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FIGURE 5.8 Return loss and active H-plane pattern of an H-plane subarray. Element aperture size 0 6 0 0 6 0 , element cell size 0 7 0 0 7 0 . (a) Return loss versus scan angle. (b) Active subarray pattern versus scan angle.

The above input admittance expression is permissible only if the aperture field vector es x is a real function of x. Using (4.1) we get Y FL = 1 Vs

(3.19a) (3.19b)

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Tr( A) = aii = l i ( A). Also note that Tr(A + B) = Tr(A) + Tr(B), and Tr(AB) = Tr(BA). We use the latter to simplify matrix expressions below. 8.1.4 Matrix Inverse, Partitioned Matrices, and Their Inverse

where b is the slope of S on the log plot,





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Given Hector s pattern of strengths and weaknesses, it is recommended that academic instruction in reading/writing emphasize multisensory approaches. In order to enhance Hector s learning/retention, new material should be presented in a concrete and organized format that is meaningful to him. The material to be learned should be pretaught, broken down into smaller components/steps, and presented in a highly structured format with frequent repetition to facilitate new learning. Instruction should be supplemented with visual aids, demonstrations, and experiential/procedural learning whenever possible. Finally, Hector should be provided with periodic review of previously learned material in order to enhance long-term retention. In light of his inattention and poor receptive language skills, there are several general rules teachers, therapists, and parents should keep in mind while interacting with Hector: Eye contact should be ensured. A prompt to look at the speaker before directions are presented will be helpful. Instructions and directions should be kept short, simple, and concrete. Requests should be limited to single commands, and instructions should be presented several times if necessary.

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This type of differential protection is biased for reasons that will be explained later on. It is activated when the differential current reaches a certain percentage of the through current.

4.5.2 Interpreting Results in the Presence of Multiple Modes Standard econometric theory states that in general we should worry primarily about the distinction between posterior7 inferences based on a t-distribution due to limited sample size and others that can comfortably be calculated on normal tail values. Greene (2003), for instance, distinguishes between Finite Sample Properties of the Least Squares Estimator and Large Sample Properties of the Least Squares and Instrumental Variables Estimators. The extension to generalized linear models uses this same distinction in measuring coef cient reliability by tail values under the null hypothesis. Suppose, however, that this limitation to two forms is not a reasonable assumption. There are essentially two types of problems. Some empirically observed likelihood functions are multimodal because of the small-sample effects or poorly implemented simulation studies. In these cases the theoretical likelihood distribution is asymptotically normal and unimodal (usually, t) as sample sizes

To conclude this section on descriptors, the following discussion concentrates on some of the trickier areas of descriptor manipulation.

The guiding principle for code structural complexity rules is the following statement: Never make code more complex than necessary. The structural complexity rules pertain to code refactoring, modularization, and levels of nesting. They specify parameters such as the recommended maximum number of methods per class, and the maximum McCabe [6] or Halstead [7] complexity of the code. McCabe s complexity, also known as cyclomatic complexity, measures the number of independent paths in a program unit. The Halstead complexity measure is derived from the number of operands and operators in a module. For example, if the code is algorithmically straightforward, its cyclomatic complexity should not exceed 10. Similarly, Halstead measure can be used to assure code modularity and readability in computationally complex implementations. The rules for restricting code complexity depend greatly on the project type and the algorithmic complexity of each code unit. Programmers who exceed the recommended maximum values are usually asked either to redesign the code to conform to the required rules or to justify the need for increased complexity by properly documenting the relevant parts of the code. The code structural complexity rules help avert algorithmic and processing defects by enforcing proper modularization and by requiring close attention to the design prior to coding.

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