Abstract
Regulations of next-generation networks (NGNs) have played a central role in the transition from copper networks to broadband networks in Fiber to The Home (FTTH), also allowing for a reduction in asymmetries between incumbent operators and their competitors. Despite common European Union directives, telecom infrastructure development varies across countries due to differences in regulation, investment models, legacy networks, operators' behavior, and public policies. This study analyzes the evolution of Very High-Capacity Networks (VHCNs), focusing on the implementation of FTTH in four European countries (Portugal, Spain, France, and Germany), and in South Korea. The latter was included as a benchmark in government-driven broadband development. The analysis considers key factors influencing FTTH development, including infrastructure regulation, fiber investment incentives, incumbent strategies, infrastructure sharing and co-investment, rural coverage plans, and demographic differences of each country. The results show that regulatory measures directly influence the pace of FTTH installation; but its effectiveness also depends on investment incentives, market competition, and demand factors. Portugal, Spain, and France have high FTTH coverage despite different regulatory and investment models. In contrast, Germany relied on xDSL over copper networks for a long time, causing significant delays, with an FTTH coverage rate of 42.5% and a penetration rate of only 12.3%, putting the European Union's 2030 goal of universal 1 Gbps coverage at risk. South Korea shows that long-term public policies, demand-side incentives, and high digital adoption accelerate mass FTTH adoption and turn telecommunications infrastructure into a key driver of economic and technological development.

Abstract
The concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.

Abstract
Time series data are essential for a wide range of applications, particularly in developing robust machine learning models. However, access to high-quality datasets is often limited due to privacy concerns, acquisition costs, and labeling challenges. Synthetic time series generation has emerged as a promising solution to address these constraints. In this work, we present a framework for generating synthetic time series by leveraging complex networks mappings. Specifically, we investigate whether time series transformed into Quantile Graphs (QG) -- and then reconstructed via inverse mapping -- can produce synthetic data that preserve the statistical and structural properties of the original. We evaluate the fidelity and utility of the generated data using both simulated and real-world datasets, and compare our approach against state-of-the-art Generative Adversarial Network (GAN) methods. Results indicate that our quantile graph-based methodology offers a competitive and interpretable alternative for synthetic time series generation.

Abstract
Efficient task allocation and coordination are critical for heterogeneous multi-agent systems operating in dynamic field environments. This paper presents a closed-loop framework that integrates Large Language Models (LLMs) with graph-based optimisation to enable end-to-end task decomposition, allocation, and adaptive execution. High-level task scripts are initially parsed by an LLM into structured execution flows, which are transformed into Directed Acyclic Graphs (DAGs) capturing action-level dependencies. A Genetic Algorithm (GA) then optimises agent-to-task assignments by minimising makespan under capability and battery constraints. To ensure robustness, the framework incorporates an LLM-driven recovery module that enables localised replanning under execution failures without interrupting unaffected agents. System-level experiments in a high-fidelity agroforestry simulation demonstrate a 37% increase (p<0.001) in harvesting productivity and a 19% reduction in human idle time compared to manual baselines. Under mid-execution failures, the system maintains significantly higher performance, with replanning latencies averaging 24 s. The framework scales to large fleets (up to 1000 agents) and effectively enhances human-robot collaboration through structured, dependency-aware coordination.

Abstract

Abstract
This study presents a methodology for forecasting municipal water consumption to support efficient resource management. Using monthly data from 2018 to 2022 for the municipality of Vouzela, Portugal, three forecasting approaches were evaluated: SARIMA, Holt-Winters, and TimeGPT. Data preparation included logarithmic transformation and stationarity assessment using the KPSS test, ensuring appropriate conditions for statistical modelling. The SARIMA model was selected automatically based on the Akaike Information Criterion (AIC), while the Holt-Winters method was fitted with additive components and a Box-Cox transformation. In addition, TimeGPT was employed as a state-of-the-art foundation model for time series forecasting. The three methods were used to predict water consumption for the 12 months of 2023, and their performance was assessed using MAE, MSE, RMSE and MAPE. Results indicate that although all methods perform adequately, Holt-Winters and TimeGPT better capture recent consumption dynamics, providing more accurate forecasts in several periods. Overall, this study shows that combining classical statistical models with advanced forecasting techniques offers local authorities reliable and computationally accessible tools to support water supply planning and sustainability.