In order to investigate the effect of aero-thermal radiation on star sensor detection capability in near-space hypersonic flight platforms, this paper proposes a star sensor simulation imaging technique that couples aerodynamic flow field thermal radiation with target star signals. First, a regional conical imaging sampling model is constructed to preprocess the flow field data, significantly improving computational efficiency. Second, a radiation transfer equation for non-equilibrium flow fields is introduced to establish an energy transfer model based on the star sensor’s optical link. Finally, the detection performance of the star sensor under different flight conditions is quantitatively analyzed, and some simulation results are compared with flight test data for validation. The results indicate that the simulation model deviates by less than 30%. The method proposed in this paper provides important data and theoretical support for star sensor navigation accuracy analysis and extreme condition assessment.

Herein, experimental and numerical studies were conducted on the excited-state radicals CO₂ ^*, CH^*, OH^*, and C₂ ^* in hydrocarbon combustion. In the experiment, an optical fiber spectrometer was employed to perform one-dimensional scanning measurements of chemiluminescent signals in methane/air laminar premixed flames with equivalence ratios ranging from 0.7 to 1.2, capturing the emission spectra of the excited-state radicals. Based on the Abel tomographic reconstruction method, the relationships between the peak chemiluminescence intensity, the peak chemiluminescence intensity ratios, and equivalence ratios of the excited-state radicals were quantitatively investigated. The findings reveal that CO₂ ^* radiation significantly influences the measurement of the chemiluminescence intensities of OH^*, CH^*, and C₂ ^*. The CO₂ ^* emission interference is quantitatively decoupled through spectral deconvolution, enabling a novel chemiluminescence-based equivalence ratio diagnostic. Calibrated against premixed flames, the C₂ ^*/OH^* and CH^*/OH^* intensity ratios exhibit monotonic correlations with equivalence ratios in 0.7–0.9 and 0.9–1.2 regimes, respectively. Finally, the existing chemical reaction mechanisms for OH^*, CH^*, and C₂ ^* were validated based on the experimental results. It mainly focuses on the relationship between the peak chemiluminescence intensity, the peak chemiluminescence intensity ratios, and the equivalence ratios. Among the existing reaction mechanisms for excited-state radicals, the simulation accuracy for CH^* is the highest, while further research is required to refine the chemical reaction mechanisms for OH^* and C₂ ^*.

As the signature gas released before coal combustion, methane’s telemetry accuracy is susceptible to environmental influences, which is a difficult problem that needs to be solved. This article provides a detailed exposition on the application of Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology in the field of gas monitoring, with particular emphasis on its advantages in coal safety detection. The coal combustion experiment is conducted to determine the required performance indexes. Through the temperature compensation algorithm and normalized signals, the impact of factors such as ambient temperature and environmental noise is reduced, effectively improving the signal-to-noise ratio and accuracy. The experiments demonstrate that this system effectively lowers the detection limit for methane while improving measurement accuracy, thereby providing robust support for the early warning of coal spontaneous combustion.

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios.

Guided wave ultrasonic testing (GWUT) in industrial environments is often limited by low signal-to-noise ratio (SNR), which reduces defect detectability. This study proposes a knowledge-guided framework that combines synthetic data generation with a tailored denoising network. From a single reference acquisition, paired clean and noisy signals are constructed using dual-Gaussian echo modeling and composite noise synthesis based on measured spectra. A Wavelet-Initialized Attention U-Net is developed with wavelet-informed kernels, a dual-decoder structure, and an attention bottleneck for efficient temporal integration. Experiments on two representative GWUT systems, a railway switch rail monitoring setup and a storage tank wall inspection robot, show that the proposed framework achieves up to 29.7 dB ROI-based SNR improvement on synthetic data, and substantial CNR improvement on real signals accompanied by a marked reduction of false detections (FP/FN), outperforming classical and deep learning baselines. The method also achieves real-time inference and efficient data generation with moderate computational cost. These results indicate that physics-guided synthesis combined with a tailored network provides a practical solution for GWUT denoising and supports reliable defect detection in industrial applications.