With the recent increase in CubeSats’ ability to undertake complex and advanced missions, they are being considered for missions such as constellations, which demand high development efficiency. From a satellite interface perspective, productivity can be maximized by implementing a flexible modular structural platform that promotes easy reconfigurability during the integration and testing phase. Thus, the structural design of a CubeSat plays a crucial role in facilitating the satellite integration process. In most cases, the mechanical interface implemented between the primary load-supporting structure and internal satellite subassemblies affects the speed and efficiency of satellite integration by adding or reducing complexity. Most CubeSat structural designs use stacking techniques to mount PCBs onto the primary structure using stacking rods/screws. As a result, the internal subsystems are interconnected. This conventional interface method is observed to increase the number of structural parts, while increasing complexity during integration. In this study, flexible 3U and 1U CubeSat platforms are developed, based on the slot concept. This innovative mounting design provides a simple method of mounting PCBs into the slots. The concept is evaluated and verified for its feasibility for mass production applications. Count and complexity analysis is carried to evaluate the proposed design against the conventional type of structural interface methods. The assessment reveals that this new concept demonstrates a significant improvement in the efficiency of the mass production process.

Adaption of laser communication terminals to airborne and lean-satellite platforms is now a vogue, made possible due to the progressing advancements in lightweight components and compactness of onboard electro-optical transceivers and control systems. This enables highly secured and superior data-transmission rates beyond multiple Gigabit/second on CubeSats and drones compared to Megabit/second rates offered by similar radio-transceivers form factors. However, laser-transmission links require a very stringent beam-pointing stability because they are easily perturbed by attitude variations and micro-vibrations generated by the host platform’s propulsion system or other mechanically active subsystems in proximity with the transmitter’s optical head. Severe line-of-sight jitter causes the downlink laser beam to drift from the targeted receiving system’s field-of-view, inducing pointing errors, increasing signal outage probability and information loss. We experimentally examine the platform jitter generated by the propellers of an hexacopter drone during ground operation and the attitude-control unit’s reaction wheels in a 6U CubeSat structure. We determined the vibration spectrum unique to these platforms and accordingly prescribe requirements for applicable optical fine pointing and disturbance isolation or suppression systems needed to achieve a high-fidelity laser-communication link.

Anomaly signals in the Attitude Determination and Control System (ADCS) of nanosatellites can significantly degrade mission performance, especially in the absence of robust fault detection, isolation, and recovery (FDIR) mechanisms. Thus, traditional threshold-based approaches, while portable and compact, may overlook subtle faults, whereby abnormal sensor signals or current spikes within the threshold may compromise the operation of the entire ADCS as a subsystem. Furthermore, the lack of interpretable detection methods further limits the development of reliable machine learning (ML) FDIR solutions. To address these limitations, this work presents a wavelet-based anomaly detection framework that introduces a two-stage hybrid architecture combining a lightweight Convolutional Neural Network (CNN) for fault detection with logistic regression for fault classification, both based on Discrete Wavelet Transform (DWT) detail coefficients extracted from sensor and actuator data. The framework was validated using a statistics-based anomaly dataset for a 1U CubeSat ADCS simulated in MATLAB, in which anomalies are introduced at the component level with controlled variations in magnitude, frequency, and waveform, ensuring 99% statistical significance. Additionally, to demonstrate operational feasibility, constraints for onboard implementation were considered by executing the proposed framework in a Processor-in-the-Loop (PIL) environment. For benchmarking, lightweight detection and classification algorithms were compared, including Out-Of-Limits (OOL) and compact machine learning approaches. Finally, to identify the framework’s limitations and trace faulty events to physical phenomena, Grad-CAM, SHAP, and impurity analysis were performed on the proposed algorithms as primary interpretability tools. Consequently, the results demonstrate accurate fault detection and identification to support both autonomous FDIR actions and ground operator decision-making. The proposed validation framework and dataset provide a reproducible basis for advancing anomaly detection onboard nanosatellites.