Abstract
Visualization can be defined as a technique that allows us to obtain the perception of an object/event in a clear and consistent way. The use of visualization in education is a key factor to explain complex information in a clear way. Therefore, it is essential to have tools capable of visualizing various types of data. An example of a data type is the weather forecast data, which includes various atmospheric data for a given place, and allows the simulation of the atmospheric evolution. It is used for decision making in many areas, such as, agriculture, fishing, tourism, etc. Thus, it is beneficial to demonstrate the usefulness of this type of visualization to better understand the meteorological phenomena, as well as to teach scientific visualization techniques in order to enable access to information that otherwise can only be interpreted by qualified people. In this article it will be discussed the scientific visualization and its benefits to the area of meteorology, and it will be presented a case study of data visualization using the ParaView tools for meteorological data visualization and analysis. ParaView is a multiplatform tool based on the Visualization Toolkit (VTK) that provides features to process, analyze, and visualize various types of data. This study aims to present a tool for scientific visualization and to demonstrate its applications and usefulness for education. © 2021, Springer Nature Switzerland AG.

Abstract

Abstract
Photovoltaic (PV) power is considered as one of the most promising sustainable energy resources in recent years. However, the existing intermittency in the nature of solar energy is a significant problem for the optimization of smart grids. In this paper, to overcome PV generation uncertainty and provide an accurate spatio-temporal (ST) PV forecast, we propose a novel deep generative convolutional graph rough variational autoencoder (CGRVAE) that captures each PV site's probability distribution functions (PDFs) of future PV generation in a modeled weighted graph. Having the learned PDFs enables CGRVAE to accurately generate the future values of PV power time series. To train and evaluate our model, we used the measurements of a set of PV sites in California, US. The sites are modeled as a weighted graph where each node represents PV measurements at each site while edges reflect their correlations. Using graph spectral convolutions the proposed model extracts the most relevant information of the graph to estimate the future PV given the historical time series for each node in the modeled graph. Experimental results show the superiority of CGRVAE over state-of-the-art forecasting approaches in terms of the root mean square error (RMSE) and mean absolute error (MAE) metric.

Abstract
Cylindrical structure analysis is important in several areas and can be performed through the evaluation of the diameter changes of these structures. Two important areas can be mentioned: pipelines for oil or gas distribution and the condition and growth of trees. In the tree diameter changes, monitoring is directly related to irrigation, since it depends on the water soil deficit and trees are important in the global circulation of heat and water. This diameter can change in the order of 5 mm for some species. In this paper, a strain gauge sensor based on a core diameter mismatch technique for diameter measurement is proposed and investigated. The sensor structure is formed by splicing an uncoated short section of MMF (Multimode Fiber) between two standard SMFs (Singlemode Fiber) called SMF–MMF–SMF (SMS); the MMF length is 15 mm. Two cylindrical structures were placed on a 3D printer, with different diameter sizes ((Formula presented.) : 80 mm and 110 mm), to assist in monitoring the diameter changes. The SMS sensor was placed on the printed structure and fixed at two points, such that, by reducing the diameter of the structure, the sensor presents dips or peaks shift of the transmittance spectrum due to the induced curvature and strain. Three values were used for the spacing between the fixation points ((Formula presented.)): (a) 5 mm, (b) 10 mm, and (c) 15 mm. For each choice of fixation points, (Formula presented.) = 80 mm: (a) a sensitivity of -0.876 nm/mm, (Formula presented.) of 0.9909 and a dynamic range of 5 mm; (b) a sensitivity of -0.3892 nm/mm, (Formula presented.) of 0.9954 and a dynamic range of 4 mm; and (c) a sensitivity of -0.768 nm/mm, (Formula presented.) of 0.9811 and a dynamic range of 2 mm. For (Formula presented.) = 110 mm, the sensor presents for each choice of fixation points: (a) a sensitivity of -0.22 nm/mm, (Formula presented.) of 0.9979 and a dynamic range of 8 mm; (b) a sensitivity of -0.2284 nm/mm, (Formula presented.) of 0.9888 and a dynamic range of 6 mm; and (c) a sensitivity of -0.691 nm/mm, (Formula presented.) of 0.9892 and a dynamic range of 3.5 mm. © 2021 by the authors.

Abstract
To increase the security level and stability of the power systems, high controllability capability is needed. The voltage security margin (VSM) index in power systems can be also affected by various transmission switching (TS) maneuvers. However, one of the major challenges in optimal power flow problems using TS, is that there is no limit to the number of switching on the network over specific periods, thus increasing the probability of a failure occurrence, reducing also the reliability and lifetime of circuit breakers. In this work, a model of optimal power flow (OPF) problem using smart TS concerning the reliability of the CBs to reduce the number of switching and operation costs is presented, formulated with a non-linear function. Thus, a linearization method is implemented to linearize CBs reliability formulation, considering as well locally reactive power compensation devices such as capacitors, allowing more active power flow through the lines to supply more loads. An optimal planning and operation of capacitors can be introduced as another solution to increase the voltage security margin and network loading. The proposed linearized AC power flow model is evaluated in a real case study of the Fars Regional Electric Network in Iran.

Abstract
The FotoInMotion (FiM) project is building a novel media creation platform, leveraging the use of semi-automated analysis and editing tools to empower creators to easily transform static visual acquisitions of real-world events into rich, animated and engaging objects, distributable through common channels. FiM transforms the content creative chain into an integrated pipeline across which media and metadata seamlessly flow and are exploited to produce more complex media objects. One of the addressed challenges consists the need for a seamless and efficient communication across such pipeline and on how to preserve, in a structured manner, all of the involved media and metadata. Existing standardized metadata tools and content wrappers are limited in expressivity and scope and incapable of fully supporting the needs of the content creative pipeline. This paper describes FiM's new structured data object, i.e. the Digital Event (DE), which acts as a universal vehicle for media and metadata. It builds on well-established and emergent MPEG standards (MPEG-21, MPEG-V, MPEG-7 and MPEG HEIF), to support data diversity, interoperability, packaging and sharing, within complex, Machine Learning enhanced, creative pipelines. Our solution has been validated by creative professionals (photojournalism, fashion marketing and festivals), who have conducted experiments within the context of different creative workflows in real world scenarios. DE's employment revealed to be advantageous, particularly in the homogenization of the media and metadata representation and packaging and in the normalization of the interaction between different pipeline components.