Том 26, № 2 (2025)

Satellite structures are subjected to extreme conditions throughout their operational lifespan, including high launch loads, thermal cycling, and space debris impacts, making them vulnerable to structural failures. Understanding the causes, effects, and mitigation strategies for such failures is crucial for enhancing satellite reliability and mission success. This review critically examines the common structural failures in satellites, categorizing them by affected components such as primary frames, joints, thermal shielding, and deployable mechanisms. The study employs a comprehensive analysis of historical and recent failures, integrating insights from case studies, experimental research, and advancements in materials science and structural health monitoring. The findings highlight key failure mechanisms, including material fatigue, vibrational stresses, and thermal degradation, and assess innovative solutions such as smart materials and in-orbit repair techniques. By synthesizing current research and industry practices, this review provides a systematic understanding of failure trends and proposes future directions for improving satellite structural resilience. The insights presented in this study aim to support the development of more robust satellite architectures, ultimately contributing to safer and more reliable space missions.

A well-designed engineering blueprint for a residential apartment building can effectively mitigate potential hazards during the preparatory phase of construction. This approach enables the consideration of factors that, due to the constraints inherent in specialized expertise, frequently go unaddressed in practice. The theory of expert systems and mathematical apparatus based on fuzzy logic are put forward as the methodological basis and fundamental research methods. The objective of the present study is to formulate mathematical principles that facilitate the determination of the engineering concept of apartment buildings at the preparatory stage of construction, based on the theory of fuzzy sets and decision support methods. The research objective is to develop general mathematical principles for solving applied problems using specialized expert systems. The research yielded the development of the mathematical foundations of a multifunctional expert system for the conceptualization of apartment buildings during the preparatory phase of construction; a fuzzy knowledge base was created. The projection of a multidimensional response surface function has been restored, reflecting the dependence of linguistic variables. Mathematical principles for determining the engineering concept of multi-family residential buildings at the preparatory stage of construction have been developed.

The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms - Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting - on a dataset containing clinical features of patients. The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis. The study used a dataset of 270 patients with 13 clinical features. The data was preprocessed, and target variables were converted into binary values for classification. The dataset was split into training and test sets in a 70-30 ratio. Five machine learning models were trained and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Confusion matrices were analyzed to gain additional insights into model performance. Logistic Regression and Random Forest showed the best results among all models, with an accuracy of 86.4 and 80.2%, respectively. The Logistic Regression showed a ROC-AUC score of 0.844, while the Random Forest showed a score of 0.88. The confusion matrices revealed the strengths and weaknesses of each model in terms of forecasting. Logistic Regression and Random Forest were identified as the most reliable models for predicting heart disease in this dataset. Future work will explore hyperparameter tuning and ensemble methods to further enhance model performance, providing valuable insights for early diagnosis and treatment of cardiovascular diseases.

The power system of Iraq aims to integrate all energy sources such as thermal power plants and renewable energy sources including wind energy. Wind speed data for 2022 in five locations were obtaied to calculate the wind energy potential of Iraq in the first part of the study. The selected locations were used to plot the graph of the regional distribution of average wind speed in Iraq. Four regions were identified according to the level of wind energy potential. Statistical analysis including wind flow power calculation was performed for each location. The second part of the study considered an energy complex including wind power plants and natural gas-fired thermal power plants. To develop such a complex, it is necessary that the maximum energy consumption is covered taking into account the unstable operating modes of wind power plants. The results show the technical feasibility in terms of flexibility and cost-effectiveness of such an energy complex.