Abstract
The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of indus-tries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capa-ble of identifying certain patterns which can represent a mal-function or deterioration in the monitored machines. There-fore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This pa-per introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and wash-ing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home ap-pliances at a repair center and included readings of elec-trical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An ex-tracted features dataset, corresponding to the collected work-ing cycles is also made available. This dataset could bene- fit research and development of AI systems for home ap-pliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption pat-terns of such home appliances.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Abstract
Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

Abstract
The fast development and adoption of IoT technologies has been enabling their application into increasingly sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices increases annually, they still present severe cyber-security vulnerabilities, becoming potential targets and entry points for further attacks. As these nodes become compromised, attackers aim to set up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes in a cloaked fashion, and network timing covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim to analyse the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol, particularly upon the DSME behaviour, one of the most promising for large-scale time critical communications. This enables us to better understand the involved risk of such threats and help support the development of active cyber-security mechanisms to mitigate these threats, which, for now, we provide in the form of practical network setup recommendations.

Abstract
High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.

Abstract

Abstract
In the evolving landscape of global education, Artificial Intelligence's (AI) integration into Learning Management Systems (LMS) promises a transformative shift. This paper presents Mozambique's journey in this domain, comparing it with global advancements. While the Mozambican higher education sector stands at the cusp of a digital revolution, its engagement with AI in LMS remains foundational. This is juxtaposed against the global trend where AI tools, such as ChatGPT, are rapidly becoming standard in many educational platforms, enhancing personalization, efficiency, and data-driven insights. The benefits of AI integration, such as tailored learning experiences and administrative automation, are counterbalanced by challenges, including data privacy concerns and over-reliance on technology. Drawing from real-world case studies, the paper highlights pioneering endeavours that showcase AI's potential in reshaping educational paradigms. As Mozambique navigates its unique challenges, insights from global best practices offer a roadmap for harnessing the transformative potential of AI in LMS, aiming to elevate its higher education sector to new heights.;Na evolução da educação global, a integração da Inteligência Artificial (IA) nos Sistemas de Gestão de Aprendizagem (LMS) promete uma transformação significativa. Este artigo investiga a jornada de Moçambique neste domínio, comparando-a com os avanços globais. Enquanto o setor de ensino superior moçambicano está à beira de uma revolução digital, seu envolvimento com a IA em LMS ainda está em uma fase inicial. Isso é contrastado com a tendência global, onde ferramentas de IA, como o ChatGPT, estão rapidamente se a se tornar padrão em muitas plataformas educativas, aprimorando a personalização, eficiência e insights baseados em dados. Os benefícios da integração da IA, como experiências de aprendizagem adaptadas e automação administrativa, são equilibrados por desafios, incluindo preocupações com a privacidade dos dados e excesso de dependência da tecnologia. Através de estudos de caso do mundo real, o artigo destaca esforços pioneiros que mostram o potencial da IA em remodelar os paradigmas educacionais. Enquanto Moçambique navega pelos seus desafios únicos, os insights das melhores práticas globais oferecem um roteiro para aproveitar o potencial transformador da IA em SGA, com o objetivo de elevar seu setor de ensino superior a novos patamares.