Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.

Abstract

Abstract
With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data-driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time-consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time-series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt-Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.

Abstract
Electronic Control Units are embedded devices that control various critical features of an automobile. Consequently, it is crucial to develop tools that enable penetration testers to identify security vulnerabilities within these ECUs as efficiently as possible. Fuzzing, a widely-used technique, can help uncover vulnerabilities in various types of applications. Fuzzing can then be applied to test ECUs through their communication protocols, the most common being the Controller Area Network (CAN). We present oCANada, a generation-based fuzzer which can be utilized in order to craft CAN messages for fuzzing. Many existing CAN fuzzers rely on simple mutation-based fuzzing, which involves randomly changing bits in the CAN payload. This paper introduces a novel generation-based fuzzing approach that leverages CAN database files (DBCs) in order to craft syntactically correct messages. oCANada also incorporates State-of-the-Art CAN reverse engineering techniques in order to enable syntax-aware fuzzing even when DBCs are not available. Additionally, this paper discusses test oracle techniques employed for fuzzing ECUs over CAN in both greybox and blackbox environments. Finally, we present our results while running the tool which we used two CANoe simulations, a Gateway ECU, and a modified version of the instrument cluster simulator ICSim. In these results, we also compare our fuzzer to the well-known CaringCaribou fuzzer.

Abstract
[No abstract available]

Abstract
Games with purposes beyond entertainment, the so-called serious games, have been useful tools in professional training, especially in engaging participants. However, their evaluation and, also, their adaptable characteristics to different scenarios, audiences and contexts remain challenges. This paper examines the application of serious games in professional training, their results and adaptable ways to achieve certain goals. Using the Design Science Research (DSR) methodology, a framework was built to develop and evaluate serious games to improve user experience, learning outcomes, knowledge transfer to work situations, and the application of the skills practised in the game in real professional settings. At this stage, the investigation presents a framework regarding the triangulation of data collected from a systematic literature review, focus groups and interviews. Following the DSR methodology, the next steps of this investigation, listed at the end of the paper, are the demonstration of the framework in serious game development and the evaluation and validation of this artefact.