I didn't check your calculations but finding widely different standard deviations/sample size estimates from very small pilot studies is not surprising. Just like the sample mean, the sample standard deviation is a noisy estimate of its population counterpart. That's why it's not such a good idea to use a small pilot study to compute power or decide on sample size (even if it is, as you noted, standard advice). I discussed this further [in these comments](https://stats.stackexchange.com/questions/65641/linear-regression-sample-size-advice/65654#comment126784_65654) and, indirectly, in [this answer to an unrelated question](https://stats.stackexchange.com/questions/64388/calculating-when-significance-will-be-reached-in-a-b-test-chi2-test-of-the/64393#64393).

As to whether or not the pilot data would be included in the final data set, the usual answer would be negative but thinking about it I am not sure if I can see a compelling reason, at least not if you are not using the mean or difference but only the standard deviation in your sample size calculations. On a related note, collecting data and stopping when your CI reaches a certain width is in fact sound (whereas repeatedly testing and stopping when the mean is significantly different from some value is a big no-no). It seems related to the [accuracy in parameter estimation](https://stats.stackexchange.com/questions/16985/how-to-report-general-precision-in-estimating-correlations-within-a-context-of-j#30287) approach, which might be relevant to you.

PS: Note that some of your statements about confidence intervals seems a little unclear at times. For example, by construction, 95% of them should contain the true mean, what increasing the sample size does is reduce their width, not change this frequency.