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741.
以甲基三甲氧基硅烷(MTMS)和二甲基二甲氧基硅烷(DMDMS)为前驱体,通过溶胶-凝胶、常压干燥制备出柔性有机硅气凝胶,研究了MTMS与DMDMS的摩尔比对其化学组成和微观结构的影响;采用莫来石纤维毡作为增强体,制备出密度为0.25 g/cm~3的柔性有机硅气凝胶复合材料。实验结果表明,所制复合材料具有优异的热稳定性,其室温热导率在0.03 W/(m·K)以内;当MTMS和DMDMS的摩尔比为3.8∶1.2时,复合材料的均匀伸长率达3.6%、残重率达82.4%;复合材料经高温处理后,有机硅气凝胶转变为无机SiO_2气凝胶,较好地保持煅烧前的微观形貌和隔热性能;通过500 s石英灯静态加热,发现复合材料的表面有陶瓷化反应,厚度方向无收缩,背部温升81℃,表现出烧蚀/隔热的双重特性。 相似文献
742.
743.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(1):286-297
This paper investigates the ionospheric storm of December 19–21, 2015, which was initiated by two successive CME eruptions that caused a G3 space weather event. We used the in situ electron density (Ne) and electron temperature (Te) and the Total Electron Content (TEC) measurements from SWARM-A satellite, as well as the O/N2 observations from TIMED/GUVI to study the ionospheric impact. The observations reveal the longitudinal and hemispherical differences in the ionospheric response to the storm event. A positive ionospheric storm was observed over the American, African and Asian regions on 20 December, and the next day showed a negative storm. Both these exhibited hemispheric differences. A positive storm was observed over the East Pacific region on 21 December. It is seen that the net effect of both the disturbance dynamo electric field and composition differences become important in explaining the observed variability in topside ionospheric densities. In addition, we also discuss the Te variations that occurred as a consequence of the space weather event. 相似文献
744.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(6):2566-2574
Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty). 相似文献
745.