Abstract
Resumo: A evolução das tecnologias digitais e das redes de comunicação também digitais propiciaram o surgimento de uma sociedade reticular marcada pela conectividade entre diferentes entidades, o que tem provocado mudanças acentuadas na economia e no mercado de trabalho, impulsionando o nascimento de novos paradigmas, modelos, processos de comunicação educacional e novos cenários de ensino e de aprendizagem. No entanto, não se imaginava, nem mesmo os professores que já adotavam ambientes online nas suas práticas, que seria necessária uma mudança tão rápida e emergencial, devido à expansão do coronavírus que inviabilizou a presença física de professores e estudantes no espaço geográfico das instituições educacionais, e obrigou os professores a transpor metodologias e práticas, adotadas em salas de aula presencial física, para os meios online, resultando em práticas de ensino remoto, de ensino a distância, distintas das práticas consolidadas neste domínio e sustentadas pela pesquisa na área. Tendo, pois, em consideração este contexto, o objetivo deste artigo, de natureza eminentemente teórica é, por um lado, contribuir para  delimitação de conceitos fundamentais no domínio da Educação mediada pelo digital, como Ensino Remoto ou Ensino a Distância, Educação a Distância ou eLearning, dentre outros e que muitas vezes são usados de forma indiferenciada sem rigor conceitual, e por outro, apresentar a proposição de um novo conceito e paradigma que designámos de Educação Digital OnLife.

Abstract
En este documento se presenta una evaluación experimental del estado del aislamiento de un transformador trifásico de 50 kVA sumergido en aceite a través de la aplicación de impulsos atmosféricos estándar de alto voltaje y su respuesta en frecuencia realizando un Análisis de la Respuesta en Frecuencia al Impulso (IFRA). La evaluación se basa en tener dos estados representativos del aislamiento, por ejemplo, cuando el transformador usa aceite dieléctrico deteriorado y luego este aceite es reemplazado por uno nuevo, por lo que se realiza el cambio del aceite dieléctrico. Se lleva a cabo una valoración general del estado del transformador en ambos estados representativos mediante la ejecución de pruebas de rutina. El análisis de la respuesta en frecuencia se desarrolla mediante la obtención de la respuesta del transformador en los dos estados representativos del aislamiento y bajo diferentes configuraciones de medición. Posteriormente se evalúa a través de una comparación gráfica entre las respuestas en frecuencia, valorando cualitativamente y cuantitativamente las figuras obtenidas; y relacionándolas con el estado del aislamiento. Por ello, se proporciona una metodología experimental como una opción al uso de equipo especializado para análisis de respuesta en frecuencia por medio de la acción de impulsos atmosféricos estándar de alto voltaje, usando el equipo disponible en el Laboratorio de Alto Voltaje de la Escuela Politécnica Nacional destinado para pruebas de impulso.

Abstract
On-sky testing of new instrumentation concepts is required before they can be incorporated within facility-class instrumentation with certainty that they will work as expected within a real telescope environment. Increasingly, many of these concepts are not designed to work in seeing-limited conditions and require an upstream adaptive optics system for testing. Access to on-sky AO systems to test such systems is currently limited to a few research groups and observatories worldwide, leaving many concepts unable to be tested. A pilot program funded through the H2020 OPTICON program offering up to 15 nights of on-sky time at the CANARY Adaptive Optics demonstrator is currently running but this ends in 2021. Pre-run and on-sky support is provided to visitor experiments by the CANARY team. We have supported 6 experiments over this period, and plan one more run in early 2021. We have recently been awarded for funding through the H2020 OPTICON-RADIO PILOT call to continue and extend this program up until 2024, offering access to CANARY at the 4.2m William Herschel Telescope and 3 additional instruments and telescopes suitable for instrumentation development. Time on these facilities will be open to researchers from across the European research community and time will be awarded by answering a call for proposals that will be assessed by an independent panel of instrumentation experts. Unlike standard observing proposals we plan to award time up to 2 years in advance to allow time for the visitor instrument to be delivered. We hope to announce the first call in mid-2021. Here we describe the facilities offered, the support available for on-sky testing and detail the eligibility and application process. © 2020 SPIE.

Abstract
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

Abstract
Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may change over time, leading to concept drift. The literature is abundant on providing supervised methods based on error monitoring for explicit drift detection. However, these methods may become infeasible in some real-world applications-where there is no fully labeled data available, and may depend on a significant decrease in accuracy to be able to detect drifts. There are also methods based on blind approaches, where the decision model is updated constantly. However, this may lead to unnecessary system updates. In order to overcome these drawbacks, we propose in this paper a semi-supervised drift detector that uses an ensemble of classifiers based on self-training online learning and dynamic classifier selection. For each unknown sample, a dynamic selection strategy is used to choose among the ensemble's component members, the classifier most likely to be the correct one for classifying it. The prediction assigned by the chosen classifier is used to compute an estimate of the error produced by the ensemble members. The proposed method monitors such a pseudo-error in order to detect drifts and to update the decision model only after drift detection. The achievement of this method is relevant in that it allows drift detection and reaction and is applicable in several practical problems. The experiments conducted indicate that the proposed method attains high performance and detection rates, while reducing the amount of labeled data used to detect drift.

Abstract
Data processing (or the transformation of data into knowledge and/or information) has become an indispensable tool for decision-making in many areas of engineering. Engineering optimization problems with many objectives are common. However, the decision-making process for these problems is complicated since there are many trade-offs that are difficult to identify. Thus, in this work, multivariate statistical methods, Principal Component Analysis (PCA) and Cluster Analysis (CA), have been studied and applied to analyze the results of many objective engineering optimization problems. PCA reduces the number of objectives to a very small number, CA through the similarities and dissimilarities, creates groups of solutions, i.e., bringing together in the same group solutions with the same characteristics and behaviors. Two engineering optimization problems with many objectives are solved: a mechanical problem consisting in the optimal design of laminated plates, with four objectives and a problem of optimization of the radar waveform, with nine objectives. For the problem of the design of laminated plates through PCA allowed to reduce to two objectives and through CA it was possible to create three distinct groups of solutions. For the problem of optimization of the radar waveform, it was possible to reduce the objectives from nine to two objectives representing the greatest variability of the data, and CA defined three distinct groups of solutions. These results demonstrate that these tools are effective to assist the decision-making processes in the presence of a large number of solutions and/or objectives.