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Dataset results
44 results for “LTE”
dataset for "Non-LTE Synthetic Observables of a Multidimensional Model of Type Ia Supernovae"
<p>Included here are datafiles supplementary to the article "Non-LTE Synthetic Observables of a<br>Multidimensional Model of Type Ia Supernovae" (Boos, Dessart, Townsley, Shen), submitted <br>to ApJ/arxiv in October 2024. This is a revised version of the dataset using an improved<br>method to generate the 1D LTE observables (1D LTE and pseudo-2D non-LTE observables have<br>thus changed from the previous version).</p> <p>We include in this dataset the 2D double detonation ejecta model (produced in Boos et al. 2021),<br>as well as the 15 wedge profiles constructed from this model, which were used in the radiative<br>transfer calculations of this work. These profiles are provided in Sedona input format. The<br>headers/columns for the 1D profiles are as follows:<br>1: Sedona model type<br>2: num_cells, rmin, ejecta time, num_isos<br>3: isos<br>4-: v_r, rho, T, X_a, X_b, etc.</p> <p>We also include the synthetic spectra and photometry from this work. These synthetic observables<br>are given for each set of calculations (1D LTE, 2D LTE, 1D non-LTE, and pseudo-2D non-LTE) between<br> -3 and +15 d from maximum light. The photometry is given in absolute magnitude in the Vega system.<br>The spectra have been rebinned such that the bins are the same between the Sedona and CMFGEN<br>calculations. The flux in these spectral files is given for a distance of 1 kpc.</p> <p><br>-Samuel J. Boos (sjboos@crimson.ua.edu)</p>
Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics
<p>The following provides a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, tram and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information.</p> <p>To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.</p>
Machine Learning for LTE Energy DetectionPerformance Improvement
<p>LTE signal generated using matlab. LTE signal is transmitted through fading channel with shadowing. Energy Detection is performed on received LTE data. Energy Detection results are saved in the text files. Those results can be used in Python programs to test out k-Nearest Neighbors and Random Forest Machine Learning algorithms. Results can be then used in matlab programs to show on plots improvement in terms of probability of detection and false alarm.</p>
Composition and thermophysical properties of metallurgical plasmas - LTE calculations
<div> <p>This dataset contains two subsets:</p> <p>First - elemental compositions of the equilibrium gas phase over a variety of metallurgical processes conducted in direct-current plasma arc furnaces at a range of slag temperatures between the typical process temperature and the vaporisation temperature (defined as the lowest temperature at which only a gas phase exists). These were obtained from process simulations using thermochemical calculation software.</p> <p>Second - plasma composition and thermophysical properties at a range of plasma temperatures between 2000 K and 30000K under local thermodynamic equilibrium conditions, as calculated using minplascalc software (https://github.com/quinnreynolds/minplascalc, commit 967faf3). All calculations are at atmospheric pressure, and use the elemental gas phase compositions for each slag temperature to define the mass conservation constraints.</p> </div> <h2></h2>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
DATASET: IQ SAMPLES OF LTE, 5G NR, WI-FI, ITS-G5, AND C-V2X PC5
<div> <div> <div> <h1>Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5</h1> <p>The dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset collected was used for technology recognition and traffic charchterization model. More details on the structure and performance of the model can be found <a href="https://www.sciencedirect.com/science/article/abs/pii/S2214209622001103">here</a>.</p> <p> </p> </div> </div> </div> <div> <div> <div> <h1>Description of Dataset Collection</h1> <h2>LTE</h2> <p>For LTE dataset collection, <a href="https://www.srslte.com/" target="_blank" rel="nofollow noopener">srsRAN</a>, an open-source SDR platform, is used. This LTE SDR implementation is used to collect samples for the LTE dataset. The indoor testbed setup used to collect our dataset consists of one eNB host PC and one UE host PC. Each host PC is connected to a USRP X310 board, which is used as the RF front end. We use the latest srsRAN version 21.04, which is installed on each host PC. For the LTE dataset collection, the FDD mode with a 10 MHz bandwidth and a 5.9 GHz center frequency is used for the down-link traffic. For the LTE dataset, the traffic load was varied between 5 and 50 Mbps, and the MCS used was varied by manually configuring from MCS index 1 to 28 .</p> <h2>5G NR</h2> <p>The <a href="https://openairinterface.org/" target="_blank" rel="nofollow noopener">OpenAirInterface</a> SDR solution is used for 5G NR dataset collection. OpenAirInterface is an open source SDR platform that provides a 3GPP compliant implementation of eNB, UE, and EPC. The OpenAirInterface SDR solution also includes a 5G non-stand alone (NSA) mode which supports 5G networks by using existing 4G infrastructure. This SDR-based 5G network setup is used to collect the IQ samples for the 5G NR dataset. For the 5G NR dataset collection, a 1:1 static TDD configuration is used for up-link and down-link traffic in NSA mode. Numerology 1 is used at 10 MHz bandwidth and center frequency of 5.9 GHz. For the 5G NR dataset, the MCS used was varied by manually configuring different values ranging from MCS index 1 to 28, and the traffic load was varied between 5 and 50 Mbps.</p> <h2>Wi-Fi</h2> <p>For the WiFi dataset collection, the <a href="https://github.com/open-sdr/openwifi" target="_blank" rel="nofollow noopener">openwifi</a> SDR solution is used. openwifi is an open-source full-stack IEEE 802.11 a/g/n SDR implementation based on the Xilinx Zynq System on Chip that includes a Field Programmable Gate Array (FPGA) and an ARM processor. For our dataset, we used an IEEE802.11n access point and a client connected to it. WiFi traffic generated cover a wide range of traffic loads, i.e., 10–200 packets per second (pps), with packet sizes ranging from 500 to 1500 bytes. The MCS used was varied by manually configuring the MCS index value between 0 and 7.</p> <h2>ITS-G5 and C-V2X PC5</h2> <p>The <a href="https://www.marquez-barja.com/images/papers/vehicuularcomms21-openaccess-version.pdf" target="_blank" rel="nofollow noopener">CAMINO</a> framework was used for the ITS-G5 and C-V2X PC5 dataset collection. CAMINO is a core framework for managing the V2X communication technologies, including ITS-G5, C-V2X PC5 and C-V2X Uu (5G/4G). The CAMINO framework is used to dynamically generate standardized C-ITS service packets, including Cooperative Awareness (CA), Decentralized Environmental Notification (DEN), and Infrastructure to Vehicle Information (IVI) message packets. The CAMINO software is implemented on the infrastructure deployed as part of the <a href="https://ieeexplore.ieee.org/document/9621229" target="_blank" rel="nofollow noopener">Belgian Smart Highway testbed</a>. The Smart Highway is a testbed deployed by IMEC on the E313 highway, near Antwerp, Flanders. The Smart Highway testbed consists of eight RSUs and 2 OBUs. Each RSU and OBU includes a general purpose CPU running the CAMINO software, and Cohda MK5 and MK6c modules, which are COTS ITS-G5 and C-V2X modules respectively. In our dataset collection, RSU4 is used as a transmitter, and a USRP N310 connected to RSU3 is used to capture and store the samples. To represent a wide range of traffic characteristics, different packet sizes of 300B for CA, 300B and 600B for DEN, and 600B for IVI packets were used at inter-packet intervals of 20, 50, 100, and 200 seconds. The MCS used were varied by manually changing the MCS index value in the configuration files of the Cohda devices. The MCS index of ITS-G5 varied from 0 to 7. For the C-V2X PC5, the MCS is varied between 0 and 20.</p> </div> </div> </div> <div> </div>
EPFL HYMEX_LTE_SOP2 32 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 32 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/32.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP2 10 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 10 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/10.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP2 13 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 13 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/13.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP2 33 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 33 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/33.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP2 31 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 31 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/31.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP2 11 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP2 11 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP2/metadata/11.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 30 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 30 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/30.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 31 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 31 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/31.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 12 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 12 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/12.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 10 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 10 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/10.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 32 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 32 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/32.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP4 33 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 33 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/33.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP3 12 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP3 12 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP3/metadata/12.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL HYMEX_LTE_SOP3 22 disdrometer station data
Disdrometer measurements of the EPFL HYMEX_LTE_SOP3 22 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP3/metadata/22.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
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
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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.