Abstract
In this article, we investigate the secure spectral efficiency of an ultrareliable low-latency communication system, where communications occur with short packets due to delay constraints, so that a finite blocklength formulation is considered. In addition, we assume that no feedback channel is available to implement automatic repeat request schemes, so that packet replication (PR) and interface diversity (ID) strategies are used to improve performance, which are then compared in terms of physical-layer security while considering a Nakagami-m fading channel. Furthermore, we assume no knowledge of the instantaneous channel state information at Alice, neither with respect to Bob nor Eves, while the position of multiple colluding eavesdroppers are specified according to a Poisson point process. Numerical results show that the joint optimization of the blocklength, the transmit power, and the amount of information bits per codeword are crucial to maximize the secure spectral efficiency. In addition, we also show that ID outperforms the PR strategy in most scenarios when the number of replications/interfaces increases.

Abstract
Li-ion battery State-of-Charge (SOC) estimation is a complex challenge for battery management systems designers, due to the battery's non-linear behaviour at different operating conditions and ageing levels. As a possible solution, multiple machine learning models have been proposed for SOC estimation throughout the years. These provide an advantage over model-based methods, as they do not require a deep knowledge and study of the battery's internal behaviour. However, many of these proposed models could not be considered due to their complexity. The high number of required stored parameters and/or elevated memory consumption during estimation may pose challenges to the application of these methods. Therefore, in this paper, several feedforward neural network models are proposed for SOC estimation, with an efficient method for online input preprocessing and low parameter requirement in storage. These models are simulated and validated using battery data, taken at different temperatures with several driving cycles and charge cycles, achieving lowest estimation Root Mean Squared Error (RMSE) of 1.096% over the whole validation dataset.

Abstract

Abstract
It is indisputable that technology is present in everyday life. In this digital era, brands need to adapt to the changing pace of the needs and desires of society to distinguish themselves from their adversaries, especially in the electronic environment. Hence, they must have well-defined and successful marketing and advertising strategies to achieve a place on the podium of preference of consumers. This work intends to understand how the communication strategies of Apple and Samsung affect the decisions of consumers in Portugal to buy electronic devices. To this end, a survey was conducted, and the responses of 700 individuals who reside in Portugal were analysed through descriptive and inferential (chi-square test of independence) statistics. The survey results show that cost-benefit, price, technical specifications, and performance are the characteristics that weigh the most when purchasing electronic devices, as well as the perceived prestige of the brand. Additionally, an association was found between having only one device and having more products of the brand, with Apple users having more frequently more than one of the brand's products than Samsung users. We thus concluded that Apple consumers are more loyal. It was also found that the store where the devices are brought is not independent, in Portugal, of the brand of the devices. Apple users buy more brand products from the brand store, both physical and online, than Samsung users. Finally, advertisements and word-of-mouth were found to be fundamental for consumers to acknowledge brand devices, and the degree in which this happens is also not independent of the brand, in Portugal, as a chi-square independence test showed.

Abstract
The need for faster and energy-efficient computing technologies has recently pushed for major developments on alternative computing paradigms to the common von Neumann architecture. Amongst those, reservoir computing framework is an emerging concept that leverages a simple training process and eases transference to hardware implementations, allowing any given nonlinear physical system to act as a computing platform. In this work, we explore how we can make use of a discrete chain of solitons to create an effective reservoir computing framework, investigating not only the ability to learn data but also to predict models depending on the strength of the nonlinear interaction of the media. Probing the role of the nonlinear separation for tasks involving nonlinear separable data, these results open new possibilities for a multitude of physical implementations in the context of optical sciences, from optical fibers to nonlinear crystals.

Abstract
As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project's results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions. © 2021 The Institution of Engineering and Technology.