In this paper, an analytical model for PV panels and arrays based on extracted physical parameters of solar cells is developed. The proposed model has the advantage of simplifying mathematical modelling for different configurations of cells and panels without losing efficiency of PV system operation. The effects of external parameters, mainly temperature and solar irradiance have been considered in the modelling. Due to their critical effects on the operation of the panel, effects of series and shunt resistances were also studied. The developed analytical model has been easily implemented, simulated and validated using both Spice and Matlab packages for different series and parallel configurations of cells and panels. The results obtained with these two programs are in total agreement, which make the proposed model very useful for researchers and designers for quick and accurate sizing of PV panels and arrays.

Dataset quality significantly affects the effectiveness of a machine learning (ML) model in an intrusion detection system (IDS) for cyber-physical industrial control systems (CPS/ICS) and Industrial Internet of Things (IIoT). Existing surveys compare datasets qualitatively or along limited dimensions, whereas this review introduces quantitative documentation and decision-aid scoring across 23 ICS/OT/IIoT datasets. These datasets are analyzed along seven measurable axes, with their attacks mapped to MITRE ATT&CK for ICS tactics. Quantitatively, 14 of the 23 datasets (60.9%) are built on physical testbeds, and 22 of the 23 map to MITRE ATT&CK for ICS, spanning 11 of the 12 tactics. We introduce a checklist for documentation completeness (0–7) and a decision-aid rubric (0–15) covering realism, attack diversity, class imbalance, documentation, and reproducibility. Protocol coverage across these datasets is skewed toward Modbus (13 of 23 datasets, 57%), while many other protocols (such as Profinet and OPC UA) are underrepresented relative to their industry deployment. The available datasets show structural gaps in capturing multi-stage adversary behavior. In practice, dataset selection should pair a realism-anchored dataset with a high-reproducibility one, and account for protocol diversity and APT representation.

Emerging technologies such as artificial intelligence and unmanned vehicle systems present promising opportunities for enhancing environmental monitoring in the oil and gas industry. Traditional methods often fall short in delivering real-time data, broad spatial coverage, and rapid response. This chapter focuses on AI-powered multi-drone systems designed to detect oil contamination in marine environments. To understand their function, the chapter first outlines the essential components of unmanned systems, highlighting recent advances in machine vision that have significantly improved detection, navigation, and obstacle avoidance in harsh settings. Deploying unmanned aerial vehicle(s) offers advantages such as broader area coverage, reduced mission duration, and increased reliability. However, coordinating these systems introduces complexities, especially in communication, data exchange, real-time decision-making, power management, and collision avoidance. For instance, as per communication, wireless telecommunication is emphasized as a cornerstone of successful multi-drone operations. The chapter presents relevant simulation scenarios alongside field experiment insights to validate not only the communication but also system capabilities. Given the intricacy of multi-drone operations, the integration of subsystems often involves tradeoffs, which necessitates the use of optimization techniques. Through a balanced exploration of system modeling, analytical methods, and hardware prototyping, the chapter addresses common misconceptions surrounding the effectiveness of AI-integrated drone/swarm drones. Ultimately, readers will gain a comprehensive understanding of how intelligent, cooperative unmanned systems can revolutionize environmental monitoring in demanding industrial environments.

This paper introduces the use of a fiber-optic communication system for interrogation and transmission of data from fiber Bragg grating (FBG) sensors. Signal coding and dispersion compensation methods, usually used in communication systems, are used to improve the sensing system’s performance. Moreover, communication system parameters are used to assess the improvement in the FBG sensing performance. This is done through the determination of the profiles of Q-factor and eye diagrams. The sensors considered in the simulations are strain sensors subjected to a wide range of strains. The results obtained show a clear improvement of the sensing system with symmetrical compensation. The proposed method is suitable for applications where fiber-optic links are available near both the sensing location and information-collection and-processing unit, even if these two are hundreds of kilometers apart. A good example of where such a system can be applied is the oil industry. The rigs are usually very far from headquarters, but both might be close to a fiber node.

Combining both voice and data on the same network infrastructure results in need for an advanced network which is to be simple and scalable. This resulted in new approach for Ethernet where Differentiated Service (Diff-Serv) is introduced. This is taken into consideration as well as QoS as a way of providing class of service to end users. Therefore, optimizing available bandwidth efficacy is one of the goals of this work which is centered on investigating the impact of crucial factors on performance of implementing a fuzzy logic controller. These factors can be external or internal and will be manipulated by Fuzzy Logic controller that will work as bandwidth broker to give each user his optimal Code Point (CP). In this work, the CP will not only consider packet loss rate as external factor to check the congestion, but it will also consider the internal factors which are a combination of both service-level agreement (SLA) and the type of application being used. This CP will be marked in the transmitted packets, and then the router will check that and will treat it as agreed between user and administrator.