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
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 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

Abstract
The intensive use of electronic equipment and the growing offer of services over the Internet has increased the incidence of computer crime. Although there are public measures in Portugal aimed at promoting the digital skills of citizens in matters of security and privacy of electronic equipment, they need to address the more complex aspects of this type of crime. Due to this specificity, preventive measures of the phenomenon may benefit from the know-how and experience of entities with legal powers in the area, especially the National Center for Cybersecurity (CNCS), the Public Prosecutor's Office (MP), and the Judicial Police (PJ). In the public administration in Portugal, emerging technologies based on artificial intelligence (AI) are being adopted to enhance communication between the State and citizens. Awareness-raising extensive actions should make use of these technological tools. Thus, this article describes the research leading to the identification of an efficient electronic device (artifact) in an e-government context aimed at informing and raising awareness among citizens about the growing phenomenon of cybercrime.

Abstract
ABSTRACT This research investigates the use of playful resources structured in Power Point for the bilingual education of deaf students in a school located in the municipality of Timon, in the state of Maranhão. This study aims to analyze how digital games contribute to language acquisition. Methodologically, this is a exploratory and qualitative field research. Class observation, a semi-open questionnaire, and bibliographic research were carried out for data collection and analysis. Results show that the games used promote engagement and motivation for learning. We also observed that the presence of deaf cultural elements creates a more empathetic and inclusive school environment.