Abstract
The growing demand for sustainable and environment-friendly energy sources resulted in extensive research in the field of renewable energy. Biomass, derived from organic materials such as agricultural waste, forestry products, and wastewater treatment plant (WWTP) sludge, holds great potential as a renewable energy resource that can reduce greenhouse gas emissions and offer sustainable solutions for energy production. This study focused on diverse biomass materials, including sludge from WWTPs, forest biomass, swine waste, cork powder, and biochar. Chemical and physicochemical characterizations were performed to understand their energy potential, highlighting their elemental composition, proximate analysis, and calorific values. Results showed that different biomasses have varying energy content, with biochar and cork powder emerging as high-energy materials with net heating values of 32.56 MJ/kg and 25.73 MJ/kg, respectively. WWTP sludge also demonstrated considerable potential with net heating values of around 14.87 MJ/kg to 17.44 MJ/kg. The relationships between biomass compositions and their heating values were explored, indicating the significance of low nitrogen and sulphur content and favourable carbon, hydrogen, and moisture balances for energy production. Additionally, this study looked into the possibility of mixing different biomasses to optimize their use and overcome limitations like high ash and moisture contents. Mixtures, such as 75% Santo Emiliao WWTP Sludge + 25% Biochar, showed impressive net heating values of approximately 21.032 MJ/kg and demonstrated reduced emissions during combustion. The study's findings contribute to renewable energy research, offering insights into efficient and sustainable energy production processes and emphasizing the environmental benefits of biomass energy sources with low nitrogen and sulphur content.

Abstract
One of the biggest obstacles when it comes to the electrification of the vehicle fleet is the charging time of an electric vehicle (EV), which means that users of these vehicles, when they need to charge their car, always look for fast charging stations. This is the charging method that has the shortest duration. In the coming years, an increase in the proliferation of electric vehicles will lead to a greater demand for electricity, which in turn will put the distribution network to the test. Since the number of non-linear loads increases day by day, putting the distribution network under increasing stress, it is wise to study the effects of the high penetration of electric vehicles (EV) on the power quality of the network. This paper aims to study the impact of charging EV on the quality of energy in the distribution network. First, an analysis of the harmonics and electronics (non-linear loads) associated with EV chargers will be done. Then, a simulation will be performed on the IEEE 33 network using the Matlab/Simulink simulation software. Finally, a comparison will be made of the results obtained in the simulation and possible ways of mitigating the harmonics that non-linear loads inject into the electricity distribution network. Keywords. Electrical Vehicle (EV), Harmonic Analysis, Matlab/Simulink, EV DC Fast Charger, Non-Linear Loads.

Abstract
Accurate dynamics modelling of Unmanned Under-water Vehicles (UUV s) is critical for optimizing mission planning, minimizing collision risks, and ensuring the successful execution of tasks in diverse underwater environments. This paper presents a structured approach to estimating the hydrodynamic coeffi-cients of UUV s. Initially, it follows a detailed methodology for estimating hydrodynamic coefficients using simple geometries, a sphere and a spheroid, using the Computational Fluid Dy-namics (CFD) software OpenFoam, and comparing the results to analytical solutions, enabling the validation of the simulations approach. Following this, the paper provides an in-depth analysis of the damping and added mass coefficients for the Raya UUV, offering valuable insights into its hydrodynamic behaviour. © 2024 IEEE.

Abstract
According to the Portuguese Roadmap for Carbon Neutrality 2050 (RNC2050), Portugal aims to achieve carbon neutrality by 2050. To achieve this goal, it is necessary to decrease the consumption of primary energy from non-renewable sources and increase the consumption of energy from renewable sources. Portugal has a high potential for energy production through solar energy, and the country has a large solar potential that can be used. Thus, this work focuses on the study of the reliability of charging electric vehicles through photovoltaic energy, being sized electric vehicles charging stations, with different topologies, for domestic consumption, for different types of user profiles. At the same time this study evaluated technically and economically the proposed solutions. The research concluded that this type of technology proves to be a viable solution, especially if storage systems do not need to be implemented, as the limited useful lifetime of batteries substantially increases investment amortization times. Key words. Photovoltaic Systems, Electric Vehicle, Charging Stations, Energy Efficiency, Techno-Economic Study.

Abstract
This study explores the prediction and mitigation of pallet collapse during transportation within the glass packaging industry, employing a machine learning approach to reduce cargo loss and enhance logistics efficiency. Using the CRoss-Industry Standard Process for Data Mining (CRISP-DM) framework, data were systematically collected from a leading glass manufacturer and analysed. A comparative analysis between the Decision Tree and Random Forest machine learning algorithms, evaluated using performance metrics such as F1-score, revealed that the latter is more effective at predicting pallet collapse. This study is pioneering in identifying new critical predictive variables, particularly geometry-related and temperature-related features, which significantly influence the stability of pallets. Based on these findings, several strategies to prevent pallet collapse are proposed, including optimizing pallet stacking patterns, enhancing packaging materials, implementing temperature control measures, and developing more robust handling protocols. These insights demonstrate the utility of machine learning in generating actionable recommendations to optimize supply chain operations and offer a foundation for further academic and practical advancements in cargo handling within the glass industry.

Abstract
The underwater domain presents a myriad of challenges for perception systems that must be overcome to achieve accurate object detection and recognition. To augment the performance and safety of existing solutions for intricate O&M (Operations and Maintenance) procedures, AUVs must perceive the surroundings and locate potential objects of interest based on the perceived information. A depth gradient methodology is employed to survey the seabed using a multibeam sonar to perform a coarse reconstruction of the scenario that it later used to locate and identify foreign objects. This could include rocks, debris, wreckage, or other objects that may pose potential exploratory interest. First results show that the proposed method was able to detect 100 % of the objects present in the scenario with an average chamfer distance error of 0.0238m between models and respective reconstruction. © 2024 IEEE.