Sensors

This dataset presents both raw and processed data from testing of two SMF-28 Ultra fibers heated up to 1000 deg. C.  The data was collected using an OBR 4600. Benchmark temperatures were collected using thermocouples co-located at points along the fiber.  See Readme file.

In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer.

This dataset consists of the training and the evaluation datasets for the LiDAR-based maritime environment perception presented in our journal publication "Maritime Environment Perception based on Deep Learning." Within the datasets, LiDAR raw data are processed using Deep Neural Networks (DNN). In the training dataset, we introduce the method for generating training data in Gazebo simulation. In the evaluation datasets, we provide the real-world tests conducted by two research vessels, respectively.

Cu (II) ion has potential roles in a lot of living metabolisms that its detection and removal are extremely important for flagship branches of science. In this paper, fluorescent and easy-make silica gel immobilized BODIPY were successfully prepared via a simple synthesis procedure for the rapid recognition of Cu (II) ions. The optimal emission and excitation wavelength of the synthesized hybrid material, Si-APTMS-BODIPY, was 543 nm and 460 nm, respectively.