This article introduces the Rayleigh autoregressive moving average (RARMA) model, which is useful to interpret multiple different sets of remotely sensed data, from wind measurements to multitemporal synthetic aperture radar (SAR) sequences. The RARMA model is indeed suitable for continuous, asymmetric, and nonnegative signals observed over time. It describes the mean of Rayleigh-distributed ...

In this manuscript we evaluate the potential of photovoltaic systems to meet some dedicated energy demand in specific geographic locations. Our approach is based on location-specific constraints rather than on pre-established, location-independent methodologies or assumptions. First, we propose that a thorough analysis of the socio-economic and technical possibilities of a location must act as the ...

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, ...

Super-resolution fluorescence microscopy is a widely employed technique in cell biology research, yet remains relatively unexplored in the field of histopathology. Here, we describe the sample preparation steps and acquisition parameters necessary to obtain fluorescent multicolor super-resolution structured illumination microscopy (SIM) images of both formalin-fixed paraffin-embedded and cryo-preserved ...

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and ...

Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow ...

In this study, we compare the retrieval results for the dielectric properties of verified oil slick, acquired using airborne multifrequency synthetic aperture radar. A polarimetric two-scale model was used to invert the radar imagery by first employing solely the co-polarization channels, and then by employing the co-polarization channels in conjunction with the cross-polarization channels, and ...

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose ...

The spatial and temporal characterization of trapped charged particle trajectories in magnetospheres has been extensively studied in dipole magnetic field structures. Such studies have allowed the calculation of spatial quantities, such as equatorial loss cone size as a function of radial distance, the location of the mirror points along particular field lines (<i>L</i>‐shells) as a function of the ...

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively ...