Abstract
With the growing interest in underwater exploration and monitoring, autonomous underwater vehicles have become essential. The recent interest in onboard deep learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This article aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and simultaneous localization and mapping. Furthermore, this article systematizes sonar-based state-of-the-art data sets, simulators, and robustness methods, such as neural network verification, out-of-distribution, and adversarial attacks. This article highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based data set and bridging the simulation-to-reality gap.

Abstract

Abstract
Electric vehicles (EVs) allow a significant reduction in harmful gas emissions, thus improving urban air quality. However, the widespread adoption of this technology is limited by several factors, resulting in heterogeneous deployment in urban areas. This raises challenges regarding the planning of public electric vehicle charging infrastructure (EVCI), requiring adaptive strategies to ensure comprehensive and efficient coverage. This study introduces an innovative method that leverages geographic information systems to pinpoint appropriate sizes and suitable locations for public EVCI within urban environments. Initially, a Bass diffusion model is employed to estimate EV adoption rates by regions, enabling the determination of the appropriate sizes of EVCI necessary for each of them. Subsequently, a multi-criteria decision-making approach is applied to identify the suitable locations for EV charger installation within each region. In this way, EVCI locations are selected using spatial criteria, which ensure they are near common areas of interest and easily accessible through the road network. To validate the effectiveness and applicability of the proposed method, tests using geospatial data from a city in Brazil were carried out. The findings suggest that EVCI planning without proper spatial analysis may result in inefficient locations and inadequate sizes, which may discourage potential EV adopters and hinder widespread adoption of this technology.

Abstract
The increasing integration of Artificial Intelligence (AI) into Building Energy Management Systems (BEMS) is revolutionizing energy optimization by enabling real-time monitoring, predictive analytics, and automated control. While these advancements improve energy efficiency and sustainability, the opacity of AI models poses challenges in interpretability, limiting user trust and hindering widespread adoption in operational decisionmaking. Ensuring transparency is crucial for aligning AI insights with building performance requirements and regulatory expectations. This paper presents EI-Build, a novel Explainable Artificial Intelligence (XAI) framework designed to enhance the interpretability of intelligent automated BEMS. EI-Build integrates multiple XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Partial Dependence Plots, Feature Permutation Importance, and correlation-based statistical analysis, to provide comprehensive explanations of model behavior. By dynamically tailoring the format and depth of explanations, EI-Build ensures that insights remain accessible and actionable for different user profiles, from general occupants to energy specialists and machine learning experts. A case study on photovoltaic power generation forecasting applied to a real BEMS context evaluates EI-Build's capacity to deliver to deliver both global and local explanations, validate feature dependencies, and facilitate cross-comparison of interpretability techniques. The results highlight how EI-Build enhances user trust, facilitates informed decision-making, and improves model validation. By consolidating diverse XAI methods into a single automated framework, EI-Build represents a significant advancement in bridging the gap between complex AI energy models and real-world applications.

Abstract
In this paper is presented a mixed-integer linear programming (MILP) model that maximizes the Photovoltaic-based (PV-based) hosting capacity (HC) in unbalanced and active distribution networks. The model takes into account the controlled charge of electric vehicles (EVs) and incorporates a demand-response program (DRP), for demand-side load shifting. The model's solution determines the optimal operation of distributed generators (DGs), switched capacitor banks (SCBs), energy storage devices (ESDs), coordination of the EVs charging, and DRP. Linear formulation is obtained from a mixed-integer non-linear programming (MINLP) model, ensuring tractability and guarantee convergence, since it can be efficiently solved using commercial optimization solvers of convex optimization. The model's effectiveness is demonstrated through tests on a 123-bus, three-phase unbalanced distribution system. Four case studies are conducted to assess the effect of different distributed energy resources (DERs). Results show that the simultaneous optimization of DERs, EVs charging and DR scheduling can significantly increase the PV-based HC -reaching up more than the substation capacity- while reducing total power losses. These findings demonstrate the technical potential of integrated DER coordination in enhancing PV penetration and improving the operational efficiency of active distribution systems.

Abstract
[No abstract available]