Abstract
Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings.

Abstract

Abstract
Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered.

Abstract
The growing demands of the Blue Economy are increasingly supported by sensing platforms, including as Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). Multimodal Underwater Wireless Networks (MUWNs), which may combine acoustic, radio-frequency, and optical wireless technologies, enhance underwater data transmission capabilities. Although Delay-Tolerant Networks (DTNs) address connectivity intermittency in such environments, not all data streams are delay-tolerant, and transmitting high-bandwidth DTN traffic over narrowband links can lead to significant inefficiencies. This paper presents QoS-MUWCom, a Quality of Service (QoS)-aware communication solution designed to manage both real-time and delay-tolerant traffic across dynamically selected multimodal interfaces. Experimental evaluations conducted in a freshwater tank demonstrate that QoS-MUWCom achieves near-zero packet loss for low-demand traffic even under link saturation, improves throughput for prioritized flows up to three times in mobility scenarios, and adapts to link availability and node mobility. The results confirm that QoS-MUWCom outperforms conventional multimodal strategies, contributing to more robust, resilient and efficient underwater communications.

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.

Abstract
Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment.