Abstract
This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings to harness wind power, similar to a kite, while maintaining VTOL functionality. A distinguishing feature is the purpose-built autopilot system, with custom hardware and software components specifically engineered for this application. The study presents the system identification process for obtaining five MIMO (Multiple-Input Multiple-Output) transfer functions that characterize the dynamics between roll-yaw commands and responses, including the tether angle feedback. To address the inherent coupling effects and uncertainties in the system, robust mixed sensitivity (H-infinity) MIMO controllers are developed. The controllers were validated through both simulations and experimental flights, demonstrating effective performance in handling cross-coupling effects and maintaining stability under various operating conditions. According to flight test findings, the system can precisely manage the tether angle while adjusting for ground effect disturbances. This allows for accurate tethered navigation, a stable attitude, and the maintenance of an adequate yaw heading.

Abstract
Underwater environments pose unique challenges to optical systems due to physical phenomena that induce severe data degradation. Current imaging sensors rarely address these effects comprehensively, resulting in the need to integrate complementary information sources. This article presents a multimodal data fusion approach to combine information from diverse sensing modalities into a single dense and accurate tridimensional representation. The proposed fusiNg tExture with apparent motion information for underwater Scene recOnstruction (NESO) encoder-decoder network leverages motion perception principles to extract relative depth cues, fusing them with textured information through an early fusion strategy. Evaluated on the FLSea-Stereo dataset, NESO outperforms state-of-the-art methods by 58.7%. Dense depth maps are achieved using multi-stage skip connections with attention mechanisms that ensure propagation of key features across network levels. This representation is further enhanced by incorporating sparse but millimeter-precise depth measurements from active imaging techniques. A regression-based algorithm maps depth displacements between these heterogeneous point clouds, using the estimated curves to refine the dense NESO prediction. This approach achieves relative errors as low as 0.41% when reconstructing submerged anode structures, accounting for metric improvements of up to 0.1124 m relative to the initial measurements. Validation at the ATLANTIS Coastal Testbed demonstrates the effectiveness of this multimodal fusion approach in obtaining robust tri-dimensional representations in real underwater conditions.

Abstract
When directional data fall in the positive orthant of the unit hypersphere, a folded directional distribution is preferred over a simple directional distribution for modeling the data. Since directional data, especially axial data, can be modeled using a Watson distribution, this paper considers a folded Watson distribution for such cases. We first address the parameter estimation of this distribution using maximum likelihood, which requires a numerical algorithm to solve the likelihood equations. We use the Expectation-Maximization (EM) algorithm to obtain these estimates and to analyze the properties of the concentration estimator through simulation. Next, we propose the Bayes rule for a folded Watson distribution and evaluate its performance through simulation in various scenarios, comparing it with the Bayes rule for the Watson distribution. Finally, we present examples using both simulated and real data available in the literature.

Abstract
This paper presents a semantic characterization of leakage-freeness through timing side-channels for Jasmin programs. Our characterization covers probabilistic Jasmin programs that are not constant-time. In addition, we provide a characterization in terms of probabilistic relational Hoare logic and prove the equivalence between both definitions. We also prove that our new characterizations are compositional and relate our new definitions to existing ones from prior work, which could only be applied to deterministic programs. To provide practical evidence, we use the Jasmin framework to develop a rejection sampling algorithm and provide an EasyCrypt proof that ensures the algorithm's implementation is leakage-free while not being constant-time.

Abstract
By connecting maritime and land transport, container terminals play a critical role in global logistics systems, as part of broader intermodal networks. The evolution of containerisation and technological advances, along with increased demand and volumes, led to significant adaptations in these terminals, as a way to improve productivity, reduce costs and increase competitiveness, while coping with spatial and operational constraints. For strategic decision-making, managing these complex systems can be enhanced by simulation models allowing the analysis of different scenarios in dynamic, uncertain environments. This work, presents a simulation-based decision support tool developed in the FlexSim software, to analyse different container terminal configurations, with a particular focus on automation and on sustainable practices to reduce the energy consumption of terminals. A discrete event simulation model was developed to study multiple scenarios impacting productivity, resource utilisation, and waiting times. The proposed approach allows the test and evaluation of management strategies for port operations, with preliminary results showing that sizing and planning of the fleets of automated guided vehicles (AGV) can significantly affect the total operating time, the energy consumed, and the costs associated with battery charging operations. Future research should explore additional factors affecting container terminal operations, such as the reorganisation of the storage area, while incorporating optimisation elements for work planning and resource allocation. Moreover, the simulation model will be tested and validated in a real case study, designed for the Port of Sines in Portugal. © 2024 The Authors.

Abstract
The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C - Software Considerations in Airborne Systems and Equipment Certification - serves as the primary certification standard used by agencies such as the FAA and EASA to review and approve software-based systems. Although DO-178C aims to ensure system safety while providing evidence for certification, it does not prescribe a specific software development process, allowing flexibility for traditional Waterfall, Agile, or hybrid methods with appropriate adaptations for the aerospace context. This study proposes Scrum4DO178C, an Agile process based on Scrum, to meet the demanding requirements of aerospace software, including safety, robustness, reliability, and integrity. Scrum4DO178C introduces novel process enhancements specifically tailored to meet these criticality needs, while aligning with the standard. Unlike previous proposals that lack detail, this research presents a comprehensive, validated process applied in a real-world industry project at the highest criticality level (Level A - Catastrophic), offering insights beyond theoretical scenarios. The findings demonstrated that the Scrum4DO178C process improves project performance, allows frequent and manageable requirement changes, reduces Verification & Validation (V&V) effort, and increases efficiency while maintaining full compliance with DO-178C. The study also identifies areas for further improvement and suggests exploring the process in additional case studies, both within the aerospace industry and other domains with similarly stringent safety-critical requirements. Finally, it confirms that appropriate automation, namely for documentation production, is a central element to further improve the process. © 2013 IEEE.