Práce byla dokončena s výrazným předstihem a bylo tak dostatek prostoru pro konzultaci její finální podoby.

Student projevil schopnost aktivního a samostatného vyhledávání nejen studijních materiálů k teoretické části, ale i co se týká dostupných zkušeností z reálných misí a potřebných dostupných testovacích dat.

Evaluation level: je v obvyklém rozmezí

The submitted technical report clearly meets the required length criteria and is likely within or slightly above the typical recommended range, based on the provided structure and chapter distribution.

The work contains a comprehensive and well-balanced structure, covering all essential parts of a research-oriented thesis.

At the same time, the distribution of content indicates that the thesis does not appear excessively inflated with non-essential material. Sections such as on-board performance evaluation, edge device testing, and anomaly reporting system development appropriately support the main objective and contribute meaningfully to the overall scope.

The technical report is well-structured and consistently formatted. The chapter organization, numbering, and hierarchical structure of sections are clear and logically arranged, supporting good readability and orientation within the document.

From a typographic perspective, the document is of high quality. Equations and technical notation are correctly formatted, references are properly integrated, and visual elements are consistently labeled and placed. The overall layout is clean and professional, with only minor and infrequent inconsistencies that do not affect readability.

The language quality is also strong. Although there are occasional minor grammatical imperfections or slightly non-native phrasings, they are rare and do not interfere with understanding.

Overall, the formal and typographic quality of the thesis is excellent, with only minimal flaws, corresponding to grade A.

The technical realization of the thesis is at a very high level and demonstrates a well-designed and fully functional implementation of a complex anomaly detection system for satellite telemetry. The student successfully implemented and compared multiple neural network architectures and integrated them into a unified experimental framework.

The software solution is functionally complete and covers all required stages of the workflow. The system is additionally extended by synthetic data generation and an anomaly exploration/reporting module, which significantly increases the practical value of the implementation.

 The experimental setup is carefully designed, allowing consistent measurement of performance metrics such as PR AUC, inference time, and model size.

Validation and verification are performed thoroughly. The use of multiple datasets (synthetic data, NASA SMAP/MSL, ESA AD benchmark, and Kaggle subset) provides strong evidence of robustness and generalization testing. Additionally, hardware-based validation on embedded and edge devices strengthens the practical relevance of the results.

Overall, the realization output is of excellent quality, demonstrating both strong engineering capability and a high level of practical applicability,  corresponding to grade A.

Evaluation level: obtížnější zadání

The assignment can be evaluated asabove-average in difficulty and research-intensive, as it addresses the complex and safety-critical domain of anomaly detection in satellite telemetry. The work requires not only a theoretical understanding of statistical, machine learning, and deep learning approaches, but also their adaptation to both time-independent and, in particular, time-dependent telemetry data, which is essential for real satellite systems.