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4,325 results for “real”
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 Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.
<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements </p><p>Dune1</p><p> </p><p>Multi-grid solver</p><p> </p><p>11</p><p> </p><p>3</p><p> </p><p>2,304</p><p> </p><p>Complex..</p><p> </p><p>2,304</p><p> </p><p>HSMGP1</p><p> </p><p>Stencil-grid solver</p><p> </p><p>14</p><p> </p><p>3</p><p> </p><p>3,456</p><p> </p><p>..equation..</p><p> </p><p>3,456</p><p> </p><p>HiPAcc1</p><p> </p><p>Image processing framework</p><p> </p><p>33</p><p> </p><p>2</p><p> </p><p>13,485</p><p> </p><p>..solving..</p><p> </p><p>13,485</p><p> </p><p>Trimesh2</p><p> </p><p>Triangle mesh library</p><p> </p><p>13</p><p> </p><p>4</p><p> </p><p>239,360</p><p> </p><p>..time</p><p> </p><p>239,360</p><p> </p><p>GEC</p><p> </p><p>Generic edge computing</p><p> </p><p>552</p><p> </p><p>2</p><p> </p><p>~5.3*108</p><p> </p><p>Energy Consumption</p><p> </p><p>132500</p><p> </p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</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>
Ecuador Real State data set
<pre>This set of data contains information on different characteristics of the properties for rent on the website https://arriendayvende.com, through which analyzes can be carried out on the distribution of prices according to location, area, among others.</pre>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Point cloud data sets of real and virtual Chenopodium alba
<p>This data set contains:</p> <p>- 5 annotated point clouds of real Chenopodium alba plants obtained from multi-view 2D camera imaging. Annotations consist of 5 classes: leaf blade, petiole, apex, main stem, branch. .txt files contain both 3D coordinates and annotations. .ply files are also provided for raw 3D point data without annotations.</p> <p>- 24 annotated point clouds of virtual Chenopodium alba that were generated by a L-system simulation program. Annotations consist of 3 classes: leaf blade, petiole, stem. 3D coordinates and annotations are in separated .txt files. </p> <p>These files have been used in a companion paper.</p> <p> </p>
EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb
<p>EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects. Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music. We also include annotations for parameter settings of the effects we used.</p> <p>More details can be found in <a href="http://egfxset.github.io">egfxset.github.io</a></p> <p>The dataset can also be accessed with <a href="https://mirdata.readthedocs.io/en/stable/source/mirdata.html#module-mirdata.datasets.egfxset">mirdata</a></p> <p>Effects and parameters included:</p> <table> <tbody> <tr> <td>Effect</td> <td>Model</td> <td>Effect Type</td> <td>Knob Names</td> <td>Knob Type</td> <td>Setting</td> </tr> <tr> <td>blues driver</td> <td>Boss BD-2 Blues Driver</td> <td>distortion</td> <td>['level', 'tone', 'gain']</td> <td>['volume','eq','effect amount']</td> <td>[0.5,0.5,1.0]</td> </tr> <tr> <td>tube screamer</td> <td>Ibanez Mini Tube Screamer</td> <td>distortion</td> <td>['tone', 'overdrive', 'level']</td> <td>['eq','effect amount','volume']</td> <td>[0.5,1.0,0.5]</td> </tr> <tr> <td>distortion</td> <td>Pro Co Sound RAT2 Distortion</td> <td>distortion</td> <td>['distortion', 'filter', 'volume']</td> <td>['effect amount','eq','volume']</td> <td>[1.0, 0.5,1.0]</td> </tr> <tr> <td>chorus</td> <td>Boss CE-3 Chorus</td> <td>modulation</td> <td>['rate', 'depth', 'stereo mode']</td> <td>['rate','effect amount','selector']</td> <td>['120 bpm', 1.0, False]</td> </tr> <tr> <td>flanger</td> <td>Mooer E-Lady</td> <td>modulation</td> <td>['color', 'type', 'range', 'rate']</td> <td>['eq','selector','effect amount','rate']</td> <td>[0.5, 'normal', 1.0, '120 bpm']</td> </tr> <tr> <td>phaser</td> <td>MXR Phase 45</td> <td>modulation</td> <td>['speed']</td> <td>['rate']</td> <td>['120 bpm']</td> </tr> <tr> <td>tape echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['tape echo', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>digital delay</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['digital delay', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>sweep echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (sweep speed)', 'tweez (sweep depth)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'rate', 'effect amount', 'effect amount']</td> <td>['sweep echo', '120 bpm', 0.6, '120 bpm',1.0,0.5]</td> </tr> <tr> <td>plate reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'plate', 1.0, 0.2, True]</td> </tr> <tr> <td>hall reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'hall', 1.0, 0.2, True]</td> </tr> <tr> <td>spring reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'spring', 1.0, 0.2, True]</td> </tr> </tbody> </table> <p><br>Please cite these papers if using EGFxSet:</p> <p>Pedroza HE, Abreu W, Corey R, Roman IR. "Leveraging real electric guitar tones and effects to improve robustness in guitar tablature transcription modeling." <em>In 27th International Conference on Digital Audio Effects (DAFx),</em> 2024.</p> <p>Pedroza, Hegel, Gerardo Meza, and Iran R. Roman. "EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb." <em>ISMIR Late Breaking Demo, </em>2022.</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"
<p>This is the underlying dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"</p>
Lab513/CyberSwitch: Real time control of a genetic toggle switch
<p>Release 1.0 | CyberSwitch | Master Branch</p> <p>This repository contains the code and data that were used in the paper:</p> <p>Lugagne, J.-B., Carillo, S. S., Kirch, M., Köhler, A., Batt, G., & Hersen, P. (2017). Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nature Communications. </p> <p>This article is accessible in open access : http://rdcu.be/A0lH</p> <p> </p>
Nonperturbative study of the electroweak phase transition in the real scalar singlet extended Standard Model: dataset
<p>Collection of simulation results as presented in figures of the paper "Nonperturbative study of the electroweak phase transition in the real scalar singlet extended Standard Model".</p>
Context-based Entity Recommendation on Real-Life Knowledge Work in Context (RLKWiC dataset)
<h2><a href="../records/11059573">RLKWiC</a> Add-on: Benchmarking Dataset for Entity Recommendation</h2> <p>This benchmark, built on top of the Real-Life Knowledge Work in Context (<a href="../records/11059573">RLKWiC</a>) dataset, is designed to evaluate context-based entity recommendation by simulating a scenario where participants receive entities extracted from their activities across their defined contexts. </p> <p>In total, 1850 entity recommendations were generated across 56 contexts. After deduplication, these entities were presented to participants for explicit relevance assessment on a 3-point scale:</p> <ul> <li>0 [Irrelevant]: Signifying a lack of relevance between the recommended entity and the context.</li> <li>1 [Relevant] Denoting a connection between the entity and the context, although it may not fully represent it.</li> <li>2 [Representative]: The entity closely aligns with the context, indicating a high level of relevance where the context can be inferred to be about this entity.</li> </ul> <p>Participants could also suggest additional relevant entities. The resulting dataset comprises 1067 entities with explicit relevance scores, offering a resource for benchmarking entity recommendation in real-life knowledge work.</p> <h3><strong>Paper: </strong><a href="https://dl.acm.org/doi/10.1145/3640457.3688068" target="_blank" rel="noopener">Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data</a></h3>
Medimania: entscheide dich zwischen Real und Fake
<p><strong>Medimania </strong>ist ein Bewegungsspiel in einfacher Sprache für Berufseinstiegklassen, mit dem du die Informationskompetenz deiner Schüler steigern kannst. Im Datenpaket findest du Dateien zur Eigenproduktion ("Premium" und "Standard (DIY)"). Auch bekommst du Anleitungen zur Herstellung des Bewegungsspiels inklusive aller benötigten Spielmaterialien. Weiterhin sind Blanko-Karten enthalten, die du selbst beschriften kannst und "weitere Real- und Fake-Karten", die du zusätzlich ausdrucken kannst, wenn du das Spiel öfter spielst. </p> <ul> <li>"Premium-Version": für die Erstellung dieser Version benötigst du Werkzeug oder einen Lasercutter. Es entsteht ein hochwertiges und länger haltbares Spiel. Das Spielmaterial ist farbenfroh gestaltet. </li> <li>"Standard (DIY)": diese Version kannst du mit dem Drucker herstellen. Insgesamt hat das Spielmaterial weniger Farbanteile (z.B. "fehlt" bei den Spielkarten die bunte Rückseite). Die restlichen Spielmaterialen können aus Dingen des täglichen Bedarfs erstellt werden. </li> <li>Es ist durchaus möglich Premium-Spielmaterial und Standard-Material zu mischen. Je nachdem, welche Möglichkeiten du hast.</li> </ul> <p>Wir freuen uns über Feedback an bib@hs-hannover.de</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>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
UnLoc dataset (Synthetic + Real)
<p>This dataset contains the synthetic and real data used in the article " Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration " to train 3 convolutional neural networks (CNNs) in order to perform uncalibrated relative localization of a cuboid block with respect to a robot, and to evaluate them. </p> <p>It consists of a dataset composed of synthetic pictures for training the CNNs and a dataset of real pictures for evaluation. </p> <p>The "synthetic" dataset is composed 3 sub-datasets (each of them composed of thousands of synthetic pictures and corresponding groundtruth) for training : </p> <ol> <li>a dataset for coarse relative localization subtask : coarse_estimation_data (~14.6 GB when extracted)</li> <li>a dataset for the tool localization subtask : tool_detection_data (~4.3 GB when extracted)</li> <li>a dataset for the fine relative localization subtask : fine_estimation_data (~13.3 GB when extracted)</li> </ol> <p>These sub-datasets are composed of raw data as well as post-treated data ready to be used for training CNNs, in CSV format and in Torch format (.t7). </p> <p>The "real" dataset is composed of real pictures with a precisely localized cuboid block for evaluation only : UnLoc_real (~2.5 GB when extracted). </p> <p>More information available on the project page : <a href="http://imagine.enpc.fr/~loingvi/unloc/">http://imagine.enpc.fr/~loingvi/unloc/</a></p>
SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3
<p>R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.</p> <p>Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible.</p> <p>Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood.</p> <p>What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?</p> <p>INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.</p> <p>The <strong>SIMBED </strong>project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios.</p> <p>By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach. </p> <p>These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.</p> <p> </p> <p>The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on:</p> <ul> <li><strong><em>SubExp#1: </em></strong><em>Static point-to-point Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#1.1:</em></strong><em> Using w-iLab.2 (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#1.2:</em></strong><em> Using w-iLab.2 (low SNR scenarios)</em></li> <li><strong><em>SubExp#1.3:</em></strong><em> Using NITOS</em></li> <li><strong><em>SubExp#1.4:</em></strong><em> Using w-iLab.1 (datacenter room)</em></li> </ul> </li> <li><strong><em>SubExp#2: </em></strong><em>Static point-to-point Wi-Fi communications using fixed</em> rate</li> <li><strong><em>SubExp#3: </em></strong><em>Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)</em></li> <li><strong><em>SubExp#4: </em></strong><em>Static multiple access Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#4.1:</em></strong><em> Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#4.2:</em></strong><em> Using w-iLab.2 (bidirectional) (low SNR scenarios)</em></li> <li><strong><em>SubExp#4.3:</em></strong><em> Using NITOS (bidirectional)</em></li> <li><strong><em>SubExp#4.4:</em></strong><em> Using w-iLab.1 (bidirectional)</em></li> <li><strong><em>SubExp#4.5:</em></strong><em> Using NITOS (2 STAs)</em></li> <li><strong><em>SubExp#4.6:</em></strong><em> Using w-iLab.2 (2 STAs)</em></li> </ul> </li> <li><strong><em>SubExpExample</em></strong>: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results.</li> </ul> <p>Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the <strong>parsed</strong> experimental results, which contains</p> <p>This folder contains the details and parsed logs of the experiment, as follows:</p> <ul> <li><em>date_time</em><strong>.cfg </strong>– configuration details of the experiment</li> <li><em>date_time_NodeID<sup><a href="#_ftn1"><strong>[1]</strong></a></sup>_SenderID<sup><a href="#_ftn2"><strong>[2]</strong></a></sup>_ReceiverID<sup><a href="#_ftn3"><strong>[3]</strong></a></sup>_FlowType<sup><a href="#_ftn4"><strong>[4]</strong></a></sup>_Params<sup><a href="#_ftn5"><strong>[5]</strong></a></sup></em><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </li> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </li> <li><em>NodeID</em><strong>.</strong><strong>waypoints</strong> – coordinates of the static nodes</li> <li><em>date_time_MobileNodeID</em><strong>.</strong><strong>waypoints</strong> – waypoints of the mobile nodes (when applicable)</li> </ul> <p>The experiment’s folder also contains a folder for the simulations <strong>output</strong> with the simulations statistics files, for the multiple simulations approaches considered, as follows:</p> <ul> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em>.<strong>simstats </strong>– logs of the packets received (simulation)</li> </ul> <p> </p> <p><sub><a href="#_ftnref1">[1]</a> ID of the node Logging node</sub></p> <p><sub><a href="#_ftnref2">[2]</a> ID of the Sender node</sub></p> <p><sub><a href="#_ftnref3">[3]</a> ID of the Receiver node</sub></p> <p><sub><a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><sub><a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></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>
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.