Study on Sleep Positions using a Wearable Device

This device constantly collects data about acceleration in three directions (tri-axial) 50 times a second (50 Hz). It has several components: a microcontroller for gathering and sending data (ESP8266), a battery (lithium-ion), a sensor for measuring acceleration (ADLX345 accelerometer), and a protective case made of plastic. The device can also store data temporarily on a microSD card in case the wireless connection is lost.


SETCD (Satellite and ERA5-based Tropical Cyclone Dataset), a comprehensive dataset encompassing satellite imagery data and ERA5 data for all TCs recorded between 1980 and 2022. Our dataset is derived from two publicly available data sources: GridSat-B1 and ERA5. To capture relevant information associated with TC, SETCD adopts the latitude and longitude positions provided by IBTrACS as the center points. The satellite data within the SETCD dataset consists of three channels from GridSat-B1: infrared, water vapor, and visible.


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.


This project is a instruction for the parameters of the case studies in our paper "A Behavior-Based and Fast Convergence Energy Sharing Mechanism for Prosumers Community".

two_prosumer.npy:The parameters of the case studies on communities with 2 prosumers.
ten_prosumer.npy:The parameters of the case studies on communities with 10 prosumers.
fifty_prosumer.npy:The parameters of the case studies on communities with 50 prosumers.
hundred_prosumer.npy:The parameters of the case studies on communities with 100 prosumers


The  Sentinel-2 L2A multispectral data cubes include two regions of interest (roi1 and roi2) each of them containing 92 scenes across Switzerland within T32TLT, between 2018 and 2022, all band at 10m resolution These areas of interest show a diverse landscape, including regions covered by forests that have undergone changes, agriculture and urban areas.


This study proposes a more competitive and sample-efficient algorithm: Memory-GIC-PPO, specifically to address POMDPs in UAV path planning. The effectiveness of the proposed algorithm is thoroughly evaluated through simulations conducted on the Airsim platform. The results convincingly demonstrate that Memory-GIC-PPO enables the UAV to achieve optimal path planning in complex environments and outperforms the benchmark algorithms in terms of sampling efficiency and success rates.


The dataset consists of undirected weighted multi-graphs stored in .pkl or .net formats. These undirected graphs form instances for the multi-trip multi-depot rural postman problem. The Multi-trip multi-depot Rural Postman Problem is a variant of the Capacitated Arc Routing Problem which is to find a set of routes for vehicles having limited capacity to traverse a set of arcs from a node called depot in an undirected graph in the least possible time. The dataset consists of instances generated by modifying instances from the literature and also real-world road networks.


The dataset consists of NumPy arrays for each alphabet in Indian Sign Language, excluding 'R'. The NumPy arrays denote the (x,y,z) coordinates of the skeletal points of the left and right hand (21 skeletal points each) for each alphabet. Each alphabet has 120 sequences, split into 30 frames each, giving 3600 .np files per alphabet, using MediaPipe.

The dataset is created on the basis of skeletal-point action recognition and key-point collection.


Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.


Simulation data for the following paper

DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver (K. Chae and Y. Kim)