More and more satellites are populating the sky nowadays in the Low Earth orbits (LEO). Most of the targeted applications are related to broadband and narrowband communications, Earth observation, synthetic aperture radar, and internet-of-Things (IoT) connectivity. In addition to these targeted applications, there is yet-to-be-harnessed potential for LEO and positioning, navigation, and timing (PNT) systems, or what is nowadays referred to as LEO-PNT. No commercial LEO-PNT solutions currently exist and there is no unified research on LEO-PNT concepts. Our survey aims to fill the gaps in knowledge regarding what a LEO-PNT system entails, its technical design steps and challenges, what physical layer parameters are viable solutions, what tools can be used for a LEO-PNT design (e.g., optimisation steps, hardware and software simulators, etc.), the existing models of wireless channels for satellite-to-ground and ground-to-satellite propagation, and the commercial prospects of a future LEO-PNT system. A comprehensive and multidisciplinary survey is provided by a team of authors with complementary expertise in wireless communications, signal processing, navigation and tracking, physics, machine learning, Earth observation, remote sensing, digital economy, and business models.

The Smart Grid's objective is to increase the electric grid's dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid's overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%.

In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning techniques in several GNSS use cases. In most cases, the models using the machine learning techniques in these GNSS use cases outperform the traditional GNSS models. ML models are promising in their utilization in GNSS. However, the application of ML models in the industry is still limited. Thus, more effort and incentives are needed to facilitate the utilization of ML models in the PNT context. Therefore, based on the findings of this review, we provide recommendations for researchers and guidelines for practitioners.

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.