Update README.md

README.md

@@ -55,10 +55,10 @@ The dataset is structured as a collection of multivariate time-series sequences.
 
 ### Use Cases
 - Benchmarking multivariate time-series anomaly detection algorithms.
-- Developing predictive maintenance and fault identification systems for quadrotors.
+- Developing predictive maintenance and fault identification systems for UAVs.
 
 #### Data Collection and Processing
-Data was harvested from a fleet of custom-built, PX4-based quadrotors. 
+Data was harvested from a fleet of custom-built, PX4-based UAVs. 
 - **Hardware:** Multiple airframes with variations in motor sizes, weights, and sensor suites. Check paper for hardware details.
 - **Environments:** Flight missions conducted in indoor hangars, warehouses, and open outdoor spaces.
 
@@ -66,7 +66,7 @@ Data was harvested from a fleet of custom-built, PX4-based quadrotors.
 Annotation was performed via **Physically-Constrained Expert Evaluation**. Human experts identified state estimation divergences by comparing autopilot estimates with real-world observed outcomes. Labels were verified against physical flight dynamics to ensure the statistical anomalies correlate with actual physical events.
 
 ### Bias, Risks, and Limitations of Data Collection
-- **Platform Specificity:** Limited to multi-rotor dynamics (quadrotors). Limited outdoor flights with GPS availability.
+- **Platform Specificity:** Limited to multi-rotor dynamics. Limited outdoor flights with GPS availability.
 
 
 ## DATASET STATISTICS