Sensors
The JU-Impact Radiomap Dataset is a comprehensive dataset designed for research and development in indoor positioning systems. It comprises 5431 instances characterized by readings from 105 static Wi-Fi Access Points (APs) and spans 152 distinct virtual grids. Each virtual grid represents a 1x1 square meter area, derived by dividing a physical floor of a university building into reference coordinate points (x, y). The dataset was collected over a period of 21 days using four mobile devices: Samsung Galaxy Tab, Moto G, Redmi Note 4, and Google Pixel.
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This dataset contains information gathered from an experiment in which loads were applied at nine specific locations on a multimode fiber (MMF). The data captures the system's response to these loads, providing valuable insights into the fiber's behavior under different stress conditions. Each load was designed to be random in magnitude and duration, ensuring a diverse range of effects on the MMF. The dataset records key parameters such as the applied force, the locations of the loads, and the corresponding changes in the fiber's properties.
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The dataset corresponding to the measurements performed in the article "Freehand System for Probe-Fed Antenna Diagnostics by Means of Amplitude-Only Acquisitions" is provided. In this work, a freehand acquisition system is used to characterize an integrated antenna fed by a GSG probe by means of amplitude-only measurements, with phase retrieval based on an indirect holography technique for broadband antennas. Spatial filtering and time-gating techniques are applied to eliminate the effect of the feeding probe.
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This is a dataset: a database of radar cross sections of models; The characteristics of the data set include radar frequency, polarization mode (currently only the co-polarized VV and HH), radar Angle of sight (pitch and azimuth), and target characteristics RCS. According to these input feature information, the highly nonlinear feature rcs of the predicted target is estimated. Prediction is actually an interpolation method, which can be divided into two kinds, interpolation and extrapolation. We can try different methods according to the data set provided, please show your skills!
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We introduce a wireless potato root tuber sensing (WPS) dataset comprising multi-channel received signal strength(RSS) data from a wireless network and ground truth annotations for potato root tubers. We design a testbed called spin, which is based on a multi-channel wireless network, the wireless network is consist of 16 TI CC25231 nodes deploy on a white rack, using this testbed, we conduct extensive measurement expriments. We first perform expriments in a static environment.
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128 Views
The given data contains the results from laboratory trials related to the paper "Optimizing Congestion Management andEnhancing Resilience in Low-Voltage Grids Using OPF and MPC Control Algorithms Through Edge Computing and IEC 61850 Standards" currently in publication in IEEE Access.
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144 Views
The datasets are sourced from the Caltrans Performance Measurement System (PeMS) in California, which monitors and collects real-time traffic data from over 39,000 sensors deployed on major highways throughout the state. The PeMS system collects data every 30 seconds and aggregates it into 5-minute interval, with each sensor generating data for 288 time steps daily. Additionally, road network structure data is derived from the connectivity status and actual distances between sensors.
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265 Views
This dataset results from a 5-month-long Cloud Telescope Internet Background Radiation collection experiment conducted during the months of October 2023 until February 2024.
A total amount of 130 EC2 instances (sensors) were deployed across all the 26 commercially available AWS regions at the time, 5 sensors per region.
A Cloud Telescope sensor does not serve information. All traffic arriving to the sensor is unsolicited, and potentially malicious. Sensors were configured to allow all unsolicited traffic.
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408 Views
In this study, experiments were conducted to etch SiO₂ and Si₃N₄ by introducing N₂ at flow rates of 0, 2, 4, 6, and 8 sccm into a CF₄/O₂ plasma. OES (Optical Emission Spectroscopy) data were systematically collected and analyzed under each condition to understand the impact of N₂ addition on plasma chemistry. Machine learning techniques were applied to identify specific OES wavelengths that are critical to the etch rate and selectivity of both materials. Furthermore, the importance of the selected wavelengths was determined using XAI (Explainable Artificial Intelligence) methods.
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128 Views
This graph illustrates the visualization trend of a subset of the dataset I have uploaded, which comprises 6500*9 data points. The dataset consists of nine columns representing underwater speed (UWS), underwater course (UWC), depth below the surface (DBS), rate of change in speed (RCS), rate of change in course (RCC), rate of change in depth (RCD), trend A and B of vibrational signals (TVS_A, TVS_B) and electromagnetic noise trend (TEN) recorded by the AUV.
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92 Views