Abstract
This article describes a Kernel Principal Component Regressor (KPCR) to identify Auto Regressive eXogenous (ARX) Linear Parmeter Varying (LPV) models. The new method differs from the Least Squares Support Vector Machines (LS-SVM) algorithm in the regularisation of the Least Squares (LS) problem, since the KPCR only keeps the principal components of the Gram matrix while LS-SVM performs the inversion of the same matrix after adding a regularisation factor. Also, in this new approach, the LS problem is formulated in the primal space but it ends up being solved in the dual space overcoming the fact that the regressors are unknown. The method is assessed and compared to the LS-SVM approach through 2 Monte Carlo (MC) experiments. Every experiment consists of 100 runs of a simulated example, and a different noise level is used in each experiment,with Signal to Noise Ratios of 20db and 10db, respectively. The obtained results are twofold, first the performance of the new method is comparable to the LS-SVM, for both noise levels, although the required calculations are much faster for the KPCR. Second, this new method reduces the dimension of the primal space and may convey a way of knowing the number of basis functions required in the Kernel. Furthermore, having a structure very similar to LS-SVM makes it possible to use this method in other types of models, e.g. the LPV state-space model identification.

Abstract
Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test.An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment.A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals. © The Institution of Engineering and Technology 2019.

Abstract
Forecasting is a task with many concerns, such as the size, quality, and behavior of the data, the computing power to do it, etc. This letter proposes the dynamic mode decomposition (DMD) as a tool to predict the annual air temperature and the sales of a stores' chain. The DMD decomposes the data into its principal modes, which are estimated from a training data set. It is assumed that the data is generated by a linear time-invariant high order autonomous system. These modes are useful to find the way the system behaves and to predict its future states, without using all the available data, even in a noisy environment. The Hankel block allows the estimation of hidden oscillatory modes, by increasing the order of the underlying dynamical system. The proposed method was tested in a case study consisting of the long term prediction of the weekly sales of a chain of stores. The performance assessment was based on the best fit percentage index. The proposed method is compared with three neural networkbased predictors.

Abstract
Batalha Abbey is a 14th century UNESCO world heritage site that shows signs of decay. During the last years, high resolution geophysical methods have been used to contribute to the knowledge of its construction characteristics and to an informed maintenance and rehabilitation project. Here in it is presented a multimethod high-resolution geophysical investigation of its main tower. A 3D resistivity survey was carried out on the surface around the tower to investigate the ground beneath it. A GPR survey was used on the tower walls surface to investigate its interior. Three frequencies, 250MHz, 500MHz and 800MHz, were used. Finally, a seismic tomography study was done around the tower with both geophones and sources on the tower walls to provide a 3D velocity image of the tower interior. 3D resistivity results give a clear image of the walls foundations and of the ground beneath the tower. GPR 250MHz data provide a complete GPR image across the tower, although of low resolution. Higher resolution GPR results provided clearer information on the constructive elements of the tower. Finally, the seismic tomography results gave, for the first time, a complete image of the tower interior and proved it a compact construction with no voids.

Abstract
Water from mountainous regions is a strategic natural resource. In Mediterranean mountainous regions, which, in many cases, correspond to protected areas, high-altitude roads are often the main threat to the sustainability of water resources. In these regions, the regular socioeconomic functioning requires frequent road de-icing operations which normally consist of spreading NaC1 and other chemicals, such as CaCl2, in pavements. The main purpose of this research is to assess the environmental impact of road de-icing on groundwater resources in a Mediterranean mountainous region and to describe it by means of a hydrogeological conceptual model. The research focused in a cross-sectional sector located in Serra da Estrela (Central Portugal), where a hydrogeological inventory was carried out, followed by hydrogeochemical and hydrogeophysical studies. The results clearly identify different hydrogeo-chemical signatures in polluted (Cl-Na facies and higher EC) and unpolluted (HCO3-Na, Cl-Na, and very low EC). The relation of hydrogeochemistry and altitude is complex and depends on both natural processes (namely, water-rock interaction) and anthropic processes (de-icing operations). The hydrogeophysical survey systematically identified the presence of a pollution plume migrating downstream from roads.

Abstract
A dangerous problem that many countries have to face is the existence of buried explosive devices, responsible for high numbers of civilian fatalities. Their detection and removal are therefore mandatory, which has led in recent years to the development of different techniques that can ensure safer and more efficient demining operations. Geophysical techniques have been employed, since allow ground search in a non-invasive, rapid and cost-effective way, with special interest being given to ground penetrating radar (GPR). In the current work, it was buried in a sandy soil and in a clayey soil (27m2 each), one of two similar sets of different inert explosive devices. GPR profiles of the subsoil were obtained with a 3D-GPR system, being then processed with the ReflexW software. Three dimensional cubes of the two study sites were constructed for better target signal visualization. The preliminary results confirm the efficiency of this technique, since all buried inert explosive devices were detected in both soil types. © SGEM2019.