Abstract
In this article, a proof-of-concept study on the use of a hybrid design technique to reduce the number of phase shifters of a beam-scanning reflectarray (RA) is presented. An extended hemispherical lens antenna with feeds inspired by the retrodirective array is developed as a reflecting element, and the hybrid design technique mixes the lenses with the microstrip patch elements to realize a reflecting surface. Compared to the conventional designs that only use microstrip antennas to realize a reflecting surface, given a fixed aperture size the presented design uses 25 percent fewer array elements while shows comparable beam-steering performance. As a result of using fewer elements, the number of required phase shifters or other equivalent components such as RF switches and tunable materials is reduced by 25 percent, which leads to the reduction of the overall antenna system's complexity, cost, and power consumption. To verify the design concept, two passive prototypes with a center frequency at 12.5 GHz were designed and fabricated. The reflecting surface was fabricated by using standard PCB manufacturing and the lenses were fabricated using 3D printing. Good agreement between the simulation and measurement results is obtained. The presented design concept can be extended to the design of RAs operating at different frequency bands including millimetre-wave frequencies with similar radiation performances. The presented design method is not limited to the microstrip patch reflecting elements and can also be applied to the design of the hybrid RAs with different types of reflecting elements.

Abstract
Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

Abstract

Abstract
There is a widespread social awareness for the need of environment protection and sustainable systems in different areas of human activity. In particular, the catering industry is responsible for a significant share of sewage systems pollution, due to daily leaks of food remnants containing Fat, Oil and Grease (FOG). This work focuses on building a combined IoT monitoring solution to automate the remote management of industrial FOG-Separators, aiming to prevent or reduce leakage of FOG and food debris into sewer systems. The proposed solution adopted the use of custom-made in-premises sensor motes integrating two particular sensors: an in-the-house developed conductivity sensor, built specifically to distinguish levels of water and FOG in industrial FOG-Separators; an off-the-shelf turbidity sensor integrated to assess the amount of water debris. Briefly, this work has four major fold contributions: i) design and implementation of a combined IoT sensing solution; ii) most significant was the development, test, and integration of the capacity-based sensor coupled to local sensor motes, for assessing Water/FOG levels; iii) assessing and profiling edge motes energy autonomy; iv) finally, deploying the combined IoT architecture to validate the entire process of monitoring and scheduling the maintenance of industrial FOG-Separators. © 2022 IEEE.

Abstract
Driven by the need for decarbonizing energy carriers across sectors, and the increasing availability of low-cost renewable electricity generation future energy systems will see a rise of power-to-gas technology. For example, hydrogen and its derivates can make enable the usage of carbon-neutral electricity for hard-to abate industry sectors and serve as long-term seasonal storage. Given recent drafts of ambitious political hydrogen strategies around the world, the question arises which power-to-gas configurations provide the highest value for money from a power system perspective. This work provides a flexible framework to compare the performance of current power-to-gas sites all over the world. Power-to-gas technologies are assessed with a benchmarking framework based on Composite Indicators to compare the system value of existing conversion technologies, plant sizes, cost structures, and configurations. Our analysis confirms recent research that suggests that plant performance is higher for larger projects and improves as projects move from research stage over pilot stage to commercial stage. Our findings inform policy makers and electricity system planners who aim to identify the economically and technically most promising solutions for investment.

Abstract