The Microgrid allows for more efficient and lower-cost power provisions, and is therefore more flexible than traditional power ecosystems. However, the increasingly integrated nature of these systems into network and internet-connected IT systems also potentially makes them susceptible to cyber-attack. This paper examined different challenges related to the cyber attacks threatening wireless microgrid systems from the experimental view. Wireless communication and transmission methods are widely used for secondary control of energy re-sources. However, there are risks of cyber attacks during the communication process, such as Denial-of-Service (DoS) attacks. This paper reports the investigation of potential cyber attacks on wireless communications of microgrid systems. Furthermore, this paper evaluates the practical impacts of cybersecurity breaches targeting microgrid systems, with special attention to those in Australia. In brief, the main goal of this paper is to enhance the mitigation countermeasures for cyber attacks linked to wireless microgrid systems, thus guaranteeing reliable wireless communications within these systems.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime's understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open-accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.