Sensors
SeaIceWeather Dataset
This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518
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Various modes of transportation traverse our roadways, highlighting the importance of object classification for improving traffic safety. Optical sensors that rely on visual data encounter challenges in adverse weather conditions, where poor visibility hinders target classification. In this project we use an off-the-shelf millimeter wave Frequency Modulated Continuous Wave (FMCW) radar -- Texas Instruments IWR1843BOOST module to classify on road objects. By combining the radar module, Robot Operating System (ROS), and Python scripts, we extracted a dataset of 3D point cloud images.
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The uploaded project is the code and dataset for Charging Efficiency Optimization Based on Swarm Reinforcement Learning under Dynamic Energy Consumption for WRSN. The details of each document in the uploaded project are as follows. Document data: The data file contains network data and simulation data. Document iostream: The iostream file contains the program for reading data and writing data. Document main: The main file contains the main program that executes the simulation. Document network: The network file
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In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress.
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Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts.
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The dataset presents a comprehensive collection of environmental sensor measurements conducted under conditions typical of the harsh environment found in high-energy physics detectors. The dataset includes measurements of relative humidity obtained from a capacitive-based humidity sensor:
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The dataset consists of experimental data collected in an anechoic tank, with a specific setup involving single-source transmission and reception by a 6-element circular array with a radius of 0.046 meters. The transmitted signals include common wideband signals used in underwater positioning and communication, such as chirps, single-carrier QPSK, multi-tone signals, and OFDM signals. The transmitter and receiver are located at the same depth, and the receiving array rotates 360 degrees with 30-degree intervals.
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As semiconductor devices have become increasingly miniaturized, the ability to control very small Critical Dimensions (CDs) during the etching process has become crucial through controlled plasma processes. Hence, diagnosing plasma and reflecting this in the process to enhance yield is of paramount importance. Typically, a Single Langmuir Probe (SLP) is utilized for plasma diagnostics.
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Recent semiconductor devices have embraced structural modifications, including vertical stacking, to overcome the limitations of miniaturization. Particularly, memory devices have seen improvements through the transition to 3D stack structures. To address the challenges of etching high aspect ratio contact holes, the Bosch process, which alternates between deposition of a passivation layer on the pattern wall to prevent sidewall etching and etching steps, has been utilized.
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The Partial Discharge - Localisation Dataset, abbreviated: PD-Loc Dataset is an extensive collection of acoustic data specifically curated for the advancement of Partial Discharge (PD) localisation techniques within electrical machinery. Developed using a precision-engineered 32-sensor acoustic array, this dataset encompasses a wide array of signals, including chirps, white Gaussian noise, and PD signals.
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