The Martian magnetic field, characterized by its complex and heterogeneous structure, poses significant challenges for modeling due to the inaccessibility of the planet's interior and noisy satellite data. This study presents an iterative approach using the S-approximation method, a robust analytical method for handling large but fragmented data sets from orbital missions on Mars. By representing the magnetic field as a sum of fields generated by simple and double layers on predefined surfaces, this method allows for the construction of high-resolution models that reflect the observed magnetic field. The approach allows for flexible selection of carrier geometries (planes, dihedral angles, spheres, ellipsoids) to accommodate different study scales and problem geometries, facilitating the modeling of local and global magnetic anomalies. By choosing different model parameters and integrating different data sets, this approach improves the accuracy and spatial coverage of Martian magnetic field models.

We discuss the process of forming satellite data samples by considering various selection strategies, including sampling measurements using different grid systems, selecting data obtained at specific altitudes above the Martian surface, and choosing data collected in the absence of direct sunlight (nighttime data). These criteria are essential for creating an optimal data sample necessary for constructing an accurate and reliable model of the Martian crust’s magnetic field using the S-approximation method.

The diagnostics of exchange of data between the elements of the top and base level systems of the Automated process control system (APCS) of a Nuclear power plant (NPP) is considering. The methods and the algorithms of diagnostics of the software and the hardware of the APCS of the NPP of the exchange of data are proposing.

The paper studies the optimal control problem for a simple model of collective action campaign. The initial model has the form of an ordinary differential equation for the variable, has the meaning of the phenomenon. The formulated management system, implemented on the basis of the Pontryagin maximum principle, is determined by optimal management.

This paper deals with the possibility of constructing models neural network spatial and surface of the Earth fields for navigation of uncrewed vehicles. The authors present the possibility of constructing a neural network model of a multigeophysical field based on their combination. An example illustrates the neural network model realization for a relief field.