Abstract
The growing trend to support parallel computation to enable the performance gains of the recent hardware architectures is increasingly present in more conservative domains, such as safety-critical systems. Applications such as autonomous driving require levels of performance only achievable by fully leveraging the potential parallelism in these architectures. To address this requirement, the Ada language, designed for safety and robustness, is considering to support parallel features in the next revision of the standard (Ada 202X). Recent works have motivated the use of OpenMP, a de facto standard in high-performance computing, to enable parallelism in Ada, showing the compatibility of the two models, and proposing static analysis to enhance reliability. This paper summarizes these previous efforts towards the integration of OpenMP into Ada to exploit its benefits in terms of portability, programmability and performance, while providing the safety benefits of Ada in terms of correctness. The paper extends those works proposing and evaluating an application transformation that enables the OpenMP and the Ada runtimes to operate (under certain restrictions) as they were integrated. The objective is to allow Ada programmers to (naturally) experiment and evaluate the benefits of parallelizing concurrent Ada tasks with OpenMP while ensuring the compliance with both specifications.

Abstract
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system's operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Abstract
Facial expression analysis is a widespread technology applied in various research areas, including sports science. In the last few decades, facial expression analysis has become a key technology for monitoring physical exercise. In this paper, a deep neural network is proposed to recognize seven basic emotions and their corresponding probability values (scores). The score of the neutral emotion was tracked throughout the exercise and related with heart rate and power generation by a stationary bicycle. It was found that in a certain power range, a participant changes his/her expression drastically. Twelve university students participated in the sub-maximal physical exercise in stationary bicycles. A facial video, heart rate,and power generation were recorded throughout the exercise. All the experiments, including the facial expression analysis, were carried out offline. The score of the neutral emotion and its derivative was plotted against maxHR% and maxPower%. The threshold point was determined by calculating the local minima, with the threshold power for all the participants being within 80% to 90% of its maximum value. From the results, it is concluded that the facial expression was different from one individual to another, but it was more consistant with power generation. The threshold point can be a useful cue for various purposes, such as: physiological parameter prediction and automatic load control in the exercise equipment, such as treadmill and stationary bicycle. © 2020, Springer Nature Switzerland AG.

Abstract
Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. This paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. The approach could be used for training other object detection models used in traffic scenarios. © 2020, Springer Nature Switzerland AG.

Abstract
<p class="western" align="justify"><span lang="en-US">The study of the electrical properties of the atmospheric marine boundary layer is important as the effect of natural radioactivity in driving near surface ionisation is significantly reduced over the ocean, and the concentration of aerosols is also typically lower than over continental areas, allowing a clearer examination of space-atmosphere interactions. Furthermore, cloud cover over the ocean is dominated by low-level clouds and most of the atmospheric charge lies near the earth surface, at low altitude cloud tops. </span></p> <p class="western" align="justify"><span lang="en-US">The relevance of electric field observations in the marine boundary layer is enhanced by the the fact that the electrical conductivity of the ocean air is clearly linked to global atmospheric pollution and aerosol content. The increase in aerosol pollution since the original observations made in the early 20th century by the survey ship Carnegie is a pressing and timely motivation for modern measurements of the atmospheric electric field in the marine boundary layer. Project SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer) addresses this challenge by means of an unique monitoring campaign on board the ship-rigged sailing ship NRP Sagres during its 2020 circumnavigation expedition. </span></p> <p class="western" align="justify"><span lang="en-US">The Portuguese Navy ship NRP Sagres departed from Lisbon on January 5th in a journey around the globe that will take 371 days. Two identical field mill sensors (CS110, Campbell Scientific) are installed </span><span lang="en-US">o</span><span lang="en-US">n the mizzen mast, one at a height of 22 m, and the other at a height of 5 meters. </span><span lang="en-US">A visibility sensor (SWS050, Biral) was also set-up on the same mast in order to have measurements of the extinction coefficient of the atmosphere and assess fair-weather conditions.</span><span lang="en-US"> Further observations include gamma radiation measured with a NaI(Tl) scintillator from 475 keV to 3 MeV, cosmic radiation up to 17 MeV, and atmospheric ionisation from a cluster ion counter (Airel). The</span><span lang="en-US"> 1 Hz measurements of the atmospheric electric field</span><span lang="en-US"> and from all the other sensors</span><span lang="en-US"> are </span><span lang="en-US">linked to the same rigorous temporal reference frame and precise positioning through kinematic GNSS observations. </span></p> <p class="western" align="justify"><span lang="en-US">Here the first results of the SAIL project will be presented, focusing on fair-weather electric field over the Atlantic. The observations obtained in the first three sections of the circumnavigation journey, including Lisbon (Portugal) - Tenerife (Spain), from 5 to 10 January, Tenerife - Praia (Cape Verde) from 13 to 19 January, and across the Atlantic from Cape Verde to Rio de Janeiro (Brasil), from January 22nd to February 14th, will be presented and discussed.</span></p>

Abstract
The distribution network operation problem (DNOP) is an optimization problem in which the objective function is the total operation cost of the distribution company (Disco), to be minimized considering the technical constraints of the network. In the presence of distributed energy resources (DERs) and microgrids (MGs), new decision makers, including MG and DER operators or managing entities, are emerging and are changing the decision-making framework for distribution systems. To describe the cooperation and competition between the Disco, MG and DER operators, different frameworks and models have been proposed in the literature. Moreover, different computational techniques and metaheuristic algorithms have been used to solve the optimal operation problems. Hence, this paper considers DNOP as one of the timely problems under study and of major interest for future research, presenting a comprehensive review on the decision-making frameworks referring to DNOP in the presence of DERs and MGs, as a new contribution to earlier studies. The focus is set on the comparison among different frameworks characterized by increasingly higher level of participation of the DER managers to the distribution system operation, offering a complementary view with respect to available reviews on similar topics based on technical aspects of the DER connection and integration in MGs and distribution networks, which is noteworthy.