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This dataset extracts the entropy of each of the PE sections of benign and ransomware reports to be used for detecting ransomware. Several machine learning classifiers were trained on this dataset such as Decision Tree, Random Forest, KNN, XGBoost and Naive Bayes. From the results, PE entropy can accurately detect ransomware with a decision tree classifier yielding the overall best result with a 98.8% accuracy and an AUC of 0.969. The latency with the prediction of the decision tree classifier was extremely quick with a result of 1.509 milliseconds.

IMUs have gained popularity for tracking joint kinematics due to their portability and versatility. However, challenges such as limited accuracy, lack of real-time data analysis, and complex sensor-to-segment calibration procedures have hindered their widespread use. To address these limitations, we developed a portable system that integrates four IMUs to collect treadmill walking data, with ground truth values obtained from a Motion Capture System.

Readily available animal tissue such as ground beef is a convenient material for mimicking the dielectric properties of biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materials depends on the accurate characterization of their properties. Tissue water content is a dominant factor in microwave frequency tissue properties, thus the effect of dehydration must be considered. The dependence of tissue properties on hydration is also important for new applications of microwave sensing for hydration monitoring.

This dataset has EV charging data from 2019 to the present day. SFU's Burnaby campus currently has two different types of Electric Vehicle Charging Stations on campus. There is no additional charge to use the station; however, the Permit or Daily Rate required in each lot remains in effect for the EV Reserved stalls.