ID 原文 译文
46506 设计了 2 个矩阵：空间调制矩阵和符号矩阵， There were two matrices in this scheme: the spatial mod-ulation matrix and the symbol matrix.
46507 前者通过设置其中非零元素的位置来激活不同的发射天线，后者采用正交空时分组码（OSTBC）作为基本码块来构造符号矩阵。 The former was aimed to activate different transmit antennas by setting the positionof nonzero elements, and the latter structured symbolic matrix by using orthogonal space-time block codes (OSTBC) asthe basic code block.
46508 所提方案具有可获得满发射分集、频谱效率高等优点， The proposed scheme could obtain full transmit diversity and higher spectral efficiency comparedwith the conventional DSM schemes.
46509 同时支持线性最大似然（ML, maximum likelihood）译码。 Moreover, the OSTBC-DSM supported linear maximum likelihood (ML) decoding.
46510 仿真结果表明，所提方案在不同的频谱效率下均获得了比其他几种方案更好的误比特率（BER, bit error rate）性能。 The simulation results show that under different spectral efficiencies, the proposed OSTBC-DSM scheme has better biterror rate (BER) performance than other schemes.
46511 分析 Spark 的作业执行机制，建立了执行效率模型和 Shuffle 过程模型，给出了分配适应度（AFD, allocationfitness degree）的定义，提出了算法的优化目标。 The job execution mechanism of Spark was analyzed, task efficiency model and Shuffle model were estab-lished, then allocation fitness degree (AFD) was defined and the optimization goal was put forward.
46512 根据模型的相关定义求解，设计了渐进填充分区映射算法（PFPM,progressive filling partitioning and mapping algorithm），通过扩展式分区和渐进填充映射， On the basis of themodel definition, the progressive filling partitioning and mapping algorithm (PFPM) was proposed.
46513 建立适应 Reducer 计算能力的数据分配方案，有效缩减 Shuffle 过程的同步延时，提高集群计算效率。 PFPM established thedata distribution scheme adapting Reducers’ computing ability to decrease synchronous latency during Shuffle processand increase cluster the computing efficiency.
46514 实验表明该算法提高了 Shuffle 过程数据分配的合理性，优化了并行计算框架 Spark 的作业执行效率。 The experiments demonstrate that PFPM could improve the rationality ofworkload distribution in Shuffle and optimize the execution efficiency of Spark.
46515 由于 k-匿名方法不仅能降低用户的计算开销，还能提供准确的查询结果，已被广泛用于位置隐私保护。 Since k-anonymity method can reduce the users’ computation cost and provides the precise query results, it hasbeen widely used to protect the user’s privacy in location-based service.