Abstract
The telecommunications sector faces a major challenge of high customer churn. Despite this, there is still a lack of research that explores the switching intention for telecommunication services, particularly with bundle services that currently dominate the market. This study aims to provide insight into consumer behaviour regarding bundle telecommunication services by examining the factors that impact satisfaction and switching intention, both directly and indirectly. Eighteen hypotheses were defined based on the literature, and were tested through a quantitative study with 910 bundle service customers using structural equation modelling with Smart-PLS. The results show that internet and television services have the strongest indirect impact on switching intention, mediated by overall satisfaction and loyalty. Additionally, the results indicate that switching costs and barriers do not significantly affect switching intention, and surprisingly, perceived contractual lock-in positively influences switching intention. This study provides a comprehensive understanding of the customer experience with bundled telecommunications services and offers relevant insights for telecommunication managers to prevent customer loss to competitors.

Abstract
O 6.º Encontro Internacional sobre Jogos e Mobile Learning (EJML) é organizado na Faculdade de Psicologia e de Ciências da Educação, no âmbito das atividades do Laboratório de Tecnologia Educativa (LabTE) da Universidade de Coimbra e do Centro de Estudos Interdisciplinares, em coorganização com a UNISINOS, a Universidad de La Laguna e o Instituto Politécnico de Tomar. Os autores partilham as suas investigações nas áreas de jogos educativos (serious games), Mobile Learning e Formação de Professores e as múltiplas literacias. As comunicações reportam estudos com diferentes públicos etários, desde os mais jovens até aos séniores. As temáticas abarcam desenvolvimento de jogos, aprendizagem baseada em jogos, avaliação da aprendizagem com dispositivos móveis, educação inclusiva, cyberbullying, gamificação, ambientes imersivos de aprendizagem, realidade virtual, realidade aumentada e inteligência artificial no ensino. Todas as comunicações foram submetidas para avaliação, sendo analisadas por três membros da Comissão Científica, através de um processo de blind review. A Comissão Científica é constituída por investigadores de Portugal, Brasil, Espanha, Moçambique e Reino Unido. O evento integra comunicações longas e breves, que estão publicadas nestas atas, relatos de experiências numa outra publicação e onze workshops, cujos tutoriais constituem uma terceira publicação do evento.

Abstract
No atual cenário de crescimento tecnológico, à semelhança da maioria dos produtos agrícolas, o vinho apresenta um volume de dados disponibilizado muito reduzido ou com poucos elementos, o que limita a exploração científica, como é o caso nos sistemas de recomendação. Este artigo apresenta e avalia uma nova base de dados denominada X-Wines no seu primeiro ano de publicação. Ela é constituída por 100.646 rótulos de vinhos produzidos em 62 países e 21 milhões de classificações reais dos consumidores encontrados na Web aberta em 2022. X-Wines é disponibilizada para ser livremente utilizada em sistemas de recomendação, aprendizado de máquina e uso geral, como uma contribuição à ciência de dados.

Abstract
In the present paper, we propose a control approach to perform docking of an autonomous surface vehicle (ASV) while avoiding surrounding obstacles. This control architecture is composed of two sequential controllers. The first outputs a feasible trajectory between the vessel's initial and target state while avoiding obstacles. This trajectory also minimizes the vehicle velocity while performing the maneuvers to increase the safety of onboard passengers. The second controller performs trajectory tracking while accounting for the actuator's physical limits (extreme actuation values and the rate of change). The method's performance is tested on simulation, as it enables a reliable ground truth method to validate the control architecture proposed.

Abstract
Ego -motion estimation plays a critical role in autonomous driving systems by providing accurate and timely information about the vehicle's position and orientation. To achieve high levels of accuracy and robustness, it is essential to leverage a range of sensor modalities to account for highly dynamic and diverse scenes, and consequent sensor limitations. In this work, we introduce TEFu-Net, a Deep -Learning -based late fusion architecture that combines multiple ego -motion estimates from diverse data modalities, including stereo RGB, LiDAR point clouds and GNSS/IMU measurements. Our approach is non -parametric and scalable, making it adaptable to different sensor set configurations. By leveraging a Long Short -Term Memory (LSTM), TEFu-Net produces reliable and robust spatiotemporal ego -motion estimates. This capability allows it to filter out erroneous input measurements, ensuring the accuracy of the car's motion calculations over time. Extensive experiments show an average accuracy increase of 63% over TEFu-Net's input estimators and on par results with the state-of-the-art in real -world driving scenarios. We also demonstrate that our solution can achieve accurate estimates under sensor or input failure. Therefore, TEFu-Net enhances the accuracy and robustness of ego -motion estimation in real -world driving scenarios, particularly in challenging conditions such as cluttered environments, tunnels, dense vegetation, and unstructured scenes. As a result of these enhancements, it bolsters the reliability of autonomous driving functions.

Abstract
Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.