The physical model of the movement of liquids in capillaries proposed by Laplace and the Jurin formula obtained on its basis are in good agreement with the experiment in terrestrial conditions, but they are not applicable in zero gravity conditions. In this paper, it is experimentally shown that the capillary motion of a liquid is not caused by excessive Laplace pressure, but only by the forces of adhesion (Van der Waals forces) and cohesion. It has been experimentally shown that the phenomenon of capillary movement of a liquid over the surface of a solid body upon their contact occurs under normal conditions even in the absence of narrow slits or small-diameter cavities. The authors postulate a new hypothesis of the physical mechanism of the phenomenon of fluid movement on the surface of bodies, including in capillaries.

Keywords: capillary, Laplace overpressure, liquid, adhesion, Van der Waals force, cohesion, meniscus, gravity, electromagnetism, weightlessness

In this paper, a star sensor tracking method without a star library based on the angular distance chain algorithm is proposed to solve the problem that traditional star sensors rely on a fixed star library and need to be configured to work with multiple units in the tracking mode. This method achieves star map matching by dynamically generating angular distance chains, avoiding the dependence on the global star library. Experiments show that the recognition time of the algorithm in the tracking mode is reduced to milliseconds, and the maximum pose determination error is no more than 0.035°, which proves its effectiveness and reliability. The study provides key technical support for the development of low-cost and lightweight star sensors that are suitable for scenarios such as deep space exploration and near-Earth satellite clusters.

Keywords: angular distance chain algorithm, star sensor without star library, star map recognition, tracking mode, orientation, dynamic matching, deep space exploration

A set of data on potentially dangerous asteroids for the Earth is analyzed. According to descriptive statistics, a preliminary analysis and data processing is performed. The correlation between the parameters allows you to identify those that will be used to train the models. With the help of machine learning models, asteroids from the database are classified into hazardous and non-hazardous. Methods of logistic regression, k-nearest neighbors; decision tree and others are used. Using cross-validation, the best method is found, then its optimal hyperparameters are determined. The quality of the classifier model is evaluated by the metrics of completeness (Recall) and its standard deviation, as well as using the error matrix (confusion matrix) and the average absolute error in percent (MAPE). The results of analysis and modeling in Python are presented, demonstrating the high accuracy of predicting the resulting model.

Keywords: machine learning, predictive model, data analysis, imbalanced data, logistic regression, k-nearest neighbors, decision tree, random forest, support vector machine, cross-validation