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Meanwhile, controlling each of these dynamics separately can lead to unacceptable results. Common approaches to controller design rely on the use of analytical methods. Although this approach can provide formal guarantees of stability and performance, the analytical design of hybrid controllers can become quite cumbersome. Alternatively, modeling and simulation (M&amp;S)-based design techniques have proven successful for hybrid controllers, providing robust results based on Monte Carlo techniques. This requires simulation models and platforms capable of seamlessly composing the underlying hybrid domains. Unmanned Aerial Vehicles (UAVs) are CPSs with sensitive physical\u2013computational couplings. We address the development of a hybrid model and simulation platform for a data collection application involving UAVs with onboard data processing. The quality of control (QoC) of the physical dynamics must be ensured together with the quality of service (QoS) of the onboard software competing for scarce processing resources. In this scenario, it is imperative to find safe trade-offs between flight stability and processing throughput that can adapt to uncertain environments. The goal is to design a hybrid supervisory controller that dynamically adapts the use of resources to balance the performance of both aspects in a CPS, while ensuring system-level QoS. 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