Abstract
Aims. The goal of this work is to characterize the polarization effects of the beam path of the Very Large Telescope Interferometer (VLTI) and the GRAVITY beam combiner instrument. This is useful for two reasons: to calibrate polarimetric observations with GRAVITY for instrumental effects and to understand the systematic error introduced to the astrometry due to birefringence when observing targets with a significant intrinsic polarization. Methods. By combining a model of the VLTI light path and its mirrors and dedicated experimental data, we constructed a full polarization model of the VLTI Unit Telescopes (UTs) and the GRAVITY instrument. We first characterized all telescopes together to construct a universal UT calibration model for polarized targets with the VLTI. We then expanded the model to include the differential birefringence between the UTs. With this, we were able to constrain the systematic errors and the contrast loss for highly polarized targets. Results. Along with this paper, we have published a standalone Python package that can be used to calibrate the instrumental effects on polarimetric observations. This enables the community to use GRAVITY with the UTs to observe targets in a polarimetric observing mode. We demonstrate the calibration model with the Galactic Center star IRS 16C. For this source, we were able to constrain the polarization degree to within 0.4% and the polarization angle to within 5 degrees while being consistent with the literature values. Furthermore, we show that there is no significant contrast loss, even if the science and fringe-tracker targets have significantly different polarization, and we determine that the phase error in such an observation is smaller than 1 degrees, corresponding to an astrometric error of 10 mu as. Conclusions. With this work, we enable the use by the community of the polarimetric mode with GRAVITY/UTs and outline the steps necessary to observe and calibrate polarized targets with GRAVITY. We demonstrate that it is possible to measure the intrinsic polarization of astrophysical sources with high precision and that polarization effects do not limit astrometric observations of polarized targets.

Abstract
Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes’ movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.

Abstract
This paper presents an experimental investigation of sigma-delta modulation (SDM) as a means of improving the performance of underwater optical communication systems. The study considers the impact of the key parameters of SDM, including oversampling ratio, the system's signal-to-noise ratio, bandwidth, and optical link distance. The results of this study provide insights into the design and optimization of SDM-based underwater optical communication systems, paving the way for future research in this field. A fully digital solution, albeit operating at a lower bit rate than previously published OFDM counterparts, provides immunity against nonlinearities of the system and robustness to noise, which is relevant in harsh environments. Moreover, the proposed solution based on a first-order bandpass SDM architecture avoids the employment of a DAC at the receiver, simplifying its operation and reducing costs. An experimental investigation is carried out for the transmission of 16-QAM over SDM, and a transmission distance of 4.8 m over the underwater channel is achieved with a maximum transmission rate of 400 Mbit/s with an MER of 28 dB.

Abstract
This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around 15 degrees for a 1 m transmission length, increasing to 22 degrees for a shorter distance of 0.5 m but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of 5 mm (6 mm) and 0.157 rad (0.174 rad) for a distance between the transmitter and receiver of 1 m and 0.5 m, respectively.

Abstract
The Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 participants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 experiments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios. (c) 2023 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract
Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer's focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user's visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.