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This paper continues the publication cycle on developing the ion plasma electrically powered spacecraft propulsion (EPSP) of the spacecraft. For monitoring and control of the EPSP operation, a feedback system based on a signal proportional to the EPSP plasma radiation intensity has been proposed to be used. It was assumed that the radiation intensity in the ultraviolet, visible and infrared ranges being proportional to the instantaneous thrust value of the EPSP. Accordingly, the introduction of a signal from the radiation registration detector into the feedback loop should allow to create an onboard closed system for monitoring and control of the EPSP operation. A photodetector based on a dynamic pin-diode integrator was considered for use in this system.

A number of characteristics of ionic and ion-plasma accelerators laboratory samples designed for electrically powered spacecraft propulsion were investigated in the experimental research. In a large-sized vacuum chamber (2.4 m^3, 10^-3 Pa) in the MCA named after A.F. Mozhaysky provided the necessary physical and technological processes, methods and means of measurement, parameters and operation modes of ion accelerators with contact ionization were provided in a large-sized vacuum chamber (2.4 m3, 10-3 Pa) in A.F.Mozhaysky`s Military-Space Academy. The theoretical analysis, including by computer simulation, of samples design features, as well as physical processes and operating parameters was carried out. It is established that the implemented and tested measurement methods, technologies, and ion-physical laboratory samples characteristics correspond to the tasks of developing promising electrically powered spacecraft propulsion.

Crop losses are a major threat to the wellbeing of rural families, to the economy and governments, and to food security worldwide. The goal of our research is to develop a multi-functional platform to help the farming community to tilt against plant diseases. In our previous works, we reported about the creation of a special database of healthy and diseased plants’ leaves consisting of five sets of grapes images and proposed a special classification model based on a deep siamese network followed by k-nearest neighbors (KNN) classifier. Then we extended our database to five sets of images for grape, corn, and wheat – 611 images in total. Since after this extension the classification accuracy decreased to 86 %, we propose in this paper a novel architecture with a deep siamese network as feature extractor and a single-layer perceptron as a classifier that results in a significant gain of accuracy, up to 96 %.

Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented.

Mathematical modelling of heating and current drive as well as yields and distributions of fusion products in a magnetically confined plasma subject to neutral beam injection requires, in turn, modelling of distributions of fast ions, which is a complex task including calculations of the source of suprathermal particles, i.e., the number of fast ions occurring in unit volume during unit time owing to the injection of fast atoms. The knowledge of the magnetohydrodynamic equilibrium, beam injection geometry and spatial distribution of the magnetic field are the necessary prerequisites. Explicit general analytical formulae for the source of fast ions have been obtained by two different methods. In addition, a method of statistical modelling is presented. Calculations of spatial and angular distributions of the fast ion source for a tokamak and verifications of the obtained results have been performed by a number of methods. [ABSTRACT FROM AUTHOR]

Featured Application: Numerical modelling of yields and distributions of nuclear fusion products in magnetically confined plasma. Velocity distributions of fuel nuclei enter the formulae for distributions of products of fusion reactions in plasma. The formulae contain multiple integration, which is a computationally heavy task. Therefore, simplifications of the integrand are advantageous. One of possible simplifications is the use of closed-form analytical distributions of fast deuterons and tritons, accounting for slowing down and pitch-angle scattering and neglecting the speed diffusion. The plausibility of such a model has been studied from the viewpoint of its influence on the calculated spectra of fusion neutrons. Calculations have shown that the speed diffusion effect on suprathermal ion distribution tails does not significantly alter the qualitative behaviour of energy and angle distributions of fusion products in a beam-heated plasma. [ABSTRACT FROM AUTHOR]

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip in GEM detectors due to the appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greaterthan for true hits, one faces with the quite serious difficulty to unravelpossible track-candidates via true hits ignoring fakes. On the basis of our previous two-stage approach based on hits preprocessing using directed K-d tree search followed by a deep neural classifier we introduce here two new tracking algorithms. Both algorithms combine those two stages in one while using different types of deep neural nets. We show that both proposed deep networks do not require any special preprocessing stage, are more accurate, faster and can be easier parallelized. Preliminary results of our new approaches for simulated events are presented.

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip and GEM detectors due to appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes. We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier. We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector. We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage. Preliminary results of our approach for simulated events of the BM@N GEM detector are presented.

Cloud computing has become a routine tool for scientists in many fields. The JINR cloud infrastructure provides JINR users with computational resources to perform various scientific calculations. In order to speed up achievements of scientific results the JINR cloud service for parallel applications has been developed. It consists of several components and implements a flexible and modular architecture which allows to utilize both more applications and various types of resources as computational backends. An example of using the Cloud&HybriLIT resources in scientific computing is the study of superconducting processes in the stacked long Josephson junctions (LJJ). The LJJ systems have undergone intensive research because of the perspective of practical applications in nano-electronics and quantum computing. In this contribution we generalize the experience in application of the Cloud&HybriLIT resources for high performance computing of physical characteristics in the LJJ system.

The article discloses the conceptual apparatus of innovation for the knowledge of its nature in order to improve the quality of governance. An analysis of the conceptual apparatus in the retrospective aspect allowed to assess the process of innovation at different stages of economic development. The comparative characteristic of interpretations of "innovation" and "innovation" in order to understand their nature and the definition of scientific approaches to the control, as a separate step of the innovation process, as well as a set of elements to obtain economic benefits.

The time-of-flight-type neutral particle analyzer has an ability of horizontal scanning from 40 to 100° of the pitch angle. The information from the spatially resolved energy spectrum gives not only the ion temperature but also the information of the particle confinement and the electric field in plasmas. We have been studying the energy distributions at various magnetic configurations in the neutral beam injection (NBI) plasma. The spatially resolved energy spectra can be observed during long discharges of the NBI plasma by continuous scanning of the neutral particle analyzer. The shape of spectra is almost similar from 44° to 53°. However, the spectra from 55° are strongly varied. They reflect the injection pitch angle of the beam. The pitch angle scanning experiment during the long discharge of NBI plasma has also been made under the reversal of the magnetic field direction. NBI2 becomes counter injected with the reversal. We can easily observe the difference between co- and counter injections of NBI. During the electron cyclotron heating in the low-density plasma for the formation of the internal thermal barrier, large neutral particle increase or decease can be observed. The degree of the increase/decrease depends on the energy and the density. The reason for the variation of the particle flux is that the orbit of the trapped particle changes due to the electric field formed by the strong electron cyclotron heating. [ABSTRACT FROM AUTHOR]

Neutral particle fluxes measured by neutral particle analyzers can provide information about the ion temperature as well as the non-Maxwellian anisotropic ion distribution tails from neutral beam injection and ion cyclotron radio frequency heating. In the case of multidirectional diagnostics employing high resolution atomic energy spectrometers, the neutral atomic flux source is not localized in contrast to pellet charge exchange or diagnostic neutral beam methods. The correct interpretation of such measurements from plasma in a complex toroidally asymmetric geometry, like that of LHD, requires careful numerical modeling of the neutral flux formation. Previously a measured neutral flux calculation scheme was developed and was used for the LHD geometry and a suitable analytic expression for ionization cross sections σs(z)(E,ne,Te,Zeff) of impurities was formulated by Janev et al. [Nucl. Fusion 29, 2125 (1989)]. In this paper, the attenuation of fast particles by impurities is incorporated into the neutral flux calculation scheme and the influence of impurities on the calculated neutral flux spectra is shown. Finally, the behavior of the calculated and experimental suprathermal particle distributions is compared for pure hydrogen and for argon impurity seeded plasmas. [ABSTRACT FROM AUTHOR]