Digital microfluidics are a unique technique for operation of nano-to-micro liter droplets based on electrowetting on dielectric. It has great application potential in the field on clinic diagnosis, life science and environment monitoring. Due to the fast droplet moving speed and high degree of freedom for droplet manipulation, it is urgent to develop automated and intelligent approaches for droplet monitoring and control.

 three-level network topology experimental environment was constructed based on a typical home network. An optical network terminal (Bell XE-140W-TD) was used as the starting point for Internet access, and a Gigabit wired connection was established with the core router (Huawei TC30) via a shielded Cat 6 cable. This router is a Huawei beta version that can obtain specific device information and corresponding traffic after NAT. The experimental evaluation of traffic attribution association in subsequent chapters will use this as ground truth label data for comparison.

This is the dataset for paper VI-BEVSEG training and testing. For more details about the dataset structure, please refer to Nuscense or V2X-Sim. We only use the number 1 vehicle in the V2X-Sim dataset. Replace the folder in this dataset, and only keep the vehicle 1 six onboard cameras information, infrastructure camera information and their semantic camera information in 'sweeps' and 'samples' folder. Then replace the 'v1.0-trainval' folder with ours and put the h5 maps under V2X-Sim folder.

This dataset presents longitudinal measurements of plant growth characteristics under varying lighting conditions. Collected for 30 individual plants, the data spans multiple time points and includes key variables such as plant fresh weight (g), plant height (mm), plant width (mm), and number of leaves. Each measurement is associated with a specific plant, date (DD/MM/YYYY), experimental group, and a lighting recipe defined by the percentage composition of Red, Green, and Blue light.