, these measurement locations are indicated by a non-dimensional parameter, r/D, and the angle, ϑ (theta).</p> <h3>E3. Combined Downburst and ABL Profile Development</h3> <p>With the unique capability of the WindEEE test chamber, profile measurements were taken while operating various combinations of the ABL and downburst-like flow configurations. The natural occurrence of a downburst in a storm acted as a driver for this segment of profile development.</p> <h3>E4. Downburst with Radial Trip Profile Development</h3> <p>For this test, wooden trips about 15cm tall were evenly placed all around the edge of the turntable. The downburst-like flow was generated similar to the downburst profile development section.</p> <p> </p> <h2>S2. 1-100 Scaled 50m Lattice Tower Model (T50)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <h3>E4. Downburst with Radial Trip Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 4.</p> <p> </p> <h2>S3. 1-100 Scaled 90m Lattice Tower Model (T90)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <p> </p> <h2>S4. Aerodynamic lattice tower sectional model</h2> <p>This specimen included a 1 m tall section of the top of the 50 m tower at a scale of 1:10. It is constructed of brass, steel, 3D printed nylon, PolyJet 3-D printed material, and steel screws. The antennas, railing and central ladder are all removable. The model was mounted on a rig made up of a 12.7 cm diameter steel pipe and a wooden base plate. The rig stands 60cm tall so that the model is above the sheared surface flow. The base and top plates of the rig were 90cm in diameter. The experiments were performed with three different configurations of the model.</p> <h3>E1. Aerodynamics of the Bare structure without Top Plate</h3> <p>During this test, the model was measured as a bare structure (no antennas, ladders, or other components). The model was tested under ABL flow to outline the aerodynamic effects of the baseline model.</p> <h3>E2. Aerodynamics of the Structure with Top Plate</h3> <p>During this test, a top plate hovered over the model for the entirety of the test program. This plate encourages 2-D flow properties in ABL flow to mimic aerodynamic properties seen in horizontal testing in traditional wind tunnels.</p> <h3>E3. Aerodynamics of the Structure with Ancillary Components</h3> <p>During this test, ancillary components including ladders, railing, and antenna were attached to the model. These items act to increase the frontal area of the model which are expected to change the aerodynamic properties of the model.</p> <p> </p> <p><strong>Note:</strong> Given the number of data files captured in this program, the files required to be uploaded in compressed '.zip' folders.</p>
Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window - Open Data
<p>This is a supplementary upload attached to the paper titled "Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window" 10.1038/s41566-021-00878-9.</p> <p><strong>Figures</strong></p> <p>All figure data from the publication can be obtained from the original MATLAB .fig files. If one does not have access to MATLAB the figures can be opened using the open source software GNU Octave.</p> <p><strong>FDFD Simulation</strong></p> <p>Also in the upload is the original matlab code used to perform the simulations presented in the paper.</p> <p>"FDFD_2D_Ez_Hz_DFB_laser_UPLOAD" - Variable gain FDFD solver is uploaded as .mat and .pdf files.</p> <p>To run the code the functions "Dgen" and "gen2xDFB" are required and the .mat files containing the refractive indices "PbS1520" and "Al2O3".</p> <p>Parameters to vary can be found in the "DASHBOARD" section of the code. The uploaded code solves for the out-of-plane electric field (Ez Mode).</p>
Dataset: Comtech Telecommunications Corp. (CMTL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Technology & Telecommunication Acquisition Corporation (TETEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Technology & Telecommunication Acquisition Corporation (TETEU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Technology & Telecommunication Acquisition Corporation (TETE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Shenandoah Telecommunications Company (SHEN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
An Ultra Low-Loss Silicon-Micromachined Waveguide Filter for D-Band Telecommunication Applications
<p>The dataset contains S-parameter measurements between 110-170 GHz, for a silicon micromachined filter. It also contains quality factor data for the filter and the complex propagation constant data.</p>
Design and development remote monitoring agent for energy service companies in the telecommunication industry
<p>Data set downloaded from the remote monitoring agent (RMA) over its local webpage using WiFi connection.</p>
Telecommunication Enhanced Asthma Management
ClinicalTrials.gov study NCT00958932. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Telecommunications System in Sleep Apnea
ClinicalTrials.gov study NCT00232544. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Documents - scientific production in telecommunications
Open the record for dataset details and reuse information.
Telecommunication Via Cell Phones During Post-Operative Treatment After Lung Transplantation
ClinicalTrials.gov study NCT00536926. IPD Sharing: Not stated. Countries: 1. Publications: 1.
An Educational Program to Improve Cardiac Arrest Diagnostic Accuracy of Ambulance Telecommunicators
ClinicalTrials.gov study NCT03894059. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effect and Experience of PreHomeCare of Preterm Infants Using Telecommunication and Smartphone Application
ClinicalTrials.gov study NCT02581800. IPD Sharing: Not stated. Countries: 1. Publications: 2.
MODELING OF TELECOMMUNICATION NETWORKS BASED ON FUZZY LOGIC TECHNOLOGY
Open the record for dataset details and reuse information.
Unlocking Transformation: AI Implementation for Enhanced Telecommunication Managed Services
<div> <div> <div> <p>In the rapidly evolving landscape of technology, the integration of Artificial Intelligence (AI) stands out as a powerful catalyst for optimizing operational efficiency and elevating service quality. This paper delves into the strategic process of implementing AI within telecommunication managed services, spotlighting its profound effects on predictive maintenance, network optimization, incident management, and service provisioning. The study presents a holistic approach encompassing data audit and preparation, AI model development, rigorous testing, seamless integration, and continuous training and support. By documenting this journey, the paper not only chronicles the process but also serves as an enlightening guide for businesses embarking on AI-driven transformations. Through this exploration, the paper underscores how AI has the potential to reshape managed services, leading to streamlined resource allocation, minimized downtime, improved service quality, and operational excellence.</p> </div> </div> </div>
Evaluation of User Experience for Dashboard Project Management Information System within the Project Operation Unit of a Telecommunication Company
Open the record for dataset details and reuse information.
Telecommunication Networks as Knowledge Graph Research Datasets
<p><strong>Telecommunication Networks as Knowledge Graph Research Datasets.</strong></p> <p>All the datasets were created using the PC with following parameters: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD</p> <p>All the software which is used for the datasets creation is available here: https://github.com/kulikovia/TN_KG_research<strong> </strong></p> <p>The datasets and supplementary files description of follow:</p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>1.</p> </td> <td> <p>Computational_complexity_v5_ (ENG).pdf</p> </td> <td> <p>SPARQL performance tests report</p> </td> </tr> <tr> <td> <p>2.</p> </td> <td> <p>Synthesis_performance_tests_results_v1.pdf</p> </td> <td> <p>Inductive and deductive synthesis performance tests report</p> </td> </tr> <tr> <td>3.</td> <td>Computational_complexity_Parallel_v2_ (ENG).pdf</td> <td>Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege </td> </tr> <tr> <td>4.</td> <td>Synthesis_additional_experiments_v4.pdf</td> <td>Additional Inductive and deductive synthesis performance tests report (with different elements distribution by levels)</td> </tr> <tr> <td> <p>5.</p> </td> <td> <p>Dataset_10k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>6.</p> </td> <td> <p>Dataset_10k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>7.</p> </td> <td> <p>Dataset_1k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>8.</p> </td> <td> <p>Dataset_1k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>9.</p> </td> <td> <p>Dataset_200k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>10.</p> </td> <td> <p>Dataset_200k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>11.</p> </td> <td> <p>Dataset_500k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>12.</p> </td> <td> <p>Dataset_500k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>13.</p> </td> <td> <p>Synthesis_Hierarchical_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>14.</p> </td> <td> <p>Synthesis_Linear_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>15.</p> </td> <td> <p>Hierarchy_model_results_Exp_10M.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> <tr> <td>16.</td> <td>Hierarchy_model_results_Exp_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>17.</td> <td>Hierarchy_model_results_Exp_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>18.</td> <td>Hierarchy_model_results_Linear_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>19.</td> <td>Hierarchy_model_results_Linear_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>20.</td> <td>Hierarchy_model_results_Linear_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>21.</td> <td>Hierarchy_model_results_Quadro_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>22.</td> <td>Hierarchy_model_results_Quadro_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>23.</td> <td>Hierarchy_model_results_Quadro_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>24.</td> <td>Hierarchy_model_results_Uniform_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>25.</td> <td>Hierarchy_model_results_Uniform_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>26.</td> <td>Hierarchy_model_results_Uniform_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td> <p>27.</p> </td> <td> <p>Linear_model_resuts.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level model in RDF/XML format</p> </td> </tr> </tbody> </table>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.