*.csv; *.tdms; *.stl; *.mp4

This figures show a sequence of photos of puncturing into the zerbrafish embryo. Initially, the tip approached the embryo orthogonally to the slot and punctured into the embryo at the instant that it was in contact with the cell membrane. Subsequently, the USPA caused the tip to retract from the embryo; this process changed the embryo’s position possibly because of the dragging force between the pipette and the membrane [48].

This is the sample tracking result of our AdaMOT algorithm.

This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques.