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higher ef ciency as there is less antenna reactance to offset. Maximum ef ciency occurs for a loading point between 0.3 and 0.4 from the feed, although the ef ciency varies slowly with load point. There is no apparent variation of the maximum point with Q or with monopole length. It should be noted that when the loading coil is located at the feed, the ef ciency is always lower. Thus for maximum ef ciency the inductive load should be located at roughly 0.4h. However, in most cases a small sacri ce in ef ciency should be made to obtain a higher input resistance. Figures 1.17 through 1.19 show the input (radiation) resistance versus , and the tradeoff may be made between these gures and Figures 1.24 and 1.25. Antenna copper loss is not included as it is always much smaller than the loading coil loss. An ef ciency transition formula has been derived. Ef ciency is: = Q Rr Q Rr + X (1.24)



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116 Deploying the System based on point-and-click graphical user interfaces (GUIs). These include the usual diagnostics, as speci ed in the standards documents that were discussed in 2. When 1550 nm is used for outbound multichannel analog TV, two unusual requirements must be met that are not involved in POTS, namely digital data or digital video service. As we saw in 2, higher power must be delivered from the OLT at this wavelength through the use of EDFAs. Moreover, extreme linearity is required in the modulation of the analog mix of many TV channels onto the laser s output, as we saw in 3. Both of these requirements add considerably to the cost of OLTs when analog video must be carried, and they also add somewhat to ONU costs.





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Gert and Clouser bring the following accusations . . . (1) the principles are little more than checklists or headings for lists of values worth remembering, and so the principles have no deep moral substance and do not produce directive guidelines for moral conduct; (2) principles analyses fail to provide a theory of justi cation or a theory that ties the principles together so as to generate clear, coherent, speci c rules, with the consequence that the principles and so-called derivative rules are ad hoc constructions without systematic order; (3) these prima facie principles must often compete in dif cult circumstances, yet the underlying account is unable to decide how to adjudicate the con ict in particular cases and unable theoretically to deal with a con ict of principles.83

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If the government comes in and imposes a tax of 1 per kilo, it affects how much you have to pay the suppliers to motivate them. If you still want 10 million kilos of beef, you have to pay the original amount required to motivate the suppliers to supply you with that much beef ( 5 per kilo), as well as enough money to pay the taxes on that much beef ( 1 per kilo). Graphically, this means that point A on supply curve S shifts up by the 1 amount of the tax to become point B on the S + tax curve. For the same motivational reasons, point E on the supply curve must shift up to point F on the S + tax curve. That is, if you have to pay suppliers 4.50 per kilo to motivate them to supply you with 9 million kilos of beef in a world in which 1 per kilo must go to the government in taxes, you have to collect a total of 5.50 per kilo. And that s exactly what happens at point F. Every point on the supply curve, S, must shift up vertically in the same way that points A and E do, so the S + tax curve captures what the supply curve looks like after the tax is imposed. With this shift in mind, you re ready to discover how this sort of taxation causes deadweight losses.

where wk is a zero-mean white Gaussian noise process with known covariances Qk and the vector x represents the state of a dynamic system. This model for ``marginally random'' dynamics is quite useful for representing physical systems (e.g., land vehicles, seacraft, aircraft) with zero-mean random disturbances (e.g., wind gusts or sea surface currents). The state transition matrix Fk represents the known dynamic behavior of the system, and the covariance matrices Qk represent the unknown random disturbances. Together, they model the propagation of the necessary statistical properties of the state variable x. Example 7.6 Harmonic Resonator with White Acceleration Disturbance Noise. If the disturbance acting on the harmonic resonator of Examples 7.1 7.5 were zero-mean white acceleration noise with variance s2 disturbance , then its disturbance noise covariance matrix would have the form 0 Q 0 s2 disturbance 0 ! : 7:65

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