Abstract
[No abstract available]

Abstract
Spectral imaging is a broad term that refers to the use of a spectroscopy technique to analyze sample surfaces, collecting and representing spatially referenced signals. Depending on the technique utilized, it allows the user to reveal features and properties of objects that are invisible to the human eye, such as chemical or molecular composition. However, the interpretability and interaction with the results are often limited to screen visualization of two-dimensional representations. To surpass such limitations, augmented reality emerges as a promising technology, assisted by recent developments in the integration of spectral imaging datasets onto three-dimensional models. Building on this context, this work explores the integration of spectral imaging with augmented reality, aiming to create an immersive toolset to increase the interpretability and interactivity of the results of spectral imaging analysis. The procedure follows a two-step approach, starting from the integration of spectral maps onto a three-dimensional models, and proceeding with the development of an interactive interface to allow immersive visualization and interaction with the results. The approach and tool developed present the opportunity for a user-centric extension of reality, enabling more intuitive and comprehensive analyses with the potential to drive advancements in various research domains.

Abstract
Li-ion batteries are widely used in electric vehicles, grid storage, and portable electronics. Battery Management Systems play a crucial role in ensuring the safety, efficiency, and longevity of Li-ion batteries. Accurate battery modelling is essential for effective battery management functionality, enabling precise state of charge/ state of health estimation, as well as protection against hazardous conditions such as overcharging or overheating. This article explores system identification techniques for battery modelling using a piecewise LTI approach where separate LTI models are identified for different state of charge intervals. A modified Thevenin circuit is employed, where the open-circuit voltage is represented by a capacitor that models the bulk charge storage. The capacitance of this element is dependent on the state of charge, reflecting the nonlinear nature of the battery's charge storage mechanism. Additionally, parallel resistor-capacitor networks capture transient voltage recovery dynamics. The identification process estimates the battery parameters from experimental data, and the resulting piecewise models are interpolated using cubic splines to construct a linear parameter-varying (LPV) representation of the system. The proposed methodology was validated through experimental results, demonstrating its effectiveness in enhancing battery management performance. Namely, (i) the model accurately captures the battery's voltage response with minimal error. (ii) the LPV model obtained by fitting splines to the estimated parameters demonstrates a level of accuracy comparable to that of the piecewise LTI model. (iii) the model robustness was validated through a continuous discharge test, showing strong agreement with experimental data and, therefore, demonstrating its reliability in real-world operating conditions. These results highlight the potential of the proposed methodology in improving battery management systems. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
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Abstract

Abstract
Hexapod robots, with their six-legged design, excel in stability and adaptability on challenging terrain but pose significant control challenges due to their high degrees of freedom. While reinforcement learning (RL) has been explored for robot navigation, few studies have systematically compared on-policy and off-policy methods for multi-legged locomotion. This work presents a comparative study of SARSA and Q-Learning for trajectory control of a simulated hexapod robot, focusing on the influence of learning rate (alpha), discount factor (gamma), and eligibility trace (lambda). The evaluation spans eight initial poses, with performance measured through lateral deviation (Ey), orientation error (E theta), and iteration count. Results show that Q-Learning generally achieves faster convergence and greater stability, particularly with higher gamma and lambda values, while SARSA can achieve competitive accuracy with careful parameter tuning. The findings demonstrate that eligibility traces substantially improve learning precision and provide practical guidelines for robust RL-based control in multi-legged robotic systems.