## 7 Conclusions  
This study demonstrated a comprehensive and systematic approach to the optimization and validation of the CUbest Solar Polarimeter (CUSP) payload, leveraging advanced computational techniques to address the multifaceted design challenges of CubeSat missions. The integration of reduced-order models (ROMs) and radial basis function (RBF) mesh morphing proved pivotal in achieving a balance between computational efficiency and the precision of numerical simulations, enabling real-time evaluation of field quantities such as stress and temperature distributions across varying design parameters. The structural and thermal performance of the payload was rigorously assessed through a series of multiphysical finite element analyses. Quasi-static analyses validated the payload's capacity to withstand extreme mechanical stress scenarios with loads up to 15 g per axis, while modal analyses confirmed compliance with the critical mission requirement of maintaining the first resonance mode above 120 Hz, ensuring stability under launch and operational conditions. Additionally, thermo-elastic and random vibration analyses identified specific areas requiring refinement, notably the scintillator frame and the platform interface, which were subsequently optimized through iterative morphing and response surface methodologies. The design optimization process was further enhanced by employing advanced techniques such as the Genetic Aggregation Response Surface (GARS), enabling the integration of multiple response surfaces for robust prediction and optimization. The adoption of additive manufacturing for the fabrication of optimized components facilitated the rapid development of a technological demonstrator, bridging the gap between numerical simulations and physical validation. Future work will focus on detailed environmental testing of the demonstrator to validate its performance under simulated orbital conditions, including extreme thermal cycles and vibration profiles representative of launch and operational stresses. Further refinement of the payload design will incorporate manufacturing constraints and material limitations to enhance robustness and manufacturability. The integration of real-world test data into the ROM framework will enable dynamic updates to the design process, ensuring a higher degree of accuracy and reliability. This iterative and multidisciplinary approach underscores the potential of combining advanced computational methods with experimental validation in the design of CubeSat payloads. The methodologies developed and validated in this study not only strengthen the CUSP mission's readiness for operational deployment, but also set a precedent for future small satellite missions, particularly those addressing complex scientific objectives in challenging operational environments.  
Conceptualization, G.L. and S.F.; methodology, G.L., E.D.M. ([PERSON]) and A.L.; software, G.L., A.L., M.E.B. and M.C.; validation, S.F., E.D.M. ([PERSON]), D.B., I.D., S.N., A.T. and E.Z.; formal analysis, M.E.B.; investigation, M.E.B.; resources, G.L., M.E.B. and S.F.; data curation, G.L., M.E.B. and S.F.; writing--original draft preparation, G.L.; writing--review and editing, G.L.; visualization, G.L.; supervision, S.F. and M.E.B.; project administration, G.L.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This work was funded by ASI Phase A contract 2022-4-R.0.  
The authors declare no conflicts of interest.