Update README.md

README.md

@@ -75,6 +75,8 @@ Script >>> Contains Python data acquisition and computational processing scripts
 -- Guillermo Montero, Jeremy Yin, Katherine A. Flanigan, Mario Bergés, James D. Brooks, "Anomaly identification algorithms for indirect structural health monitoring using a laboratory-scale railroad track system," Proc. SPIE 12488, Health Monitoring of Structural and Biological Systems XVII, 124881L (25 April 2023); https://doi.org/10.1117/12.2658463
 -- Jeremy Yin, Guillermo Montero, Katherine A. Flanigan, Mario Bergés, James D. Brooks, "Open-source hardware and software for a laboratory-scale track and moving vehicle actuation system used for indirect broken rail detection," Proc. SPIE 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 1248609 (18 April 2023); https://doi.org/10.1117/12.2658438
 
+- **Known issues:** (From previous FPS fluctuation) Image-to-location alignment was anchored at the start and end of each run; between these two anchor points, frame timestamps reflect when each frame began saving rather than its capture instant. Under variable-FPS conditions this introduces a non-constant drift that can displace an image by up to ~15 m from its true location. Location-dependent analyses should therefore validate image positions against in-scene references (e.g., the surveyed cones at Point A, Point B, and each anomaly) rather than relying on timestamps alone.
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 - Methods for processing the data: Raw accelerometer data was digitized at 2 kHz, converted to physical units (g), and passed through a 500 Hz low-pass Butterworth filter.
 
 - Instrument-specific information needed to interpret the data: None. The vibration system uses Silicon Designs Model 2012 accelerometers. The vision system uses a See3CAM 50CUGM industrial camera.