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20 results for “Video streaming”
Dataset of a 5G RTSP video streaming use case
<p><strong>About the project: monitoring 5G RTSP video streaming</strong></p> <p>This dataset collects data from a 5G video streaming use case. A video is streamed by a cvlc server (realized as a Kubernetes pod) through RTSP to a variable number of 5G UE clients that activate according to a daily traffic pattern. The values of the 4 dataset features (number of active UEs, gNB's downlink bit rate, pod's outbound traffic, and pod's CPU usage) are collected by a custom monitoring system deployed in the context of the MONB5G project.</p> <p><strong>Setup/Equipment</strong></p> <p>The Kubernetes cluster, including the server pod, runs in a COTS server. The 5G core and gNB is realized through Amarisoft Callbox Ultimate. The UEs are emulated through Amarisoft Simbox. In order to display the video in ffplay clients, we use the Remote UE from Amarisoft, so traffic from Simbox is forwarded to an external VM with GUI.</p> <p><strong>Video</strong></p> <p>The streamed video is Big Buck Bunny at 30 FPS from <a href="https://peach.blender.org/">https://peach.blender.org/</a>.</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table> <p><strong>What does this Zenodo project contain?</strong></p> <ol> <li>The csv file of the dataset (dataset.csv)</li> <li>A picture displaying an overview of the setup (overview.png)</li> <li>A picture displaying Grafana charts for each featuer (grafana.png)</li> <li>A picture displaying a screenshot of the Remote UE VM with multiple UEs playing the video (ues.png)</li> </ol> <p><strong>Dataset</strong></p> <p>The dataset has 5 columns (time + 4 features). Features:</p> <ol> <li><em>Time</em>: timestamp in epoch format.</li> <li><em>Number of active UEs (N)</em>: number of UEs that are currently downloading more than 100 kbps. No unit.</li> <li><em>gNB's downlink bit rate (R)</em>: aggregate downlinkg bitrate from the gNB to all the UEs. In Mbps.</li> <li><em>Outbound traffic (O)</em>: outboun traffic at the pod's interface, transmitting the video(s) packets. In Mbps.</li> <li><em>CPU (C)</em>: CPU usage at the server pod. In millicores (mc). Each iteration represents a whole day, composed of 24 "demand periods". Each demand period takes 2 minutes and is given by the number of active UEs consuming the video stream (N). N is included for informative reasons. Sampling rate is 10 seconds, but some parameters are refreshed at a lower frequency given monitoring limitations. This means that some parameters repeat the same value in consecutive measurements.</li> </ol>
DMC sCMOS video stream: 2012-12-25
<p>sCMOS BG3-filtered auroral camera. 6x8 degree FOV.</p> <p>See preview of uncompressed 16-bit data https://zenodo.org/deposit/572265</p>
Dataset to ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive Video Streaming – A Holistic Analysis"
<p>Dataset for the ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive<br> Video Streaming – A Holistic Analysis".<br> The dataset includes</p> <ul> <li>Results from video encoding (using variable and fixed segment durations)</li> <li>Video sequences used for streaming evaluations</li> </ul>
Raw Measurements for Low Delay Video Streaming on the Internet of Things Using Raspberry Pi
<p>Raw measurements for article "Low Delay Video Streaming on the Internet of Things<br /> Using Raspberry Pi"</p>
Data from: Effectiveness of Online Off-the-Job Training in Attracting Participants and Video-On-Demand Streaming in Improving Work-Life Balance: A Study Focusing on Medical Technologists
<p>The Nara Association of Medical Technologists has introduced online Off-Job Training (Off-JT) starting from FY2020 in response to the COVID-19 pandemic. This study aims to evaluate the online Off-JT, which differs from the traditional face-to-face format. Firstly, we compared the online format's ability to attract participants with the face-to-face format based on the number of training sessions and attendees. Despite having fewer training sessions (40.8% less), the online format had an average attendance of 105.4% higher (39.7 vs. 19.3) than the face-to-face format. To enhance participant convenience, we offered a limited number of live and video-on-demand (VOD) sessions on YouTube, evaluating their usefulness through an online survey focusing on work-life balance (WLB). The survey results showed that 81.9% (458/559) of respondents reported an improvement in WLB. The effect on WLB improvement varied depending on the viewing method, with VOD sessions showing 84.1% (376/447) and live sessions showing 73.2% (82/112). We believe that the increased ability to attract participants in the online Off-JT is mainly due to the elimination of travel burdens through internet-connected devices. The combination of live and VOD sessions on YouTube allowed participants to adjust their viewing time, leading to better allocation of free time and improved WLB. The online Off-JT and VOD delivery have shown to enhance convenience for participants by removing geographical and time constraints, resulting in positive effects.</p>
Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study [Secondary documentary sources]
<p>Secondary documentary sources used in the paper entitled "Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study"</p>
Integration-based Extraction and Visualization of Jet Stream Cores - Supplemental Video
<p>Supplemental video for the publication "Integration-based Extraction and Visualization of Jet Stream Cores", which extracts jet stream core lines from ERA5 data using a predictor corrector algorithm.</p>
NANCY SNS JU Project - VR Video Streaming & iPerf3 on O-RAN 5G Testbed Dataset
<p>This dataset was developed in the context of the NANCY project and it is the output of the experiments involving streaming a virtual reality (VR) video in a 5G coverage expansion scenario. Additionally, iPerf3 experiments in both TCP and UDP modes were carried out. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services.</p> <p>The dataset includes network traffic, which was captured and stored in a .pcap files, as well as various performance metrics that were collected by an xApp running in the near-real-time Radio Access Network Intelligent Controller.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Secure Video Streaming Using Dedicated Hardware
<p>Demo video of Secure Video Streaming Using Dedicated Hardware</p>
Streaming video course data
<p>This case study data reflects the use of videos in four courses at a small liberal arts university in Halifax, Nova Scotia. The authors sought to analyze types of videos used, access to the course videos, including restrictions and unavailable videos, and accessibility features. This case study also analyzed the percentage of videos that were linked through the library’s streaming video collections versus those that were found on free sites. Recorded class lectures and student videos were not included in the study.</p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
Open the record for dataset details and reuse information.
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
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Uplink-based Live Session Model for Stalling Prediction in Video Streaming
<p>Dataset to model streaming session only by uplink requests with the goal of quality impairment estimation like quality changes or stallings. </p>
Dataset for Uplink-based Live Session Model for Stalling Prediction in Video Streaming
<p>This dataset presents aggregated YouTube streaming data used for uplink based quality impairment estimation. </p>
Dispatch of Emergency Call Using Video Streaming Compared With Traditional Telephone Communication
ClinicalTrials.gov study NCT05742412. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Video Streaming with Virtual Reality Glasses on Pain and Anxiety
ClinicalTrials.gov study NCT06776497. IPD Sharing: NO. Countries: 1. Publications: 1.
welborn_24_streaming_large_scale_electron_microscopy_data_si_videos
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Subjective Test Dataset and Meta-data-based Models for 360° Streaming Video Quality
<p>During the last years, the number of 360° videos available for streaming has rapidly increased, leading to the<br> need for 360° streaming video quality assessment. In this paper, we report and publish results of three subjective 360° video<br> quality tests, with conditions used to reflect real-world bitrates and resolutions including 4K, 6K and 8K, resulting in 64 stimuli<br> each for the first two tests and 63 for the third. As playout device we used the HTC Vive for the first and HTC Vive Pro<br> for the remaining two tests. Video-quality ratings were collected using the 5-point Absolute Category Rating scale. The 360°<br> dataset provided with the paper contains the links of the used source videos, the raw subjective scores, video-related meta-data,<br> head rotation data and Simulator Sickness Questionnaire results per stimulus and per subject to enable reproducibility of the<br> provided results. Moreover, we use our dataset to compare the performance of state-of-the-art full-reference quality metrics such<br> as VMAF, PSNR, SSIM, ADM2, WS-PSNR and WS-SSIM. Out of all metrics, VMAF was found to show the highest correlation<br> with the subjective scores. Further, we evaluated a center-cropped version of VMAF ("VMAF-cc") that showed to provide a similar<br> performance as the full VMAF. In addition to the dataset and the objective metric evaluation, we propose two new video-quality<br> prediction models, a bitstream meta-data-based model and a hybrid no-reference model using bitrate, resolution and pixel<br> information of the video as input. The new lightweight models provide similar performance as the full-reference models while<br> enabling fast calculations.</p>
The Effect of Video Streaming With Virtual Reality Before Coronary Angiography
ClinicalTrials.gov study NCT06458647. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
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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.