Artificial Intelligence

This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

To ensure the usability and reliability of the collected data, one Hikvision monitoring camera (iDS-TCV900-AE/25) is deployed at the entrance of Taijia Expressway in Shanxi province in China for image capturing. This camera is installed on the roadside pole with a height of 5.8 meters and uses the infrared flash as the supplementary lighting. The captured images cover two lanes of the expressway, with the resolution being 4096*2160. All images are captured during the period of November 2019 to April 2020.

This dataset is used in the experiment of the paper "A Data Embedding Scheme for Efficient Program Behavior Modeling with Neural Networks" accepted by IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI). System calsl and their relevant branch sequences are contained in the tar.gz file. For a detailed description, please refer to the paper.

Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis and metabolic disorders, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected.