Sea level oscillations are the superposition of many contributions. In particular, tide is a sea level up-down water motion basically depending on three different phenomena: the Earth-Moon-Sun gravitational relationship, the water surface fluid reaction to atmospheric meteorological dynamic, and the Newtonian vertical adjustment of the sea surface due to atmospheric pressure variations. The first tide component (astrotide) is periodic and well known in all points of the Earth surface; the second one is directly related to the meteorological phenomenon, and then it is foreseeable; the Newtonian component, on the contrary, is not readily predictable by a general hydrostatic law, because theJfactor that represents the Newtonian transfer (from the atmospheric weight to the consequent sea level) is variable in each harbor area. The analysis of the gravity field permits to forecast the sea level variation due to meteorological tide events, and its metrological analysis highlights a compensation in the inverse hydrobarometric factor to be taken into account to correctly compensate atmospheric pressure variations in semibinding basins. This phenomenon has several consequences in Harbor Waterside management and in water quality control as shown by the reported case studies and introduces a new reference parameter: the so-called Water 1000.
With the decrease in energy consumption of electronic sensors, the concept of harvesting renewable energy in human surrounding becomes a must. In this context, piezoelectric generators seems promising since they can harvests mechanical vibrations and in general energy available in nature. The aim of this paper is to design a piezoelectric generators, optimized for different kind of sensors and electronic equipments, able to work under the sea or into rivers, which can work with natural water vibrations generated by solid-fluid objects interactions (Von-Karman Vortex) and then to test the effectiveness of the device developing an electronic board to analyze the behavior of the generator
We describe an inversion method for 3D geomagnetic data based on approximation of the source distribution by means of positive constrained Gaussian functions. In this way, smoothness and positivity are automatically imposed on the source without any subjective input from the user apart from selecting the number of functions to use. The algorithm has been tested with synthetic data in order to resolve sources at very different depths, using data from one measurement plane only. The forward modeling is based on prismatic cell parameterization, but the algebraic nonuniqueness is reduced because a relationship among the cells, expressed by the Gaussian envelope, is assumed to describe the spatial variation of the source distribution. We assume that there is no remanent magnetization and that the magnetic data are produced by induced magnetization only, neglecting any demagnetization effects. The algorithm proceeds by minimization of a X 2 misfit function between real and predicted data using a nonlinear Levenberg-Marquardt iteration scheme, easily implemented on a desktop PC, without any additional regularization. We demonstrate the robustness and utility of the method using synthetic data corrupted by pseudorandom generated noise and a real field data set
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