Abstract: A recreational vehicle includes a power source, a first electrical junction box, a second electrical junction box, a chassis power subsystem, and a recreational vehicle (RV) power subsystem. The first electrical junction box is electrically coupled between the power source and the second electrical junction box, the first electrical junction box is electrically coupled between the power source and the chassis power subsystem, and the second electrical junction box is electrically coupled between the first electrical junction box and the RV power subsystem.

Abstract: An aircraft-based method for deriving synthetic air data (SyAD) independent of traditional pneumatic air data systems includes a first stage driven by machine learning (ML) algorithms trained throughout the aircraft's flight envelope and a second stage driven by an optimal state estimator incorporating non-linear Kalman filtering. The ML algorithms receive absolute and inertial parameter inputs (e.g., position, ground speed, attitude, angular rates, linear acceleration) sensed by absolute and inertial aircraft sensors and estimate a first-stage SyAD triplet (true airspeed, angle of attack, sideslip angle). The optimal state estimator blends the first-stage SyAD estimate with a subset of absolute and inertial parameter inputs to generate a refined blended SyAD triplet. The blended SyAD triplet output by the optimal state estimator refined estimates of the airspeed, angle of attack, and sideslip angle.