## 3 Methods and Materials  
### MBSE and the Parametric Functional Model  
Model-based systems engineering is a discipline that emphasizes the use of models to design, develop, and test all parts of a system [35]. MBSE aims to shift the systems engineering paradigm from a document-centric approach to a coherent model from which the typical systems' engineering documents are derived. The MBSE model includes a functional architecture (encompassing functions, use cases, activities, actions, and states of the system) and a physical one (including physical elements, resources, and variation law of those resources). In this study, the functional architecture includes the operational capabilities that the system can perform. Each of these capabilities is associated with a physical element that can perform them. The first step is the definition of the functional architecture. During the design formulation phases, it is possible to generalize a set of operational capabilities for a given system, as studied in [36]. The operational capabilities are high-level functions responding to a mission statement [36]. From a SysML point of view, they can be identified as primary use cases. Regardless of the mission architecture, the authors propose to generalize a set of common operational capabilities for rover-like planetary exploration systems:  
* Observe target;
- Collect target data;
* Communicate telemetry;
- Receive command;
- Recharge batteries;
- Check resources;
- Check goal;
- Navigate to goal;
- Wait instructions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Application** & **Design Phase** & **Autonomy** & **MBSE-Based** & **ConOps Validation** \\ \hline
[23] & Cars & Preliminary design & Yes & Yes & Yes, by expert review \\ \hline
[34] & Satellites & Preliminary design & Yes & No & Not specified \\ \hline
[26] & Cars & Preliminary design & Yes & No & Yes, by simulation \\ \hline
\begin{tabular}{c} Preliminary design \\ and operations \\ \end{tabular} & Yes & Yes & Yes, by simulation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the literature employing MBSE or operations-based simulations and the study of this paper. The technologies that guarantee those operational capabilities are met may differ based on the mission. For example, \"navigate to goal\" can be a shared operational capability for a rover or a drone, even if the mobility system is quite different. Even how those operational capabilities would be detailed or executed can vary. Different missions may require the system to check different types of resources or execute a different logic. For example, \"collect target data\" may encompass sampling activities, temperature or radiation recording activities, mapping activities or similar. Some of these operational capabilities are heavily linked to autonomous decision-making. For instance, a planetary rover can check the list of goals provided by the control centre and remove some or send a warning to the control centre if it detects that reaching them may endanger it. In comparison, other capabilities are shared between autonomous and automated platforms, as for \"communicate telemetry\". Some of these listed operational capabilities may not be considered based on the mission's objectives. After identifying which high-level functions the system under study should perform, it is possible to focus on the physical architecture and identify the components. These components would impact resources such as power consumption, generated data or storage space. These parameters can derive from a database of components, previous studies, or a scale-up of existing systems. There is extensive literature on the rapid sizing of physical hardware for robotic space systems, as in [28; 37; 38]. This resource consumption can change or be refined during the different design phases. The MBSE model matures with the system development, encoding all the related information in a coherent model.