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A soft-decision decoder for the convolutional code described in Figure 6.12, whose trellis is also seen in Figure 6.13, is presented in order to show differences between hard- and soft-decision decoding. In this example the message sequence is m = (10101), which after being encoded and transmitted in polar format, produces the code sequence c = (+1 +1 1 +1 +1 + 1 1 1 + 1 +1). This sequence is transmitted over an AWGN channel. Let s r = (+1.35 0.15 1.25 +1.40 0.85 0.10 0.95 1.75 +0.5 + 1.30) be the received sequence. The soft-decision VA [1 6] will be applied to decode this sequence. Then a comparison with a hard-decision decoder is also presented. The rst step in the application of the soft-decision decoding algorithm is to calculate the Euclidean distance between the samples of the received signal and the corresponding outputs for each transition of the trellis: Message sequence Code sequence Received sequence m = 10101 c = +1 + 1 1 +1 +1 + 1 1 1 +1 + 1 s r = +1.35 0.15 1.25 +1.40 0.85 0.10 0.95 1.75 +0.5 +1.30

An extensive review of OC and BC concentrations is given by Chung and Seinfeld [22] Carbonaceous material is the main component of FT particulate matter Background FT values of up to 940 ng m 3 OC and 610 ng m 3 BC (or EC) have been reported for high alpine sites in previous studies [131] Recent AMS measurements at the Jungfraujoch have shown that more than 50% of ne particles (<1 m) in summer are composed of organic matter [132] Putaud et al [133] found a relative amount between 16 and 29%, depending on particle size, for European background sites Raes et al [8] give a range from 31 to 56% for the submicron OC contribution in the background FT over the North Atlantic.

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regression coef cients, variances, and covariances that are to be estimated (ie, the number of asterisks in a diagram) If there are more data points than parameters to be estimated, the model is said to be overidenti ed, a necessary condition for proceeding with the analysis If there are the same number of data points as parameters to be estimated, the model is said to be just-identi ed In this case, the estimated parameters perfectly reproduce the sample covariance matrix, the chi-square test statistic and degrees of freedom are equal to zero, and the analysis is uninteresting because hypotheses about the model s adequacy cannot be tested However, hypotheses about speci c paths in the model can be tested If there are fewer data points than parameters to be estimated, the model is said to be underidenti ed, and parameters cannot be estimated.

A separation of total carbon into BC, WSOC and WINSOC fractions revealed that BC amounted to 22%, WSOC to 47% and WINSOC to 31% during a summer period at the Jungfraujoch [131] Carboxylic acids were found in mean concentrations of 49 ng m 3 (oxalic acid), 15 ng m 3 (malonic acid), 11 ng m 3 (succinic acid) and 9 ng m 3 (formic acid), contributing 65% to the total WSOS mass Heald et al [134] report a free tropospheric background of 1000 3000 ng m 3 of OC over the Paci c Ocean, observed by aircraft during ACE-Asia [28] The high OC values observed are best explained as the result of SOA formation due to VOC oxidation and condensation favoured through the low temperatures In their study they show that the observed OC concentrations are a factor of 10 100 higher than calculated with current models.

They conclude that there is a large sustained source of SOA in the FT currently not re ected in today s models, and that SOA is the dominant component of aerosol mass in the FT SOA formation is also a characteristic of ageing air masses, typical for the FT..

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The number of parameters needs to be reduced by xing, constraining, or deleting some of them A parameter may be xed by setting it to a speci c value or constrained by setting the parameter equal to another parameter In the Acceptance of Risky Behavior example of Figure 242, there are 5 measured variables and thus 15 data points: 5(5 + 1)/2 = 15 (5 variances and 10 covariances) There are 12 parameters to be estimated in the hypothesized model: 5 regression coef cients, 6 variances, and 1 covariance The hypothesized model has 3 fewer parameters than data points, so the model may be identi ed The second step in determining model identi ability is to examine the measurement portion of the model The measurement part of the model deals with the relationship between the measured indicators and the factors.

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