This paper presents a set of Ground Penetrating Radar (GPR) data obtained from in situ measurements conducted in four ornamental stone quarries located in Italy (Botticino quarry) and Romania (Ruschita, Carpinis, and Pietroasa quarries). The GPR is a Non-Destructive Testing (NDT) technique that enables the detection and localization of fractures without damage to the surface, among other capabilities. In this study, two instruments of ground-coupled GPR were used to detect and locate the fractures, discontinuities, or weakened zones. The GPR data contains radargrams for discontinuities and fracture detection, besides the geographic location of the measures. For each measurement site, a set of radargrams has been acquired in two orthogonal directions, allowing for a 3D reconstruction of the investigated site.

In the general context, the phenomenon of globalization causes an increase in risks to critical infrastructures. In order to order this set, the criteria of dependence and interdependence were imposed. Thus, was proposed a model with seven levels, the first being sector analysis and the second one the study of interdependencies.The first level is organized in critical sectors and dependencies between them. This division, which engendered the public-private partnership, is show on the EU and NATO documents. The second level of the interdependence study aims to determine the vital elements and essential components of a system. In this paper we show some considerations on the dimensionality of interdependencies between critical infrastructures, interdependence between critical infrastructures from the energy sector and Information and Communication Technology (ICT). After a breakdown of SCADA systems, we presented at the end of the paper few examples of cybernetic attack against energy infrastructures

Travertines are susceptible to fracturing/ cracking, making it difficult to extract large uncracked blocks of significant commercial value. In our study, we used the GPR – Ground Penetrating Radar and other geophysical methods (resistivity and seismic methods) to evaluate the degree of fracturing. Following the interpretation of the longitudinal and transversal GPR profiles, corroborated with the resistivities minima, led us to identify fractures and highly fracturing areas in the Carpinis quarry. In addition, using these combined methodologies and corroborating geophysical, geological, tectonic and geomorphological information creates the premises for an effective investigation of Carpinis quarry, which assists with optimising the mining process.

In this paper, we present the results obtained for the geomagnetic data acquired at the Surlari Observatory, located about 30 Km North of Bucharest - Romania. The observatory database contains records from the last seven solar cycles, with different sampling rates.We used AR, MA, ARMA and ARIMA (AutoRegressive Integrated Moving Average) type models for time series forecasting and phenomenological extrapolation. ARIMA model is a generalization of an autoregressive moving average (ARMA) model, fitted to time series data to predict future points in the seriesWe made spectral analysis using Fourier Transform, that gives us a relevant picture of the frequency spectrum of the signal component, but without locating it in time, while the wavelet analysis provides us with information regarding the time of occurrence of these frequencies. Wavelet allows local analysis of magnetic field components through variable frequency windows. Windows with longer time intervals allow us to extract low-frequency information, medium-sized intervals of different sizes lead to medium-frequency information extraction, and very narrow windows highlight the high-frequencies or details of the analysed signals.We extend the study of geomagnetic data analysis and predictive modelling by implementing a Long Short-Term Memory (LSTM) recurrent neural network that is capable of modelling long-term dependencies and is suitable for time series forecasting. This method includes a Gaussian process (GP) model in order to obtain probabilistic forecasts based on the LSTM outputs. The evaluation of the proposed hybrid model is conducted using the Receiver Operating Characteristic (ROC) Curve that provides a probabilistic forecast of geomagnetic storm events. In addition, reliability diagrams are provided in order to support the analysis of the probabilistic forecasting models.The implementation of the solution for predicting certain geomagnetic parameters is implemented in the MATLAB language, using the Toolbox Deep Learning Toolbox, which provides a framework for the design and implementation of deep learning models.Also, in addition to using the MATLAB environment, the solution can be accessed, modified, or improved in the Jupyter Notebook computing environment.