file structure reflected

Datasheet.md

@@ -30,45 +30,40 @@ Both sub-records cover the same physical scenario under matched conditions, maki
 
 ## Q6. How many instances are there in total?
 
-The dataset contains 3,000 instances (scenarios), each contributing 11 files:
+No labels were created prior to simulation or by external human annotators. Annotation is fully automated and embedded within the simulation models. The dataset contains 3,000 scenarios, each identified by a four-digit, zero-padded index (0001-1600). There are 1600 scenarios simulated for the GFM control mode, named xxxx_M where xxxx is in [0001, 1600], and 1400 scenarios simulated for the GFL control mode, named xxxx_L where xxxx is in [0001, 1400]. Each control mode scenario contributes one metadata file and 2 signal CSV files (one from the EMT simulation and one from the RMS phasor-domain simulation). The signal file follow the naming convention xxxx_[M|L]_[EMT|RMS].csv, where the EMT or RMS denotes the simulation domain. 
 
-- 1 metadata file (xxxx_meta.csv)
-- Four EMT signal CSV files (xxxx_[M|L]_EMT_[A|I|P|V].csv)
-- Four RMS signal CSV files (xxxx_[M|L]_RMS_[A|I|P|V].csv)
-
-This yields a total of 33,000 files across the full dataset. Each scenario is identified by a four-digit zero-padded index (0001–3000). The dataset covers both GFM (M) and GFL (L) control modes across the 3,000 scenarios.
+The metadata file follows the naming convention xxxx_[M|L]_meta_data.csv. Given there are 1,600 GFM scenarios and 1,400 GFL scenarios, the total number of CSV files are 1,600 × 3 + 1,400 × 3 = 9,000.
 
 ## Q7. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?
 
-The dataset is a sample from the theoretically infinite space of all possible time-domain trajectories producible by the SIIB system under GFM and GFL control across all possible combinations of initial conditions and disturbances. The 3,000 scenarios were generated by stochastic sampling of the scenario parameter space, active and reactive power references, grid impedance scaling, grid voltage sag scaling, disturbance type, disturbance timing, and disturbance configuration, using uniform distributions over predefined ranges encoded in the scenario generation scripts. The sample is representative of the parameter space as defined by those ranges, but is not representative of the real-world distribution of operating conditions observed in actual systems. The sampling procedure does not weight scenarios by their likelihood of occurrence in practice; all parameter combinations within the defined ranges are equally probable. Additionally, the 3,000 scenarios are not stratified to guarantee equal representation of stable and unstable outcomes; the class proportions are emergent properties of the physics. Users performing classification tasks should assess class balance before training. Further discussion of representativeness is provided in the accompanying dataset card.
+The dataset is a sample from the theoretically infinite space of all possible time-domain trajectories producible by the SIIB system under GFM and GFL control across all possible combinations of initial conditions and disturbances. The 3,000 scenarios were generated by changing the control mode as well as stochastic sampling of the scenario parameter space, active and reactive power references, grid impedance scaling, grid voltage sag scaling, disturbance type, disturbance timing, and disturbance configuration, using uniform distributions over predefined ranges encoded in the scenario generation scripts. The sample is representative of the parameter space as defined by those ranges, but is not representative of the real-world distribution of operating conditions observed in actual systems. The sampling procedure does not weight scenarios by their likelihood of occurrence in practice; all parameter combinations within the defined ranges are equally probable. Additionally, the 3,000 scenarios are not stratified to guarantee equal representation of stable and unstable outcomes; the class proportions are emergent properties of the physics. Users performing classification tasks should assess class balance before training. Further discussion of representativeness is provided in the accompanying dataset card.
 
 ## Q8. What data does each instance consist of?
-Each instance consists of a metadata file in CSV format, as follows:
-
-- xxxx_meta_data.csv: A 16-row key-value file recording the scenario's generating conditions:
-    - Pref: active power reference (per unit, float)
-    - Qref: reactive power reference (per unit, float; GFL only, null for GFM)
-    - grid_impedance_scale: grid impedance scaling factor (float)
-    - voltage_sag_factor: voltage sag magnitude (float, null if not applicable)
-    - disturbance type: “Short circuit” or “Load Step up” (string)
-    - disturbance duration: duration of disturbance in seconds if the disturbance is short circuit, 0 otherwise (float)
-    - sc type: short circuit type identifier, integer 1–10, if the disturbance is short circuit, 0 otherwise (integer)
-    - R1, R2, R3: per-phase resistance of random load (Ω, float)
-    - L1, L2, L3: per-phase inductance of random load (H, float)
-    - C1, C2, C3: per-phase capacitance of random load (F, float)
-
-Each instance consists of 9 signal files. These share a common time column t in seconds, covering 0 to 6.5 s and are sampled at the simulation time step. All signals are measured at the LCL filter output (point of common coupling). The files are as follows:
-
-- xxxx_[M|L]_[EMT|RMS]_V.csv: This file includes two additional columns, Vd and Vq, measuring the d- and q-axis voltages in kV, respectively.
-- xxxx_[M|L]_[EMT|RMS]_I.csv: This file includes two additional columns, id, iq, measuring the d- and q-axis currents in kA, respectively.
-- xxxx_[M|L]_[EMT|RMS]_P.csv: This file includes two additional columns, P and Q, measuring active power in MW and reactive power in Mvar, respectively.
-- xxxx_[M|L]_[EMT|RMS]_A.csv: This file includes the additional column Theta, measuring phase angle of the voltage in radians, derived from the inverter's internal synchronization signal (PLL output for GFL; droop-based frequency integration for GFM).
+
+The metadata and singal files contain the following information:
+
+- xxxx_[M|L]_meta_data.csv: located in ./metadata, these files provides information needed to reconstruct or verify the simulation conditions for any scenario, and serves as the primary scenario-level annotation for downstream tasks. It is the authoritative annotation of each scenario's initial and disturbance conditions and records 16 rows, as follows:
+  - Pref: float, active power reference (per unit)
+  - Qref: float, reactive power reference (per unit; GFL mode only)
+  - grid_impedance_scale: float, initial condition of grid impedance scaling factor (null if not applicable).
+  - voltage_sag_factor: float, initial condition of grid voltage magnitude sag factor (null if not applicable)
+  - disturbance type: string, either "Short circuit" or "Load Step up"
+  - disturbance duration: float, duration of the short circuit in seconds, otherwise 0
+  - sc type: integer, 0 for load step up, 1-10 for short circuit type identifier
+  - R1, R2, R3: float, per-phase resistance values of the stochastically sampled random load (Ω), used for load disturbance.
+  - L1, L2, L3: float, per-phase inductance values of the random load (H), used for load disturbance.
+  - C1, C2, C3: float, per-phase capacitance values of the random load (F), used for load disturbance.
+
+- xxxx_[M|L]_[EMT|RMS].csv: located in ./data, these files are structured into 8 columns as follows:
+  - Time column, t, in seconds, and the rows correspond to simulation time steps; simulations run from t=0 to t=6.5 s. 
+  - Column Theta, measuring phase angle of the voltage in radians, derived from the inverter's internal synchronization signal (PLL output for GFL; droop-based frequency integration for GFM).
+  - Columns, P and Q, measuring active power in MW and reactive power in Mvar, respectively.
+  - Columns, Vd and Vq, measuring the d- and q-axis voltages in kV, respectively.
+  - Columns, id, iq, measuring the d- and q-axis currents in kA, respectively.
 
 ## Q9. Is there a label or target associated with each instance?
-Yes. Two types of labels are associated with each instance, as follows:
 
-- xxxx_[M|L]_[EMT|RMS]_T.csv, as described before.
-- The xxxx_meta_data.csv file provides complete scenario-level annotation of the generating conditions, including disturbance type and configuration and the initial operating point. 
+Yes. A metadata file is associated with each scenario, as described before. 
 
 ## Q10. Is any information missing from individual instances?
 
@@ -193,7 +188,7 @@ No.
 No cleaning or normalization was applied at any stage to the data exported from the simulation environments. Two preprocessing operations were performed, one during format conversion and one during wrangling:
 
 - COMTRADE-to-CSV conversion: PSCAD natively writes simulation outputs in COMTRADE format, consisting of a binary data file (out.dat) and a channel configuration file (out.cfg). A dedicated Python script converts these files into the five-CSV structure described before. This is a lossless recovery procedure, not a normalization or transformation. 
-- Structural wrangling: Python scripts organize all simulation outputs into a consistent hierarchical structure and apply the standardized file naming convention (xxxx_[M|L]_[EMT|RMS]_[A|I|P|T|V].csv). Scenario metadata is assembled into xxxx_meta_data.csv files directly from the scenario generation scripts. These operations are purely structural and do not modify signal values.
+- Structural wrangling: Python scripts organize all simulation outputs into a consistent hierarchical structure and apply the standardized file naming convention (xxxx_[M|L]_[EMT|RMS_meta_data].csv). These operations are purely structural and do not modify signal values.
 
 ## Q34. Was the raw data saved in addition to the preprocessed/cleaned/labeled data?
 
@@ -302,8 +297,4 @@ Will be discussed upon publication.
 
 ## Q57. Any other comments?
 
-This datasheet follows the framework proposed by Gebru et al. (2021): "Datasheets for Datasets," Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723. The datasheet should be read in conjunction with the accompanying Hugging Face dataset card, which provides more detailed technical documentation of the dataset's scope, data pipeline, quality, and management. Together, these two documents are intended to provide dataset consumers with all the information needed to make informed decisions about using this dataset for their chosen tasks.
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+This datasheet follows the framework proposed by Gebru et al. (2021): "Datasheets for Datasets," Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723. The datasheet should be read in conjunction with the accompanying Hugging Face dataset card, which provides more detailed technical documentation of the dataset's scope, data pipeline, quality, and management. Together, these two documents are intended to provide dataset consumers with all the information needed to make informed decisions about using this dataset for their chosen tasks.