ID	原文	译文
6054	以某双尾撑固旋翼无人机为算例,利用所提方法分析了该无人机抗风特性及其影响因素。	Taking a double tail stay solid rotor unmanned aerial vehicle (uav) for example, using the proposed method analyzes the characteristics of the unmanned aerial vehicle (uav) wind resistance and its influencing factors.
6055	分析结果表明该无人机在机头来流的工况下具有很强的抗风能力,侧面来流工况下无人机抗风能力较差,	The analysis results show that the unmanned aerial vehicle (uav) in the nose to flow under the condition of strong wind resistance, side to flow under the condition of uav wind resistance is poorer,
6056	可以通过增加旋翼桨盘倾角、改变旋翼电机相对于重心的位置、优化尾翼等部件的气动外形等方法来改善无人机抗风能力。	can increase the rotor plate Angle, change the position of the rotor motor relative to the center of gravity, optimization of the tail parts of aerodynamic shape and other methods, such as to improve the wind resistance ability of the unmanned aerial vehicle (uav).
6057	飞行试验验证了所提方法的可行性和分析结果的准确性。	Flight test to verify the proposed method is feasible and the accuracy of the analysis results.
6058	卫星所搭载的再生伪码(pseudo-noise,PN)测距系统已广泛应用于测量双星或星地距离。	Satellites carry regeneration of pseudo code (pseudo - noise, PN) ranging system has been widely used in measurement of binary or star distance.
6059	由于星地间的相对运动更为明显导致伪码测距信号存在着较大的多普勒频移,从而影响利用相关运算获得的双程延时测距精度。	Due to the relative motion between the stars, more obvious the pseudo code ranging signal there is a large doppler frequency shift, which affects use of related calculation for round-trip delay ranging accuracy.
6060	根据伪码测距系统的实现结构,推导出当多普勒频移存在时的测距延时计算公式,进而提出了一种补偿方法。	according to the realization of the pseudo code ranging system structure is deduced when the doppler frequency shift is the distance formula of time delay, and then presents a compensation method.
6061	同时考虑到测距系统的实际应用环境,对补偿公式进行了简化。	Considering the practical application environment ranging system at the same time, the compensation formula is simplified.
6062	仿真及实际测试结果表明,经过补偿后的双程延时测量值精度(中强信噪比下)可达到0.14ns(4cm),补偿造成的精度影响为0.01ns(3mm),同时公式简化及补偿引入的误差可以忽略不计。	Simulation and practical test results show that after the compensation of the round-trip delay measurement accuracy (under the strong signal to noise ratio) can achieve 0.14 ns (4 cm), impact the accuracy of the compensation of 0.01 ns (3 mm), at the same time, the introduction of simplified formula and compensation error is negligible.
6063	针对长期演进-车辆(long term evolution-vehicle,LTE-V)下的车辆随机竞争接入网络容易造成网络拥塞的问题,提出基于深度强化学习(deep reinforcement learning,DRL)为LTE-V下的车辆接入最佳基站(evolved node B,eNB)的选择算法。	In view of the long-term evolution - vehicle (long term evolution - vehicle, LTE - V) vehicle under random competition access network is easy to cause network congestion problem is put forward based on the depth of reinforcement learning (deep reinforcement learning, DRL) for LTE - V vehicle access to the best of base station (evolved node B, eNB) selection algorithm.