Abstract
Energy consumption is a major concern in Wireless Sensor Networks (WSNs) since nodes are powered by batteries. Usually, batteries have low capacity and can not be replaced due to economic and/or logistical issues. In addition, batteries are complex devices as they depend on electrochemical reactions to generate energy. As a result, batteries exhibit non-linear behaviour over time, which makes difficult to estimate their lifetime. Analytical battery models are abstractions that allow estimating the battery lifetime through mathematical equations, taking into account important effects such as rate capacity and charge recovery. The recovery effect is very important since it enables charge gains in the battery after its electrochemical stabilization. Sleep scheduling approaches may take advantage of the recovery effect by adding sleep periods in the node activities in order to extend the network lifetime. This work aims to analyse the recovery effect within WSN context, particularly regarding low-power nodes. To do so, we use an analytical battery model for analysing the battery performance over time, during the node execution. © Springer International Publishing AG 2017.

Abstract
The MOOC (Massive Open Online Courses) are the latest training model offered. They are online training courses, open and free, and for massive access. But are these features enough to attract potential participants? What are the characteristics of those who are most likely to enroll in a MOOC? To address these and other underlying issues a quantitative methodology was adopted, in the form of an online survey. The study was applied to the adult population of Aveiro district (Portugal) with over nine years of schooling. The sample consists of 424 individuals, and its sociodemographic characteristics equivalent to the population under study. 86.6% of the participants were unaware of the MOOC concept, but there are no significant differences in perceptions about the MOOC among those who knew and those who did not know the concept. The intention to participate in a MOOC is higher among the younger, the ones who have an academic degree, the more autonomous in terms of learning, the ones that have higher Internet and social network skills, the ones who already knew the concept, and who predict change on their employment status. This study provides clues to the identification of target segments and promotion strategies for MOOCs offered in Portugal.

Abstract
Minimum-variance control of adaptive optics (AO) systems relies on a stochastic dynamical model of the per-turbation and on models of the components, including loop delays. Resulting LQG controllers have been imple-mented in SCAO and WFAO both on laboratory benches and on-sky. Their efficiency has been recognized in several modes of operation, e.g. I) on-sky control of TT or low-order modes with vibration mitigation (SPHERE, GPI, CANARY, Raven, GeMS, in H2 formulation at the McMath-Pierce solar telescope) ii) full SCAO mode (CANARY) and MOAO mode (CANARY, Raven) and iv) in general it is advocated to control the low-order modes in laser tomography systems (E-ELT HARMONI LTAO, NFIRAOS). We first point out two examples related to VLT AO controllers to illustrate the need for RTC exibility. The implementation of LQG control in the framework of the future ELTs raises many questions related both to real-time control computation and associated parameter updates (at a far lower rate), and to the performance that can be reached compared with simpler control strategies. By gathering many lab and on-sky results, we draw the performance trends observed so far. We then outline some promising research directions for control design and implementations for future ELTs AO systems.

Abstract
A control technique is proposed in this paper for control of modular multilevel converters (MMC) in high-voltage direct current (HVDC) transmission systems. Six independent dynamical state variables are considered in the proposed control technique, including two ac currents, three circulating currents, and the dc-link voltage, for effectively attaining the switching state functions of MMCs, as well as for an accurate control of the circulating currents. Several analytical expressions are derived based on the reference values of the state variables for obtaining the MMC switching functions under steady state operating conditions. In addition, dynamic parts of the switching functions are accomplished by the direct Lyapunov method to guarantee stable operation of the proposed technique for control of MMCs in HVDC systems. Moreover, the capability curve of MMC is developed to validate maximum power injection from MMCs into the power grid and/or loads. The impacts of the variations of MMC output and dc-link currents on the stability of dc-link voltage are also evaluated in detail by small-signal analysis.

Abstract
In Virtual Reality (VR), the action of selecting virtual objects outside arms-reach still poses significant challenges. In this work, after classifying, with a new taxonomy, and analyzing existing solutions, we propose a novel technique to perform out-of-reach selections in VR. It uses natural pointing gestures, a modifiable cone as selection volume, and an iterative progressive refinement strategy. This can be considered a VR implementation of a discrete zoom approach, although we modify users' position instead of the field-of-view. When the cone intersects several objects, users can either activate the refinement process, or trigger a multiple object selection. We compared our technique against two techniques from literature. Our results show that, although not being the fastest, it is a versatile approach due to the lack of errors and uniform completion times.

Abstract