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.

Abstract
Over recent decades, precision agriculture has revolutionized farming by optimizing crop yields and reducing resource use through targeted applications. Existing portable spray quality assessors lack precision, especially in detecting overlapping droplets on water-sensitive paper. This proposal aims to develop a smartphone application that uses the integrated camera to assess spray quality. Two approaches were implemented for segmentation and evaluation of both the water-sensitive paper and the individual droplets: classical computer vision techniques and a pre-trained YOLOv8 deep learning model. Due to the labor-intensive nature of annotating real datasets, a synthetic dataset was created for model training through sim-to-real transfer. Results show YOLOv8 achieves commendable metrics and efficient processing times but struggles with low image resolution and small droplet sizes, scoring an average Intersection over Union of 97.76% for water-sensitive spray segmentation and 60.77% for droplet segmentation. Classical computer vision techniques demonstrate high precision but lower recall with a precision of 36.64% for water-sensitive paper and 90.85% for droplets. This study highlights the potential of advanced computer vision and deep learning in enhancing spray quality assessors, emphasizing the need for ongoing refinement to improve precision agriculture tools.

Abstract
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In addition to the existing audit capabilities in Aequitas, the Aequitas Flow module provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling easy-to-use and rapid experiments and analysis of results. Aimed at ML practitioners and researchers, the framework offers implementations of methods, datasets, metrics, and standard interfaces for these components to improve extensibility. By facilitating the development of fair ML practices, Aequitas Flow hopes to enhance the incorporation of fairness concepts in AI systems making AI systems more robust and fair.

Abstract
Enterprise Architecture (EA) is defined as a set of principles, methods, and models that support the design of organizational structures, expressing the different concerns of a company and its IT landscape, including processes, services, applications, and data. One role of EA management is to automate modeling tasks and maintain up-to-date EA models while reality changes. However, EA modeling still relies primarily on manual methods. Contributing to EA modeling automation, EA Mining is an approach that uses data mining techniques for EA modeling and management. It automatically captures existing information in operational databases to generate architectural models and views. This paper presents an ontology for EA Mining that focuses on generating architectural models from API gateway log files. An ontology defines the concepts and relationships among them to uniquely describe a domain of interest and specify the meaning of the terms. API Gateways are information technology components that serve as a facade between information systems and enterprise business partners. The ontology development methodology followed the SABiO process, whereas the Unified Foundational Ontology provided the foundations of the ontology and OntoUML, the ontology modeling language. An experiment in an e-commerce application scenario was conducted to evaluate the theoretical feasibility and applicability of the ontology. Automatic semantic and syntactic validation tools and semi-structured expert interviews were used to confirm the desired ontology properties. This study aims to contribute to the evolution of the knowledge base of EA Management.

Abstract
Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.

Abstract
Classical non-perturbative simulations of open quantum systems' dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate nonperturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearestneighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.