Abstract
This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including Radio Frequency (RF) communication, Optical Wireless Communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy dynamically reconfigures RIoT nodes by adapting transmission rates, wake/sleep scheduling, and access technology selection. Results demonstrate that the proposed framework, combining digital twin technology, hybrid optical-radio integration, and data-driven energy modeling, substantially enhances the performance, resilience, and sustainability of 6G IoT networks.

Abstract
Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations to support vehicular users and Vehicle-to-Everything (V2X) applications. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Existing approaches rely on probabilistic blockage models or require dedicated infrastructure such as Reconfigurable Intelligent Surfaces. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that complements these approaches by autonomously extracting environmental information-such as obstacle geometries and UE locations-via computer vision, enabling infrastructure-free deployment. The algorithm employs Particle Swarm Optimization to determine UAV positions that maximize aggregate throughput while prioritizing LoS connectivity and accounting for heterogeneous traffic demands. VTOPA is particularly suited for rapid deployment scenarios such as emergency response and temporary events. Evaluated through simulations in ns-3, VTOPA achieves up to 50% increase in aggregate throughput and 50% reduction in delay, outperforming state of the art benchmarks in obstacle-rich environments.

Abstract
Integrated Access and Backhaul (IAB) technology in cellular networks operating in the 3.x GHz band combines access and backhaul functionalities within a wireless framework, reducing dependence on fiber-based solutions and enabling cost-efficient, flexible network expansion. Deploying a mobile IAB (MIAB) in obstruction-prone environments, such as seaports, offers on-demand capacity and resilience but poses unique challenges due to severe shadowing from dense physical obstacles. This paper presents a three-dimensional, obstacle-aware model for optimal MIAB placement and scheduler selection in networks comprising user equipments (UEs) and fixed IABs (FIABs). We evaluate user and backhaul association patterns under different scheduling strategies, including Round-Robin (RR) and Weighted Round-Robin (WRR), ensuring that both MIABs and FIABs meet UE application-layer capacity demands without exceeding backhaul limits. A genetic algorithm (GA)-based optimizer is employed to explore deployment configurations under varying FIAB densities, number of UEs, and obstacles. Results show that MIAB assistance yields the greatest benefits in sparse FIAB networks and low-UE scenarios, with capacity gains reaching up to 350%. MIAB delivers the greatest added value in the presence of obstacles. In contrast, dense FIAB deployments exhibit diminishing returns from MIAB integration. Across most of the evaluated conditions, WRR outperforms RR by enabling fairer and more adaptive resource blocks (RBs) allocation. These findings provide practical guidance for targeted MIAB deployment strategies that balance infrastructure investment, environmental constraints, and scheduling policies.

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
Accurate retrieval of post-surgical images plays a critical role in surgical planning for breast cancer patients. However, current content-based image retrieval methods face challenges related to limited interpretability, poor robustness to image noise, and reduced generalization across clinical settings. To address these limitations, we propose a multistage retrieval pipeline integrating saliency-based explainability, noise-reducing image pre-processing, and ensemble learning. Evaluated on a dataset of post-operative breast cancer patient images, our approach achieves contrastive accuracy of 77.67% for Excellent/Good and 84.98% for Fair/Poor outcomes, surpassing prior studies by 8.37% and 11.80%, respectively. Explainability analysis provided essential insight by showing that feature extractors often attend to irrelevant regions, thereby motivating targeted input refinement. Ablations show that expanded bounding box inputs improve performance over original images, with gains of 0.78% and 0.65% contrastive accuracy for Excellent/Good and Fair/Poor, respectively. In contrast, the use of segmented images leads to a performance drop (1.33% and 1.65%) due to the loss of contextual cues. Furthermore, ensemble learning yielded additional gains of 0.89% and 3.60% over the best-performing single-model baselines. These findings underscore the importance of targeted input refinement and ensemble integration for robust and generalizable image retrieval systems.

Abstract
Breast segmentation has a critical role for objective pre and postoperative aesthetic evaluation but challenged by limited data (privacy concerns), class imbalance, and anatomical variability. As a response to the noted obstacles, we introduce an encoderdecoder framework with a Segment Anything Model (SAM) backbone, enhanced with synthetic depth maps and a multiterm loss combining weighted crossentropy, convexity, and depth alignment constraints. Evaluated on a 120patient dataset split into 70% training, 10% validation, and 20% testing, our approach achieves a balanced test dice score of 98.75% a 4.5% improvement over prior methods with dice of 95.5% (breast) and 89.2% (nipple). Ablations show depth injection reduces noise and focuses on anatomical regions, yielding dice gains of 0.47% (body) and 1.04% (breast). Geometric alignment increases convexity by almost 3% up to 99.86%, enhancing geometric plausibility of the nipple masks. Lastly, crossdataset evaluation on CINDERELLA samples demonstrates robust generalization, with small performance gain primarily attributable to differences in annotation styles.