Data processing updated

README.md

@@ -62,7 +62,7 @@ Each simulation run requires approximately 7 minutes of CPU execution time. Assu
 The dataset creation involves a trade-off between simulation fidelity and computational and environmental cost. EMT simulations were necessary to accurately capture fast inverter dynamics, provide reliable ground truth for machine learning models, and enable cross-resolution learning between EMT and RMS domains. At the same time, the dataset size of 4000 scenarios was selected to provide sufficient diversity for machine learning applications, while keeping the overall computational footprint within a manageable range. 
 
 # 3- Data Pipeline
-All data were produced by running 4,000 distinct operational scenarios across four simulation configurations and GFM and GFL control modes, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. Each simulation runs for 10 simulated seconds. 
+All data were produced by running 4,000 distinct operational scenarios across four simulation configurations and GFM and GFL control modes, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. Each simulation starts from the system being in steady-state and runs for 6.5 simulated seconds. 
 
 The simulations are grounded in established models of inverter-based systems and standard control strategies. That said, the dataset does not capture the nuances and complexities of a real-world power system dynamics, and must be interpreted as a significant approximation of real-world behavior. Moreover, the EMT and phasor domain models are not perfectly equivalent representations of the same physical system, they differ in modeling fidelity by design. The EMT model captures electromagnetic dynamics through the explicit representation of the inverter’s DC link and switching stage, and allows three-phase unbalanced operation in the random load. The phasor domain model operates at a coarser resolution, averaging switching behavior and representing the system in the positive sequence. This resolution gap is the primary axis of variation the dataset is designed to study, and the differences between EMT and phasor trajectories for matched scenarios are therefore a feature, not a defect.
 
@@ -116,6 +116,8 @@ Data processing for this dataset consists exclusively of post-simulation wrangli
 
 The Python API for the RMS simulation models in MATLAB Simulink write five signal files directly to disk in CSV format at the end of each simulation run: voltage magnitude in d-q axis (Vd, Vq in kV), current (id, iq in kA), powers (P in MW, Q in kvar), voltage angle (phase angle in radians), and an oscillatory state flag. These files are sampled at 1 ms resolution, yielding approximately 10,001 rows per file for a 10-second simulation. No conversion step is required, MATLAB writes these directly in the target format. The decision to write CSVs directly from Simulink was made to minimize pipeline wrangling and reduce the risk of conversion errors. The Python API for PSCAD writes recorded signals in COMTRADE format first, and a dedicated Python conversion script converts these files to the same 5-CSV structure as the MATLAB outputs. For each simulation run in both MATLAB and PSCAD, Python scripts assemble two scenario descriptor files. One records the disturbance type and timing and the other records the stochastically sampled random load parameters. Two CSV files record the active and reactive power reference time-series applied during the simulation. These files are generated directly from the Python scenario-sampling scripts and written alongside the simulation outputs, ensuring that every scenario folder is self-describing.
 
+Each scenario outputs a time-series label file recording the system's oscillatory state at each time step. In MATLAB Simulink, the trigger signal is written directly as Boolean strings. In PSCAD, the equivalent signal is recorded as a digital channel and converted to the same Boolean string format during wrangling. This conversion step ensures that file has an identical format and interpretation across both simulators, allowing paired EMT and phasor-domain trigger signals to be compared directly. The Trigger signal is False (non-oscillatory/steady-state) once active and reactive power signals simultaneously satisfy two conditions: their rate of change falls below a threshold of 0.01 pu, sustained for a confirmation window of 0.1 s with an on-delay of 0.05 s. This time-series label supports multiple downstream annotation strategies. A scenario-level binary label can be derived by checking whether the trigger ever reaches False after the disturbance is applied and within the simulation window. Scenarios in which the trigger never transitions are fully oscillatory for their entire duration. Users may also use the trigger as a temporal segmentation signal, distinguishing the transient phase from the post-disturbance steady-state phase within each trajector
+
 Note that the native-resolution PSCAD outputs and the MATLAB outputs are not directly time-aligned. Hence, users working with paired EMT/RMS data must account for this resolution difference explicitly, which is the primary intended use case of this dataset.