Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly used as wireless communications nodes, serving as Wi-Fi Access Points and Cellular Base Stations. To enable energy-efficient access networks, we previously introduced the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of UAVs as Flying Access Points (FAPs) to serve Ground Users (GUs). However, SUPPLY did not address the backhaul link. This paper presents the Simple Gateway Positioning (SGWP) solution, which optimizes the position of a Gateway (GW) UAV to ensure backhaul connectivity in a two-tier network. We integrate SUPPLY for FAP positioning with SGWP for GW placement and evaluate their combined performance under various scenarios involving different GUs' Quality of Service (QoS) requirements and positions. Our results demonstrate that SUPPLY and SGWP can be used jointly in a two-tier network with minimal performance degradation.

Abstract
Nowadays, there is the paradox of technology: although smartphones have revolutionized our way of living, bringing convenience and connectivity, they have also introduced new challenges, notably distracted driving. This paper addresses the issue of visual distraction, one of the main contributors to traffic accidents, through the development of an innovative system that combines the application of convolutional neural networks and the functionality of mobile devices. The adopted methodology focused on the collection of a broad set of images to train an artificial intelligence model capable of classifying a qualitative variable with two distinct categories: attention and distraction of a driver. In particular, the study concentrated on creating a mobile application that uses a smartphone's camera to monitor the driver and issue auditory alerts if it detects prolonged distraction. The achieved results highlighted the efficacy of the model, especially after its optimization for the TensorFlow Lite format, suitable for implementation on mobile devices due to its efficiency in terms of speed and resource consumption.

Abstract
Data management solutions became highly expensive and ineffective mainly due to the lack of transparent processes and procedures to measure and provide clear guidance on the steps needed to implement them. The organizations and specialists agree that the only manner solve the data management issues requires the implementation of data governance. Many of those attempts had failed previously because they were based only on IT, with rigid processes and activities frequently split by systems or the areas supported by systems and their data. It shows that Data governance has been acquiring significant relevance. However, a consensus or even a holist approach was not achieved so far. This paper that is part of an ongoing thesis research that aims to identify the main gaps and opportunities by summarizing and study the literature consistently and as result at the end of the research it will propose a standard framework for data governance measuring its impact on the Data Governance maturity level before and after its implementation and thus as contribute to the community by trying to mitigate the problems found.

Abstract

Abstract
This paper presents a comprehensive procedure for conducting a black start service from an offshore wind farm (OWF) by integrating grid-forming (GFM) control. The proposed strategy utilizes a grid-forming battery energy storage system (BESS) to provide black start service within an OWF that is equipped with grid-following wind turbines. The paper elaborates on the modeling of controllers and the operational methodology taking wind variability during the black start procedure. To showcase the effectiveness of the proposed control and operation principles, the study utilizes an OWF as a case study. Simulation analyses are performed using Matlab/Simulink software to validate the feasibility of the proposed strategy. © Energynautics GmbH.

Abstract
The 6G paradigm and the massive usage of interconnected wireless devices introduced the need for flexible wireless networks. A promising approach lies in employing Mobile Robotic Platforms (MRPs) to create communications cells on-demand. The challenge consists in positioning the MRPs to improve the wireless connectivity offered. This is exacerbated in millimeter wave (mmWave), Terahertz (THz), and visible light-based networks, which imply the establishment of short-range, Line of Sight (LoS) wireless links to take advantage of the ultra-high bandwidth channels available. This paper proposes a solution to enable the obstacle-aware, autonomous positioning of MRPs and provide LoS wireless connectivity to communications devices. It consists of 1) a Vision Module that uses video data gathered by the MRP to determine the location of obstacles, wireless devices and users, and 2) a Control Module, which autonomously positions the MRP based on the information provided by the Vision Module. The proposed solution was validated in simulation and through experimental testing, showing that it is able to position an MRP while ensuring LoS wireless links between a mobile communications cell and wireless devices or users.