Abstract
This article addresses the problem of wheelchair path planning. In particular, to minimize the length of the trajectory within an environment containing a variable number of obstacles. The positions and quantities of these obstacles are pre-determined. To tackle this challenge, we present a methodology that integrates optimisation techniques and heuristic algorithms to find trajectories both optimal and collision-free. The effectiveness of this methodology is illustrated through a practical example, demonstrating how it successfully generates a collision-free trajectory, even when a large number of obstacles is present in the workspace. In the future, we intend to continue investigating the same problem, taking into account energy consumption as well as time minimisation. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
This work focuses on the application of image processing techniques to segment and analyze images of brain sections with the aim of facilitating early diagnosis of brain tumors. The aim is to delineate specific regions of the brain, such as the cranial, intracranial, and encephalic regions, for subsequent geometric analysis. The process involves image pre-processing, conversion to polar coordinates, determination of contour points, Fourier Series approximation, and the use of the Least Square Method to obtain accurate representations of the regions. The proposed approach was tested on Magnetic Resonance Images of three different brains, showing its capability to accurately delineating the targeted regions. The results highlight the potential of signal processing techniques for analyzing brain images and provide insights for further research in this area. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
Solar wind forecasting is a core component of Space Weather, a field that has been the target of many novel machine-learning approaches. The continuous monitoring of the Sun has provided an ever-growing ensemble of observations, facilitating the development of forecasting models that predict solar wind properties on Earth and other celestial objects within the solar system. This enables us to prepare for and mitigate the effects of solar wind-related events on Earth and space. The performance of some simulation-based solar wind models depends heavily on the quality of the initial guesses used as initial conditions. This work focuses on improving the accuracy of these initial conditions by employing a Recurrent Neural Network model. The study's findings confirmed that Recurrent Neural Networks can generate better initial guesses for the simulations, resulting in faster and more stable simulations. In our experiments, when we used predicted initial conditions, simulations ran an average of 1.08 times faster, with a statistically significant improvement and reduced amplitude transients. These results suggest that the improved initial conditions enhance the numerical robustness of the model and enable a more moderate integration time step. Despite the modest improvement in simulation convergence time, the Recurrent Neural Networks model's reusability without retraining remains valuable. With simulations lasting up to 12 h, an 8% gain equals one hour saved per simulation. Moreover, the generated profiles closely match the simulator's, making them suitable for applications with less demanding physical accuracy.

Abstract
The progressive replacement of thermal power plants by converter-interfaced generation, such as wind and solar power plants, reduces the synchronous component available in the system. Additionally, as converter-interfaced renewable energy sources do not directly provide inertia to the power grid, electric power systems are facing a notorious inertia reduction. When facing disturbances affecting the balance between the generation and demand, reduced inertia systems exhibit higher and faster frequency deviations and dynamics. This can result in the disconnection of generation units as well as load shedding, provoking cascading effects that can compel severe power outages. This work examines the impacts of the progressive integration of converter-interfaced renewable energy sources in the frequency stability, considering critical disturbances involving short-circuits in different locations. To simulate the dynamic behaviour of a network containing high shares of renewable energy generation, the IEEE 39-bus system is used while resorting to the PSS/E simulation package. After obtaining a scenario with reduced synchronous generation, the network's stability is assessed in face of key frequency indicators (frequency nadir and Rate of Change of Frequency, RoCoF). Regarding the critical disturbances applied in a low inertia scenario, different control solutions for the mitigation of frequency stability problems are tested and their performance is assessed comparatively. This involves the investigation of the performance of the active power-frequency control in the renewable energy sources, of synchronous condensers, or fast active power-frequency regulation services from stationary energy storage. Moreover, the influence of the location and apparent power of synchronous condensers (SCs) and Battery Energy Storage Systems (BESS) on the frequency indicators is evaluated.

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
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.