Initial condition sampling is updated

README.md

@@ -112,7 +112,7 @@ The control gains, filter components, and droop coefficients listed above are re
 
 Scenario diversity is achieved through stochastic parameterization of initial conditions and two disturbance categories, all generated in Python and injected into the simulation models via their respective APIs.
 
-Initial conditions include the power references set-points, grid impedance scale, and grid voltage sag scales. These initial conditions are sampled from uniform distributions. The voltage sag scale is randomly selected from a uniform distribution between 0.8 and 0.99. The grid impedance scale is sampled from a uniform distribution between 1 and 7. The two scales are mutually exclusive, i.e., one is scaled and the other scaling factor remains 1 based on a 50-50% selection chance.
+Initial conditions include the power references set-points, grid impedance scale, and grid voltage sag scales. These initial conditions are sampled from uniform distributions. Active power in both the GFM and GFL modes is sampled uniformly from [0.5-1.7]. Reactive power in the GFL mode is uniformly sampled from [0.2-0.8]. The voltage sag scale is randomly sampled from a uniform distribution between 0.8 and 0.99. The grid impedance scale is sampled from a uniform distribution between 1 and 7. The two scales are mutually exclusive, i.e., one is scaled and the other scaling factor remains 1 based on a 50-50% selection chance.
 
 Scenarios start from a steady-state point. A disturbance is applied at t = 0.5 s. Disturbances are sampled as follows: