Abstract
CubeSats are becoming an alternative challenge for space exploration. Research in the technology and applicability of these small platforms has received an increasing interest in the last years. They represent an emergent technological market (CAGR growth of 37.91 % in the 2017-2021 period), while a variety of fields like meteorology, climatic research, transportation safety, or navigation is resorting to this technology. As more complex CubeSats missions are defined, a natural increase in the mission power demand occurs. In a scarce-resource environment like the space, this demands the development of new ways of harvesting spacecraft electrical energy. An alternative to traditional energy harvesting systems composed of solar panels and batteries is Wireless Energy Transfer (WET). It originates in the electromagnetic transfer, proven to have two important limitations: high power efficiency decrease at distances bigger than coil size and the need of mobile parts. A new approach is proposed as a solution to these limitations: the possibility of mounting on a 3U CubeSat photo-thermoelectric generator array devices that can convert photon energy to electrical energy via thermal gradient generation. For creating the thermal gradient, a long-range laser source targets cells from each array forming the hybrid photo-thermoelectric plasmonic system (HPTP). Two possible scenarios are presented in terms of mission requirements and analysis: a controlled pulsed large-range laser source located on Earth, in the case of Earth-orbiting missions, or on a hub system, in a deep-space mission. For Earth, Mars and Jupiter, a simulation of the total energy produced by solar panels and the HPTP system is presented to illustrate the potential use of the WiPTherm technology. In each of the scenarios, key measures of effectiveness will be analysed to overcome potential CubeSat and constituent subsystems overheat, by comparison with nominal component and shield temperature profiles in both eclipse and illuminated cases when the HPTP system is not used. Pointing budget accuracy and jitter for targeting the HPTP generator cells and required laser link budget for a planned energy transfer efficiency of up to 10 % of the source power are other challenges covered in the presentation, apart from research topics from a multidisciplinary group covering nanomaterials science, optics, photonics, and CubeSats power systems engineering. Copyright

Abstract
This paper presents a preliminary study on the energy consumption of two popular web browsers. In order to properly measure the energy consumption of both environments, we simulate the usage of various applications, which the goal to mimic typical user interactions and usage. Our preliminary results show interesting findings based on observation, such as what type of interactions generate high peaks of energy consumption, and which browser is overall the most efficient. Our goal with this preliminary study is to show to users how very different the efficiency of web browsers can be, and may serve with advances in this area of study.

Abstract
Abstract Background: The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs.Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.

Abstract
This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.

Abstract
This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.

Abstract
The development of flexible and efficient communication mechanisms is of paramount importance within the context of the Internet of Things (IoT) paradigm. IoT has been used for industrial, commercial, and residential applications, and the IEEE 802.15.4/ZigBee standard is one of the most suitable protocols for this purpose. This protocol is now frequently used to implement large-scale Wireless Sensor Networks (WSNs). In industrial settings, it is becoming increasingly common to deploy cluster-tree WSNs, a complex IEEE 802.15.4/ZigBee-based peer-to-peer network topology, to monitor and control critical processes such as those related to oil or gas, mining, or certain specific chemicals. The remote monitoring of critical events for hazards or disaster detection in large areas is a challenging issue, since the occurrence of events in the monitored environment may severely stress the regular operation of the network. This paper proposes theDynamic REconfiguration mechanism of cluster-Tree WSNs(DyRET), which is able to dynamically reconfigure large-scale IEEE 802.15.4 cluster-tree WSNs, and to assign communication resources to the overloaded branches of the tree based on the accumulated network load generated by each of the sensor nodes. A complete simulation assessment demonstrates the proposed mechanism's efficiency, and the results show that it can guarantee the required quality of service level for the dynamic reconfiguration of cluster-tree networks.