Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

Owing to the increased demand of long endurance flight, lightweight, and high strength envelope becoming the main point of the stratospheric airship design, especially the research on the envelope mechanical properties a novel stratospheric airship envelope material was developed in this paper. According to the structure of the woven fabric composite, nonlinearity and orthotropy are two main characteristics of the envelope. Uniaxial tensile test was performed to study the stress–strain curves of the envelope material. It is seen that the force–displacement curve can be divided into two significant nonlinear regions and three quasi-linear regions by the analysis of the test results. To analyze the force–displacement curve with different regions by the statistical method, Monte Carlo simulation based on the Ising model was developed. It is found that the simulation curves coincide well with the experiment curves both in the warp and weft directions. And the results show that parameters of the envelope material including fabric strength, functional layer strength, interfacial bonding strength, and so on, are the important factors affecting the mechanical properties and they decide the force–displacement curve shape.

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) and transformer architectures have each contributed to considerable advancements. CNNs possess potent local feature representation skills, whereas transformers excel in learning global features, offering a complementary strength. Nevertheless, both architectures are limited by static receptive fields, which hinder their accuracy in delineating subtle boundary discrepancies. To mitigate the identified limitations, we introduce a novel dual-branch adaptive convolutional transformer (DBACT) network architecture featuring an adaptive multi-head self-attention mechanism. The architecture begins with a triadic parallel stem structure for shallow feature extraction and reduction of the spectral dimension. A global branch with adaptive receptive fields performs high-level global feature extraction. Simultaneously, a local branch with a cross-attention module provides detailed local insights, enriching the global perspective. This methodical integration synergizes the advantages of both branches, capturing representative spatial-spectral features from HSI. Comprehensive evaluation across three benchmark datasets reveals that the DBACT model exhibits superior classification performance compared to leading-edge models.

This paper reports the biaxial tensile mechanical properties of the envelope material through experimental and constitutive models. First, the biaxial tensile failure tests of the envelope material with different stress ratio in warp and weft directions are carried out. Then, based on fiber-reinforced continuum mechanics theory, an anisotropic hyperelastic constitutive model on envelope material with different stress ratio is developed. A strain energy function that characterizes the anisotropic behavior of the envelope material is decomposed into three parts: fiber, matrix and fiber–fiber interaction. The fiber–matrix interaction is eliminated in this model. A new simple model for fiber–fiber interaction with different stress ratio is developed. Finally, the results show that the constitutive model has a good agreement with the experiment results. The results can be used to provide a reference for structural design of envelope material.

Stratospheric airships with a solar array have demonstrated overwhelming superiority in many aspects, such as earth observation, meteorological survey, and communication relay. The solar array supplies sufficient power for the airship to be in flight for months, but excessive heat is also transferred to the airship, causing high overpressure of inner gas. However, the optimal arrangement of the insulation layer on the airship has not been investigated. The theoretical method, including the geometry, thermal, and energy models, is developed and validated. The distribution of the temperatures and power of the solar cells, with different installation angles on the curved surface, is investigated. The thickness of insulation layer has a significant effect on the solar output power and internal pressure of the airship. An optimized configuration of the insulation structure is proposed, in order to improve the total output energy of solar array. The optimized configuration of insulations helps to reduce the structural mass by 24.9% and increase the payload mass by 9%. Moreover, the optimized arrangement improves the output energy of solar array in a year, and the maximum improvement is 8.2% on the winter solstice. The work displays the optimization of the thermal insulation layer for the stratospheric airship with a solar array, in order to improve the everyday energy acquirement during flight in a year.