Large-scale space projects rely on a thorough Assembly, Integration, and Verification (AIV) process to provide the upmost reliability to spacecraft. While this has not traditionally been the case with CubeSats, their increasing role in space science and technology has led to new verification approaches, including in educational CubeSats. This work describes the integration and verification approach for ISTSat-1, which is an educational CubeSat from the Instituto Superior Técnico in Portugal that partially discards the typical stage-gate approach to spacecraft development in favor of a more iterative approach, allowing for the system-level verification of unfinished prototypes. Early verification included software functional testing on a flatsat model, thermal vacuum and vibration testing on a battery model, ionizing radiation testing on the on-board computer, and non-ionizing radiation (EMC) testing on all subsystems. The testing of functional prototypes at an early development stage led to uncovering system-level errors that would typically require hardware redesign at a later project stage. The team considers the approach to be useful for educational projects that employ a small, co-located team with low non-recurring engineering costs.

In this work, we evaluate the impact of numerical integration methods and perturbation models on the computational speed and position accuracy of orbit propagation techniques. With increasing numbers of satellites in orbit, space traffic management may require near real-time satellite operations, for which computational speed may play a more important part in orbit propagation than positional accuracy. The aim of this work is to identify the most suitable propagation parameters for different mission scenarios and outline the perturbations to be considered based on the target orbit characteristics. We analyze the impact of the integrators’ tolerance on accuracy and runtime, as well as quantify the dominant perturbations for each orbit type. We use a Starlink satellite as a reference case, propagating it across multiple orbital regimes. The results are presented in the form of Pareto fronts trading off runtime and positional accuracy. These Pareto fronts outline some important results, for instance, how gravitational models beyond $32\times 32$ yield no accuracy improvements while significantly increasing runtime. We also verify that drag is critical in VLEO, LEO, SSO, and HEO (Molniya), while third-body effects play a major role in HEO (Molniya and Tundra), GEO, and GSO, and solar radiation pressure becomes significant in HEO (Tundra), GEO, and GSO. These results can be incorporated into collision avoidance optimization strategies for real-time satellite operations, thereby contributing to more efficient space traffic management.

The increasing number of satellites in orbit has created a need for efficient spacecraft monitoring solutions to support situational awareness and reduce operator workload. Sparse and irregular telemetry, due to limited communication and memory constraints, makes conventional methods ineffective. This paper introduces Phase-Folded Kernel Density Estimation (PFKDE), an unsupervised approach that takes advantage of the periodic structure of an orbit to detect anomalies. By transforming the time axis into orbital phase, measurements from successive cycles are aligned within a common phase-based representation. Within this phase domain, kernel density estimation is applied to model the distribution of telemetry values as a function of phase. PFKDE provides intuitive visualizations that enable operators to more easily interpret anomalous behaviour. Enhancements such as adaptive bandwidth selection and phase smoothing are presented to explain how the PFKDE can be adapted to handle different operational contexts efficiently. Experiments conducted on a real-world dataset demonstrate that PFKDE can accurately identify anomalies while providing interpretable visualizations, thereby addressing key challenges in small satellite operations.