Abstract
The growing number of battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV) brings the need of more fast charging stations across cities and highway stops. This charging stations toned to be connected to the electrical grid via existent facilities, causing constraints such as power availability. This study brings an approach for the planning and operation of such energy hubs by coping with this challenge by deploying a Battery-based Energy Storage System (BESS). With the BESS integration, it is expected to minimize utilization and overall energy costs, preventing infrastructure upgrades, and enhancing the integration of renewable energy resources. This approach sizes a stationary energy storage system with lithium-ion technology batteries through a co-optimization of the planning and operation stages, integrated in an electrical installation that will implement fast charging stations. This sizing is a result of an optimization based on the interior point algorithm, where the objective is to minimize the costs of maintenance, operation, and installation of a BESS, while properly modelling the different resources such as the BESS, the charging station and EV charging and PV generation. © 2023, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
This paper discusses the problem of information sharing and data interoperability in a B2B context. Therefore, this paper presents a case study on the scope of data-sharing in collaborative networks in an industrial cluster. It explores the feasibility of International Data Spaces in the context of the footwear industry cluster. This work also discusses how the adoption of digital processes might contribute to support data-based management to optimize the production planning of a footwear industry. As a result, it is defined and specified the foundations for the development and implementation of an dataspace oriented IIoT architecture, following a fully compliant Industry 4.0 solution for the footwear industry cluster. This paper discusses the problem of information sharing and data interoperability in a B2B context. Therefore, this paper presents a case study on the scope of data-sharing in collaborative networks in an industrial cluster. It explores the feasibility of International Data Spaces in the context of the footwear industry cluster. This work also discusses how the adoption of digital processes might contribute to support data-based management to optimize the production planning of a footwear industry. As a result, it is defined and specified the foundations for the development and implementation of an dataspace oriented IIoT architecture, following a fully compliant Industry 4.0 solution for the footwear industry cluster.

Abstract
Team automata describe networks of automata with input and output actions, extended with synchronisation policies guiding how many interacting components can synchronise on a shared input/output action. Given such a team automaton, we can reason over communication properties such as receptiveness (sent messages must be received) and responsiveness (pending receivesmust be satisfied). Previouswork focused on how to identify these communication properties. However, automatically verifying these properties is non-trivial, as it may involve traversing networks of interacting automata with large state spaces. This paper investigates (1) how to characterise communication properties for team automata (and subsumed models) using test-free propositional dynamic logic, and (2) how to use this characterisation to verify communication properties by model checking. A prototype tool supports the theory, using a transformation to interact with the mCRL2 tool for model checking.

Abstract
This study presents and applies global metrics for the analysis of the center of pressure (COP) excursion during impulse phases at different standard maximum vertical jump (MVJ) with long, short and no countermovement (CM) at countermovement jump (CMJ), drop jump (DJ) and squat jump (SJ) expanding COP analysis from static to dynamic condition of CM in association with lower limb muscle stretch–shortening cycle (SSC) and complementing previous studies on time structural analysis of COP excursion during impulse phase at standard MVJ. Whereas literature is abundant on COP excursion at gait, run and orthostatic standing position, there is a lack of studies on COP analysis at standard MVJ with an open issue on its contribution to long, short and no CM performance. Fifty-four trial tests were assessed with the selection of the best CMJ, DJ and SJ for each subject based on vertical flight height hflight. During trial tests ground reaction forces (GRF) and force moments were acquired and the COP coordinates were computed during the impulse phases. COP stabilograms and statokinesigrams were plotted and global metrics were computed namely the COPxA antero-posterior and COPyA mediolateral amplitudes of COP excursion, mean radial distance R, the length L of the path and the average velocity v during COP excursion. Statistical significative differences were detected at 5% significance, with higher mean COPxA than COPyA and higher mean COP global metrics at CMJ than SJ both higher than DJ, with DJ higher velocity of COP excursion than CMJ both higher than SJ. Global correlational analysis presented a positive linear association of COP metrics with hflight whereas at segmented MVJ this association wasn’t detected, thus rejecting the negative impact of larger COP excursion on MVJ performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.

Abstract
Deep learning-based models are widely used for disease classification in chest radiographs. This exam can be performed in one of two projections (posteroanterior or anteroposterior), depending on the direction that the X-ray beam travels through the body. Since projection visibly affects the way anatomical structures appear in the scans, it may introduce bias in classifiers, especially when spurious correlations between a given disease and a projection occur. This paper examines the influence of chest radiograph projection on the performance of deep learning-based classification models and proposes an approach to mitigate projection-induced bias. Results show that a DenseNet-121 model is better at classifying images from the most representative projection in the data set, suggesting that projection is taken into account by the classifier. Moreover, this model can classify chest X-ray projection better than any of the fourteen radiological findings considered, without being explicitly trained for that task, putting it at high risk for projection bias. We propose a label-conditional gradient reversal framework to make the model insensitive to projection, by forcing the extracted features to be simultaneously good for disease classification and bad for projection classification, resulting in a framework with reduced projection-induced bias. © 2023 CC-BY 4.0, S.C. Pereira, J. Rocha, A. Gaudio, A. Smailagic, A. Campilho & A.M. Mendonça.