This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station.

Unattended Wireless Sensor Networks (UWSNs), characterized by the intermittent presence of the sink, are exposed to attacks aiming at tampering with the sensors and the data they store. In order to prevent an adversary from erasing any sensed data before the sink collects them, it is common practice to rely on data replication. However, identifying the most suitable replication rate is challenging: data should be redundant enough to avoid data loss, but not so much as to pose an excessive burden on the limited resources of the sensors.

In the framework of the exact factorization of the time-dependent electron-nuclear wave function, we investigate the possibility of solving the nuclear time-dependent Schrödinger equation based on trajectories. The nuclear equation is separated in a Hamilton-Jacobi equation for the phase of the wave function, and a continuity equation for its (squared) modulus. For illustrative adiabatic and nonadiabatic one-dimensional models, we implement a procedure to follow the evolution of the nuclear density along the characteristics of the Hamilton-Jacobi equation.

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In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem.

This article is concerned with the analysis of the one-dimensional compressible Euler equations with a singular pressure law, the so-called hard sphere equation of state. The result is twofold. First, we establish the existence of bounded weak solutions by means of a viscous regularization and refined compensated compactness arguments. Second, we investigate the smooth setting by providing a de- tailed description of the impact of the singular pressure on the breakdown of the solutions.

Motivated by the upcoming Internet of Things, designing light-weight authentication protocols for resource constrained devices is among the main research directions of the last decade. Current solutions in the literature attempt either to improve the computational efficiency of cryptographic authentication schemes, or to build a provably-secure scheme relying on the hardness of a specific mathematical problem.