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22 results for “LoRa”
Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset
<p>LoRa Radio Testing Datasets for preliminary performance tests. These datasets were taken in order to ensure that the LoRa radios were capable of transmitting through concrete and testing various preamble settings of the radio. As per the paper,</p> <p>"Although these testing methodologies were indicative but not exact or perfect, to test in a manner that was qualitative would have been both costly and beyond the scope of the project." </p> <p>These tests were to help us verify feasibility of the chosen LoRa Radio</p>
EXpLoRa - EXperimenting with LoRa products across realistic environments
<p>LoRa has gained significant momentum among industrial and research communities, with several device manufacturers adopting the LoRa technology in their IoT solutions. However, in spite of LoRa’s wide penetration, the resulting performance under realistic conditions and real-life scenarios has only recently been considered and mainly under line-of-sight environments. For instance, issues like communication across multiple floors and the performance degradation impact of interference have not been analyzed in detail. In the EXpLoRa proposal, we aim to answer the previously identified inquiries from the side of a technology adopter company, namely domX SME, which develops custom IoT monitoring solutions and considers the adoption of LoRa technology for covering the company’s needs.</p> <p>Through the EXpLoRa experiment, we managed to collect a first set of experimental data and familiarize with the LoRa technology and conduct an exhaustive set of experiments with LoRa-compatible devices, under realistic city-scale and varying channel conditions (range of ~35 dB, LOS/ NLOS). For the purposes of our experiment, NITOS testbed provided full access to 12 LoRa nodes and one LoRa Gateway with an additional Monitor interface to capture the prevailing channel occupancy. The set of conducted experiments employed all 12 LoRa end nodes, the 8 available channels, 3 different transmission power levels (0,7,14 dBm), 10 Transmission modes (modulation settings) and multiple payload sizes. In total, more than 100K LoRa packets were transmitted and analysed within the RSSI range of -102 and -137 dBm.</p>
LoRa RSSI vs distance
<p>The data set has been used in CityScan experiment within <a href="https://www.fed4fire.eu/">Fed4Fire+</a> continuous call. RSSI and packet loss between LoRa end device sending data packets and a set of stationary LoRa nodes were studied depending on the distance and other parameters.</p> <p>The data set contains location information transferred over LoRa from a single tag and registered by 10 nodes from <a href="https://doc.lab.cityofthings.eu/wiki/Nodes">CityLab testbed</a>:<a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node2"> Node 2</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node4">Node 4</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node15">Node 15</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node18">Node 18</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node20">Node 20</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node21">Node 21</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node27">Node 27</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node33">Node 33</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node35">Node 35</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node36">Node 36</a>.</p> <p>The data is stored in CSV format (with the following columns):<br> <strong>node</strong> - id of the CityLab testbed node<br> (<strong>node_lat</strong>, <strong>node_lon</strong>) - GPS coordinates of the node<br> <strong>tag</strong> - id of the CityScan tag<br> (<strong>tag_lat</strong>, <strong>tag_lon</strong>) - GPS coordinates of the tag<br> <strong>tag_speed</strong> - Speed of the moving tag<br> <strong>dist_m</strong> - Distance between corresponding tag and node (meters)<br> <strong>ts</strong> - timestamp (milliseconds since the UNIX epoch: 1 January 1970)<br> <strong>num</strong> - a sequential number of submitted packet<br> <strong>rssi</strong> - received signal strength indication<br> <strong>received</strong> - Boolean attribute indicating whether or not the data packet was received</p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
А – типовые местонахоЖдениЯ: Зал. ЛаврентиЯ (красный маркер), б. ПровидениЯ (Зеленый маркер); B, B' – иЗобраЖениЯ раковины (B) и Зуба радулы (B') Bela violacea var. nodulosa. Вр=14.5 мм, ДЗ=0.25 мм, иЗ: Krause [1885, pl. 18, figs. 4, 12]; C, C' – синтип Bela violacea var. nodulosa (C) и увеличенный участок предпоследнего оборота (C'), ZMB 37860, Вр=12 мм (фотографиЯ – с раЗрешениЯ ZMB); D–I – иЗменчивость Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN беЗ номера, ЗФИ, о-в Аполлонова, Американский Залив, 3–4 м. Вр=8.2 мм; E – Defrancia becki. ZIN беЗ номера, ЗФИ, о-в Кука, 3–4 м. Вр=9.1 мм; F – Bela violacea var. laevior. Вр=12 мм, иЗ: Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Баренцево море, Югорский Шар, 13 м. Вр=8.7 мм; H – Pleurotoma bicarinata. ZIN 41203/156, ЗФИ, о-в ГрЭм-БЭм, 12–15 м. Вр=8.4 мм; I, I' – Зубы радулы типичной (I) и беЗкилевой (I') форм. ДЗ=0.12 мм и 0.21 мм, соответственно, иЗ: [Sars, 1878, pl. 9, figs. 7, 8]; J – иЗобраЖение раковины Lora inequita. Вр=11 мм, иЗ: Dall [1919, pl. 16, fig. 9]; K – голотип Lora inequita, USNM 222238. Вр=11 мм (фотографиЯ – с раЗрешениЯ USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: раковины (L) и Зуб радулы (L'). Вр=12 мм и 11.6 мм, соответственно, ДЗ=0.15 мм, иЗ: Богданов [1990, рис. 175, 176, 422 (7)]. A – type localities: Lawrence Bay (red circle), Providence Bay (green circle); B, B' – images of the shell (B) and tooth of the radula (B') of Bela violacea var. nodulosa. H=14.5 mm, L=0.25 mm, after Krause [1885, pl. 18, figs. 4, 12]; C, C' – a syntype of Bela violacea var. nodulosa (C) and the enlarged section of the penultimate whorl (C'), ZMB 37860, H=12 mm (photo – courtesy of ZMB); D–I – variability of Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN uncatalogued, Franz Josef Land, Apollonova Isl., American Gulf, 3–4 m. H=8.2 mm; E – Defrancia becki. ZIN uncatalogued, Franz Josef Land, Cook Isl., 3–4 m. H=9.1 mm; F – Bela violacea var. laevior. H=12 mm, after Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Barents Sea, Ugra Shar, 13 m. H=8.7 mm; H – Pleurotoma bicarinata. ZIN 41203/156, Franz Josef Land, Graham-Bam Isl., 12–15 m. H= 8.4 mm; I, I' – teeth of typical (I) and keelless (I') forms. L=0.12 mm and 0.21 mm, respectively; after Sars [1878, pl. 9, figs. 7,8]; J – image of Lora inequita. H=11 mm, after Dall [1919, pl.16, fig. 9]; K – the holotype of Lora inequita, USNM 222238. H=11 mm (photo – courtesy of USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: shells (L) and tooth (L'). H=12 mm and 11.6 mm, respectively, L=0.15 mm, after Bogdanov [1990, figs. 175, 176, 422 (7)]. in Curtitoma nodulosa (Krause, 1885) comb. nov. (Gastropoda: Mangeliidae), a rare species twice described from the northern part of Bering Sea
А – типовые местонахоЖдениЯ: Зал. ЛаврентиЯ (красный маркер), б. ПровидениЯ (Зеленый маркер); B, B' – иЗобраЖениЯ раковины (B) и Зуба радулы (B') Bela violacea var. nodulosa. Вр=14.5 мм, ДЗ=0.25 мм, иЗ: Krause [1885, pl. 18, figs. 4, 12]; C, C' – синтип Bela violacea var. nodulosa (C) и увеличенный участок предпоследнего оборота (C'), ZMB 37860, Вр=12 мм (фотографиЯ – с раЗрешениЯ ZMB); D–I – иЗменчивость Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN беЗ номера, ЗФИ, о-в Аполлонова, Американский Залив, 3–4 м. Вр=8.2 мм; E – Defrancia becki. ZIN беЗ номера, ЗФИ, о-в Кука, 3–4 м. Вр=9.1 мм; F – Bela violacea var. laevior. Вр=12 мм, иЗ: Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Баренцево море, Югорский Шар, 13 м. Вр=8.7 мм; H – Pleurotoma bicarinata. ZIN 41203/156, ЗФИ, о-в ГрЭм-БЭм, 12–15 м. Вр=8.4 мм; I, I' – Зубы радулы типичной (I) и беЗкилевой (I') форм. ДЗ=0.12 мм и 0.21 мм, соответственно, иЗ: [Sars, 1878, pl. 9, figs. 7, 8]; J – иЗобраЖение раковины Lora inequita. Вр=11 мм, иЗ: Dall [1919, pl. 16, fig. 9]; K – голотип Lora inequita, USNM 222238. Вр=11 мм (фотографиЯ – с раЗрешениЯ USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: раковины (L) и Зуб радулы (L'). Вр=12 мм и 11.6 мм, соответственно, ДЗ=0.15 мм, иЗ: Богданов [1990, рис. 175, 176, 422 (7)]. A – type localities: Lawrence Bay (red circle), Providence Bay (green circle); B, B' – images of the shell (B) and tooth of the radula (B') of Bela violacea var. nodulosa. H=14.5 mm, L=0.25 mm, after Krause [1885, pl. 18, figs. 4, 12]; C, C' – a syntype of Bela violacea var. nodulosa (C) and the enlarged section of the penultimate whorl (C'), ZMB 37860, H=12 mm (photo – courtesy of ZMB); D–I – variability of Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN uncatalogued, Franz Josef Land, Apollonova Isl., American Gulf, 3–4 m. H=8.2 mm; E – Defrancia becki. ZIN uncatalogued, Franz Josef Land, Cook Isl., 3–4 m. H=9.1 mm; F – Bela violacea var. laevior. H=12 mm, after Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Barents Sea, Ugra Shar, 13 m. H=8.7 mm; H – Pleurotoma bicarinata. ZIN 41203/156, Franz Josef Land, Graham-Bam Isl., 12–15 m. H= 8.4 mm; I, I' – teeth of typical (I) and keelless (I') forms. L=0.12 mm and 0.21 mm, respectively; after Sars [1878, pl. 9, figs. 7,8]; J – image of Lora inequita. H=11 mm, after Dall [1919, pl.16, fig. 9]; K – the holotype of Lora inequita, USNM 222238. H=11 mm (photo – courtesy of USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: shells (L) and tooth (L'). H=12 mm and 11.6 mm, respectively, L=0.15 mm, after Bogdanov [1990, figs. 175, 176, 422 (7)].
LoRa signal quality and GPS positioning time series dataset
<p>This time series dataset contains measurements taken by moving devices equipped with LoRa communication and GPS positioning capabilities. The measurements have been captured at the Sálvora Archipelago in Galicia (Spain), which belong to the Atlantic Islands of Galicia National Park, in which we have deployed three LoRa gateways at the following locations (latitude, longitude, altitute):</p> <ul> <li>Gateway 1 (42.46972, -9.01345, 73)</li> <li>Gateway 2 (42.49955, -9.00654, 5)</li> <li>Gateway 3 (42.50893, -9.04902, 31)</li> </ul> <p>The dataset is provided as a single comma-separated values (CSV) file, with the following data in each line:</p> <ul> <li>Device identification</li> <li>Received signal strength indicator (RSSI) from LoRa Gateway 1</li> <li>Signal-to-noise ratio (SNR) from Gateway 1</li> <li>RSSI from LoRa Gateway 2</li> <li>SNR from Gateway 2</li> <li>RSSI from LoRa Gateway 3</li> <li>SNR from Gateway 3</li> <li>LoRa spreading factor</li> <li>Timestamp</li> <li>Device latitude received from GPS</li> <li>Device longitude received from GPS</li> <li>Device altitude received from GPS</li> </ul>
Data Descriptor: LoRa in Buildings
<p>The uploaded data contains measurement data and further processed data to evaluate the quality of a LoRa network in the building NW1 of the University of Bremen. The file "LoRa_Measurement_28-04-23.csv" contains the measurement data of the signal strength per location. The other files contain data for further evaluation of the quality of the LoRa network, such as the distances between receiver and transmitter, the exact time at which the packets were sent and the reception ratio per location calculated from the measured data set. In addition to these data sets, the LaTeX files used to write the paper and the MATLAB scripts to create the heatmaps are also uploaded.</p> <p>The data and paper were gathered and written as part of the "Scientific Practice" lecture at the University of Bremen.</p>
Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning
<p>This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows:</p> <ul> <li>_id: Unique MongoDB document ID</li> <li>chirp: Base 64 encoded binary float complex I/Q data</li> <li>field: Symbol location inside a LoRa frame</li> <li>tag: Name of the device that sent the frame</li> <li>date: Time and date of reception</li> <li>fn: Frame number</li> <li>rand: Random number for sorting</li> </ul> <p><strong>How to import</strong></p> <p>Extract the tar archive. Inside the directory, run the following command to import the lora2msps database:</p> <p><em>mongorestore --gzip -d lora2msps ./lora2msps</em></p> <p>This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing:</p> <p><em>mongorestore --gzip . </em></p> <p><strong>How to use</strong></p> <p>After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.:</p> <p><em>./tf_train.py train conf/experiment_lora2msps_mlp.conf</em></p>
Effects of Body Shadowing in LoRa Localization systems
<p>The purpose of this database of measurements with and without body obstruction is to provide the scientific community with a tool to evaluate different localisation methods in these scenarios. The measurements were performed in an outdoor environment with a semi-dense and dense vegetation, located at the Universidad del Norte in Colombia. The data presents the RSSI values transmitted by a LoRa WisTrio RAK5205 node and received by four Risinghf LoRaWAN gateways composing the network, operating at 915 MHz. Additionally, the node and gateways locations in geographic coordinates are provided. Measurements were made by placing the node on a tripod for each test position (scenario without human shadowing - NHS) and then placing the node on a person's chest (scenario with human shadowing - HS) for the same positions.</p> <p>The database is composed of three groups:<br> - Raw data:<br> RSSI_NHS_raw.csv: Containing 50 packets per position in No Human Shadowing scenario.<br> RSSI_HS_raw.csv: Containing 50 packets per position with reception at 3 or 4 gateways, in Human Shadowing scenario.<br> - Cleaned data: Only for the 16 node positions where the messages were received at the four gateways, after outlier removal (See https://www.mathworks.com/matlabcentral/fileexchange/62954-mean_removing_outliers_tukey-x-rmzerovals?s_tid=mwa_osa_a).<br> NHS_16ps.csv: Containing 40 packets per position in No Human Shadowing scenario.<br> HS4GWs_16ps.csv: Containing 40 packets per position in Human Shadowing scenario.</p> <p>Additionally, the geographical coordinates for the gateways are provided in the PosGWs.csv file.</p> <p>The methodology for data collection and the first results will be published in a research journal.</p>
An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology
<div><strong>Overview</strong>:</div> <div>The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Falcade, Italy) at 1870 m in the Italian Dolomites, at two different times of the year: March and April 2024. The depth and conditions of the snow are different: in March, the snow is mostly dry and over one meter deep, while in April, the snow is wetter, with a greater presence of liquid water, and approximately 55 centimeters deep.</div> <div> </div> <div>The dataset includes three test typologies:</div> <div> <ol> <li>Cross test: 1 buried transmitter, at different depths, and 4 receivers on a tripod, positioned at 10 different distances from the burial point along 4 orientations: North, South, East, West. Distances are: 0.6 m, 1.2 m, 1.8 m, 3 m, 5 m, 10 m, 20 m, 30 m, 40 m, 50 m.</li> <li>Maximum Distance test: 1 buried transmitter and 1 receiver, held in hand and moved away from the burial point until the signal is completely lost. The receiver stops periodically, collecting 2 minutes data in specific markers.</li> <li>Drone Flyover test: 1 buried transmitter and 1 receiver mounted on the bottom of a quadcopter professional drone. The drone stands on 121 measurement points, creating a precise grid covering an area of 100 square meters, with the burial location at the center.</li> </ol> </div> <div>All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the receivers and the burial depth of the transmitter. The dataset is organized in three folders, one for each test: cross, max_dist and drone. In a separate folder, the snow profiles for the two data collection periods, march and april 2024, are also included, according to the AINEVA Model 4.</div> <div> </div> <div>The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system.</div> <div> </div> <div><strong>How to use the dataset</strong>:</div> <div>Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:</div> <div> </div> <div>1. Cross test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>rx_pos</li> <li>distance</li> <li>depth</li> <li>polarization</li> </ul> </div> <div>2. Maximum Distance test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>depth</li> <li>id_marker</li> <li>longitude</li> <li>latitude</li> </ul> </div> <div>3. Drone Flyover test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>longitude</li> <li>latitude</li> <li>x</li> <li>y</li> <li>depth</li> </ul> </div> <div><strong>How to cite this dataset</strong>:</div> <div>- DOI number of this datsaset: 10.5281/zenodo.12750580</div> <div>- M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in <em>IEEE Access</em>, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654</div>
Lora Experiment Dataset
<p>This file contains Dataset of a Lora Communication Experiment which has been done in the NW1 building of University of Bremen on 03.05.2023 .</p>
Performance Analysis of LoRa in Indoor Settings: A Data Descriptor
<p>This work is a description of the experiment conducted to understand the reception<br> of LoRa in closed environments, such as a building.<br> The experiment was carried out on 04/05/2023, in the NW1 building of University of<br> Bremen. The data’s primary goal is to provide researchers with the understanding of<br> factors such as distance, obstacles, interference with other wireless devices that<br> dictates LoRa’s performance.</p>
SNR and RSSI from an urban LoRa Network
<p>The dataset contains various SNR and RSSI reading from a LoRa Network in the city of Portland, Maine. The dataset consists in a single sheet file, where each row corresponds to a specific LoRa node. Note that: i) the first row corresponds to the LoRa Gateway; ii) each row contains information over the node ID, its coordinates, the packet frequency, bandwidth, , Transmission Power, Spreading Factor, Coding Rate, SNR and RSSI.</p>
Raw results of the simulation experiments performed to evaluate different Aloha-based schemes for LoRa-based DtS uplink transmissions
<p>This zip file contains the gnuplot and data files needed to generate the figures showing the experimental results presented in [1].</p> <p>[1] S. Herrería-Alonso, M. Rodríguez-Pérez, R. F. Rodríguez-Rubio and F. Pérez-Fontán, "Improving Uplink Scalability of LoRa-Based Direct-to-Satellite IoT Networks," in <em>IEEE Internet of Things Journal</em>, vol. 11, no. 7, pp. 12526-12535, April 2024, doi: 10.1109/JIOT.2023.3333934.</p>
FIGURES 2‒7. Palemnema lorae n in Dragonflies of Cusuco National Park, Honduras; checklist, new country records and the description of a new species of Palaemnema Selys, 1860 (Odonata: Platystictidae)
FIGURES 2‒7. Palemnema lorae n. sp. holotype male (2‒4, 6), Palaemnema gigantula Calvert (5), habitus, lateral view (2, 3, 5), S8‒10 lateral view (4), Head, thorax, S1‒2 lateral view (6,7).
LoRa on Ice: dataset to evaluate the LoRa radio technology for sea ice research in Antarctica
<p>We present the design and implementation of a wireless sensor network (WSN) tailored for sea ice research. Our custom-built data acquisition unit employs Long Range (LoRa) radio technology and the Long Range Wide Area Network (LoRaWAN) protocol. We describe the deployment of a scientific measurement system in the vicinity of the Neumayer III research station in Antarctica, utilizing IoT technologies.<br>This dataset contains different measurements to evaluate the performance of the LoRa radio link and the LoRaWAN protocol. In this work we use a data acquisition unit sending data via LoRa using the LoRaWAN protocol. The data acquisition unit is a custom made electronics designed to sample data from connected sensors used for sea ice research in Antarctica. Three datasets are available to evaluate the performance of the LoRa radio technology and the LoRaWAN protocol: one range test on a motor boat in Germany and two range tests performed in Antarctica near the Neumayer III station. </p>
LoRa LBT Simulation
Open the record for dataset details and reuse information.
AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion
<p>Images related to the paper *AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion* and the specially trained LoRA model for restoring Yangshao pottery patterns.</p>
LoRA_Data_Descriptor_Experiment
<p>The file includes the Received Signal Strength indicator values of a LoRa network installed in the NW1 building at the University of Bremen, Germany. The network consists of 4 gateways and the data is acquired to analyse the placement of these gateways. The analysis reveals that at certain locations across multiple floors, not all packets were successfully received. Factors such as signal interference and gateway placement were identified as potential causes for packet loss. The performance of four gateways was also analyzed, highlighting variations in reception rates and outliers. The results emphasize the importance of strategic gateway placement and coverage optimization for ensuring reliable LoRa network performance. Recommendations are provided for improving gateway placement to enhance reception rates. This study contributes valuable insights for the planning and implementation of LoRa networks in real-world environments.</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.