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The numerical simulation results for the total eld intensity attenuation, according to the proposed multiparametric model, are presented as a 3D picture in Figure 11.12. The comparison between theoretical predictions, using formulas (5.94) to (5.99) and the corresponding experimental data, shows that the mean error and the standard deviation do not exceed 6 dB (Err 5:2 dB, STD 5:6 dB). We must note that close results were also obtained in other cities with different terrain pro les (see [19 21]). Experiments in Lisbon. To understand and assess the accuracy of the present stochastic approach, and to nd its limits for predicting path loss characteristics, speci c experiments were carried out in Lisbon (Portugal) [24,25]. As mentioned in References [24,25], the Lisbon terrain pro le is hilly with various densities of buildings and vegetation surrounding the base station antenna, which was 93 m high. Two typical examples of these pro les in various experimental sites (identi ed by numbers at the top of the gure) are presented in Figures 11.13a 11.13b. In the experiments, the receiver antenna was mounted on a crane and changed its height from a line-of-sight (LOS) position down to a position behind the building in NLOS conditions, when the signal amplitude was reduced to the noise level. The clearance between terminals, the transmitter, and receiver is shown in each gure by introducing the rst Fresnel zone, as an ellipse, clearly showing the extent of obstruction within such an elliptical cross section. By reducing the receiver antenna height,

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FIGURE 11.13(a). Typical examples of pro les of various experimental sites identi ed as #25.

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Selection is a pretty simple concept, but there are a couple of basic variants worth discussing Because selection is typically concerned with objects, these variants are driven by two broad categories of selectable data In some cases, data is represented by distinct visual objects that can be manipulated independently of other objects Icons on the Desktop and vector objects in drawing applications are examples These objects are also commonly selected independently of their spatial relationships with each other We refer to these as discrete data, and their selection as discrete selection Discrete data is not necessarily homogeneous, and discrete selection is not necessarily contiguous Conversely, some applications represent data as a matrix of many small contiguous pieces of data The text in a word processor or the cells in a spreadsheet are made up of hundreds or thousands of similar little objects that together form a coherent whole.

TIP H1s can be a difficult factor for clients to understand (mostly due to the variety of ways they can be visually formatted) so I try to include an image of an H1 on their website so that they can see what element I am talking about.

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FIGURE 11.13(b). The same as in Fig. 11.13a, but for experimental site #16.

These objects are often selected in contiguous groups, and so we call them contiguous data, and selection within them contiguous selection Both contiguous selection and discrete selection support single-click selection and click-and-drag selection Single-clicking typically selects the smallest useful discrete amount and clicking and dragging selects a larger quantity, but there are other significant differences There is a natural order to the text in a word processor s document it consists of contiguous data Scrambling the order of the letters destroys the sense of the document The characters flow from the beginning to the end in a meaningful continuum and selecting a word or paragraph makes sense in the context of the data, whereas random, disconnected selections are generally meaningless.

we continuously move from conditions of full clearance (LOS) to NLOS conditions, that is, from fully illuminated paths to shadow zones due to the obstructing building. Figures 11.14a to 11.14c show a comparison of the measured and theoretically predicted path loss in some experimental points in Lisbon. Each gure shows the following: The measured path loss, denoted by a thick line bar connecting the minimum and maximum loss measured at that point. The results of the model described in 5 using (5.98) and (5.99) with (5.94), (5.96), and (5.97) with the values of the parameters taken from a topographic map of Lisbon city. These results are depicted by asterisks, connected by a thin line indicating the results for high, medium, and low values of the obstacle density n in km 2 . The results of various knife-edge models, described in Reference [17], indicated by circles. For the sites point #44 and #51, we used a single diffraction model according to Lee s approximate formulas (see 5). For points #4, #25 and #41, we used a double diffraction model based on the

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