(which were cleaned from solar bursts and background variations caused by charged particles) were processed by the standard epoch-folding technique [see, e.g. @Ter92], which was modified to accommodate the specific features of the Prognoz 9 and GRIF data. The intervals of observations were broken up into segments with duration equal to the trial period under consideration. The sequences of count rates that corresponded to these segments were added together, and an average phase dependence of the count rate was constructed for this trial period. The amplitude of the periodicity corresponding to the trial period (the actual or randomly simulated one) can be described by the rms deviation (${\sigma}^2$) of the numbers $M_i$ that constitute the average phase dependence: $$\sigma^{2} ={\sum\limits_{i=1}^k (M_i - \bar{M}) \over {k-a}}.$$ Here $\bar{M}$ is the mean count rate determined from the entire analyzed time series, $k = {T \over 
\Delta T}$, where $\Delta T$ is the bin duration of the count rates that constitute the mean phase profile; and $T$ is the trial period. If the periodicity related to the period under study contains both a real periodic component and a noise component, then because of their independence, ${\sigma}^2$ can be represented as the superposition: $$\sigma^2 