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A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization
<p><strong>Overview</strong>:<br> The dataset contains measurements of Angle of Arrival (AoA) and Received Signal Strength (RSS)collected from Bluetooth 5.1 tags and a set of 4 anchor nodes deployed in an indoor environment. The data collection campaign has been conducted in a wide-open room of 110 square meters located in a wide open room.<br> The dataset includes four application scenarios covering typical use-cases for indoor localization:</p> <ol> <li>calibration: 4 anchors and 1 tag mounted on a tripod and positioned in 119 different locations;</li> <li>static: 4 anchors and 1 tag held by a person resting in 36 different locations. The tag is locked on a lanyard around the person's neck, we collect data with the person oriented toward North, South, East and West;</li> <li>mobility: 4 anchors and a person holding the tag around the neck a moving along 3 different paths;</li> <li>proximity scenario: 4 anchors and tags held by groups of people. We reproduce proximity with dyads, triplets and of groups of 4 people approach and distancing along the time.</li> </ol> <p>All the scenarios include an accurate Ground Truth (GT) annotation. The dataset is organized in four folders one for each scenario: Calibration, Static, Mobility and Proximity.</p> <p>The dataset enables the study of how AoA varies under stationary or mobility conditions, facilitating the analysis of an anchor's AoA model. In addition, the data collected can be analyzed to create a simulator of AoA values, which is useful for rapid prototyping and evaluation of indoor localization algorithms.</p> <p><strong>How to use the dataset</strong>:<br> Please, read the README.txt file detailing the dataset's format and the data collection camping. In summary, collected data include:<br> - Timestamp<br> - Tag identifier<br> - RSS on the 1<sup>st</sup> antenna's polarization<br> - AoA azimuth<br> - AoA elevation<br> - RSS on the 2<sup>nd</sup> antenna's polarization<br> - Bluetooth channel<br> - Anchor identifier</p> <p><strong>How to cite this dataset</strong>:</p> <p>- DOI number of this datsaset : 10.5281/zenodo.7759557</p> <p>- M. Girolami, F. Furfari, P. Barsocchi and F. Mavilia, "A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization," in <em>IEEE Access</em>, vol. 11, pp. 81763-81776, 2023, doi: 10.1109/ACCESS.2023.3301126.</p>
Indoor UWB CIR Data Set for Material Prediction
<p><strong>ABOUT</strong></p> <p>This data set contains spatially distributed CIR of multipath components in indoor environment acquired with ultra wideband (UWB) radio technology in microwave frequency range.<br> The data is labeled with the materials of the surfaces bounding the space (floor, ceiling, walls).<br> The data was collected for training and evaluating machine learning models for CIR-based indoor material prediction, but it may be also used for other studies based on indoor radio propagation data. </p> <p> </p> <p><strong>AUTHORS</strong></p> <p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p> </p> <p><strong>DATA COLLECTION</strong></p> <p>The synthetic data is obtained using <a href="https://www.remcom.com/wireless-insite-em-propagation-software">Remcom Wireless InSite</a> v.3.3.3.<br> The CIR is estimated in 16,875 rooms in total. <br> These rooms belong to 5,625 distinct room types and each room type is considered in three sizes. <br> The number of distinct room types comes from the materials used for the floor, ceiling, and walls, having nine floor-ceiling material combinations and 625 wall-material combinations.</p> <p> </p> <table align="center"> <caption>Room Sizes</caption> <thead> <tr> <th scope="col"> ROOM SIZE</th> <th scope="col">FLOOR/CEILING DIMENSIONS </th> <th scope="col">WALL DIMENSIONS</th> </tr> </thead> <tbody> <tr> <td>S</td> <td>3 m x 3 m</td> <td>3 m x 3 m </td> </tr> <tr> <td>M</td> <td>5 m x 5 m</td> <td>5 m x 3 m </td> </tr> <tr> <td>L</td> <td>7 m x 7 m </td> <td>7 m x 3 m </td> </tr> </tbody> </table> <p> </p> <p><strong>MATERIALS</strong></p> <ul> <li> floor: concrete, wood, floorboard</li> <li> ceiling: concrete, plaster, wood</li> <li> walls: brick, concrete, glass, plaster, wood</li> </ul> <p> </p> <table align="center"> <caption>Electrical properties of materials*</caption> <thead> <tr> <th scope="col">MATERIAL</th> <th scope="col">RELATIVE PERMITTIVITY</th> <th scope="col">CONDUCTIVITY</th> </tr> </thead> <tbody> <tr> <td>brick</td> <td>3.75</td> <td>0.038</td> </tr> <tr> <td>concrete</td> <td>5.31</td> <td>0.120</td> </tr> <tr> <td>glass</td> <td>6.27</td> <td>0.029</td> </tr> <tr> <td>plaster</td> <td>2.94</td> <td>0.036</td> </tr> <tr> <td>wood</td> <td>1.99</td> <td>0.026</td> </tr> <tr> <td>floorboard</td> <td>3.66</td> <td>0.039</td> </tr> </tbody> </table> <p> </p> <p><strong>COMMUNICATION SYSTEM CONFIGURATION</strong></p> <p>Ultra wideband (UWB) radio technology is considered. The parameters of the communication system are set according to 802.15.4-2011** standard.<br> The configuration of system parameters is summarized as follows:</p> <table align="center"> <caption>System configuration</caption> <thead> <tr> <th scope="col"> PARAMETER</th> <th scope="col">CONFIGURATION</th> </tr> </thead> <tbody> <tr> <td>frequency</td> <td>3494.4 MHz</td> </tr> <tr> <td>bandwidth</td> <td>466.2 MHz</td> </tr> <tr> <td>Tx/Rx height</td> <td>1.5 m</td> </tr> <tr> <td>antenna type</td> <td>omni</td> </tr> <tr> <td>polarization</td> <td>vertical</td> </tr> </tbody> </table> <p> </p> <p><strong>RADIO NODE POSITIONS</strong></p> <p>The data is collected using three acquisition layouts as follows:</p> <p>1. Layout 1 </p> <p> Tx in the center of the room and Rx moved over uniform grid covering the room.</p> <p>2. Layout 2</p> <p>Tx in eight positions following circular pattern around the center of the room and Rx moved over uniform grid covering the room. </p> <p>The distance from the center of the room to the circumference of the circle is 0.5 m, and the spacing between the radio nodes is pi/4 rad.</p> <p>3. Layout 3</p> <p>Tx in four positions near the corners of the room (0.375 m from the walls) and Rx moved over uniform grid covering the room.</p> <p>The corners of the grid are 0.25 m apart from the walls and the distance between the nodes is also 0.25 m.</p> <p>Since the grid size is defined relatively to the room size, the total number of grid node positions is different in rooms with different sizes.</p> <p>The total number of grid node positions is 121, 361, and 729 in S, M, and L rooms, respectively.</p> <p> </p> <p><strong>DATA ORGANIZATION</strong></p> <p>Data is saved in .csv files. Each file starts with a header line specifying the column names. <br> The column names included in the .csv files are:</p> <p>- Column 0: layout {center, circle, corners}<br> - Column 1: tx_point_id {1} for Layout 1, {1-8} for Layout 2, and {1-4} for Layout 3<br> - Column 2: rx_point_id {1-121} in S-rooms, {1-361} in M-rooms, and {1-729} in L-rooms<br> - Column 3: 1_phase_deg NUMERIC<br> - Column 4: 1_toa_ns NUMERIC<br> - Column 5: 1_power_dbm NUMERIC<br> - Column 6: 1_power_nw NUMERIC<br> - Column 7: 2_phase_deg NUMERIC<br> - Column 8: 2_toa_ns NUMERIC<br> - Column 9: 2_power_dbm NUMERIC<br> - Column 10: 2_power_nw NUMERIC<br> - Column 11: 3_phase_deg NUMERIC<br> - Column 12: 3_toa_ns NUMERIC<br> - Column 13: 3_power_dbm NUMERIC<br> - Column 14: 3_power_nw NUMERIC<br> - Column 15: 4_phase_deg NUMERIC<br> - Column 16: 4_toa_ns NUMERIC<br> - Column 17: 4_power_dbm NUMERIC<br> - Column 18: 4_power_nw NUMERIC<br> - Column 19: 5_phase_deg NUMERIC<br> - Column 20: 5_toa_ns NUMERIC<br> - Column 21: 5_power_dbm NUMERIC<br> - Column 22: 5_power_nw NUMERIC<br> - Column 23: 6_phase_deg NUMERIC<br> - Column 24: 6_toa_ns NUMERIC<br> - Column 25: 6_power_dbm NUMERIC<br> - Column 26: 6_power_nw NUMERIC<br> - Column 27: 7_phase_deg NUMERIC<br> - Column 28: 7_toa_ns NUMERIC<br> - Column 29: 7_power_dbm NUMERIC<br> - Column 30: 7_power_nw NUMERIC<br> - Column 31: 8_phase_deg NUMERIC<br> - Column 32: 8_toa_ns NUMERIC<br> - Column 33: 8_power_dbm NUMERIC<br> - Column 34: 8_power_nw NUMERIC<br> - Column 35: 9_phase_deg NUMERIC<br> - Column 36: 9_toa_ns NUMERIC<br> - Column 37: 9_power_dbm NUMERIC<br> - Column 38: 9_power_nw NUMERIC<br> - Column 39: 10_phase_deg NUMERIC<br> - Column 40: 10_toa_ns NUMERIC<br> - Column 41: 10_power_dbm NUMERIC<br> - Column 42: 10_power_nw NUMERIC<br> - Column 43: 11_phase_deg NUMERIC<br> - Column 44: 11_toa_ns NUMERIC<br> - Column 45: 11_power_dbm NUMERIC<br> - Column 46: 11_power_nw NUMERIC<br> - Column 47: 12_phase_deg NUMERIC<br> - Column 48: 12_toa_ns NUMERIC<br> - Column 49: 12_power_dbm NUMERIC<br> - Column 50: 12_power_nw NUMERIC<br> - Column 51: 13_phase_deg NUMERIC<br> - Column 52: 13_toa_ns NUMERIC<br> - Column 53: 13_power_dbm NUMERIC<br> - Column 54: 13_power_nw NUMERIC<br> - Column 55: 14_phase_deg NUMERIC<br> - Column 56: 14_toa_ns NUMERIC<br> - Column 57: 14_power_dbm NUMERIC<br> - Column 58: 14_power_nw NUMERIC<br> - Column 59: 15_phase_deg NUMERIC<br> - Column 60: 15_toa_ns NUMERIC<br> - Column 61: 15_power_dbm NUMERIC<br> - Column 62: 15_power_nw NUMERIC<br> - Column 63: room_size_surf_m2 {9} for S-rooms, {25} for M-rooms, and {49} for L-rooms<br> - Column 64: room_size_name {S} for S-rooms, {M} for M-rooms, and {L} for L-rooms<br> - Column 65: room_shape {square}<br> - Column 66: floor_mat {concrete, wood, floorboard}<br> - Column 67: ceiling_mat {concrete, plaster, wood}<br> - Column 68: wall1_mat {brick, concrete, glass, plaster, wood}<br> - Column 69: wall2_mat {brick, concrete, glass, plaster, wood}<br> - Column 70: wall3_mat {brick, concrete, glass, plaster, wood}<br> - Column 71: wall4_mat {brick, concrete, glass, plaster, wood}</p> <p>Each row corresponds to separate radio link defined with the Tx and Rx nodes. <br> It includes information about <br> (i) the CIR-acquisition layout (position of the Txs and Rxs), <br> (ii) the Tx and Rx, <br> (iii) the CIR of 15 strongest multipath components, <br> (iv) the room geometry, and <br> (v) the materials of the surfaces bounding the space. </p> <p>- Column 0 specifies the layout. <br> The following maping is used: <br> Layout 1 -> center, <br> Layout 2 -> circle, and <br> Layout 3 -> corners.<br> - Column 1 specifies the Tx identifier.<br> - Column 2 specifies the Rx identifier.<br> - Columns 3-62 are the input attributes. <br> The input attributest represent the phase (in deg), ToA (in ns), received power (in dBm), and received power (in nW) for 15 strongest multipath components. <br> The column naming is X_Y_Z, where X is the multipath component identifier (1 to 15), Y is the propagation characteristic (phase, toa, or power), and Z is the unit (deg, ns, dbm, or nw).<br> - Column 63 specifies the surface of the room in square meters.<br> - Column 64 specifies the room size category (S, M, or L).<br> - Column 65 specifies the room-base shape.<br> - Columns 66-71 are the target attributes specifying the material of the floor, ceiling, wall 1, wall 2, wall 3, and wall 4, respectively. </p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The folder <em>indoor_CIR_data</em> contains:<br> - one subfolder named <em>CIR_data</em><br> It contains three .csv files with CIR data named by the size of the rooms where the data is acquired.<br> For naming the .csv files the following mapping is considered:<br> - Small.csv -> S-rooms<br> - Medium.csv -> M-rooms<br> - Large.csv -> L-rooms<br> - one subfolder named <em>CIR_acquisition_details</em><br> It contains .png file with schematic representation of the radio node positions considered for obtaining the data.<br> - README.txt file</p> <p>The folder structure is:<br> - CIR_data<br> - Small.csv<br> - Medium.csv<br> - Large.csv<br> - CIR_acquisition_details<br> - radio_node_positions.png<br> - README.txt</p> <p> </p> <p><strong>REFERENCES</strong></p> <p>* R. sector of International Telecommunication Union (ITU-R), “Effects of building materials and structures on radio wave propagation above about 100 MHz,” International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p> <p>** IEEE, “Standard for local and metropolitan area networks–Part 15.4: Low-rate wireless personal area networks (LR-WPANs),” IEEE, Standard IEEE 802.15.4-2011, 2011.</p>
Dataset for indoor carbon dioxide readings using low-cost sensors
<p>The dataset was originated from four sensors: 2 units of MG–811, a Metal Oxide Semiconductor, and 2 units of MH–Z16, a Non-Dispersive Infra-Red Sensor.</p>
assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set
<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>
Validation of an indoor real-time location system for tracking sheep
Open the record for dataset details and reuse information.
Tramadol effects on physical performance and sustained attention during a 20-min indoor cycling time-trial: A randomised controlled trial
<p>Objectives: To investigate the effect of tramadol on performance during a 20-min cycling time-trial (Exper- iment 1), and to test whether sustained attention would be impaired during cycling after tramadol intake (Experiment 2). Design: Randomized, double-blind, placebo controlled trial. Methods: In Experiment 1, participants completed a cycling time-trial, 120-min after they ingested either tramadol or placebo. In Experiment 2, participants performed a visual oddball task during the time-trial. Electroencephalography measures (EEG) were recorded throughout the session. Results: In Experiment 1, average time-trial power output was higher in the tramadol vs. placebo condition (tramadol: 220 W vs. placebo: 209 W; p < 0.01). In Experiment 2, no differences between conditions were observed in the average power output (tramadol: 234 W vs. placebo: 230 W; p > 0.05). No behavioural differences were found between conditions in the oddball task. Crucially, the time frequency analysis in Experiment 2 revealed an overall lower target-locked power in the beta-band (p < 0.01), and higher alpha suppression (p < 0.01) in the tramadol vs. placebo condition. At baseline, EEG power spectrum was higher under tramadol than under placebo in Experiment 1 while the reverse was true for Experiment 2. Conclusions: Tramadol improved cycling power output in Experiment 1, but not in Experiment 2, which may be due to the simultaneous performance of a cognitive task. Interestingly enough, the EEG data in Experiment 2 pointed to an impact of tramadol on stimulus processing related to sustained attention. Trial registration: EudraCT number: 2015-005056-96.</p>
Dataset for Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals
<p>Dataset of paper "Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals" presented in International Conference on Localization and GNSS (ICL-GNSS) 2020.</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted at an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing.</p> <p>The file "indoorMapping_processing.m" shows how to process and plot the shared data.</p> <p> </p> <p> </p>
Build-in-Wood Regulation Analysis – Energy and Indoor Environment
<p>This dataset contains an analysis of selected EU Member State building regulations covering energy and indoor environment in multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Data from: Efficacy of Aedes aegypti control by indoor Ultra Low Volume (ULV) insecticide spraying in Iquitos, Peru
Background: Aedes aegypti is a primary vector of dengue, chikungunya, Zika, and urban yellow fever viruses. Indoor, ultra low volume (ULV) space spraying with pyrethroid insecticides is the main approach used for Ae. aegypti emergency control in many countries. Given the widespread use of this method, the lack of large-scale experiments or detailed evaluations of municipal spray programs is problematic. Methodology/Principal Findings: Two experimental evaluations of non-residual, indoor ULV pyrethroid spraying were conducted in Iquitos, Peru. In each, a central sprayed sector was surrounded by an unsprayed buffer sector. In 2013, spray and buffer sectors included 398 and 765 houses, respectively. Spraying reduced the mean number of adults captured per house by ~83 percent relative to the pre-spray baseline survey. In the 2014 experiment, sprayed and buffer sectors included 1,117 and 1,049 houses, respectively. Here, the sprayed sector's number of adults per house was reduced ~64 percent relative to baseline. Parity surveys in the sprayed sector during the 2014 spray period indicated an increase in the proportion of very young females. We also evaluated impacts of a 2014 citywide spray program by the local Ministry of Health, which reduced adult populations by ~60 percent. In all cases, adult densities returned to near-baseline levels within one month. Conclusions/Significance: Our results demonstrate that densities of adult Ae. aegypti can be reduced by experimental and municipal spraying programs. The finding that adult densities return to approximately pre-spray densities in less than a month is similar to results from previous, smaller scale experiments. Our results demonstrate that ULV spraying is best viewed as having a short-term entomological effect. The epidemiological impact of ULV spraying will need evaluation in future trials that measure capacity of insecticide spraying to reduce human infection or disease.
Data from: Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in Acapulco, Mexico
The markedly anthropophilic and endophilic behaviors of Aedes aegypti (L.) make it a very efficient vector of dengue, chikungunya, and Zika viruses. Although a large body of research has investigated the immature habitats and conditions for adult emergence, relatively few studies have focused on the indoor resting behavior and distribution of vectors within houses. We investigated the resting behavior of Ae. aegypti indoors in 979 houses of the city of Acapulco, Mexico, by performing exhaustive indoor mosquito collections to describe the rooms and height at which mosquitoes were found resting. In total, 1,403 adult and 747 female Ae. aegypti were collected, primarily indoors (98% adults and 99% females). Primary resting locations included bedrooms (44%), living rooms (25%), and bathrooms (20%), followed by kitchens (9%). Aedes aegypti significantly rested below 1.5 m of height (82% adults, 83% females, and 87% bloodfed females); the odds of finding adult Ae. aegypti mosquitoes below 1.5 m was 17 times higher than above 1.5 m. Our findings provide relevant information for the design of insecticide-based interventions selectively targeting the adult resting population, such as indoor residual spraying.
Indoor maps
<p>Indoor maps of three testing buildings</p>
WiFi RTT RSS dataset for indoor positioning
<p>This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code>https://doi.org/10.5281/zenodo.11558792</code> for the second batch.</p> <h2><strong>Please do use version 2 for better quality.</strong></h2> <p>We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition</strong> label.</p> <p>1.Building Floor</p> <p>This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.</p> <p>2. Office</p> <p>Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP). </p> <p>3.Apartment</p> <p>Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed. </p> <p> </p> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the datasets</strong></h2> <p><strong>The features of the building floor dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 92 × 15 m2</p> <p>Grid size: 0.6 × 0.6 m2</p> <p>Number of AP: 13</p> <p>Number of reference points: 642</p> <p>Samples per reference point: 120</p> <p>Number of all data samples: 77040</p> <p>Number of training samples: 57960</p> <p>Number of testing samples: 19080</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Collection time interval: 3 days</p> </blockquote> <p><strong>The features of the office dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 4.5 × 5.5 m2</p> <p>Grid size: 0.455 × 0.455 m2</p> <p>Number of AP: 3</p> <p>Reference points: 37</p> <p>Samples per reference point: 120</p> <p>Data samples: 4,440</p> <p>Training samples: 3,240</p> <p>Testing samples: 1,200</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: A LOS scenario</p> </blockquote> <p><strong>The features of the apartment dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 7.7 × 9.4 m2</p> <p>Grid size: 0.48 × 0.48 m2</p> <p>Number of AP: 4</p> <p>Reference points: 110</p> <p>Samples per reference point: 120</p> <p>Data samples: 13,200</p> <p>Training samples: 9,720</p> <p>Testing samples: 3,480</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: Contains an AP with NLOS paths for most of the RPs</p> </blockquote> <h2><strong>Dataset explanation</strong></h2> <p>The columns of the dataset are as follows:</p> <p>Column 'X': the X coordinates of the sample.</p> <p>Column 'Y': the Y coordinates of the sample.</p> <p>Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.</p> <p>Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.</p> <p>Column 'LOS APs': indicating which AP has a LOS to this reference point.</p> <p>Please note:</p> <ul> <li>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</li> <li>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</li> </ul> <h2><strong>Citation request<br></strong></h2> <p>When using this dataset, please cite the following two items:<br><br><code>Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code><br><br><code>@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor \& Francis} }</code></p>
Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.
<p>Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.</p><p><strong>Keywords: </strong>prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.</p>
Audio recordings and soundscape assessments from the study: "Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings"
<h1><strong><span>Content</span></strong></h1> <p><span>The dataset contains processed audio files employed in a listening test performed at the Here East Audio Lab of the University College London to derive a model of acoustic perception in residential buildings [1]. The study followed a full factorial design by combining five urban environments (Factor A) and four indoor sound scenarios (Factor B). The audio files are available in both B-format (Ambix) and A-format. Furthermore, the component scores of each participant in the three derived perceptual dimensions (i.e., comfort, content, familiarity) are made available, along with the psychoacoustic analysis of 20 binaural recordings, each lasting 1 minute, corresponding to the 20 acoustic scenarios to which the 32 participants were exposed.</span></p> <h1><strong><u><span>Audio files</span></u></strong></h1> <p><strong><span>Factor A (Outdoor sounds)</span></strong></p> <p><span>Factor A audio recordings were performed in indoor spaces without audible indoor sound sources and with windows partially opened to different urban contexts in the city of London. Sound material was recorded @24bit/48kHz using a First Order Ambisonics (FOA) microphone (Sennheiser AMBEO VR Mic) positioned at the average listener’s ear height in endfire position, with accompanying portable multi-channel audio recorder (Sound Devices MixPre-10T) with channels 1-4 linked for the FOA setting, together with a sound level meter (NTi Audio XL2), both microphones oriented towards the window openings. By recording outdoor acoustic environments in indoor spaces, the effects of reverberation and window filtering were intrinsically embedded in the collected recordings.</span></p> <p><strong><span>Factor B (Indoor sounds)</span></strong></p> <p><span>Factor B audio recordings were made in low-noise indoor environments using the equipment described above with both microphones oriented roughly towards the sound source of interest.</span></p> <p><strong><span>Combinations of Factors A & B</span></strong></p> <p><span>Excluding the two “no added sounds” scenarios, a total of seven audio stimuli were played and combined during the listening tests, as described in [1], resulting in total 20 scenarios where no more than 2 sounds were overlapped. </span></p> <p><strong><span>Audio Editing and Processing</span></strong></p> <p><span>Audio samples were edited and processed in A format in the Digital Audio Workstation Reaper (Cockos) @24bit/48kHz. The edits were performed in terms of removing extraneous sound events by trimming the audio track and creating the necessary crossfades, in order to bring the audio material as close as possible to the scenario it represented. Audio processing was conducted using the Sennheiser Ambeo plugin to generate the B-format audio files, to be correctly spatialized using a playback system of choice. In the process of conversion to B format, the default Ambisonics Correction Filter was engaged and the Low Cut Filter was switched off, while the Microphone Rotation was set to correct for the endfire position used during the recordings. One-minute excerpts were finally extracted. No further audio editing, nor processing was done. Full details about sound recordings and playback levels used in the experiment are available in [1] and in the supplementary materials.</span></p> <p><span>The audio files are intended to be employed in future listening tests.</span></p> <h1><strong><u><span>Psychoacoustic Analysis and Soundscape scores (.xlsx file)</span></u></strong></h1> <p><span>The xlsx file is formatted with a row for each individual participant's component scores per each of the 20 experimental conditions, then includes the psychoacoustic analysis of the 60s binaural recording corresponding to each acoustic condition. Details about the psychoacoustic analyses and component scores derivation are provided in [1] and in the related supplementary material. In the sheet "Legend_Exposure_Conditions", the coding of the 20 conditions is provided. The numbers of the levels for factors A and B refer to Table 1 in [1].</span></p> <p><span> </span></p> <p><span>[1] Torresin, S., Albatici, R., Aletta, F., Babich, F., Oberman, T., Siboni, S., & Kang, J. (2020). </span><span>Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment, 182, 107152.</span></p>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Datasets of Indoor UWB Measurements for Ranging and Positioning in Good and Challenging Scenarios
<p>This is a dataset of ranging and positioning measurements collected from an UWB development board. The Real Time Location System based on UWB is set up in a laboratory. Data were captured in the static laboratory environment with different conditions that affects to the positioning performance. In the lab, scenarios with different propagation conditions between the nodes and different geometries were set up. We consider good, challenging, and intermediate scenarios with: Line of Sight (LOS) and Non-LOS propagation conditions as well as easy and challenging geometries. These datasets may be used, for example, for investing and validating ranging and positioning algorithms in different scenarios. A detailed description is provided in the file README.pdf</p>
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Real-Life Indoor Sound Event Dataset (ReaLISED) for Sound Event Classification (SEC)
<p>The Real-Life Indoor Sound Event Dataset (ReaLISED) offers the scientific community the possibility of testing Sound Event Classification (SEC) algorithms with new real indoor audio event recordings. The full set is made up of 2479 sound recordings of 18 events. The 18 event classes are the following: beater, cooking, cupboard/wardrobe, dishwasher, door, drawer, furniture movement, microwave, object falling, smoke extractor, speech, switch, television, vacuum cleaner, walking, washing machine, water tap, and window. There are 2479 clips of isolated sounds, which result in 3624.51 seconds. The number of events in each class is between 104 for the "Window" class and 190 for the “Speech” class, with a mean value of 138 events and a standard deviation of 25.</p> <p>Four Olympus LS-100 recorders were used. The sampling frequency was set to 44.1 kHz and 24 bits per sample. The stereo mode was used, and a medium sensitivity of the microphone was set. The distance between the recorder and the sound source was set to approximately 30-40 cm.</p> <p>Apart from the labels related to the class of event, extra information for each recording is provided in order to be exploited if necessary in the future, with other research purposes. This extra information completes the description of the sound source.</p> <p>The dataset is introduced to the scientific community by providing all the .flac files which composed it. The name of the files is built with 5 pieces of information, separated with underscores (“_”), with the format “abc_123_45_67_8.flac′′:</p> <ul> <li> <p>“abc”: the first three letters indicate the source that produces the sound. This segment can take 18 different values: ‘bea’ (beater), ‘coo’ (cooking), ‘cup’ (cupboard/wardrobe), ‘dis’ (dishwasher), ‘doo’ (door), ‘dra’ (drawer), ‘fur’ (furniture movement), ‘mic’ (microwave), ‘obj’ (object falling), ‘smo’ (smoke extractor), ‘spe’ (speech), ‘swi’ (switch), ‘tel’ (television), ‘vac’ (vacuum cleaner), ‘wal’ (walking), ‘was’ (washing machine), ‘wat’ (water tap), win’ (window).</p> </li> <li> <p>“123”: this set of digits identifies the event among the number of events produced by the source identified with “abc”. This segment can take all the values between ‘001’ and ‘190’, which is the maximum number of events of a particular class we can find in the dataset (speech).</p> </li> <li> <p>“45”: this set of digits identifies the action that produce the sound. This segment can take 11 different values: ‘01’ (close), ‘02’ (open), ‘03’ (throw), ‘04’ (turn on), ‘05’ (turn off), ‘06’ (move), ‘07’ (plug), ‘08’ (unplug), ‘09’ (raise), ‘10’ (lower), and ‘00’ (there is no information about the action).</p> </li> <li> <p>“67”: this set of digits identifies the material the sound source is made of. This segment can take 14 different values: ’01’ (wood), ’02’ (glass), ’03’ (metal), ’04’ (plastic), ’05’ (ceramic), ’06’ (synthetic), ’07’ (cardboard), ’08’ (marble), ’09’ (floating platform), ’10' (platelet), ’11’ (wicker), ’12’ (carpet), ’13’ (medium-density fibreboard MDF), and ’00’ (there is no information about the material).</p> </li> <li> <p>“8”: the last digit gives approximate information about the intensity of the recorded sound. It can take 4 different values: ’1’ (low intensity), ’2’ (medium intensity), ’3’ (high intensity), ’0’ (there ir no information about the intensity).</p> <p>For clarity, some examples of audio file names with this code are shown hereunder:</p> </li> <li> <p>“doo_040_02_00_3.flac” is the name of the 40th file in the Door class, described as “opening a door of unknown material with high intensity”.</p> </li> <li> <p>“fur_058_06_01_2.flac” is the name of the 58th file in the furniture movement class, described as “moving a wooden furniture with medium intensity”.</p> </li> <li> <p>“vac_001_00_00_0.flac” is the name of the 1st audio file in the vacuum cleaner class, described as “using the vacuum cleaner, without information about the action, neither the material or the intensity”.</p> </li> </ul>
Pre-Analysis Bioinformatics Files for The Microbiome and Volatile Organic Compounds Reflect the State of Decomposition in an Indoor Environment
<p>Data statistics before and after trimming, FastQC reports before and after trimming, MultiQC reports before and after trimming, commands for the Kraken2-Bracken analysis, and classification reports. Read the read.me file for file names and descriptions. </p>
Indoor Positioning Simulation For Examination And Correction Of Occupancy Limits In Architectural Design
<p>Dataset contains results of the simulation, statistical analysis and images. "Read me" file contains explanations on the content.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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OpenNeuro
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