In broadband reconnaissance scenarios，achieving high signal detection accuracy often entails significant computational costs. To address this，a multi-scale convolution attention sparse detection(MSCAS) method is proposed，which incorporates prior knowledge of signal spectrograms by capturing long-range temporal dependencies and suppressing irrelevant frequency-domain interference. MSCA-S introduces a multiscale horizontal convolution attention(MSHCA) mechanism that jointly extracts multi-dimensional signal features，enhancing detection accuracy while reducing computational complexity through horizontal convolution. Building on MSHCA，a hierarchically stacked broadband signal detection framework is developed，and sparse feature parameters are used to further optimize computational efficiency. MSCA-S is evaluated on a real-world and simulated broadband signal dataset(2.5 MHz spectrum) collected in Qingdao，achieving an average detection accuracy of 95.6% across varying signal-to-noise ratios. Compared with the frequency-sensitive signal detector，the Swin-Transformer-based protocol recognition method，and the Res-101 detection method，MSCA-S improves accuracy by 0.05%，2.94%，and 6.14%，respectively，while reducing computational costs by 1.53×10¹⁰，1.79×10¹⁰，and 4.59×10¹⁰，respectively.

Automatic Dependent Surveillance-Broadcast IN(ADS-B IN) applications can provide numerous conveniences for pilots during flight operations, with safety being the prerequisite for realizing the advantages of ADS-B IN applications. A Bow-tie model-based safety assessment method is introduced to address potential safety issues associated with ADS-B IN applications in actual flight activities. By analyzing pre-defined hazards,the maximum acceptable probability of hazard occurrence is determined,which in turn leads to the derivation of safety requirements such as the failure rate of ADS-B equipment or the integrity of communication data links necessary to achieve this probability. Based on the explanation of this method,a specific implementation case is presented to further illustrate its application.

For the problem that the existing deep learning modulation recognition algorithms are not robust enough and have insufficient generalization ability in complex signal environments，a multi-channel network based on phase parameter estimation and spatial reconstruction(PET-SAMCL) is proposed. First，the input in-phase quadature(IQ) signal is converted by phase parameter estimation and divided into three modules to extract the amplitude-phase feature，IQ combination and branching features of IQ respectively. A spatial reorganization unit(SRU) is added to the feature extraction module to reduce the influence of redundant features. The spatial features are refined and fused by global average pooling and soft attention operations，and the temporal and spatial features are extracted by gated recurrent units(GRU) and bidirectional gated recurrent units(BiGRU) . Ablation study determines the optimal model structure. The model performs well on the RML2016.10a dataset，achieving a maximum recognition accuracy of 93.9% at 14 dB，and the average recognition rate is increased by 7.7% compared with that of other models.

It's difficult to implement maintainability design and evaluation effectively in the early stages of development of space TT&C ground system. To solve this problem,a maintainability design and evaluation system is constructed based on virtual reality technology,a maintainability design and evaluation workflow is developed,and a comprehensive evaluation criteria for maintainability design is proposed. In the virtual maintenance scenario,virtual maintenance prototypes and virtual maintenance resources are used to verify the overall process,and immersive simulation is conducted to verify the accessibility,visibility,and human body comfort of the maintenance of components of a vehicle mounted space TT&C ground system. Thus the quantitative comprehensive evaluation of virtual maintainability design is achieved. The results show that this method reduces costs by 68% , shortens duration by 61% compared with traditional method. It may serve as a practical reference for visualizing,quantifying carrying out maintainability design and evaluation in the early stages of development of space TT&C ground system.

In response to challenges in sea clutter modeling within the classical algorithms,including the lack of fitting accuracy due to the inability to satisfy multiple statistical characteristics simultaneously and the limitations in controllably generating accurate class-based results,combining the generative power of U-Net with the potential of complex-valued neural networks to deal with complex nonlinear problems in the electromagnetic domain, a novel approach is proposed. This approach integrates complex-valued network layers and a classifier-free guidance module, establishing an interpretable mapping mechanism for input conditions,resulting in complex-valued guided diffusion model(CVG-DM). This model is centered on the direct utilization of the complex-valued baseband signals from the In-phase and Quadrature(IQ) path of sea clutter, as well as the exploration of the relationship between sea clutter and strong targets in the background. This enables controlled generation of the model under varying conditions of target presence or absence, and assessment based on amplitude distribution, temporal and spatial correlation, nonlinear characteristics,and Doppler spectrum. Simulation experiment validates CVG-DM's capability in realizing sea clutter data augmentation under varying conditions. The simulated clutter can simultaneously take into account above five statistical properties, surpassing the completeness of real number network-based evaluation metrics and further enhancing fidelity.

In order to achieve low-latency and low-energy offshore communication, the dynamic service cache update mechanism is introduced into the complex neural network, and the mobile edge dynamic service caching policy (MEDSCP ) based on double deep Q network (DDQN ) is proposed by cleverly designing the complex neural network structure based on offshore communication scenarios. The policy firstly obtains the optimal offloading decision set through the user terminal task offloading decision game, and then utilizes mobile edge computing(MEC) and dynamic service caching update to reduce the delay and energy cost of task execution in the offshore communication environment, aiming to improve the efficiency of task processing in offshore communication and to expand the development potential of this industry. Simulation experimental results show that the proposed MEDSCP strategy can achieve fast convergence of the algorithm while guaranteeing the training effect,and also effectively reduce the delay-energy weighted sum of offshore communications compared with existing work.

Vehicular edge computing(VEC) converges the computing resources of cloud servers to the edge of the network closer to the vehicle side, allowing vehicles to offload vehicular computing tasks to the network edge servers,thus providing vehicles with low latency,high bandwidth and high reliability services. However,the highly dynamic network topology,strict low-delay constraints,and massive data of vehicular tasks of VEC pose significant challenges for implementing efficient offloading. The digital twin(DT)-driven VEC model can enable real-time monitoring of the state of the VEC network,thus assisting in making sound offloading decisions in the physical world. Firstly, the research progress of edge computing, available vehicles and DT-driven VEC task offloading methods are introduced. Then,the scenario architecture of DT-driven task offloading for VEC is elaborated. Finally,the future research challenges and solutions of DT-driven VEC task offloading methods are discussed,in hope of providing certain guidance for solving the problem of DT-driven VEC task offloading.

For the problem of target azimuth estimation under low signal-to-noise ratio (SNR) for active sonar in underwater environments,a direction of arrival(DOA) estimation multi-beamforming sonar imaging method based on fractional Fourier transform (FrFT )-enhanced iterative adaptive approach (IAA ) is proposed. Firstly, the echo signals received by hydrophones are subjected to FrFT preprocessing, transforming the wideband linear frequency modulation (LFM ) signals into narrowband signals in the fractional domain to avoid the influence of cross-interference terms. Then, focusing on LFM signals and suppressing noise in the FrFT domain is achieved. Finally,the iterative adaptive method is implemented in the FrFT domain,optimizing the power spectrum estimation method for accurate DOA estimation. Compared with traditional DOA estimation methods, the proposed method achieves better estimation accuracy and smaller root mean square error under low SNR conditions without increasing sensor array elements. It significantly improves imaging effectiveness, as indicated by simulation results showing sidelobe levels-13.364 dB for peak sidelobe ratio. in the range direction and -9.723 dB for integrated sidelobe ratio,-13.874 dB for peak sidelobe ratio in the azimuth direction and -10.034 dB for integrated sidelobe ratio.

To reduce the computations of parameters estimation in high-dynamic and long integration global navigation satellite system(GNSS) signal detection applications,the authors propose a low-computation GNSS acquisition method (LGAM) suitable for high-dynamic environment. The goal of LGAM is to apply the synthetic Doppler frequency hypothesis testing (SDHT) method to the acquisition of high dynamic GNSS signals with Doppler rate and bit flipping. Firstly,sparse Doppler frequency(SDF) process is implemented by coarse Doppler estimation,and post-correlation signal model is derived based on SDF structure. Then,in order to improve the detection efficiency of Doppler and Doppler rate, double-FFT based detection is proposed based on the post-correlation signal model for parameters estimation. The results demonstrate that in high dynamic environments, when the signal-to-noise ratio (SNR ) is higher than -43 dB, the computational complexity based on FFT method is 15 times that of LGAM1 and 780 times that of LGAM2.

Object detection technology aims to locate and identify specific category targets in images or videos. However，in low-illumination scenarios，problems such as low contrast，blurred boundaries，and noise interference，result in the decline of detection performance. To address this，a Color Channel Transformation Enhancement-based Object Detection (C2TEOD ) algorithm is proposed. Firstly，a color channel transformation module is constructed，and learnable parameters are introduced to transform different color channels，enhancing the flexibility of the enhancement strategy. Then，an image enhancement module is employed to preprocess the input images. This module is jointly optimized with the object detection network using detection loss functions，thereby enabling the enhancement module to learn to generate representations that explicitly facilitate the subsequent detection task. Additionally，a selective self-supervised regression loss is proposed that uses both the original low-illumination images and the enhanced images as inputs to optimize the detection network. According to detection results，the enhancement module is further optimized through self-supervised regression to improve detection performance. Experimental results show that，compared with the baseline method，the mean average precision(mAP) metrics on the Exdark，M3FD，and LLVIP datasets are improved by 2.2%，1.1%，and 0.2% respectively.