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遮蔽因子
    In this paper, we analyse the spatial overlapping behavior of sea quarks of neighboring nucleons in nuclei in the small x region and give a relation between the nuclear shadowing factor R(x, Q~2, A) and the spatial overlapping factor △V_Λ(x)/V_Λ(x).
    本文分析了小x区原子核内邻近核子间海夸克空间重叠行为,给出了核遮蔽因子R(x,Q~2,A)与空间重叠程度因子△V_Λ(x)/V_Λ(x)的关系.
    In this paper,we prove analytically that the parton evolution model can provide a physical basis to the double x-rescaling model. Then we describe the nuclear shadowing and antishadowing effect in the small x region using the modified Alteralli-Parisi equation to improve the parton evolution model. The unified description of the EMC effect,the nuclear shadowing effect,and antishadowing effect is given without introducing the nuclear shadowing factor.
    用解析方法证明了部分子演化模型可为双重X重新标度模型提供物理基础,进而利用修正的Alteralli-Parisi方程描述小x区域的核遮蔽和反遮蔽效应,以改进部分子演化模型,使我们不必引入核遮蔽因子便可统一地描述EMC效应、核遮蔽和反遮蔽效应.最后,利用改进的部分子演化模型相当好地解释了核Drell-Yan过程的实验数据.
    Introduce nuclear shadowing factor in the α s order corrections to the Drell-Yan process as its base. We calculated the K factor's of the different energy in the p-A, A-A, collision, differently and contrast considering shadowing factor's effect with not considering shadowing factor's effect.
    在Drell-Yan过程αs 修正的基础上 ,引入遮蔽因子分别计算了不同能量下 p -A、A -A碰撞的K因子 ,把考虑遮蔽因子的量和没有计入遮蔽因子的量做了对比。
    Result indicate, values of K-factor in the small x has been obviously raised after the way it is added to the nuclear shadowing factor.
    结果表明 ,加入遮蔽因子后在小x段K值明显抬高 ,能量的不同遮蔽因子对K影响的趋势不变
    Inserting a random variable distance estimation error into the "distance-loss" model results in solvable equations with maximum-likelihood algorithm. The estimation error is too small to change the distribution of log-normal shadowing factor in the model.
    首先对现有的"路径—损耗"模型进行了修正,引入距离估计误差随机变量,由于其一般误差较小,不足以改变现有的模型中遮蔽因子的概率密度函数,所以遮蔽因子仍然满足正态分布.
    Based on the indoor propagation loss model, the algorithm uses the Maximum-Likelihood (ML) method , takes advantage of an iterative method to get the distances between the user and each receiver and the corresponding estimation of the shadowing factor in the propagation loss model, and gets the location estimation.
    基于对数正态分布的室内传播损耗模型,本文利用最大似然估计算法,通过迭代估计得到用户与各个接收机之间的距离和相应的传播损耗模型中的遮蔽因子,并由此估计得到室内用户的位置。
 

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