This is the dataset for the paper Bayesian Inference of Sector Orientation in LTE Networks based on End-User Measurements published at VTC 2021 - Fall.

It includes a set of Drive-Test RSRP Pathloss Measurements with their relative position to the corresponding eNodeB. In total it contains data for 91 three-sector eNodeBs, which results in  273 sectors.

The time-to-market pressure and the continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to imp

As Science and technology evolve, the environment is getting affected daily. These cause major environmental issues like Global Warming, Ozone layer depletion, Natural resource depletion, etc. These are measured and regulated by local bodies. The data given by the local bodies are average values for a large area, those data might be inaccurate for a small sector or isolated zone. However, there are few techniques such as WSN (Wireless Sensor Networks), IoT (Internet of things) which measures and updates real-time data to a cloud server to overcome the trouble.

The dataset is generated by performing different MiTM attacks in the synthetic electric grid in RESLab testbed at Texas A&M University, US. The testbed primarily consists of a dynamic power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master and Elasticsearch's Packetbeat index. There are raw and processed files that can be used by security enthusiasts to develop new features and also to train IDS using our feature space respectively.