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1,216 results for “real-time”
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Real-time black ice detection using YOLOX on drone
<p><strong>Detailed Info:</strong> https://github.com/hsh060824/blackice-drone-dataset</p> <p> </p> <p><strong>Dataset Type</strong>: Object Detection Dataset (with bounding boxes)</p> <p> </p> <p><strong>Overview</strong></p> <p>Road safety during winter months remains a critical concern due to the elusive nature of black ice, a thin layer of ice that forms on road surfaces, making it challenging for drivers to identify and navigate safely. In an effort to address this issue, our research team at Cheongshim International Academy (CSIA) has conducted extensive studies on real-time black ice detection utilizing YOLOX, a state-of-the-art object detection algorithm, deployed on drones. As a significant contribution to the research community, we are pleased to share our meticulously curated image dataset, which encapsulates diverse scenarios and conditions representative of real-world black ice occurrences.</p> <p> </p> <p><strong>Background</strong></p> <p>Black ice poses a significant threat to road safety, especially during winter, as it is often challenging for drivers to detect, leading to increased risks of accidents and hazardous road conditions. Our dataset aims to fill the gap in existing resources by providing a comprehensive collection of images showcasing various instances of black ice under different environmental conditions. The dataset covers diverse scenarios, including different lighting conditions, road surfaces, and black ice formations, making it a valuable resource for developing and testing robust black ice detection models.</p> <p> </p> <p><strong>Significances of the Dataset</strong></p> <p>The significance of this dataset lies in its potential to advance the development of effective black ice detection algorithms. By sharing our dataset with the research community, we aim to facilitate the creation of more accurate and reliable models for real-time detection of black ice using drone technology. The dataset includes annotations in COCO format, providing detailed information about the location and characteristics of black ice instances in each image.</p> <p> </p> <p><strong>Categorization</strong></p> <p>In our pursuit of advancing the field of computer vision and contributing to ongoing research endeavors, we proudly introduce three distinct image datasets meticulously curated by our research team. These datasets, categorized as "White," "Black," and "Outdoors (OD)," cater to unique scenarios and are designed to fuel the development of specialized models addressing specific challenges in visual recognition.</p> <p> </p> <p><strong>White Dataset: </strong></p> <ul> <li><strong>Composition:</strong> This dataset comprises 413 images, each meticulously annotated with an average of 1.1 annotations per image, depicting the unique optical characteristics of black ice.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is 3.16%, emphasizing the subtlety of the black ice formations. The average image brightness is measured at 149.358.</li> <li><strong>Capture Environment:</strong> The images were taken in controlled indoor laboratory conditions, ensuring consistency and repeatability.</li> <li><strong>Creation Method:</strong> The dataset was generated by cooling asphalt samples in a freezer to temperatures ranging from -4°C to -20°C. Subsequently, 4°C water was sprayed onto the sample surfaces, creating black ice. The dataset captures the optical properties of black ice, showcasing its interaction with light.</li> <li><strong>Significance: </strong>Valuable for highlighting the optical characteristics of black ice, enhancing model accuracy in well-lit scenarios.</li> </ul> <p> </p> <p><strong>Black Dataset: </strong></p> <ul> <li><strong>Composition:</strong> This dataset comprises 814 images, with a detailed annotation structure averaging 3.5 annotations per image, showcasing the challenges of recognition in low-light conditions.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is notably higher at 12.37%, reflecting the complex and varied formations of black ice. The average image brightness is measured at 123.028.</li> <li><strong>Capture Environment:</strong> Similar to the White Dataset, images were captured in a controlled indoor laboratory environment. Asphalt pelt was placed under the black iced asphalt pieces to replicate realistic scenarios.</li> <li><strong>Creation Method:</strong> The dataset creation involved the same process of cooling asphalt samples, followed by spraying water to create black ice. To simulate real-world conditions, asphalt pelt was used as a background, and various shapes of black ice were randomly placed in each image.</li> <li><strong>Significance:</strong> Realistic emulation of black ice using backgrounds made up of asphalt pelts, providing essential drark images for robust model training.</li> </ul> <p> </p> <p><strong>Outdoor (OD) Dataset</strong></p> <ul> <li><strong>Composition:</strong> This dataset is the most extensive, consisting of 1624 images, with an average of 1.5 annotations per image, capturing the challenges of recognizing black ice in outdoor winter conditions.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is 12.34%, mirroring the complexity of real-world outdoor scenarios. The average image brightness is significantly lower at 56.575.</li> <li><strong>Capture Environment:</strong> Unlike the indoor datasets, the OD dataset was captured outdoors in winter conditions where black ice naturally forms.</li> <li><strong>Creation Method:</strong> Black ice was created on the asphalt road of Cheongsim International High School by spraying +4°C water onto the surface. DJI Tello's built-in camera was used for capturing images from various angles, simulating drone-like perspectives. This dataset is designed to closely replicate real-world scenarios, providing a valuable resource for training models for outdoor applications.</li> <li><strong>Significance: </strong>Represents real-world outdoor scenarios, offering a unique perspective for developing models capable of handling diverse and challenging conditions.</li> </ul> <p> </p> <p><strong>Cameras: </strong></p> <ul> <li>iPhone SE2 (Apple, California)</li> <li>iPhone SE3 (Apple, California)</li> <li>iPhone 12 (Apple, California)</li> <li>iPhone 14 Pro (Apple, California)</li> <li>Q9 (LG Electronics, Seoul, Korea)</li> <li>V30 (LG Electronics, Seoul, Korea)</li> <li>Tello (DJI, ShenZhen, China)</li> </ul>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021)
<p>The datasets consists on 91 daily files, in IONEX format (<a href="http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf">http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf</a>) , corresponding to three months of global ionospheric maps (GIM) of vertical total electron content (VTEC) computed in real-time from the assessed and combined real-time GIMs generated by four analysis centers. Indeed, the Real-Time Working Group (RTWG) of International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) navigation, positioning, timing, and Earth observations. As one of the important part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) have been generated by real-time weighting technique with the help of RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). Compared with IGS rapid Global Ionosphere Maps (GIMs) (corg, ehrg, emrg, esrg, igrg, jprg, uhrg, uprg, uqrg, whrg) and IGS final combined GIM (igsg), the IGS combined RT-GIM (irtg) is equivalent to the post-processed GIMs and even better than some rapid GIMs. The IGS RT-GIMs are reliable sources of real-time global VTEC information and has great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes)</p>
Real-time ZTD and gradients from 161 EPN stations; year 2020
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2020 for 161 EPN (http://epncb.oma.be/) stations.</p> <p>The processing strategy can be found in https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered.</p> <p>For convenience, products are stored in 3 formats (but each contains identical data):</p> <p>1. Standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>2. Self-explanatory netCDF format (<a href="https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html">https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html</a>), which follows the parameter naming convention of the troposphere SINEX v2 format: <a href="https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf">https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf</a>. Each file contains data for all stations but only for one month.</p> <p>3. Semicolon delimited text files, with a self-explanatory header line. Each file contains daily products for one station.</p> <p> </p> <p>For visualization see:</p> <p>January https://youtu.be/oU_JaSnhoYI</p> <p>February https://youtu.be/O2LnWOjudGA</p> <p>March https://youtu.be/JVvO25oYcV8</p> <p>April https://youtu.be/2wqY7BKnw8g</p> <p>May https://youtu.be/XY62BMSlbT8</p> <p>June https://youtu.be/37cxhQ-0Y7U</p> <p>July https://youtu.be/MSJVtC-DnVM</p> <p>August https://youtu.be/lfG2GnrYiBo</p> <p>September https://youtu.be/CUrdDYr_lOY</p> <p>October https://youtu.be/pKPsTjKmvtE</p> <p>November https://youtu.be/s5gbtEdwiSM</p> <p>December https://youtu.be/rrn1SuzlM2k</p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Simulated NGS datasets for real-time detection of novel pathogens
<p>Datasets based on the <a href="https://doi.org/10.5281/zenodo.3678563">bacterial</a> and <a href="https://doi.org/10.5281/zenodo.4312525">viral</a> simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.</p> <p>The Nanopore datasets were resimulated with <a href="https://github.com/liyu95/DeepSimulator">DeepSimulator 1.5</a> (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.</p>
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Data and Code for: Real-Time Pricing and the Cost of Clean Power
<p>Solar and wind power are now cheaper than fossil fuels but are intermittent. The extra supply-side variability implies growing benefits of using real-time retail pricing (RTP). We evaluate the potential gains of RTP using a model that jointly solves investment, supply, storage, and demand to obtain a chronologically detailed dynamic equilibrium for the island of Oahu, Hawai'i. We find that RTP reduces costs in high-renewable systems by roughly 6 to 12 times as much as in fossil systems holding demand assumptions fixed, markedly lowering the cost of clean energy integration.</p>
Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons - Dataset
<p>Dataset complement to "Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons" - includes output and video files obtained using the <a href="https://etprogram.org/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program.</p> <p>See the paper at <a href="https://doi.org/10.1103/PhysRevResearch.6.033283">https://doi.org/10.1103/PhysRevResearch.6.033283</a></p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing", published in <em>Chemosensors</em> on 03 Jan 2019.</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
SMART - Self-adaptive Machine Learning Approach for Real-time Tuning of IEEE 802.11 PHY and MAC layers
<p><strong>Introduction</strong></p> <p>Worldwide the demand for wireless access networks providing very high throughputs has been increasing exponentially, namely due to bandwidth-hungry applications such as high definition video streaming and augmented reality. In order to fulfil these requirements, the Wi-Fi standard was enriched with new amendments, such as IEEE 802.11n, IEEE 802.11ac, and recently IEEE 802.11ax (Wi-Fi 6). New parameters have been proposed for both physical (PHY) and media access control (MAC) layers, including channel bonding, short guard interval (SGI), and advanced modulation and coding schemes (MCS).</p> <p>However, the high variability of the signal strength in the wireless radio channel, allied to the channel asymmetry, makes the selection of optimal configurations for these parameters a challenge. Typically, these parameters are configured with a default value. For runtime optimization, some algorithms have already been proposed. Still, they were designed considering legacy IEEE 802.11 releases and static scenarios. Besides, these parameters have their trade-offs that need to be properly managed. To help dealing with this, machine learning has been recently introduced in wireless networks, providing the intelligence that networks need in order to be smart and self-adaptive.</p> <p>SWOP (Smart Wireless Optimization) is a cross-layer optimization approach for Wi-Fi networks extending the current Rate Adaptation (RA) approach, for instance, followed by the well-known Minstrel algorithm widely used in practice. Our approach takes advantage of Deep Reinforcement Learning (DRL) in order to learn the optimal Wi-Fi link configuration. By considering the wireless channel as the environment, the transmitter node (the agent) chooses the best link parameters (the action) in order to maximize the throughput (the reward) based on the channel metrics captured from the environment (the state). In this work we propose a simple DRL-based Wi-Fi Rate Adaptation (RA) algorithm, named Data-driven Algorithm for Rate Adaptation (DARA), which is one of the modules of Smart Wireless Optimization (SWOP)</p> <p>SMART aimed to run a set of wireless experiments on top of w-iLab.t testbeds provided by the Fed4FIRE+ project to directly validate our DRL model and learn a policy from the wireless experiments executed in a controlled environment. However, after facing difficulties with the scenarios we could achieve on the real testbed, we decided to train and test DARA using a trace-based simulation approach. In simulation, we could train our model in scenarios that are more complex and diverse whilst easy to configure, when compared to real testbeds. The w-iLab.t testbeds were still used to capture data traces (e.g. Signal-to-Noise Ratio, position of nodes, transmission power and link distance) that were then injected in ns-3 for validating DARA.</p> <p>With this work, we concluded that DARA performance is impacted when operating in scenarios with asymmetric links, which is common in the highly dynamic and unpredictable wireless environments. Furthermore, the asymmetry offset varies between scenarios and it may also change for the same scenario, as time progresses. This randomness is not addressed when solely considering the SNR as the link metric, posing a challenge in the learning phase of DARA. Despite these limitations, the results obtained show that DARA still achieves up to 14.9% higher throughput higher than Minstrel [1] and slightly lower than Ideal [2] for most of the scenarios. The results obtained will serve as a basis to support our ongoing and future research.</p> <p> </p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the SMART project, organized in different folders for each Rate Adaptation Algorithm, as well as the traces that were used to obtain such results:</p> <ul> <li><strong>DARA: </strong>Results obtained using our solution <strong>(Naming Convention #1, Folder Content #1)</strong></li> <li><strong>MIN: </strong>Results obtained using Minstrel-HT <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>ID: </strong>Results obtained using Ideal <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>TRACES: </strong>Trace files used to obtain the results present in this dataset <strong>(Naming Convention #2, Folder Content #3)</strong></li> </ul> <p><strong>Naming Convention #1 – RAA TID TP TO:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> (RAA) </strong> <ul> <li><strong>drl </strong>– Data Driven Algorithm for Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> <li>Trace ID<strong> (TID)</strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transport Protocol<strong> (TP)</strong> <ul> <li><strong>udp </strong>– User Datagram Protocol</li> </ul> </li> <li>Traffic Orientation<strong> (TO)</strong> <ul> <li><strong>normal </strong>– A<strong>-></strong>B</li> <li><strong>reversed </strong>– B<strong>-></strong>A</li> </ul> </li> </ul> <p><strong>Naming Convention #2 – TID_TXP:</strong></p> <ul> <li>Trace ID<strong> (TID) </strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transmitting Power in dBm <strong>(TXP) </strong> <ul> <li><strong>3, 5, 7, 9, 12 dBm </strong></li> </ul> </li> </ul> <p><strong>Folder Content #1: </strong></p> <ul> <li><em>checkpoint_ RAA TID TP TO</em><strong> (Folder)</strong> <ul> <li><strong>Policy Checkpoint</strong> with which the results were obtained</li> </ul> </li> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Step Counter</strong></li> <li>Column 2 – <strong>Reward Value</strong></li> <li>Column 3 – <strong>Observation Value</strong></li> <li>Column 4 – <strong>Action Value</strong></li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #2: </strong></p> <ul> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #3 - </strong>Source: <a href="https://zenodo.org/record/3713271#.YjjBVDXLdhE">https://zenodo.org/record/3713271#.YjjBVDXLdhE</a><strong>: </strong></p> <p>· <em>date_time</em><strong>.cfg </strong>configuration details of the experiment</p> <p>· <em>date_time_NodeID</em><a href="https://zenodo.org/record/3713271#_ftn1"><strong><em><sup>[1]</sup></em></strong></a><em>_SenderID</em><a href="https://zenodo.org/record/3713271#_ftn2"><strong><em><sup>[2]</sup></em></strong></a><em>_ReceiverID</em><a href="https://zenodo.org/record/3713271#_ftn3"><strong><em><sup>[3]</sup></em></strong></a><em>_FlowType</em><a href="https://zenodo.org/record/3713271#_ftn4"><strong><em><sup>[4]</sup></em></strong></a><em>_Params</em><a href="https://zenodo.org/record/3713271#_ftn5"><strong><em><sup>[5]</sup></em></strong></a><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </p> <p>· <em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </p> <p><a href="https://zenodo.org/record/3713271#_ftnref1"><sub>[1]</sub></a><sub> ID of the node Logging node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref2"><sub>[2]</sub></a><sub> ID of the Sender node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref3"><sub>[3]</sub></a><sub> ID of the Receiver node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref4"><sub>[4]</sub></a><sub> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref5"><sub>[5]</sub></a><sub> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p> <p> </p> <p><strong>References</strong></p> <p>1. F. FietKau, “Minstrel_HT: New rate control module for 802.11n [LWN.net]”. Mrt-2010.</p> <p>2. “ns-3: ns3::IdealWifiManager Class Reference,” Jan 2021, [Online; accessed 23. Jun. 2021]. Available: <a href="https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi%20manager.html">https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi manager.html</a></p>
Aurorasaurus Real-Time Citizen Science Aurora Data
<p>Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website (aurorasaurus.org) or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms. This data is offered to the scientific community for research use through an open-access database in its raw and scientific formats for the 2015-2016 period, each of which is described in detail in the following technical report:</p> <p>Kosar, B. C., MacDonald, E. A., Case, N. A., & Heavner, M. (2018). Aurorasaurus Database of Real‐Time, Crowd‐Sourced Aurora Data for Space Weather Research. <em>Earth and Space Science</em>, <em>5</em>(12), 970-980.</p> <p>For more information on the project, please contact the project leaders at aurorasaurus.info@gmail.com.</p> <p> </p>
STOP-IT REAL-TIME ANOMALY DETECTOR (RTAD)
<p>ICS were designed with automation reliability in mind and most communication technologies were proprietary with no compatibility with TCP/IP Stack. Nowadays most devices have connectivity features inheriting attacks that do not require physical access to plant or systems and organizations are dedicating resources to protect their assets converging physical, logical and IT resources. Task 5.5 objective is to detect known and unknown threats to ICS systems affecting integrated sensors or actuators and SCADA systems by monitoring and learning from its normal behaviour and giving the ICS operators the ability to detect advanced attacks and anomalies before they can cause damage or spread inside the network. Machine Learning techniques and custom attacks were developed for those purposes in order to identify attack patterns and unwanted behaviour before they can interact with sensors/actuators or spoof SCADA reporting messages for removing visibility of the threat to the operator.</p>
STOP-IT Real-Time Sensor Data Protection (RSDP)
<p>Sensors, and other devices, generate large amounts of data, which can be used for different purposes; for example, controlling the proper functioning of a critical infrastructure, performing predictive maintenance actions or making decisions that improve the productivity of an industrial plant. However, the use or analysis of erroneous or corrupt data can cause catastrophic situations. For this reason, it is very important to be able to guarantee the integrity of the data generated by sensors, or other devices, which will be used to perform relevant actions for a critical infrastructure, industrial plant, etc. The RSDP tool provides exactly that service, it checks the integrity of the data that has been previously stored in the system, and this can be guaranteed thanks to the use of Blockchain, or DLT, technologies.</p>
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.</p>
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.