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The European research initiative COST 259 developed a DCM that has gained widespread acceptance. It is very realistic, incorporating a wealth of effects and their interplay, for a number of different environments. As the model is rather involved, this section only points out some basic features. A more detailed description of the rst version of the model is described in Steinbauer and Molisch [2001], and a full account is found in Asplund et al. [2006] and Molisch et al. [2006]. The COST 259 DCM includes small-scale as well as continuous large-scale changes of the channel. This is achieved ef ciently by distinguishing between three different layers: 1. At the top layer, there is a distinction between different Radio Environments (REs) i.e., environmental classes with similar propagation characteristics (e.g., TU ). All in all, there are 13 REs: four macrocellular REs (i.e., BS height above rooftop), four microcellular REs (outdoor, BS height below rooftop), and ve picocellular REs (indoor). 2. Large-scale effects are described by their pdfs, whose parameters differ for different REs. For example, delay spread, angular spread, shadowing, and the Rice factor change as the MS moves over large distances. Each realization of large-scale fading parameters determines a DDDPS. 3. On a third layer, double-directional impulse responses are realizations of the DDDPS, created by the small-scale fading. Large-scale effects are described in a mixed geometrical stochastic fashion, applying the concept of IO clusters as described above. At the beginning of a simulation, IO clusters (one local cluster around the MS and several far IO clusters) are distributed at random in the coverage area; this is the stochastic component. During the simulation, the delays and angles between the clusters are obtained deterministically from their position and the positions of the BS and MS; this is the geometrical component. Each of the clusters has a small-scale averaged DDDPS that is exponential in delay, Laplacian in azimuth and elevation at the BS, and uniform or Laplacian in azimuth and elevation at the MS. Double-directional complex impulse responses are then obtained from the average ADPS either directly, or by mapping it onto an IO distribution and obtaining impulse responses in a geometrical way. In macrocells the positions of clusters are random. In micro- and picocells, the positions are deterministic, using the concept of Virtual Cell Deployment Areas (VCDAs). A VCDA is a map of a virtual town or of ce building, with the route of the MS prescribed in it. This approach is similar to the ray-tracing approach but differs in two important respects: (i) the city maps need not re ect an actual city and can thus be made to be typical for many cities; (ii) only the cluster positions are determined by ray tracing, while the behavior within one cluster is treated stochastically. Other standardized models are described in the Further Reading section and the appendices.
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TABLE 6.4. Comparison of Thin-Film and Single-Crystal CuGaS2 Lattice Parameters (a and c), c/a, and the Distortion Parameter x a ( ) 5.353 5.35 5.351 c ( ) 10.495 10.48 10.484 c/a 1.9606 1.959 1.9593 x 0.0394 0.0410 0.0407 Thin lm fabrication method Spray-CVD deposited lm Evaporated lm49 JCPDS card 25 0297: Single crystal prepared from the elements
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APPLICATIONS The attenuation signal in Figure. 3.4.7(b) can be reconstructed by standard tomographic techniques such as ltered back projection, since the underlying tomographic model is the standard radon transform.59 The uorescence signal (Figure 3.4.7a), however, is generated in a more complicated way. The incoming beam is attenuated along its path. At each point along the path, uorescence is excited and radiated into the full solid angle. The part of the radiation that falls into the solid angle of the uorescence detector is attenuated by an a priori unknown attenuation of the sample before it reaches the detector. If, as in this case, the sample has low density, secondary uorescence and scattering effects can be neglected. The attenuation effects of the uorescence become apparent in Figure 3.4.7(a), where the uorescence signal is slightly weaker on the left side (far side of the detector) than on the right. This asymmetry in the sinogram can be used to estimate the attenuation inside the sample self-consistently.34 The results of such a selfconsistent reconstruction are shown in Figure 3.4.8 for potassium, iron, and zinc. In Figure 3.4.8 the element distributions are reconstructed with subcellular resolution (<1 m). The surface of the root is covered with soil that is rich in iron. The outermost cellular layer of the root has died off, yielding a low uorescence signal. In the next cellular layer the mycorrhiza fungus accumulates zinc, stopping the zinc from being transported into the plant as seen by the low concentration of zinc in the central part of the root. To determine the element distribution inside plant samples, uorescence microtomography has
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FIGURE 20.13
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Highly charged ions like Xe9+ in the previous example, or higher charge, can arise when the decaying processes are completed. Meanwhile the ejected electrons and uorescence X-rays proceed ionizing further Xe neutral atoms. The nal stage is reached when there are no more electrons or photons with suf cient energy (larger than 12.1 eV) to ionize neutral atoms; as a result n primary electrons are produced. All these processes can be simulated in full detail with Monte Carlo techniques provided all relevant integral and differential cross-section data and transition rates are available.3 This way n can be calculated for the simulated X-ray absorption event. Repeating the calculations a large number of times it is possible to calculate with good accuracy its average value, n, the so-called W value (W = E/n), the variance n of n, the so called Fano factor, F = n /n, i.e. the relative variance of n and the size and spatial distribution of the primary electron cloud.3 5
FIGURE 11.14
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