This work explores the crucial roles that control theory and digital twins play in enhancing the performance of underactuated quadrotor unmanned aerial vehicles (QUAVs). It describes how the novel idea of digital twins combined with control theory could alter operations. Some basic ideas, such as the underactuated UAV model, various control schemes, and innovative techniques to improve autonomy and performance in QUAV missions in dynamic circumstances, may also be of interest to readers. It highlights recent developments and presents a game-changing idea of combining digital twin with computer vision, amalgamating artificial intelligence and internet of things like elements to improve sensing and perception better for autonomous flight control, human-UAV interaction, energy-efficient flight, and swarming UAVs. The reader may finally find suggestions for applying control theory and understandings of how incorporating digital twin technology could boost its revolutionary potential.

As satellite missions become more complex, reliable power management is crucial for success. Traditional power budgeting approaches often operate at the system level, leading to inefficiencies in power allocation, particularly during critical phases such as eclipse mode. This study presents a component-level power budgeting framework to enhance operational integrity for next-generation satellites, specifically focusing on power load calculation, power generation, and battery capacity requirements. Key challenges such as power shortages, thermal fluctuations, and battery management require efficient power allocation and thermal control to ensure system reliability. Additionally, laser communication, while a critical capability for advanced satellites, becomes more complex during eclipse mode as in this period the mission execution will totally depends on the onboard available power. This analysis is based on detailed component-level power load calculations for eclipse mode. The total power load in this scenario is 38.922 W, with an operational mode of 27.5179 W, accounting for the power consumption of other subsystems. These calculations incorporate each module's power load, weighted by its duty cycle per orbit. The solar panels generate 65 W of power, with an effective power output of 45.599 W, as simulated in STK software. The eclipse mode, at an altitude of 525 km and an inclination angle of 83 degrees, lasts for 37.7% of the orbit, resulting in a power consumption of 27.343 W during this time. The calculated power consumption in eclipse mode is 27.343 W, with a minor error of 0.175 W. To meet battery requirements, the battery must provide at least 48.34 W, considering an 80% Depth of Discharge (DOD), for a 10-second high-power communication window. The battery capacity should exceed 48.34 W to ensure safe operation in eclipse mode. Based on these calculations, the satellite's power generation and storage system are sufficient to support its operations during eclipse mode, ensuring reliable and uninterrupted performance.

Unmanned aerial vehicles, or drones, are increasingly deployed in critical applications that demand exceptional safety and reliability. However, as drones become integral to industries such as logistics, agriculture, and public safety, reliability issues with core components like propellers can lead to serious safety risks and financial losses. Propellers typically have high failure rates, especially in harsh conditions, which has encouraged research into effective health monitoring techniques for early fault detection. Despite these efforts, existing datasets on faulty propellers remain limited in scale, diversity, and coverage of fault types and severity levels. This data article introduces a comprehensive dataset composed of motion trajectories and flight logs of 130 flight sequences for a commercial quadcopter platform. This dataset includes different flight paths, fault types, and severity levels. The dataset primarily comprises onboard sensor readings from the drone and the corresponding mission trajectory logs. All faults develop while the drone operates normally, with no significant impact on performance across flight phases. These faults include minor cracks, edge damage, and holes, which are some of the popular propeller failures. This dataset is valuable for exploring effective fault detection methods and predictive maintenance capabilities.

A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert, before the swarm returns to its original formation. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulation results show that the approach works in critical environments such as warehouses, laboratories, and other secure indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.