Reflexivity updated

README.md

@@ -38,6 +38,17 @@ Overall, the benefits of enabling research in scalable and accurate power system
 
 ## 2.1- Domain Knowledge Requirements
 
+The synthetic data is generated based on the model shown in Figure 1. Developing the dataset required expertise spanning multiple technical domains, as follows:
+
+-	Power system modeling: Expertise in IBRs modeling in both EMT and RMS domains is essential, including the distinction between GFM (switch state 1 in Figure 1) and GFL (switch state 2 in Figure 1) control architectures, the structure and parameterization of inner and outer control loops, PLL controllers, and the design of LCL filters at the inverter terminal. Understanding the SIIB system as a canonical test case, including its governing differential-algebraic equations and the conditions under which it exhibits oscillatory or unstable behavior, was a prerequisite for meaningful scenario design.
+-	Power system time domain simulation: Proficiency in EMT simulation using PSCAD was required to construct the high-fidelity electromagnetic transient model, configure appropriate integration time steps, and implement signal logging. Proficiency in MATLAB Simulink was required to construct the equivalent RMS phasor-domain model and ensure that both simulators represented the same physical system under matched scenario conditions. Cross-validating simulation outputs between the two platforms required the ability to interpret and reconcile differences arising from differences in modelling resolution rather than modelling error.
+-	Data processing: The dataset required a systematic design of a stochastic scenario sampling procedure, structured file organization across thousands of simulation runs, consistent naming and indexing conventions, and the development of automated logging and verification pipelines to ensure that saved outputs correctly correspond to their intended simulation scenarios. Data processing, wrangling, and packaging were performed in Python, including parsing and aligning time-series outputs across simulators, organizing scenario metadata, and preparing the dataset in a structured format.
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+Users of this dataset are expected to have foundational knowledge of power systems and an applied ML skill set. These are necessary to leverage the dataset for its primary intended purpose of training and benchmarking surrogate models for time-domain simulation. Understanding time-series modeling, sequence-to-sequence learning, and the concept of discretization invariance will be essential for interpreting model behavior along the paired EMT/RMS resolution axis. Moreover, users should understand the physical meaning of the signals recorded, voltages, currents, and powers in the dq (Park) reference frame, active and reactive power injections, and phase angle, as well as the significance of the control mode distinction between GFM and GFL operation [5]. On the data side, reusers with basic data handling skills in any language should be able to work with the dataset directly, as all signal data is provided in CSV format. Python is not a requirement for access or use.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/69ed86a08bdc19557f6eda14/U_fIUryMHT6MWwoKAjIgK.png)
+*Figure 1: SIIB physical and control layers*
+
 [1] https://ieeexplore.ieee.org/abstract/document/9286772/ 
 
 [2] https://ieeexplore.ieee.org/abstract/document/10213230