Abstract
To ensure the safety of passengers concerning virus propagation, such as COVID-19, and keep the turnaround time at low levels, airlines should seek efficient aircraft boarding strategies in terms of both physical distancing and boarding times. This study seeks to analyze the impact of different boarding strategies in the context of the International Air Transport Association's recommendations during the pandemic to reduce interference and physical contact between passengers in airplanes. Boarding strategies such as back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal have been tested in this context. This study extends the previous literature using discrete event simulation to evaluate the impact of the occupation of the middle seat by family members only. This study also analyses the impact of having passengers carrying hand luggage and priority passengers on the performance of these strategies concerning boarding times. In general, the simulation results revealed a 15% improvement in boarding times when the reverse pyramid strategy is used compared to a random strategy, which essentially results from a reduction in the boarding interferences between passengers. The results also show that Steffen's strategy is the best performing, while the blocks strategy results in the worst performance. This study has practical implications for airline companies concerning both operation efficiency and passenger safety.

Abstract
This article addresses the 3-D localization of a stand-alone acoustic beacon based on the Principle of Synthetic Baseline using a single receiver on board a surface vehicle. The process only uses the passive reception of an acoustic signal with no explicit synchronization, interaction, or communication with the acoustic beacon. The localization process exploits the transmission of periodic signals without synchronization to a known time reference to estimate the time-of-arrival (ToA) with respect to an absolute time basis provided by the global navigation satellite system (GNSS). We present the development of the acoustic signal acquisition system, the signal processing algorithms, the data processing of times-of-arrival, and an estimator that uses times-of-arrival and the coordinates where they have been collected to obtain the 3-D position of the acoustic beacon. The proposed approach was validated in a real field application on a search for an underwater glider lost in September 2021 near the Portuguese coast.

Abstract
Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View(MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.

Abstract
Telecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top-up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top-up events, to predict the top-up monthly frequency and average value of prepaid subscribers using offline and online multi-target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500,000 events (approximately 5 months). Finally, the predicted top-up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre-emptive measures.

Abstract
The Hoffmann reflex is a is a neurophysiological test that provides insight into the functioning of the human nervous system. It is commonly used in clinical and research settings to evaluate the modulation of the monosynaptic spinal reflex. This paper focus the analysis of the Hoffmann reflex in the trapezius muscle, a muscle of particular interest for researchers and clinicians due to its importance in upper limb function and dynamic stability. However, the Hoffmann reflex analysis of this muscle bring some challenges as the need of applicating burst of electrical square impulses in each current intensity. A web-based smart dashboard, implemented in Python, which allows the user to visualize and analyze the Hoffmann reflex using various signals acquired through a constant current stimulator. The dashboard provides an intuitive and user-friendly interface that facilitates the selection of muscle signals of interest, analysis cycles, and start and end points for the signals. The visualizations offered by the dashboard, including overlapped and mean signal graphics, provide valuable insights into the Hoffmann reflex and its properties. Preliminary experiments with field experts and physiotherapists have yielded positive feedback on the usefulness of this tool, as they seek to gain a deeper understanding of the Hoffmann reflex, and we plan to further improve its capabilities in the future by employing machine learning techniques to automate the reflex detection. © 2023 ITMA.

Abstract
Accurate localization is a critical task in underwater navigation. Typical localization methods use a set of acoustic sensors and beacons to estimate relative position, whose geometric configuration has a significant impact on the localization accuracy. Although there is much effort in the literature to define optimal 2D or 3D sensor placement, the optimal sensor placement in irregular and constrained 3D surfaces, such as autonomous underwater vehicles (AUVs) or other structures, is not exploited for improving localization. Additionally, most applications using AUVs employ commercial acoustic modems or compact arrays, therefore the optimization of the placement of spatially independent sensors is not a considered issue. This article tackles acoustic sensor placement optimization in irregular and constrained 3D surfaces, for inverted ultra-short baseline (USBL) approaches, to improve localization accuracy. The implemented multi-objective memetic algorithm combines an evaluation of the geometric sensor's configuration, using the Cramer-Rao Lower Bound (CRLB), with the incidence angle of the received signal. A case study is presented over a simulated homing and docking scenario to demonstrate the proposed optimization algorithm.