*.npy

This dataset comprises a structured collection of control flow representations derived from microcontroller program execution traces, visualized as space-filling curves. The dataset is organized into eight folders, each containing 1,000 NumPy arrays representing individual image samples. These samples are grouped into four logical categories, each corresponding to a different abstraction level of program trace data: (1) complete execution traces, (2) function-call-only traces, (3) conditional-statement-only traces, and (4) scaled and truncated function-call traces.

TRANSIT dataset is the default dataset of the simulation software Traffic Anomaly Simulation Tool (TRANSIT). You can get the code of TRANSIT in the page:

bigzeze/TRANSIT: TRaffic ANomaly SImulation Tool

Satellite attitude determination is critical for accurately measuring and controlling a satellite’s orientation in orbit using a variety of sensors and methods. Currently, low-low Satellite-to-Satellite Tracking (ll-SST) missions—such as GRACE(-FO)—and upcoming missions like Magic primarily rely on quaternion data from onboard star camera sensors. To enhance attitude determination, we propose a GSCF fusion method.

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

Egocentric video and Inertial sensor data Kitchen activity dataset is the first V-S-S interaction-focused dataset for the ego-HAR task.

It consists of sequences of everyday kitchen activities involving rich interactions among the subject's body, object, and environment.

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.