## 6 Conclusions  
In this paper, we proposed a simulation framework for the performance evaluation of hybrid satellite-terrestrial network access. The framework contains an AI part that enables the intelligent selection of better network access. The framework is successfully demonstrated on one use case scenario. The UDP streaming session scenario was selected to clearly demonstrate the benefits of AI aid. This demonstration shows the potential of our proposed framework. By simply changing the experimental setup, one can easily and very quickly gain insight into the relation between network accesses and which access should be used more in a hybrid session. In our demonstration scenario, we changed the expected average SNR value. By simulating other conditions, users can gain insight into a multitude of scenarios and gain a very good understanding of multipath, i.e., hybrid, access performance for various conditions. The MP-TCP and similar multipath solutions will be tested and presented in our future work. In these scenarios, multiple factors impact the overall performance because of additional mechanisms like flow and congestion control algorithms. For example, link delays need to be included, and by simulating different relations between link delays over both accesses, one will be able to analyze the performances for various scenarios and also determine which flow/congestion control algorithms are the most suitable for hybrid satellite-terrestrial access. Note that this aspect of flexibility in testing various scenarios emphasizes the economic aspect of our proposed and also any other simulator as well. By using the same environment with different module versions or parameter setups, a vast set of scenarios can be tested. Our proposed framework includes hardware components that increase the cost of the simulation environment compared to software-only simulators, but they add to the versatility of our framework in adding potential hardware accelerators for computing intensive functions or setting the precise link delays. Also, an important factor in practice is power consumption. Currently, the proposed simulation framework does not estimate power consumption. However, it should be noted that the proposed AI model is not complex, and thus, it can be used in energy-aspect-critical devices like smartphones. Considering that the input to the neural network is a signal containing 10 values, the neural network architectures used in the authors' previous (and current) work are not of high complexity. In the authors' previous work [48], the best performing model was LSTM with 17.8 thousand parameters, with the second one being LSTM with 4.8 thousand parameters, both with a single LSTM layer and a single hidden fully connected layer. These architectures do not require substantial amounts of resources to run, even on mobile devices, and can be easily implemented with a framework designed for mobile platform use--LiteRT (formerly TensorFlow Lite). One very important property of the proposed simulation framework is its distributed nature, which provides great flexibility and its ability to adapt to different scenarios. The distributed approach enables the very easy replacement of existing modules with their other versions. And this will be utilized in our future work. One direction of our future work will focus on AI components to use other parameters besides SNR measurements like latency or user preferences in network access selection. For example, the user preference parameter can offer one network access if it satisfies a desired QoS. Link delays are important in testing the performance of the MP-TCP and other similar multipath transport protocols as well. Also, new control mechanisms in such protocols can be easily tested and evaluated and compared to existing ones. In this way, it is possible to take into account the specifics of hybrid satellite-terrestrial (wireless) network access and propose improvements in the MP-TCP and similar protocols.