CESI (Italy)

• Power-frequency voltage effects on indirect lightning performance are investigated. • Two methods within LIOV-EMTP environment are compared: a rigorous and a simplified approach. • The effects of power-frequency voltage on multi-phase flashovers are statistically evaluated. This paper investigates the influence of power-frequency voltages on the indirect lightning performance of overhead distribution lines. Analyses on the subject often disregard this factor, assuming a de-energized line. However, in certain cases, e.g., when discriminating between single and multi-phases flashovers is of interest, the effect of the power-frequency voltage might be worth of investigation. The paper presents a comparative study of two methodologies implemented within the LIOV-EMTP simulation environment to account for the power-frequency voltage effects. The first method employs a rigorous approach, incorporating voltage offsets in the Bergeron lines that interconnect LIOV and EMTP. The second method, though less precise, offers a simplified approach that has the advantage of avoiding modifications to the LIOV-EMTP interface used when the line is de-energized. A statistical analysis is conducted to quantify the effect of the power-frequency voltage on the predicted number of single and multi-phase flashovers and to evaluate the effectiveness of both methods in calculating the indirect lightning performance. By considering two lines characterized by different rated voltages, a sensitivity analysis is performed for different spacing between SAs and different values of both ground conductivity and SA grounding resistance.

This study investigates how surface temperature influences lightning activity over the Italian peninsula and adjacent seas during 2020-2021. Monthly cloud-to-ground lightning data from the SIRF network were correlated with satellite-derived land and sea surface temperatures. Results show a pronounced positive correlation over land at short temporal scales, especially in northern Italy, while marine and coastal regions exhibit slightly lower correlations that peak at intermediate time lags (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3-5$</tex> months). These patterns reflect the differing thermal responses of land and sea surfaces on convective processes. These findings highlight the distinct influence of land and sea warming on lightning occurrence, providing insights into the spatial and temporal variability of lightning discharges.

Geosynthetics are increasingly used in geotechnical works in association with vegetation, whose function has been typically considered as an improvement for the works themselves, especially due to the mechanical and hydraulic effects the root system provides. Within this context, the paper presents an experimental study performed to evaluate the long-term performance of geosynthetics, specifically drainage geocomposites, exposed to the influence of roots. Two species of plant were selected for their fast-growing and pervasive roots, Vetiver and Bamboo, and planted in a thin layer of growing medium, which buried the geosynthetic specimens. After a period of plant growth, the specimens affected by roots (‘rooted specimens’) were exhumed, and submitted to tensile and drainage capacity laboratory tests, to determine their mechanical and hydraulic behaviour. The laboratory test results were compared with those obtained on virgin specimens of drainage geocomposite, under undisturbed conditions (‘reference specimens’). The results show a decrease in drainage capacity for rooted specimens, more pronounced in the case of the highest density of roots into the drainage core. Concerning the tensile behaviour, no significant variations emerged in rooted specimens, although a slight increase in tensile strength, together with a modest decrease in elongation at maximum load, could be observed.

The theme of microplastics (MP) emissions is one of the focuses of possible environmental concerns when using geosynthetic products in the civil engineering applications. For the construction of a new aircraft parking apron at the Ostend airport in Belgium, a relevant amount of gravel has been substituted with an appropriate woven geotextile for stabilizing the ground. The initial consideration for this decision was essentially due to the important economic and ecological advantages in terms of material savings, time needed for their installation and the overall construction costs. This paper reports the successive analyses showing a very relevant saving in CO2 emissions (more than 77%), which would justify by itself the solution with geosynthetics, if compared with the use of gravel. The specificity and precision of the data available allowed the calculation of a dramatic saving (more than 97%) also in terms of MP emissions, due to the spare of trucks for the transport of materials, preventing also the MP formation connected to the truck’s tires wearing.

For an effective and economical deployment of battery-powered electric vehicles, mobile phones, laptops, and medical gadgets, the State of Charge (SoC) of the batteries must be properly assessed. It permits a safe operation, have a longer usable battery life, and prevent malfunctions. In this context, the battery management systems provide diverse SoC estimation solutions. However, the Machine Learning (ML) based SoC estimation mechanisms are becoming popular because of their robustness and higher precision. In this study, the features set is prepared using the intended battery cell charge/discharge curves for voltage, current, and temperature. Utilizing statistical analysis and the shape context, the attributes are extracted. Following that, three credible machine learning (ML) algorithms-decision trees, random forests, and linear regression-process the set of mined attributes. The applicability is tested using the Panasonic Lithium-Ion (Li-Ion) battery cells, publicly provided by the McMaster University. The feature extraction and the ML based SoC prediction modules are implemented in MATLAB. The “correlation coefficient”, “mean absolute error”, and “root mean square error” are used to assess the prediction performance. The results show an outperformance of the random forest regressor among the intended ones by attaining the correlation coefficient value of 0.9988.

The arrhythmia is one of the cardiovascular diseases which has several types. In literature, researchers have presented a broad study on the strategies utilized for Electrocardiogram (ECG) signal investigation. Automated arrhythmia detection by analyzing the ECG data is reported using a number of intriguing techniques and discoveries. In order to effectively categorize arrhythmia, a novel approach based on the hybridization of the denoising filter, QRS complex segmentation, “Empirical Mode decomposition” (EMD), “Intrinsic Mode Functions” (IMFs) based features extraction, and machine learning techniques is developed in this study. To evaluate the categorization accuracy, the 10-fold cross validation (10-CV) strategy is used. Using an arrhythmia dataset that is publically available for research, the performance of our method is evaluated. A 97% average accuracy score is secured by our method for the problem of 5-class arrhythmias. These findings are comparable or better than counterparts.

Triple junction solar cells based on InGaP/GaAs/Ge are the baseline for the space application. This paper reports the achievement in terms of development, characterization and in-flight experience for two new concepts of multijunction solar cells developed at CESI and experimented on board of a cubesat developed at Sapienza University: the triple junction solar cells with integral assembly (CTJ-AI) and the triple junction solar cells with enhanced resistance against radiation (CTJ-EOL).

Fish consumption is increasing nowadays both because of its positive role for health due to the abundant presence of unsaturated fatty acids and for its use in many new food preparations (e.g. raw fillet used for uncooked sushi and sashimi dishes). The growing food industry and increased demand for the long-term storage and preservation of food have created the need to develop methods that can easily track and preserve food freshness and safety throughout shelf-life (production, storage, shipment, and consumption). While E-nose technologies have already been used and tested for these purposes, scarce information is available in the literature on the feasibility of using other food devices to detect changes in perishable food like fish during shelf-life in order to predict and correctly manage all food storage phases. The aim of the present study was to investigate the potential of Food Sniffer® portable devices to define the quality and safety of salmon fillet and burger (Salmo salar) packaged in modified atmosphere at two refrigerated conditions (4 and 8 °C). An increase in biogenic amines and volatile compounds especially ketones and alcohols were observed, with large amounts at final storage times of 8 °C temperature. The Food Sniffer® application was able to anticipate unacceptability conditions of salmon samples also correlated with chemical and microbiological parameters. This could represent a valid support for food industry and retail to manage perishable food commodities preventing possible food risk as well. © 2020 Society of Chemical Industry.