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.

Abstract
This paper describes an application aimed at improving the current state of enforcement in the areas of food safety and economic activities in Portugal. More specifically, the application focuses on a flexible and interactive approach to generate inspection routes, to be followed by surveillance brigades with the aim of verifying Economic Operators’ compliance to national and European legislation on economic and food safety. The problem is modeled as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows, and the algorithmic approaches employed seek to maximize either the number of inspected Economic Operators or a utility function that takes into account the utility gained from inspecting each Economic Operator. The generated solutions are shown in an intuitive platform, where human operators can visualize the solutions details (including georeferenced locations in a map) and fully customize them on time by manually removing or adding Economic Operators to be targeted. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Every player in the business world is constantly looking for a competitive advantage which will place them in a leadership position in their particular market. When chosen correctly, lighting may have a significant influence on consumers’ decisions to enter a retail shop or buy a certain item. However, the illumination theme does not only apply to physical purchases, in the retail environment, but also to advertisements and online shopping. Hence, this article aims to study light characteristics and the temperature of light which best emphasize product quality, making them more appealing to the customer. Thus, an online survey was performed, which had 130 answers. After analysing the data, we concluded that a desire exists that retail firms act honestly, and that they should thus use lighting which does not distort the perceived images (which should be as real as possible) of the products which are for sale.

Abstract
This paper presents the results of the experimental tests performed to validate the functionality of a variable pitch system (VPS), designed for pitch attitude control of the novel underwater robotic vehicle explorer UX-1. The VPS is composed of a mass suspended from a central rod mounted across the hull. This mass is rotated around the transverse axis of the vehicle in order to perform a change in the inclination angle for navigation in vertical mine shafts. In this work, the equations of motion are first derived with a quaternion attitude representation, and are then extended to include the dynamics of the VPS. The performance of the VPS is demonstrated in real underwater experimental tests that validate the pitch angle control independently, and coupled with the heave motion control system.