To account for the effect of unexpected centroid shifts, a sliding-mode observer and an adaptive estimator are designed to obtain disturbance information useful for subsequent FTC controller designs. Later, an innovative adaptive FTC scheme is suggested by employing the observer in conjunction with a specific adaptive controller consisting of a sixth-order dynamic compensator, which can guarantee the achievement of the control objective without resorting to the exact knowledge of the inertial matrix. In [5], a neural network algorithm based on the combination of Labeling Neural Network and Relevant Long Short-Term Memory Neural Network is proposed. This is a semi-supervised exception detection algorithm that can be readily extended with business logic exception types. The self-learning performance of this multi-network is better adapted to the big data anomaly detection scenario, which further improves the efficiency and accuracy of network data anomaly detection and considers business scenario-based anomaly data detection. In [6], a new method for fault diagnosability research based on information geometry is proposed. The problem of the diagnosability evaluation of dynamic system faults is transformed into a distance calculation problem on a manifold. The Fisher information distance is used to realize a quantitative judgment, and a quantitative evaluation index of the fault diagnosability of a satellite attitude determination system is designed. The work [7] presents a novel predefined-time nonsingular tracking control system for a vertical-takeoff horizontal-landing (VTHL) reusable launch vehicle (RLV) in the face of parameter uncertainties, model couplings, and external disturbances. Firstly, this paper proposes a novel predefined-time prescribed performance function (PTPPF) with desired steady-state and transient performance. The convergence time of PTPPF from the transient state to the steady state can be flexibly adjusted by changing one parameter. In [8], a fault diagnosis method based on fusion convolutional neural networks (FCNNs) is proposed for an aircraft electro hydrostatic actuator (EHA). The FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively, higher than the 1 DCNN and the 2 DCNN by about 16.5% and 5.7%. The work [9] proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. In [10], to solve the problems of the existing real-time health assessments of microsatellite swarms, such as the difficulty of selecting a multisource and assessment calculation normalization, this paper proposes a real-time health assessment method applicable to mission-oriented swarms. The method divides the microsatellite swarm into three levels: a single satellite, intersatellite communication link, and swarm effectiveness, which establish a multilevel index system by adopting the reliability evaluation based on random failure and failure by loss; this is a health evaluation based on natural connectivity, and a real-time dynamic analysis based on swarm topology. The work [11] investigated the adaptive fault-tolerant control (FTC) for a flexible variable structure spacecraft in the presence of external disturbance, multiple actuator faults, and saturation. The attitude system model of a variable structure spacecraft and actuator fault model are first given. A sliding-mode-based fault detection observer and a radial basis function-based fault estimation observer were designed to detect the time of actuator fault occurrence and estimate the amplitude of an unknown fault, respectively. In [12], a variable structure PID controller with a good convergence rate and robustness for satellite attitude is proposed.