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This study includes work related to the analysis of satellite telemetry data. Prior to that, the focus is on investigating various ways of finding anomalies in telemetry data. Both traditional statistical approaches, such as outlier analysis and process control, and modern machine learning techniques, such as deep learning algorithms and anomaly detection techniques, are covered in this. It is seeking to decide on a machine learning model that can identify anomalies in telemetry data. This requires the analysis of several machine learning algorithms, their comparison, and evaluation using a small spacecraft telemetry dataset. This work needs to identify the most suitable and effective methods for detecting anomalies in satellite telemetry data.

The system presented in the study is designed to increase the share of automation in the process of interaction with small spacecraft (SSС) at all stages of operation. Automatic operation of the system will reduce the cost of human resources, the number of errors in management, and increase the time of uninterrupted and continuous operation. The system independently calculates the time of all possible sessions of a group of small spacecrafts with a ground station; forms on its basis a schedule for the operation of a ground station, taking into account historical and future calculation sessions; implements control of the rotary antenna according to the compiled session schedule; decodes telemetry data, writes it to the database, carries out preprocessing and visualization. The system was developed taking into account possible scaling to several receiving stations.

This publication explores the dependencies between factors of space weather (geomagnetic indices Dst, Kp, and Ap, Wolf solar activity index, and solar wind parameters) and the telemetry of the small satellite Cubebel-1. The importance of maintaining the functionality of such satellites in orbit is described based on the research results of specialists in this field. A correlation analysis is presented, conducted using Python language tools. The results are presented in the form of a correlation matrix. A comparison is made between the results of this study and similar results of correlation analysis conducted with data from the satellite Siriussat-1. Suggestions for further research using the telemetry of the Cubebel-1 satellite are provided.

Agriculture industry in Russia is an important part of the economy. Therefore, this industry must be highly efficient and profitable. The usage of remote sensing data for field monitoring makes it possible to simplify the process of collecting information on sown areas and increase the efficiency of this economic sector. Agricultural crops show up well on satellite images and are relatively easy to decipher both in terms of texture and spectral characteristics. However, even for deciphering well-distinguishable objects, large labor costs and high qualification of the performer are required. Recently, active work has been carried out to automate monitoring performed on satellite images, however, these methods are at the initial stage of development and are mostly created for narrowly focused individual areas of activity. The aim of the study is the development of a methodology for automated processing of spatial data of agricultural designation. The novelty of the research lies in the absence of methods for automated decoding in this area. The article describes a study of the possibilities of automated image processing, the analysis of the necessary spectral characteristics and vegetation indices, and the application of the machine learning algorithm used in the methodology. The development of software and hardware implementation of the methodology in Python programming language for geographic information system QGIS is presented. The software and hardware implementation of this methodology allowed to perform automated processing of images of agricultural designation and to identify the objects displayed on them.

This study focuses on the development and implementation of a specialized technology to address the challenges posed by space weather on the performance and operational reliability of small spacecraft, particularly CubeSats and nanosatellites in Low Earth Orbit (LEO). The research emphasizes the importance of data acquisition, processing, and visualization of energy particle fluxes in near-Earth space, with a web application demonstrating visualization of average electron values in a heat map format. The proposed technology, in conjunction with space weather prediction methods, allows for quick response to adverse conditions, contributing to increased longevity and operational efficiency of small spacecraft. In the absence of comprehensive protection systems, the research suggests simple organizational methods as an effective approach for risk mitigation and reliability improvement, applicable even to spacecraft already in orbit.

The volume of forest resources is the most important characteristic in tracking the supply chains of raw materials, along with species and quality. To date, there is no unified automated methodology designed to track the movement of forest resources. The paper considers the use of an unmanned aerial vehicle for the purposes of accounting and accurate calculation of timber volumes both in the standing and in the form of stacks of round timber. The users of the presented methodology are timber industry enterprises, as well as authorities and departments for the purpose of state control. The developed methodology will make it possible to obtain prompt and accurate information on the volume of timber along the entire path of their extraction and processing. This, in turn, will reduce the cost of the procedure for assessing the volume of timber, make the procedure for monitoring their movement more independent and transparent.

This article explores the influence of some averaged planetary geomagnetic indices, as well as solar activity indexes (Dst, Kp, Ap and Wolf numbers) on small spacecraft systems. The global experience in the field of research is described, and various directions in which leading specialists work are analysed. Methods of data analysis using Python are also provided. The procedure of correlation between space weather data and telemetry data of small spacecraft at a single moment in time is carried out. At the end of the experiment, a conclusion is drawn on which specific indicators should be focused on. Suggestions for further research on this topic are also provided.

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