Abstract
Europe's position in the current cloud market needs to be improved. This market is currently dominated by non-European players by 75%, shaping the way that Europe is deploying and using cloud services. Although these players are bound to laws and regulations of foreign powers, such as PR China and USA, generating legitimate concerns for the EU, its businesses and citizens. EU's digital future resides on having installed secure, high-quality data processing capacity. This can only be offered by cloud services both centrally and at the edge. In this context NOUS's ambition is completely in line with the European Strategy for data as aims to create the foundations for a European Cloud Service which exploits the HPC network and tackles specific-to-the-EU-economy requirements as well as leverages different data spaces (Mobility, Energy, Green Deal and Manufacturing).

Abstract
Increasing the number of Electric Vehicles (EVs) imposes several challenges in power distribution networks. Developed Electric Vehicle Supply Equipment (EVSE) provides fast and efficient charging of EVs at the Public Charging Stations (PCS). These chargers benefit from balanced three-phase chargers with considerable power consumption. Hence, the optimal management and task scheduling for EVSE should be arranged in such a way as to avoid overloading network infrastructure or imposing new peaks on the distribution networks. On the other hand, energy management in the presence of high renewable energy penetration due to installed Photovoltaic (PV) panels at the low-voltage (LV) distribution network should be elaborated to minimize the renewable power curtailment. Hence, this paper presents a novel model to address the optimal scheduling of charging stations availability and unlocking the Demand Response (DR) potentials at the distribution networks with highly penetrated PV panels. The energy management model is represented as a standard Mixed-Integer Linear Programming (MILP) problem which can be solved by open-source solvers. The proposed model is tested for a real case study in Portugal to demonstrate the functionality of the developed tool. © 2024 IEEE.

Abstract
A significant number of dependable systems rely on scheduling algorithms to achieve temporal correctness. Despite their relevance in real-world applications, only a narrow subset of the works in the literature of real-time systems are readily available to be reproduced in real-world hardware platforms. This lack of support not only hinders the reproducibility of research results, but also reduces the opportunity for new platform-specific research directions to emerge. In this work we discuss the use and development of an open-source tool named MARS capable of porting various scheduling tests and algorithms to hardware platforms used in distributed real-time dependable systems.

Abstract
Due to the exponential increase in energy consumption and CO2 emissions, new sustainable energy sources have emerged, and hydrogen (H2) is one of them. Despite all the advantages, H2 has high flammability, so constant monitoring is essential. Two optical techniques were numerically studied and compared with the goal of H2 sensing: surface plasmon polaritons (SPP) and Tamm plasmon polaritons (TPP). The H2-sensitive material used was palladium (Pd) in both techniques. The SPP structure was found to have more sensitivity to H2 than TPP, 23 and 5nm/4vol% H2, respectively. However, the latter has lower FWHM, with the minimum of the band showing reflectivity near 0%. In addition, TPP also uses more cost-effective materials and can be interrogated at normal incidence with depolarized light. The potential of using each of these optical techniques for H2 sensing was demonstrated. © The Authors.

Abstract
This paper introduces the Large Events Model (LEM) for soccer, a novel deep learning framework for generating and analyzing soccer matches. The framework can simulate games from a given game state, with its primary output being the ensuing probabilities and events from multiple simulations. These can provide insights into match dynamics and underlying mechanisms. We discuss the framework's design, features, and methodologies, including model optimization, data processing, and evaluation techniques. The models within this framework are developed to predict specific aspects of soccer events, such as event type, success likelihood, and further details. In an applied context, we showcase the estimation of xP+, a metric estimating a player's contribution to the team's points earned. This work ultimately enhances the field of sports event prediction and practical applications and emphasizes the potential for this kind of method.

Abstract
Following the lack of existing studies about the applications development and use in the event industry, this research aims to analyze the use of applications as a tool to improve event communication. The methodology used includes an exploratory method. This research methodology is based on a review of the literature, a semi-structured interview with the CEO of the company and a case study with participant observation of the French start-up Invent App which provides event applications. The study demonstrate that the event app use allows to ease the event organization, to improve the event communication but also the interactivity and the participant engagement. All of this, increasing the customer experience but also having a part in the customer digital experience improvement. Event applications could be thus considered as amarketing and event communication tool. The research has the limitation to its application within a single company in France. Furthermore, the point of viewadopted is the one of the event organizers and not the event participant one. This research helped to better understand the role that event applications can have in the event industry and particularly in the communication around an event. In addition, this made it possible to understand the perception of market requirements and the characteristics expected of an event application.