## 6 Conclusions and Future Directions  
This article presents an in-depth analysis of four different simulation environment configurations, utilizing a model-based methodology to verify and validate the ADCS from the early stages of development. This approach supports both the design and implementation of modular systems, contributing significantly to the standard systems-focused methodology. Each simulation environment is detailed to illustrate the specific requirements and facilities necessary for effective validation. The sequential approach enables designers to isolate and differentiate errors arising from mathematical modeling, computational limitations, and physical implementation. This process is particularly advantageous for solving specific problems and optimizing the Verification and Validation process. In Section 3.2, the SIL environment was utilized for optimizing the controller through sensitivity analysis. This step allowed for the identification of optimal control gains and the evaluation of the system's response to various initial conditions. SIL simulations showed that, by varying the control gains \(Kd\) and \(Kp\) within a 5.8% range, the mean pointing accuracy and standard deviation of the pointing error were effectively assessed, revealing a consistent performance across different scenarios. Section 3.1 introduced detailed models of physical components and the simulated environment to assess their impact on controller performance, serving as a verification method after the detailed design of the controller, typical of phase C in the standard design process. Despite the error margin in SIL due to dependencies and algebraic loops, it remains an ideal simulation method as it is unaffected by hardware inconsistencies. The MIL environment allowed for evaluating the controller's performance, showing a mean pointing accuracy of 11.6914 with a standard deviation of 3.5952 for radii pointing and an angular velocity norm of 0.0022 rad/s with a standard deviation of 1.3972 \(\times\) 10\({}^{-4}\) rad/s for detumbling. Using the PIL simulations described in Section 3.3.1, variables such as latency and memory usage were isolated to analyze their effects on controller performance. PIL simulations showed a slight increase in latency (4 ms for B-dot and 12 ms for nadir) and a mean pointing accuracy of 23.3497\({}^{\circ}\) with a standard deviation of 7.8226\({}^{\circ}\) for nadir pointing and an angular velocity norm of 0.0022 rad/s with a standard deviation of 1.3705 \(\times\) 10\({}^{-4}\) rad/s for detumbling. This step is crucial for understanding the impact of computational constraints on the embedded system. The HIL environment, also discussed in Section 3.3.1, provided insights into the latency impacts on system performance. HIL simulations indicated higher latencies (5.2 ms for B-dot and 15 ms for nadir) but still maintained comparable performance, with a mean pointing accuracy of 9.9649\({}^{\circ}\) and a standard deviation of 2.8167\({}^{\circ}\) for nadir pointing, and an angular velocity norm of 0.0024 rad/s with a standard deviation of 1.4217 \(\times\) 10\({}^{-4}\) rad/s for detumbling. The HIL environment accurately approximated real behavior, allowing for effective comparisons with other simulation environments. Depending on the specific characteristics of a CubeSat, it is necessary to take into account all risk components implicating new technologies, mobile parts or, in this case, COTS components. As a matter of fact, rigorous testing using specialized test environments is necessary after integration to assess system functionality and operational expectations. First of all, due to the usage of COTS, rigorous thermal testing must be performed, while all modes of operation of the ADCS must be tested to verify its performance under extreme temperature variations. Further, outgassing tests should then be considered to evaluate COTS degradation and its performance under vacuum conditions and during operation.