Abstract
This article presents a comprehensive state of the art analysis of the challenging domain of synthetic data generation. Focusing on the problem of synthetic data generation, the paper explores various difficulties that are identified, especially in real-world problems such as those is the scope of power and, energy systems, including the amount of data, data privacy concerns, temporal considerations, dynamic generation, delays, and failures. The investigation delves into the multifaceted nature of the challenges presented by these factors in the synthesis process. The review thoroughly examines different models used in synthetic data generation, covering Generative Adversarial Networks (GANs), Variational Autoencoder (VAE), Synthetic Minority Oversampling Technique (SMOTE), Data Synthesizer (DS) and E. Non-Parametric SynthPop (SP-NP). Each model is dissected with respect to its advantages, disadvantages, and applicability in different data generation scenarios. Special attention is paid to the nuanced aspects of dynamic data generation and the mitigation of challenges such as delays and failures. The insights drawn from this review contribute to a deeper understanding of the landscape around synthetic data generation, providing a valuable resource for researchers, practitioners, and stakeholders who aim to harness the potential of synthetic data in addressing real-world data challenges. The paper concludes by outlining possible avenues for future research and development in this ever-evolving field. © 2024 IEEE.

Abstract
Business Process Automation has been gaining increasing importance in the management of companies and organizations since it reduces the time needed to carry out routine tasks, freeing employees for other, more creative and exciting things. It can be applied in the most varied business areas. Organizations from any sector of activity can also adopt it. Given these benefits, the granted success in transforming business processes would be expected. However, automation initiatives still fail. Adopting this technology can raise social, technological, ethical, methodical, and organizational issues. These facts have triggered the necessity for a summary that could extract more information about how to implement Business Process Automation (BPA), attending to the administrative processes, especially when applied in Small and Medium-sized Enterprises (SMEs). This study aims not only to review the available literature on how to implement BPA but also to typify the processes that can be automated, which technologies or tools exist for making that change, and influence factors in the procedure of BPA. We have covered more than 300 research papers published between 2016 and 2023 in reputable scientific data sources like Scopus, Web of Science/Clarivate, and ScienceDirect/Elsevier. The review revealed some paths for BPA, with some common steps. In addition, some common process characteristics fundamental to automation are exposed, as well as factors that are critical to an organization for successful automation. The results indicate that BPA is an established area in Business Process Management related to technologies/tools like Robotic Process Automation (RPA) or Cognitive-Robotic Process Automation (C-RPA), Workflow Management Systems (WfMS), Enterprise Resource Planning (ERP), and Blockchain. As far as we observed, this Systematic Literature Review (SLR) is a unique study that covers all the environmental variables for applying automation in business processes.

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