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52 results for “IEEE”
Supplemental Material for IEEE VIS 2023 Short Paper 'Compact Phase Histograms for Guided Exploration of Periodicity'
<p>Supplemental video explaining the approach described in our 2023 IEEE VIS short paper "Compact Phase Histograms for Guided Exploration of Periodicity," which is going to be presented in Melbourne, Australia October 22–27, 2023.</p>
IEEE 14 bus system data set
<p>IEEE 14 bus system data set</p> <p>High quality data set for data-driven power system assessment.</p>
IEEE 39 bus system data set
<p>IEEE 39 bus system data set.</p> <p>High quality data set for data-driven power system assessment.</p>
IEEE 68 bus system data set
<p>IEEE 68 bus system data set</p> <p>High quality data set for data-driven power system assessment.</p>
IEEE MWCL_Figure Dataset_EFC surfaces for HMTSs.opj
<p>Equal frequency contours of surface waves propagating along the metasurface on 6.5 GHz for alpha = 0 deg and propagation angle phi=0-90 deg.</p>
Dataset for P. Ripka, M. Mirzaei, J. Maier: Flat Magnetic X-Y Alignment sensor, IEEE Sensors Letters Vol. 8, Iss. 7, 2024, pp. 1-4 10.1109/LSENS.2024.3414375
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Monte Carlo experiment data & IEEE 118 bus data
<p>The data files contain Monte Carlo experiment data and IEEE 118 bus data: The data generated through Monte Carlo experiments include four different spatiotemporal residual structures: no correlation, only weak spatial (cross) correlation, only temporal (auto) correlation, and weak spatial-temporal correlation; The IEEE 118 bus data contains 118 voltage observations sampled for 1000 times.</p>
2018 IEEE IUS SA-VFI Challenge
<p>The 2018 IEEE IUS SA-VFI challenge consist on estimating blood flow velocities from both simulated and measured ultrasound RF element data sets. The challenge results are meant to be presented during the next IEEE International Ultrasonics Symposium 2018 in Kobe (Japan).</p> <p>The data sets for the challenge consist on pre-beamformed RF element signals using a pre-selected synthetic aperture (SA) sequence on flow phantoms, both simulations and experiments. The details of the provided data sets and example code is given in the main document "2018_IEEE_IUS_SA-VFI_Challenge.pdf". </p> <p>A website for the participants with additional information is available at https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2018/. </p> <p> </p> <p> </p>
AMDADOS simulation results and scaling for IEEE Oceans paper
<p>Presents inputs datasets for the AMDADOS paper presented at IEEE Oceans 2018 (<a href="https://doi.org/10.5281/zenodo.1346168">10.5281/zenodo.1346168</a>) to enable replication of experiments</p>
Inputs and outputs of conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina and Boris Bellalta. The article has been sent to CSCN 2019.</p> <p>Regarding the input, we provide both the "input_node" and "input_system" files used by the Komondor simulator. In particular, up to 50,400 different scenarios are provided, which stand for 3 maps sizes 50 different random deployments (i.e., nodes allocation), 21 OBSS/PD values, and 16 traffic loads. More details are provided in the article. </p> <p>The output files collect the results gathered for all the scenarios. In addition, we include the code files used to "post-process" all the results.</p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Survey of Current Reproducibility Practices at IEEE Workshops and Conferences and in Journals
<p>The goal of the IEEE Reproducibility Practices Survey was to assess the reproducibility practices of IEEE journals, magazines, and conferences. IEEE Strategic Research (with Josephine Russo as the primary point of contact) executed the survey. As part of the survey, 422 IEEE conference organizers, magazine editor-in-chiefs, and journal editors-in-chiefs were surveyed using a self-administered, online questionnaire, yielding 132 responses for a response rate of 31%. A detailed report associated with this survey can be found at https://www.computer.org/volunteering/boards-and-committees/Open-Science-Reproducibility.</p>
Artifacts for the IEEE Internet of Things Journal Publication: Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT
<p>Artifacts for the evaluation of the publication <em>Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT </em>which will be published in the IEEE Internet of Things journal. More information is available in the provided README.md file.</p>
Vehicle-to-Infrastructure IEEE 802.11ad Wi-Fi dataset
<p><strong>1. Introduction</strong></p> <p>This dataset contains space and time-indexed data collected in a Vehicle-To-Infrastructure (V2I) communication scenario, where a moving vehicle downloaded data from a stationary Access Point (AP) using IEEE 802.11ad Wi-Fi.</p> <p>The dataset is comprised of both throughput data and detailed frame information captured with <code>tcpdump</code>. It can be used to study 802.11ad's behavior in vehicular environments, in particular in what pertains to antenna sector selection.</p> <p>This dataset is associated with the following article, which we recommend consulting for more information: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/comcom-2022-preprint.pdf"><em>Geolocation-based Sector Selection for Vehicle-to-Infrastructure 802.11ad Communication</em></a>, Mateus Mattos, António Rodrigues, Rui Meireles, Ana Aguiar, in the Elsevier Journal of Computer Communications, Volume 193, ISSN 0140-3664, 2022, <a href="https://doi.org/10.1016/j.comcom.2022.07.005">doi:10.1016/j.comcom.2022.07.005</a>.</p> <p><strong>2. Experimental setup</strong></p> <p>The AP was placed at the corner of a residential-area intersection while a mobile client vehicle drove around it, downloading data from the AP.</p> <p>Commercial Off-The-Shelf (COTS) TP-Link Talon AD7200 were used for both the stationary AP and mobile client. A third AD7200 configured in promiscuous mode was placed next to the mobile client, in order to capture the control frames being exchanged.</p> <p><strong>2.1 Experimental nodes</strong></p> <table> <tbody><tr> <th>MAC address</th> <th>Role</th> <th>Position</th> <th>Orientation</th> </tr> </tbody><tbody> <tr> <td><code>70:4f:57:72:b2:52</code></td> <td>AP</td> <td>Static, latitude: 41.111879, longitude: -8.631146, mounted of top of a parked vehicle</td> <td>Perpendicular to road</td> </tr> <tr> <td><code>50:c7:bf:97:8a:ac</code></td> <td>Client</td> <td>Mobile, mounted on roof of client vehicle</td> <td>Towards front of vehicle</td> </tr> <tr> <td><code>50:c7:bf:3c:53:1c</code></td> <td>Monitor</td> <td>Mobile, mounted on roof of client vehicle</td> <td>Towards front of vehicle</td> </tr> </tbody> </table> <p><strong>3. Trace description</strong></p> <p>The data is divided into traces. Each trace represents an uninterrupted period of data collection. The experiments were ran twice, once in 2020, and again in 2021. Environmental conditions, such as weather and topography, were consistent between the two experiment sets.</p> <p><strong>3.1 2020 traces</strong></p> <table> <tbody><tr> <th>Trace #</th> <th>Start timestamp</th> <th>End timestamp</th> <th>Mobility pattern</th> </tr> </tbody><tbody> <tr> <td>235</td> <td>1593946456</td> <td>1593946593</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>237</td> <td>1593946793</td> <td>1593946908</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>238</td> <td>1593946938</td> <td>1593947076</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>240</td> <td>1593947181</td> <td>1593947332</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>241</td> <td>1593947360</td> <td>1593947499</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>242</td> <td>1593947566</td> <td>1593947700</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>243</td> <td>1593947759</td> <td>1593947915</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>244</td> <td>1593947971</td> <td>1593948111</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>245</td> <td>1593948210</td> <td>1593948348</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>246</td> <td>1593948433</td> <td>1593948569</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>247</td> <td>1593948631</td> <td>1593948795</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>248</td> <td>1593948904</td> <td>1593949053</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>249</td> <td>1593949307</td> <td>1593950016</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> <tr> <td>250</td> <td>1593950073</td> <td>1593950643</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> <tr> <td>251</td> <td>1593950682</td> <td>1593951240</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> </tbody> </table> <p><strong>3.2 2021 traces</strong></p> <table> <tbody><tr> <th>Trace #</th> <th>Start timestamp</th> <th>End timestamp</th> <th>Mobility pattern</th> </tr> </tbody><tbody> <tr> <td>201</td> <td>1632244398</td> <td>1632244548</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>202</td> <td>1632244563</td> <td>1632244663</td> <td>Vehicle moving westwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>203</td> <td>1632244674</td> <td>1632244799</td> <td>Vehicle moving southwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>204</td> <td>1632244812</td> <td>1632244915</td> <td>Vehicle moving northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>206</td> <td>1632245138</td> <td>1632245346</td> <td>Vehicle moving eastwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>207</td> <td>1632245355</td> <td>1632245463</td> <td>Vehicle moving southwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>208</td> <td>1632245472</td> <td>1632245581</td> <td>Vehicle moving northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>209</td> <td>1632245592</td> <td>1632245790</td> <td>Vehicle moving southwards from AP and back, northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>210</td> <td>1632245798</td> <td>1632245987</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>302</td> <td>1632335672</td> <td>1632336273</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>303</td> <td>1632336286</td> <td>1632336870</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>401</td> <td>1634983869</td> <td>1634984363</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>402</td> <td>1634984410</td> <td>1634984881</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>403</td> <td>1634984908</td> <td>1634985562</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>404</td> <td>1634985666</td> <td>1634986835</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>405</td> <td>1634986980</td> <td>1634988303</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>406</td> <td>1634988352</td> <td>1634989470</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>407</td> <td>1634989490</td> <td>1634990434</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> </tbody> </table> <p><strong>4. Data description</strong></p> <p><strong>4.1 File structure</strong></p> <p>The data from the 2020 and 2021 sets of experiments can be found in subfolders <code>2020</code> and <code>2021</code>, respectively.</p> <p>Each subfolder constains the following:</p> <ul> <li><code>gps.csv</code>: client vehicle mobility trace (individual NMEA sentences);</li> <li><code>gps-merged.csv</code>: client vehicle mobility trace (summarized);</li> <li><code>thrghpt.csv</code>: application throughput data;</li> <li><code>wifi.csv</code>: summarized 802.11ad frame data;</li> <li><code>pcap/</code>: contains raw <code>.pcap</code> files used to generate <code>wifi.csv</code>, separated by trace number;</li> <li><code>configs/</code>: includes JSON file with fields and filters used by <code>tshark</code> for the generation of <code>wifi.csv</code>.</li> </ul> <p><strong>4.2 GPS data: <code>gps.csv</code></strong> and <code>gps-merged.csv</code></p> <p>GPS data was captured by a high-accuracy GPS device: Trimble Pro Series 6H <a href="http://www.windenvironmental.com/Data-Sheets/Trimble-Pro%20Series-DS.pdf">[link]</a>, and consists of a combination of fields provided by multiple NMEA <code>GP*</code> sentence codes, namely: <code>GPRMC</code>, <code>GPGGA</code>, <code>GPGLL</code>, and <code>GNGSA</code> <a href="http://aprs.gids.nl/nmea/">[link]</a>.</p> <p>Column description</p> <p><code>gps.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>timestamp</code></td> <td>UNIX system timestamp at which GPS sentence was recorded, in seconds</td> </tr> <tr> <td><code>lat</code></td> <td>Latitude, in decimal degrees</td> </tr> <tr> <td><code>lon</code></td> <td>Longitude, in decimal degrees</td> </tr> <tr> <td><code>alt</code></td> <td>Altitude, in meters</td> </tr> <tr> <td><code>speed</code></td> <td>Ground speed, in knots</td> </tr> <tr> <td><code>HDOP</code></td> <td>Horizontal dilution of precision</td> </tr> <tr> <td><code>PDOP</code></td> <td>Position dilution of precision</td> </tr> <tr> <td><code>VDOP</code></td> <td>Vertical dilution of precision</td> </tr> <tr> <td><code>heading</code></td> <td>Direction of movement as provided by GPS device, in clockwise degrees from north</td> </tr> <tr> <td><code>identifier</code></td> <td>NMEA sentence code, e.g., <code>GPRMC</code></td> </tr> <tr> <td><code>gpstime</code></td> <td>Timestamp as provided by GPS device, in seconds</td> </tr> </tbody> </table> <p><strong>Note:</strong> Because each GPS sentence only contains a subset of the listed columns, any missing values are set to -1.0.</p> <p><code>gps-merged.csv</code> is a table containing all mobility information aggregated by GPS timestamp, for ease of use. It contains the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>gpstime</code></td> <td>Timestamp as provided by GPS device, in seconds</td> </tr> <tr> <td><code>timestamp</code></td> <td>Average UNIX system timestamp at which the GPS sentences from which this row was created were recorded, in seconds</td> </tr> <tr> <td><code>lat</code></td> <td>Latitude, in decimal degrees</td> </tr> <tr> <td><code>lon</code></td> <td>Longitude, in decimal degrees</td> </tr> <tr> <td><code>alt</code></td> <td>Altitude, in meters</td> </tr> <tr> <td><code>speed</code></td> <td>Ground speed, in knots</td> </tr> <tr> <td><code>HDOP</code></td> <td>Horizontal dilution of precision</td> </tr> <tr> <td><code>PDOP</code></td> <td>Position dilution of precision</td> </tr> <tr> <td><code>VDOP</code></td> <td>Vertical dilution of precision</td> </tr> <tr> <td><code>heading</code></td> <td>Direction of movement as provided by GPS device, in clockwise degrees from north</td> </tr> </tbody> </table> <p><strong>4.3 Application layer throughput : <code>thrghpt.csv</code></strong></p> <p>Data was sent from a custom sender application running on the AP, at the maximum possible rate. A custom receiver application on the client vehicle consumes the data. A time-indexed log of the amount of data sent and received was recorded.</p> <p>Column description</p> <p><code>thrghpt.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>column</th> <th>description</th> </tr> </tbody><tbody> <tr> <td><code>timestamp</code></td> <td>UNIX system timestamp the throughput record pertains to</td> </tr> <tr> <td><code>pckt_cntr</code></td> <td>Number of packets received within the current record</td> </tr> <tr> <td><code>byte_cntr</code></td> <td>Number of bytes received within the current record</td> </tr> <tr> <td><code>elapsed_time</code></td> <td>Time elapsed since previous throughput record</td> </tr> <tr> <td><code>thrghpt</code></td> <td>Throughput for the current record, in in Megabit per second (Mbps)</td> </tr> <tr> <td><code>inter_arrival_avg </code></td> <td>Average inter-packet arrival time during the recording period, in seconds</td> </tr> <tr> <td><code>diff_local_avg </code></td> <td>Average delta between the timestamp recorded in the packet's payload (set by the sender) and the local timestamp in the receiver, in microseconds</td> </tr> <tr> <td><code>trace_nr</code></td> <td>Trace number the throughput data is associated with</td> </tr> </tbody> </table> <p><strong>4.4 802.11ad frame data: <code>wifi.csv</code></strong></p> <p>The <code>wifi.csv</code> file contains 802.11ad frame data, captured with <code>tcpdump</code>, on a Talon AD7200 router configured in promiscuous mode and colocated with the mobile client device.</p> <p>Data collection and processing</p> <p>The following <code>tcpdump</code> command was used to collect raw data frames:</p> <pre><code>tcpdump -B 100000 -s96 -i <ad-monitor-interface> -y IEEE802_11_RADIO -w <pcap-file> & </code></pre> <p>In order to create <code>wifi.csv</code>, the raw data frames were processed using <code>tshark</code> in order to filter out unnecessary information. More specifically, we ran the following command:</p> <pre><code>tshark -r <input-pcap> -2 -T fields <fields> -Y "<filter>" -E header=y -E separator=, -E quote=d -E occurrence=f </code></pre> <p>The raw input <code>.pcap</code> files for each trace are provided in the <code>pcap/</code> folder. The parameters <code><filter></code> and <code><fields></code> represent the filtering conditions and what fields we want to extract from each frame, respectively. The actual values used are provided in the <code>configs/tshark.json</code> file.</p> <p>Frames were filtered based on a single field: the WLAN frame type and subtype, or <code>wlan.fc.type_subtype</code>. Only the following types of frames were kept:</p> <table> <tbody><tr> <th>Frame type/subtype value</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>0x0000</td> <td>Association request</td> </tr> <tr> <td>0x0001</td> <td>Association response</td> </tr> <tr> <td>0x0002</td> <td>Re-association request</td> </tr> <tr> <td>0x0003</td> <td>Re-association response</td> </tr> <tr> <td>0x000a</td> <td>Disassociation</td> </tr> <tr> <td>0x000b</td> <td>Authentication</td> </tr> <tr> <td>0x000c</td> <td>De-authentication</td> </tr> <tr> <td>0x0019</td> <td>Block ACKs</td> </tr> <tr> <td>0x001d</td> <td>Clear-to-send</td> </tr> <tr> <td>0x0028</td> <td>QoS data</td> </tr> <tr> <td>0x0030</td> <td>DMG beacon</td> </tr> <tr> <td>0x0164</td> <td>Grant</td> </tr> <tr> <td>0x0167</td> <td>Grant ACK</td> </tr> <tr> <td>0x0168</td> <td>SLS</td> </tr> <tr> <td>0x0169</td> <td>SLS feedback</td> </tr> <tr> <td>0x016a</td> <td>SLS feedback ACK</td> </tr> </tbody> </table> <p>Column description</p> <p><code>wifi.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>frame.time_epoch</code></td> <td>UNIX timestamp of frame capture, in seconds (with microsecond resolution)</td> </tr> <tr> <td><code>frame.number</code></td> <td>Ordinal number attributed to captured frame</td> </tr> <tr> <td><code>frame.len</code></td> <td>Frame length, in bytes</td> </tr> <tr> <td><code>ip.src</code></td> <td>Source IP address</td> </tr> <tr> <td><code>ip.dst</code></td> <td>Destination IP address</td> </tr> <tr> <td><code>ip.flags</code></td> <td>IP flags</td> </tr> <tr> <td><code>ip.frag_offset</code></td> <td>IP fragmentation offset</td> </tr> <tr> <td><code>ip.hdr_len</code></td> <td>IP header length</td> </tr> <tr> <td><code>ip.id</code></td> <td>IP identification field</td> </tr> <tr> <td><code>ip.proto</code></td> <td>IP protocol field</td> </tr> <tr> <td><code>ip.reassembled_in</code></td> <td>Frame number in which IP packet is reassembled</td> </tr> <tr> <td><code>radiotap.channel.flags.2ghz</code></td> <td>1 if channel frequency is in 2.4 GHz range, 0 otherwise</td> </tr> <tr> <td><code>radiotap.channel.flags.5ghz</code></td> <td>1 if channel frequency is in 5 GHz range, 0 otherwise</td> </tr> <tr> <td><code>radiotap.channel.freq</code></td> <td>Channel frequency (60480 Hz in this case)</td> </tr> <tr> <td><code>radiotap.length</code></td> <td>IEEE 802.11 radiotap capture header length</td> </tr> <tr> <td><code>radiotap.mcs.index</code></td> <td>Modulation Coding Scheme index</td> </tr> <tr> <td><code>udp.srcport</code></td> <td>UDP source port</td> </tr> <tr> <td><code>udp.dstport</code></td> <td>UDP destination port</td> </tr> <tr> <td><code>wlan.ba.bm</code></td> <td>Block ACK bitmap</td> </tr> <tr> <td><code>wlan.bf</code></td> <td>Full beamforming field of WLAN frame</td> </tr> <tr> <td><code>wlan.bf.isInit</code></td> <td>Whether or not frame is SLS initiator</td> </tr> <tr> <td><code>wlan.bf.isResp</code></td> <td>Whether or not frame is SLS responder</td> </tr> <tr> <td><code>wlan.bf.num_dmg_ants</code></td> <td>Number of DMG antennas</td> </tr> <tr> <td><code>wlan.bf.num_sectors</code></td> <td>Number of SLS sectors</td> </tr> <tr> <td><code>wlan.bf.train</code></td> <td>Whether or not frame is part of SLS training</td> </tr> <tr> <td><code>wlan.fc.retry</code></td> <td>Whether WLAN frame is re-transmitted</td> </tr> <tr> <td><code>wlan.fc.type_subtype</code></td> <td>WLAN frame type and subtype</td> </tr> <tr> <td><code>wlan.fixed.ssc.sequence</code></td> <td>WLAN starting sequence number</td> </tr> <tr> <td><code>wlan.fixed.timestamp</code></td> <td>WLAN timestamp</td> </tr> <tr> <td><code>wlan.frag</code></td> <td>WLAN fragment number</td> </tr> <tr> <td><code>wlan.ta</code></td> <td>WLAN transmitter MAC address</td> </tr> <tr> <td><code>wlan.ra</code></td> <td>WLAN receiver MAC address</td> </tr> <tr> <td><code>wlan.seq</code></td> <td>WLAN frame sequence number</td> </tr> <tr> <td><code>wlan.ssw</code></td> <td>Full Sector-level Sweep (SLS) field of WLAN frame</td> </tr> <tr> <td><code>wlan.ssw.cdown</code></td> <td>SLS countdown (CDOWN) number</td> </tr> <tr> <td><code>wlan.ssw.direction</code></td> <td>SLS direction (0: frame sent by SLS initiator, 1: by SLS responder)</td> </tr> <tr> <td><code>wlan.ssw.sector_id</code></td> <td>ID of sector used for SLS frame</td> </tr> <tr> <td><code>wlan.sswf</code></td> <td>Full SLS feedback field of WLAN frame</td> </tr> <tr> <td><code>wlan.sswf.sector_select</code></td> <td>SLS Feedback Sector Select</td> </tr> <tr> <td><code>wlan.sswf.snr_report</code></td> <td>SLS Feedback SNR Report</td> </tr> <tr> <td><code>wlan_radio.11n.mcs_index</code></td> <td>WLAN MCS index</td> </tr> <tr> <td><code>wlan_radio.channel</code></td> <td>WLAN channel</td> </tr> <tr> <td><code>wlan_radio.data_rate</code></td> <td>WLAN data rate</td> </tr> <tr> <td><code>wlan_radio.duration</code></td> <td>WLAN frame duration</td> </tr> <tr> <td><code>wlan_radio.frequency</code></td> <td>WLAN channel frequency</td> </tr> <tr> <td><code>wlan_radio.noise_dbm</code></td> <td>WLAN noise level, in dBm</td> </tr> <tr> <td><code>wlan_radio.phy</code></td> <td>WLAN PHY type</td> </tr> <tr> <td><code>wlan_radio.preamble</code></td> <td>WLAN preamble</td> </tr> <tr> <td><code>wlan_radio.signal_dbm</code></td> <td>WLAN signal strength, in dBm</td> </tr> <tr> <td><code>wlan_radio.timestamp</code></td> <td>WLAN TSF timestamp</td> </tr> <tr> <td><code>data.text</code></td> <td>Data enclosed in WLAN data frame</td> </tr> <tr> <td><code>trace_nr</code></td> <td>Number of the trace the frame is associated with</td> </tr> </tbody> </table>
Dataset for "An Accuracy Study of Emulation Daemons for IEEE 802.11 Networks"
<p>This is the dataset used in "An Accuracy Study of Emulation Daemons for IEEE 802.11 Networks" presented at the 48th IEEE Conference on Local Computer Networks (LCN), October 1-5, 2023, Daytona Beach, Florida, USA.</p> <p>`raw_results` contains all the raw results redacted from the experiment hosts, including log files.<br> The `tikz` files used to generate the figures presented in the paper can be found in `tex/figures`. Executing `make` in the `tex` directory generates the `pdf` plots in the `figures` folder.<br> The `figures` folder also contains pre-processed results from the raw data set.</p>
Battery discharge characteristics for IEEE 802.15.4 based radio load profile
Open the record for dataset details and reuse information.
IEEE
<p>Supplementary data for the IEEE Transactions on Power Systems article "Modelling heat pump systems in low-carbon energy<br> systems with significant cross-sectoral integration" by Philipp Härtel and Debraj Ghosh (Fraunhofer IEE)</p> <p>Contains the case study power system time-series result data for Germany in a net-neutral energy scenario, i.e. four different heat pump system modelling approaches: detailed, lumped, aggregated without COP correction, and aggregated with COP correction</p> <p>Contains heat pump system input data, i.e. heat demands for space heat, domestic hot water heat, and corresponding COP profiles, as well as lumped and aggregated versions based on the proposed methodology in the paper.</p>
Enhanced IEEE RTS-96 test case w/ synthesized dynamic parameters
<p>Enhanced IEEE RTS-96 test case w/ synthesized dynamic parameters<br /> Compatible with Siemens PTI PSS/E v33, adapted from Grigg et al., 1999</p> <p>Salient generator models<br /> IEEE T1 exciter models<br /> IEEE G2 governor models</p>
How to cite & reference in IEEE style
<p>This short video shows how to cite and reference a research report in IEEE style, using style guide Cite Them Right.</p>
Data Corpus for the IEEE-AASP Challenge on the Acoustic Characterization of Environments (ACE)
<p>The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field.</p> <p>Several established parameters and metrics have been used to characterize the acoustics of a room. The most important are the Direct-To-Reverberant Ratio (DRR), the Reverberation Time (<em>T60</em>) and the reflection coefficient. The acoustic characteristics of a room based on such parameters can be used to predict the quality and intelligibility of speech signals in that room. Recently, several important methods in speech enhancement and speech recognition have been developed that show an increase in performance compared to the predecessors but do require knowledge of one or more fundamental acoustical parameters such as the <em>T60</em>. Traditionally, these parameters have been estimated using carefully measured Acoustic Impulse Responses (AIRs). However, in most applications it is not practical or even possible to measure the acoustic impulse response. Consequently, there is increasing research activity in the estimation of such parameters directly from speech and audio signals.</p> <p><strong>Documentation and software</strong></p> <ul> <li>Corpus instructions including software operating instructions</li> <li>Software to generate new datasets from the corpus materials (Matlab)</li> <li><em>T</em>60 and DRR measurements in fullband and <a href="http://www.iso.org/iso/catalogue_detail.htm?csnumber=1350">ISO-266</a> preferred frequency bands</li> <li>Room dimensions and approximate positions of microphones and sources</li> </ul> <p><strong>Anechoic speech</strong></p> <p>Comprising Development (Dev): 4 male talkers, 2 utterances each, and Evaluation (Eval): 5 male and 5 female talkers, 5 utterances each, recorded using the anechoic chamber at <a href="http://www.tudelft.nl/en/">TU Delft</a> at <em>fs</em>=48 kHz in 16-bit format. Plain text (.txt) transcriptions of each .wav file are included.</p> <p><strong>RIRs and noise by microphone configuration</strong></p> <p>Each archive below contains the set of <em>fs</em>=48 kHz 16-bit RIRs, ambient, fan and babble noise .wav files for each room and microphone position for that microphone configuration, recorded in 7 different rooms in the <a href="http://www3.imperial.ac.uk/electricalengineering">Dept. of Electrical and Electronic Engineering at Imperial College London</a>.</p> <p>The corpus comprises the following components:</p> <ul> <li>Single-channel (based on cruciform channel 1) 417 MB</li> <li>2-channel laptop 1.05 GB</li> <li>3-channel mobile 1.59 GB</li> <li>5-channel cruciform 2.84 GB</li> <li>8-channel linear 4.24 GB</li> <li>32-channel spherical 14.2 GB</li> </ul> <p>The corpus and the ACE Challenge are described in the following <a href="https://www.researchgate.net/publication/303854321_Estimation_of_room_acoustic_parameters_The_ACE_Challenge">journal paper</a>:</p> <ul> <li>J. Eaton; N. D. Gaubitch; A. H. Moore; P. A. Naylor, "Estimation of room acoustic parameters: The ACE Challenge," in <em><a href="http://ieeexplore.ieee.org/document/7486010/">IEEE/ACM Transactions on Audio, Speech, and Language Processing</a></em>, vol. 24, no.10, pp.1681-1693, Oct. 2016.</li> </ul> <p>Please cite this whenever you use any part of the corpus. BibTeX references are available here for the <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_IEEE_ref.bib">journal paper</a> and <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_Tech_ref.bib">technical report</a>.</p> <ul> </ul>
Indoor Positioning - de Blasio et al - dataset paper IEEE Access (2018)
<p>This zip file contains the raw BLE data in .xlsx format obtained in the tests detailed in the following article (attached pdf):</p> <p>Gabriel de Blasio, Alexis Quesada-Arencibia, Carmelo R. García, José Carlos Rodríguez-Rodríguez, Roberto Moreno-Díaz jr. A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy, IEEE Access vol. 6, pp. 33440-33450 (2018)<br>DOI: 10.1109/ACCESS.2018.2837497</p>
ScienceDex guides
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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.