ID	原文	译文
3323	在该滤波器的优化过程中，详细讨论了 3～6 腔体交叉耦合通槽的相对位置偏移量和交叉耦合通槽的长度对滤波器传输零点位置、近端和远端带外抑制特性的影响，并给出了相关的变化规律。	In the process of optimizing the filter, the influences of the rela-tive position offset of the cross-coupling through slot and the length of the cross-coupling through slot on the transmis-sion zero position, the near end and far end out of band suppression characteristics of the filter were discussed in detail,and the relevant change rules were given.
3324	经优化后滤波器性能指标如下：中心频率为 3.5 GHz，工作带宽为 200 MHz，插入损耗≤1.2 dB，回波损耗≥17 dB，近端带外抑制≥25 dB，远端带外抑制≥51 dB。	The performance indexes of the optimized filter were as follows, center fre-quency was 3.5 GHz, working bandwidth was 200 MHz, insertion loss ≤ 1.2 dB, return loss ≥ 17 dB, near end out ofband rejection ≥ 25 dB, far end out of band rejection ≥ 51 dB.
3325	根据仿真模型结构参数制备得到的样品，其性能测试结果与仿真结果吻合良好。	According to the structural parameters of the simulationmodel, the performance test results of the samples are in good agreement with the simulation results.
3326	对于电离层参数预测，通过长短期记忆（LSTM）的预测神经网络建模实现电离层参数的短期和日均值预测。	For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parame-ters was established by long short-term memory (LSTM) predictive neural network modeling.
3327	使用逐点预测和序列预测 2 种方法，并采用多维预测和经验模态分解（EMD）算法优化，预测电离层参数的每小时和每天的变化规律。	Two methods ofpoint-by-point prediction and sequence prediction were utilized. Furthermore, in order to predict the hourly and dailychanges of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empiricalmode decomposition (EMD) algorithm.
3328	实验结果验证了所提优化算法在提高预测电离层参数预测精度上的可行性。	Finally, the feasibility of the proposed optimization algorithm in improving theprediction accuracy of ionospheric parameters is verified.
3329	针对 LDPC 重建问题，提出了一种可直接重建 LDPC 稀疏校验矩阵的算法。	In order to reconstruct the sparse check matrix of LDPC, a new algorithm which could directly reconstruct theLDPC was proposed.
3330	首先，根据传统重建算法原理，详细分析了传统重建算法存在的缺陷以及缺陷存在的原因；	Firstly, according to the principle of the traditional reconstruction algorithm, the defects of the tra-ditional algorithm and the reasons for the defects were analyzed in detail.
3331	其次，基于 LDPC 稀疏矩阵的特性，通过多次随机抽取码字中部分比特序列进行高斯消元，	Secondly, based on the characteristics of sparsematrix, some bit sequences in code words were randomly extracted for Gaussian elimination.
3332	同时为了可靠实现抽取的比特序列能包含校验节点，基于一次抽取包含校验节点的概率，确定多次随机抽取的次数；	At the same time, in orderto reliably realize that the extracted bits sequence could contain parity check nodes, the multiple random variables weredetermined based on the probability of containing check nodes in one extraction.