Investigations of the Mars Upper Atmosphere with ExoMars Trace Gas Orbiter

Space Science Reviews - Time measured by an ideal clock crucially depends on the gravitational potential and velocity of the clock according to general relativity. Technological advances in...     

Onboard system for measuring the parameters of wind vector during parking,starting and takeoff-landing modes for helicopter with aerometric and ion-beacon measuring channels

The problem and features of measuring the speed and the direction angle of the wind vector relative to the longitudinal helicopter axis during parking, starting and takeoff-landing modes by onboard means are considered. The construction principles, information processing algorithms and advantages of onboard system for measuring the wind vector parameters based on ion-beacon and aerometric measuring channels, are disclosed.     

On the stability of stationary motions of a system of coaxial bodies

We consider the stability of stationary motions of a model of a spacecraft as a system of coaxial bodies with small asymmetry caused by the shift of the axes of dynamic symmetry of bodies relative to the axis of rotation. We determine the stationary motions of the system; their stability is studied with respect to both the projections of angular velocity and the position of the axis of rotation. The sufficient conditions for the stability of these stationary motions are obtained by constructing a Lyapunov function, and the necessary conditions are obtained by analyzing the corresponding linearized equations of perturbed motion.     

Methodology of using artificial neural networks for imitating the loading of a single-rotor helicopter

A methodology of creating a loading imitation model of a single-rotor helicopter with artificial neural network algorithms is considered. Stages of creation, the structure, and an algorithm of searching for an optimal neural networks configuration of loading imitation model are presented.     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.