Abstract
In this paper, we describe the design of an electrically large anechoic chamber for usage on millimetre-wave bands. Ansys Savant sotware was used to perform a simulation of the chamber, using physical optics coupled with uniform theory of diffraction (PO/UTD). Moreover, a method based on an open waveguide probe is described in this paper to obtain the electrical properties of the RF absorbers at millimetre-wave frequencies. Two different source antennas were simulated in this work and the corresponding quiet zones predicted. The largest quiet zone was 30 mm x 30 mm x 50mm, for a chamber size of 1.2 m x 0.6 m x 0.6 m.

Abstract
The European Union (EU) energy strategy towards decarbonization led EU countries to elaborate their corresponding National Energy and Climate Plans (NECP) for the period 2021 to 2030. This paper analyzes the Portuguese and Spanish NECPs concerning their power systems. CEVESA, a model for the long-term planning and operation of the Iberian electricity system, is used. The analysis is based on simulating the reference NECP scenario, as well as other alternative scenarios with different solar and wind generation shares, CO2 prices and fuel costs. Results provide insights on the MIBEL electricity market evolution under the current decarbonization national strategies. © 2020 IEEE.

Abstract
Recent developments in the field of smart grid have led to renewed interest in load monitoring strategies for achieving effective energy management schemes. There are vast amount of published studies describing the role of non-intrusive load monitoring (NILM) system based on various learning algorithms. It is widely known that the accuracy of load identification depends strongly on utilized methods and its features. Thus, the main aim of this study is to investigate the comparative accuracy of machine learning algorithms which have the same training data with different feature subsets. Afterwards, a low-cost data acquisition system for NILM using bagged tree ensemble algorithm is developed and demonstrated in detail. The proposed structure is tested on the ThingSpeak IoT platform to reveal the effectiveness of the evaluated concept.

Abstract
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.

Abstract

Abstract
Portugal is a country with a large coastal extension and a fishing and sea tradition which place it on the top of fresh, salty and deep-frozen fish consumption in the European Union. The incapacity by consumers to evaluate the degree of freshness and quality of fish for consumption may cause health problems, such as food poisoning, hence the preference of consumers for deep-frozen products instead of healthier fish that is sold fresh. In this project we intend to contribute to an improvement in the evaluation by the final consumer of the quality of fresh fish sold to them through creating a reference table of fish degradation (at this experimental stage for mackerel and sardine) and the development of a mobile application allowing, in a near future, for users to determine the rate of freshness of fish they wish to acquire. The reference table creation is based on the degradation/dehydration of the fish’s eye as well as a respective coloration (opaqueness), which changes according to the time since the fish was caught. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.