Abstract
The Transmission System Operators (TSOs) from Portugal and Spain do not procure Frequency Containment Reserve (FCR) through market mechanisms. A Virtual Power Plant (VPP) aggregating sources such as wind and solar power and hydrogen electrolysers (HEs) would benefit from participation in this ancillary service market. The methodology proposed in this paper allows to quantify the costs of the participation of the Iberian TSOs in the FCR Cooperation as well as the revenues of a VPP that aggregates wind and solar power and HEs. Results are produced using real data from past market sessions. The Portuguese TSO would have paid roughly 10 M€ to participate in this market in 2022. Using data for the same country and year, a VPP (aggregating the HEs expected to be connected by 2025) would have revenues over 2 M€. © 2023 IEEE.

Abstract
Forests worldwide have been suffering from fires damages, provoking incalculable losses in fauna and flora, economic losses, people and animals' deaths, among other problems. To avoid forest fires catastrophes, it is fundamental to develop innovative operations, such as a forest fire monitoring system. This work concentrates efforts on defining the optimum sensor allocation in a forest fires monitoring system based on a wireless sensor network. Thus, a bi-objective mathematical model is developed to solve the problem, in which the first objective consists of minimising the forest fire hazard of a given forest region, and the second objective refers to the sensors spreading into this region. The developed mathematical model was solved by genetic algorithm and the results demonstrated that the methodology was capable of presenting suitable solutions for the problem. © 2023 American Institute of Physics Inc.. All rights reserved.

Abstract
The environmental protection and energy conservation concerns have spurred the development of new solutions in the automotive industry. This has led to the popularity of electric vehicles (EV) and Plugin hybrid electric vehicles (PHEV). On the other hand, this surge in popularity has created a challenge for the development of various new technologies and services, such as charging technology systems and stations. However, unidirectional charging offers hardware simplicity and easier interconnection and enable a G2V model, while bidirectional charging solutions enables G2V and V2G solutions, which can help stabilize AC power by utilizing the energy stored in the battery. This paper presents an EV battery charging system that uses a compact and straightforward bidirectional converter. The system can draw power from either traditional electrical sources or sustainable energy sources like photovoltaic modules, with the option of using lithium rechargeable batteries and supercapacitors as an Energy Storage System (ESS). Several Simulink simulations were conducted to investigate battery behavior under different power sources, and the results show the good effectiveness of the developed system, allowing it to be used in more comprehensive studies in the field of EV charging. © 2023 IEEE.

Abstract

Abstract
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Abstract
The problem of gathering sufficiently representative data, such as those about human actions, shapes, and facial expressions, is costly and time-consuming and also requires training robust models. This has led to the creation of techniques such as transfer learning or data augmentation. However, these are often insufficient. To address this, we propose a semi-automated mechanism that allows the generation and editing of visual scenes with synthetic humans performing various actions, with features such as background modification and manual adjustments of the 3D avatars to allow users to create data with greater variability. We also propose an evaluation methodology for assessing the results obtained using our method, which is two-fold: (i) the usage of an action classifier on the output data resulting from the mechanism and (ii) the generation of masks of the avatars and the actors to compare them through segmentation. The avatars were robust to occlusion, and their actions were recognizable and accurate to their respective input actors. The results also showed that even though the action classifier concentrates on the pose and movement of the synthetic humans, it strongly depends on contextual information to precisely recognize the actions. Generating the avatars for complex activities also proved problematic for action recognition and the clean and precise formation of the masks.