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snapshots or fixed constellation topologies, a method sometimes referred to as discretization [11, 37, 38]. Then the routing problem is solved with respect to that fixed constellation. High Mobility. The above two models are interesting when the mobility of the satellite network is negligible with respect to the mobility of the users requests, e.g., if most of the requests have very low duration, let us say a few minutes, while the handover time would be one hour or more. In that case, before the network configuration changes (significantly) several (many) requests will have been satisfied. Moreover, these models do not take into account the dependence between consecutive states of the network. Thus, between two states, the complete routing scheme of the constellation should be changed. Clearly, in the case of highly dynamic constellations and/or long call durations, almost all requests may pass through several states and thus may be rerouted several times. 22.3.2 User Distribution: Common Traffic Assumptions Depending on the application, three major scenarios can be identified for satellite markets. The first and most natural one states that satellites will serve countries where the telecommunication infrastructure is insufficient or nonexistent. 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In factor analysis or covariance structure analysis, observed variables, usually continuous, map onto continuous latent variables assumed to be normally distributed (J reskog & S rbom, 1996). Latent trait analysis (Spiel, 1994) refers to discrete observed variables mapping onto a continuous latent variable. For example, a set of aptitude test items coded as correct or incorrect might be seen as indicators of the underlying latent trait, in this case ability. In latent pro le analysis, continuous observed variables map onto a discrete latent variable. Latent class analysis (LCA) models the relationship between discrete observed variables and a discrete latent variable. The present chapter focuses on this last framework, LCA. The rst section introduces the concept of a latent class and then presents the mathematical model. 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As practiced in North America, bacon is produced from boneless pork belly that is hot smoked, sliced thinly, and vacuum packed (Andersen 2004), while in Ireland and the United Kingdom, the most popular bacon is made from cured pork loins. In continental Europe, bacon lardons (cubes) are used mainly as a cooking ingredient. Salt (sodium chloride) and nitrite are essential for curing, although nitrates (sodium or potassium) are still used in some brines. Both major ingredients, salt and nitrite, are multifunctional. Salt acts as a preservative by lowering water activity, gives bacon its characteristic salty avor, and increases the water-holding capacity of meat by solubilizing myo brillar protein and increasing the myo brillar lattice spacing (Offer and Trinick 1983). Although nitrite chemistry is relatively complex, the basic functions of nitrite are well known (e.g., Pegg and Shahidi 2000; Honikel 2008). It (1) acts as a preservative, (2) promotes the formation of the cured meat color, (3) contributes to cured meat avor, and (4) acts as an antioxidant. The principles of curing have been described in an earlier Panel B. Class Prevalence Class 1 2 3 4 5 Label No depression Sad Disliked Sad + disliked Depression Prevalence at Grade 11 .34 .23 .20 .13 .09 Prevalence at Grade 12 .40 .22 .18 .12 .09 Case 5. As depicted in Figure 5.26, this experiment uses share table lock mode and creates a deadlock between T1 and T2. Both transactions lock the table in shared mode. Then, they both try to update the same row. This causes a deadlock since both transactions wait for the lock on the row to be released by the other. Case 6. This example uses a share row table lock. This scenario (Figure 5.27) is the same as above except that transaction T2 locks the table in shared row mode. 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