| ID | 原文 | 译文 |
| 2033 | 针对该问题，提出一套基于高增益天线的空间信号质量评估方法，实现了 E1 授权信号的解析，完善了 E1 信号评估体系。 | Aiming at this problem, this paper proposes a signal-in-space quality assess mentmethod based on high gain antenna, which realizes the analysis of E1 authorization signal and perfects the E1 signal evalua-tion system. |
| 2034 | 运用相关功率法来解决信号分量功率比问题，采用跟踪结果解决相位偏差估计问题，提出加权组合平均和码相位平均相结合的新型时域波形分离方法， | The related power method is proposed to solve the signal component power ratio problem. The tracking result isused to solve the phase deviation estimation problem. A new time domain waveform separation method combining weightedcombination averaging and code phase averaging is proposed, |
| 2035 | 克服了电文和码多普勒对时域波形特性评估的影响，采用 S 曲线过零点偏差(S-curve offset Biases，SCB)等参数进行信号测距偏差定量评估。 | which overcomes the influence of message and code Doppleron time domain waveform characteristics evaluation. The S-curve offset Biases (SCB)parameters are used to quantitatively evaluate the signal ranging deviation. |
| 2036 | 通过该方法对 Galileo GSAT-0214 卫星进行了评估， | In this paper, the Galileo GSAT-0214 satellite is evaluated by this method. |
| 2037 | 结果显示:该卫星 E1 各信号分量 SCB 小于 0.2ns，测距性能优异， | The results show that the signal component SCB of E1 is less than 0. 2 ns, and the ranging performance is excellent. |
| 2038 | 其复用效率达到了 97.8% ，优于 GPS L1 信号和北斗三号系统(BDS-3)B1 信号。 | The multiplexing ef-ficiency is 97. 8% , which is better than GPS L1 signal and BOS-3 system B1 signal. |
| 2039 | 为解决传统一类支持向量机对噪声数据敏感和不适用于大规模分类等问题，提出了用于大规模噪声环境的基于简约凸壳的一类模糊支持向量机(OC-FSVM-RCH)。 | The traditional one-class support vector machines are sensitive to noise data and not suitable for large-scaleclassification. In order to solve the problem, a novel one-class fuzzy support vector machine based on reduced convex hullcalled OC-FSVM-RCH is proposed for large-scale noise data classification. |
| 2040 | OC-FSVM-RCH 根据简约凸壳的定义在核空间得到代表正常类数据几何特征的样本， | According to the reduced convex hull, OC-FS-VM-RCH obtains the samples representing the geometric characteristics of normal class data in the kernel space. |
| 2041 | 然后基于改进的模糊支持向量域描述算法，使得正常类数据包含在最小超球内，异常数据与超球间隔最大化。 | Then OC-FSVM-RCH improves the fuzzy support vector domain description algorithm, in which normal class data is enclosed in thesmallest hypersphere, and the margin between abnormal class data and hypersphere is maximized. |
| 2042 | OC-FSVM-RCH 剔除正常类数据轮廓边缘处的噪声，同时对数据内部的噪声不敏感。 | OC-FSVM-RCH can elim-inate the noise at the edge of normal data contour and is insensitive to the noise inside the normal data. |