Abstract

Abstract
Mutation testing (MT) may be used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, MT is not so  commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android MT costs. It implements MT operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android MT framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the  framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant).  The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy.

Abstract

This document is intended to present a benchmark of multiple good practices in the context of internationalization studies, particularly focused on digital electronics and programmable devices, yet is not limited to them. This paper will start with a comprehensive paper desk analysis together with an in-depth research process that should lead to the selection of innovative tools applied to digital systems. International initiatives are oriented towards increasing the quality of higher education by motivating teachers of STEM disciplines to use a multidisciplinary approach and teach with the massive support of technologies like Classroom, MS-Teams, Blackboard, etc. The central goal is to suggest and recommend a model for integrating intermediate and advanced digital electronics subjects (e.g., FPGA, microcontrollers, etc.) and ICT in international teaching approaches such as Collaborative Online International Learning (COIL), Project-based Learning (PBL) and Real Remote Labs (RRL). This is the approach sought by the European Project DECEL.

Abstract
In the domain of RIS-based indoor localization, our work introduces two distinct approaches to address real-world challenges. The first method is based on deep learning, employing a Long Short-Term Memory (LSTM) network. The second, a novel LSTM-PSO hybrid, strategically takes advantage of deep learning and optimization techniques. Our simulations encompass practical scenarios, including variations in RIS placement and the intricate dynamics of multipath effects, all in Non-Line-of-Sight conditions. Our methods can achieve very high reliability, obtaining centimeter-level accuracy for the 98th percentile (worst case) in a different set of conditions, including the presence of the multipath effect. Furthermore, our hybrid approach showcases remarkable resolution, achieving submillimeter-level accuracy in numerous scenarios.

Abstract
The cooperation between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has brought new perspectives and effectiveness to production and monitoring processes. In this sense, tracking moving targets in heterogeneous systems involves coordination, formation, and positioning systems between UGVs and UAVs. This article presents a Proportional-Integral-Derivative (PID) control strategy for tracking moving target operations, considering an operating environment between a multirotor UAV and an indoor UGV. Different PID architectures are developed and compared to each other in the Gazebo simulator, whose objective is to analyze the control performance of the UAV when used to track the ground robot based on the identification of the ArUco fiducial marker. Computer vision techniques based on the Robot Operating System (ROS) are integrated into the UAV's tracking system to provide a visual reference for the aircraft's navigation system. The results of this study indicate that the PD, Cascade, and Parallel controllers showed similar performance in both trajectories tested, with the Parallel controller showing a slight advantage in terms of mean error and standard deviation, suggesting its suitability for applications that prioritize precision and stability.

Abstract