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The release and retrieval of a CubeSat from a big spacecraft is useful for the external inspection and monitoring of the big spacecraft. However, docking maneuvers during the retrieval are challenging since safety constraints and high performance must be achieved, considering the small dimensions and the actual small satellites technology. The trajectory control is crucial to have a soft, accurate, quick, and propellant saving docking. The present paper deals with the design of a tracking model predictive controller (TMPC) tuned to achieve the stringent docking requirements for the retrieval of a CubeSat within the cargo bay of a large cooperative vehicle. The performance of the TMPC is verified using a complex model that includes non-linearities, uncertainties of the CubeSat parameters, and environmental disturbances. Moreover, 300 Monte Carlo runs demonstrate the robustness of the TMPC solution.

Miniaturized electric propulsion systems are one of the main technologies that could increase interest in CubeSats for future space missions. However, the integration of miniaturized propulsion systems in modern CubeSat platforms presents some issues due to the mutual interactions in terms of power consumption, chemical contamination and generated thermal and electro-magnetic environments. The present paper deals with the validation of a flexible test platform to assess the interaction of propulsion systems with CubeSat-technologies from mechanical, electrical, magnetic, and chemical perspectives. The test platform is a 6U CubeSat hosting an electric propulsion system and able to manage a variety of electric propulsion systems. The platform can regulate and distribute electric power (up to 60 W), exchange data according to several protocols (e.g., CAN bus, UART, I2C, SPI), and provide different mechanical layouts in 4U box completely dedicated to the propulsion system. Moreover, the data gathered by the onboard sensors are combined with the data from external devices and tools providing unprecedented information about the mutual behavior of a CubeSat platform and an electric propulsion system.

The increasing complexity of modern space systems requires a more integrated and scalable approach to their design, analysis, and verification. Model-Based Systems Engineering (MBSE) has emerged as a powerful methodology for managing the complexity of systems through formalized modeling practices, but its integration with dynamic and domain-specific simulations remains limited. This paper presents the first version of the unified Modeling and Simulation (M&S) framework MOSAiC (Modeling and Simulation Architecture for integrated Complex systems), which connects MBSE with open source, multi-domain simulation environments, with the goal of improving traceability, reusability, and fidelity in the system lifecycle. The architecture proposed here leverages ARCADIA-based models as authoritative sources, interfacing with simulation tools through standardized data exchanges and co-simulation strategies. Using a representative space mission scenario, the framework ability to align functional and physical models with specialized simulations is demonstrated. Results show improved consistency between system models and simulation artifacts, reduced integration costs, and improved early validation of design choices. This work supports the broader vision of digital engineering for space systems, suggesting that a modular, standards-based approach to unifying MBSE and simulation can significantly improve system understanding and development efficiency.

Rendezvous and docking missions of small satellites are opening new scenarios to accomplish unprecedented in-obit operations. These missions impose to win the new technical challenges that enable the possibility to successfully perform complex and safety–critical manoeuvres. The disturbance forces and torques due to the hostile space environment, the uncertainties introduced by the onboard technologies and the safety constraints and reliability requirements lead to select advanced control systems. The paper proposes a control strategy based on Model Predictive Control for trajectory control and Sliding Mode Control for attitude control of the chaser in last meters before the docking. The control performances are verified in a dedicated simulation environment in which a non-linear six Degrees of Freedom and coupled dynamics, uncertainties on sensors and actuators responses are included. A set of 300 Monte Carlo Simulation with this Non-Linear system are carried out, demonstrating the capabilities of the proposed control system to achieve the final docking point with the required accuracy.

Given the role of Cubesats in the new space economy, a statistically relevant number of CubeSats have flown, and considering the high percentage of failed missions, the investigation of in-orbit anomalies becomes of paramount importance. It is rare to find data about mission failures, probably because the partial or total absence of telemetry does not encourage any analysis. The lack of data from the spacecraft in orbit can be mitigated through ad-hoc verification campaigns on satellite models when in-orbit anomalies are experienced. This paper shows an effective testing activity conducted on models of the spacecraft to understand the root cause of a severe anomaly that occurred during mission operations. The tests are part of a comprehensive methodology for root causes analysis. The paper aims at sharing the experience built upon a practical case of interest. More importantly, this work has the ambition of fostering the research on key topics of reliability, mission operations and assembly, and integration and verification/test processes, which have shown to be critical. The activity presented in this paper demonstrates that investigating the anomalies can help recover the mission of interest but can also support building a heritage that is still missing for CubeSat missions today.

This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstacles, goals, and an initial state, they can be asked to guess what a new path would look like. We performed experiments on a 64 × 64 pixel grid that generated a training set by using randomly positioned obstacles and goals. The heuristic search algorithm A* was used to create training paths due to its significant presence in the literature and ease of implementation. We experimented with architectural elements and hyperparameters, converging to a pix2pix-based architecture in which the generator was trained to generate plausible paths given obstacles and two points. A discriminator tried to determine whether these maps were real or fake. Additionally, we defined a qualitative path-generation “success rate” metric derived from the Fréchet inception distance (FID) and optimized our architecture’s parameters, ultimately reaching a 74% success rate on the validation set. Furthermore, we discuss the applicability of this approach to safety-critical settings, concluding that this architecture’s performance and reliability are insufficient to offset the downsides of a black-box approach to path generation.

Miniaturized propulsion systems can enable many future CubeSats missions. The advancement of the Technology Readiness Level of this technology passes through the integration in a CubeSat platform and the assessment of the impact and the interactions of the propulsion systems on the actual CubeSat technology and vice versa. The request of power, the thermal environmental, and the electromagnetic emissions generated inside the platform require careful analyses. This paper presents the upgraded design and the validation of a CubeSat test platform (CTP) that can interface a wide range of new miniaturized propulsion systems and gather unprecedented information for these analyses, which can be fused with the commonly used ground support equipment. The CTP features are reported, and the main achievements of the tests are shown, demonstrating the effective capabilities of the platform and how it allows for the investigation of the mutual interactions at system level between propulsion systems and the CubeSat technology.

The WIVERN (WInd VElocity Radar Nephoscope) mission, now in Phase 0 of the ESA Earth Explorer program, promises to complement Doppler wind lidar by globally observing, for the first time, the vertical profiles of winds in cloudy areas. This work describes an initial assessment of the performances of the WIVERN conically scanning 94 GHz Doppler radar, the only payload of the mission. The analysis is based on an end-to-end simulator characterized by the following novel features tailored to the WIVERN radar: the conically scanning geometry, the inclusion of cross-polarization effects and the simulation of a radiometric mode, the applicability to global cloud model outputs via an orbital model, the incorporation of a mispointing model accounting for thermoelastic distortions, microvibrations, star-tracker uncertainties, etc., and the inclusion of the surface clutter. Some of the simulator capabilities are showcased for a case study involving a full rotational scan of the instrument. Preliminary findings show that mispointing errors associated with the antenna's azimuthal mispointing are expected to be lower than 0.3 m s−1 (and strongly dependent on the antenna's azimuthal scanning angle), wind shear and non-uniform beam-filling errors have generally negligible biases when full antenna revolutions are considered, non-uniform beam filling causes random errors strongly dependent on the antenna azimuthal scanning angle, but typically lower than 1 m s−1, and cross-talk effects are easily predictable so that areas affected by strong cross-talk noise can be flagged. Overall, the quality of the Doppler velocities appears to strongly depend on several factors, such as the strength of the cloud reflectivity, the antenna-pointing direction relative to the satellite motion, the presence of strong reflectivity and/or wind gradients, and the strength of the surface clutter. The end-to-end simulations suggest that total wind errors meet the mission requirements in a good portion of the clouds detected by the WIVERN radar. The simulator will be used for studying tradeoffs for the different WIVERN configurations under consideration during Phase 0 (e.g., different antenna sizes, pulse lengths, and antenna patterns). Thanks to its modular structure, the simulator can be easily adapted to different orbits, different scanning geometries, and different frequencies.

Meeting abstracts - A single PDF containing all abstracts in this supplement is available at http://www.biomedcentral.com/content/files/pdf/1550-2783-8-S1-full.pdf.EMPTY Oxidative stress, a condition defined as unbalancing between production of free radicals and antioxidant defenses, is an important index of health status to monitor wellness and sport performance.