Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective.

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value.

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883–0.899) at the 90% nominal level and 0.947 (95% CI, 0.943–0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505–1.540) and 1.552 (95% CI, 1.534–1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883–0.899) at the 90% nominal level and 0.947 (95% CI, 0.943–0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505–1.540) and 1.552 (95% CI, 1.534–1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883–0.899) at the 90% nominal level and 0.947 (95% CI, 0.943–0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505–1.540) and 1.552 (95% CI, 1.534–1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883–0.899) at the 90% nominal level and 0.947 (95% CI, 0.943–0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505–1.540) and 1.552 (95% CI, 1.534–1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.