Abstract

Abstract

Abstract
Integrated Access and Backhaul (IAB) in cellular networks combines access and backhaul within a wireless infrastructure reducing reliance on fibre-based backhaul. This enables flexible and more cost-effective network expansion, especially in hard-to-reach areas. Positioning a mobile IAB node (MIAB) in a seaport environment, in order to ensure on-demand, resilient wireless connectivity, presents unique challenges due to the high density of User Equipments (UEs) and potential shadowing effects caused by obstacles. This paper addresses the problem of positioning MIABs within areas containing UEs, fixed IAB donors (FIABs), and obstacles. Our approach considers user associations and different types of scheduling, ensuring MIABs and FIABs meet the capacity requirements of a special team of served UEs, while not exceeding backhaul capacity. With a Genetic Algorithm solver, we achieve capacity improvement gains, by up to 200% for the 90th percentile, particularly during emergency capacity demands.

Abstract
The paper presents an application of the new control paradigm, which is based on process moments, to a model of a DC motor. The basis of the new control paradigm is that it eliminates the process transfer function within the closed loop, as it estimates the final steady-state value of the process output and compares it with the reference signal. As a result, the closed loop response is much more stable and generally without overshoots. This property makes it suitable for application to motor-driven processes where overshoots is undesirable. It was shown that the control method provides very stable closed-loop responses even when the actual motor and the model parameters differ. It was also shown that the proposed method can be applied to constrained systems as the anti-windup protection is implicitly embedded in the control solution. © 2025 IEEE.

Abstract
Regression testing is a selective retesting of a system or component to verify that modifications have not induced unintended effects and that the system or component maintains compliance with the specified requirements. However, it can be time-consuming and resource-intensive, especially for large systems. Regression testing selection techniques can help address this issue by selecting a subset of test cases to run. The Change Based technique selects a subset of the existing test cases and executes modified classes. Besides effectively reducing the test suite, this technique may reduce the capability of revealing faults. From this perspective, code smells are known to identify poor design and software quality issues. Some works have explored the association between smells and faults with some promising results. Inspired by these results, we propose combining code change and smell to select regression tests and present eight techniques. Additionally, we developed the Regression Testing Selection Tool (RTST) to automate the selection process using these techniques. We empirically evaluated the approach in Defects4J projects by comparing the techniques' effectiveness with the Change Based and Class Firewall as a baseline. The results show that the Change and Smell Intersection Based technique achieves the highest reduction rate in the test suite size but with less class coverage. On the other hand, Change and Smell Firewall technique achieves the lowest test suite size reduction with the highest fault detection effectiveness test cases, suggesting the combination of smells and changed classes can potentially find more bugs. The Smell Based technique provides a comparable class coverage to the code change and smell approach. Our findings indicate opportunities for improving the efficiency and effectiveness of regression testing and highlight that software quality should be a concern throughout the software evolution.

Abstract
Underwater exploration is vital for advancing scientific understanding of marine ecosystems, biodiversity, and oceanic processes. Autonomous underwater vehicles and sensor platforms play a crucial role in continuous monitoring, but their operational endurance is often limited by energy constraints. Various control strategies have been proposed to enhance energy efficiency, including robust and optimal controllers, energy-optimal model predictive control, and disturbance-aware strategies. Recent work introduced a variable structure depth controller for a sensor platform with a variable buoyancy module, resulting in a 22% reduction in energy consumption. This paper extends that work by providing a formal stability proof for the proposed switching controller, ensuring safe and reliable operation in dynamic underwater environments. In contrast to the conventional approach used in controller stability proofs for switched systems-which typically relies on the existence of multiple Lyapunov functions-the method developed in this paper adopts a different strategy. Specifically, the stability proof is based on a novel analysis of the system's trajectory in the net buoyancy force-versus-depth error plane. The findings were applied to a depth-controlled sensor platform previously developed by the authors, using a well-established system model and considering physical constraints. Despite adopting a conservative approach, the results demonstrate that the control law can be implemented while ensuring formal system stability. Moreover, the study highlights how stability regions are affected by different controller parameter choices and mission requirements, namely, by determining how these aspects affect the bounds of the switching control action. The results provide valuable guidance for selecting the appropriate controller parameters for specific mission scenarios.