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adjacent carrier can be while the terminal can still operate at its current frequency. Such a situation may happen between operators in real networks. 3GPP speci cations require an ACS of 33 dB. Release 5 does contain a new test case for ACS as well, but that is only to broaden test coverage and is not directly linked to HSDPA/HSUPA operation. In the terminal design, ASC is obtained by the channel lter and by baseband digital ltering (as illustrated in Figure 11.5).

You have to graft JSP and servlet features and capabilities atop HTTP to create, maintain, and track sessions In short, these features are methods available to your JSPs by accessing the aforementioned session object JSPs use the implicit session object to track the requests of a single client and to distinguish one client from another Consider an application used concurrently by multiple clients Each client progresses through a series of steps to complete a transaction A good, if not overused, example of such a multi-step transaction is an electronic shopping cart A client must complete steps A, B, and C before proceeding to step D Meanwhile, other clients are progressing through the same steps By using sessions, the application is able to identify what clients are doing and what steps the clients are executing Imagine the chaos if an application mixes up clients shopping carts!.

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When you are done with your work, click the Out button to stop the clock, at which point you are prompted to provide a description and add notes, which will be recorded in your time record. Once saved, your time record is stored in a database along with your other time and billing information for this and any other projects you create in BizTrackIt.

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A small random sample S of the data often captures certain characteristics of the entire data set. If this is the case, the sample can be maintained in memory and queries can be answered over the sample. In order to use sampling techniques in a data stream context, we rst need to address the problem of maintaining a sample of a speci ed size over a possibly unbounded stream of data that arrive online. Note that simple coin tossing is not possible in streaming applications, as the sample size would be unbounded. The standard solution is to use Vitter s reservoir sampling [63] that we describe in the following Sections. 8.3.1.1 Reservoir Sampling This technique dates back to the 1980s [63]. Given a stream of n items that arrive online, at any instant of time reservoir sampling guarantees to maintain a uniform random sample S of xed size m of the part of stream observed up to that time. Let us rst consider the following natural sampling procedure. At the beginning, add to S the rst m items of the stream. Upon seeing the stream item xt at time t, add xt to S with probability m/t. If xt is added, evict a random item from S (other than xt ). It is easy to see that at each time |S| = m as desired. The next theorem proves that, at each time, S is actually a uniform random sample of the stream observed so far. Theorem 1 [63] Let S be a sample of size m maintained over a stream = x1 , ..., xn by the above algorithm. Then, at any time t and for each i t, the probability that xi S is m/t.

Although we are concerned exclusively with coordinate systems in the three dimensions of the observable world, there are many ways of representing a location in that world by a set of coordinates. The coordinates presented here are those used in navigation with GPS and=or INS. C.3.1 Cartesian and Polar Coordinates

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