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As mentioned brie y earlier in this chapter, DFS can provide a level of redundancy to ensure that shares within a given DFS namespace are available even when a server or share becomes unavailable for some reason. DFS does this through replication, which copies the root or link (and underlying data) to one or more other servers. Because DFS returns the complete list of root replicas or share replicas in response to a query, a client can try each one in the list to nd one that functions when a particular server or share is of ine. Though DFS by default does not provide replication of a DFS root or any replicas associated with a given DFS link, you can con gure DFS to replicate entire DFS namespaces or individual shared folders (DFS links). DFS relies on the File Replication Service (FRS) included with Windows Server 2008 to perform automatic replication. After you create a namespaces replica or a share replica, you can con gure the replication policy for that object, which de nes how the object is replicated. Automatic replication is only available for domain-based DFS namespaces and only data stored on NTFS volumes can be replicated. By default, FRS replicates the data every 15 minutes. Automatic replication is not available for data stored on FAT volumes or for standalone DFS roots or replicas. In these situations, you need to use manual replication. Manual replication is just what its name implies. You replicate the data by copying the data periodically through drag-and-drop or a similar method as you would manually copy any le from one place to another. Although you could automate the manual replication through the use of

The Control Panel window appears. (See Figure 2-39.)

2 2 X m Zo + Rm Zo( Rm + X m Rm Zo ) , Rm Zo 2 2 X m Zo + Rm Zo( Rm + X m Rm Zo ) , ( Rm Zo ) 2 2 Rm Zo ( Rm + X m Rm Zo ) . Rm

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vent damage. This wood does not affect the anechoic performance of the tile to any degree. 4.2.3 Reverberation Chamber A reverberating chamber consists of a rectangular chamber with walls and a modestir paddle that disrupts RF fields internal to the enclosure. This chamber can be used as an alternate facility for conducting radiated immunity tests. The principal of operation is based on the existence of multimode resonance mixing. A typical chamber is shown in Figure 4.18. The reverberation chamber is simple in design; however, testing products is complex. This is due to the need for comprehensive theoretical analysis to describe field behavior inside the chamber and a means to correlate test results with actual operating conditions. Reverberating chambers emulate free-space conditions using the process of mode stirring in an enclosed area. Data from testing can be extensive during the monitoring process, which requires computer support. Use of a reverberating chamber for radiated susceptibility (RS) testing has advantages over other chambers. This is because the chamber provides excellent isolation from the external electromagnetic environment. In addition, these chambers are relatively inexpensive to build and are capable of yielding efficient field conversion, thus making it possible to conduct RS testing at high field levels. One disadvantage is relating the measurements made in the chamber to actual operating conditions. Polarization properties of the EUT are unknown. A reverberating chamber produces an environment where the radiated field is uniform throughout the volume of the enclosure walls. As a result, several modes of

Assume that our data contain 1% gross errors. Then it makes a tremendous conceptual difference whether the sample size is 1000 or 5. In the former case, each sample will contain around 10 grossly erroneous values, while in the latter, 19 out of 20 samples are good. In particular, it is not at all clear whether conclusions derived from an asymptotic theory remain valid for small samples. Many people are willing to take a 5% risk (remember the customary levels of statistical tests and confidence intervals!), and possibly, if we are applying a nonrobust optimal procedure, the gains on the good samples might more than offset the losses caused by an occasional bad sample, especially if we are using a realistic (i-e., bounded) loss function. The main purpose of this chapter is to show that this is not so. We shall find exact, finite sample minimax estimates of location, which, surprisingly, have the same structure as the asymptotically minimax M-estimates found in 4, and they are even quantitatively comparable.

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