Abstract
This paper presents a flight-test data reduction and visualization framework applied to telemetry records obtained during the experimental campaign of a 60% scale Super Cub demonstrator. The study addresses the transformation of raw flight-test measurements into physically interpretable representations suitable for the evaluation of aircraft dynamic behaviour, propulsion response and control activity. Experimental recordings are complemented by calibrated synthetic datasets generated from identified flight parameters, allowing controlled validation of the proposed processing sequence. The methodology integrates zero-phase Butterworth low-pass filtering, Savitzky-Golay smoothing, Min-Max normalization, automated event detection and Largest-Triangle-Three-Buckets (LTTB) geometric downsampling. Particular emphasis is placed on the first flight record, F1, which is used as a reference case for assessing the influence of signal conditioning on visualization-level data reduction. The results indicate that applying LTTB after signal conditioning preserves the main trends, local extrema and relevant transient events while reducing visual density and improving interpretability. The proposed workflow is suitable for post-flight analysis of multi-parameter telemetry datasets and can support future developments in automated flight-test reporting and real-time monitoring.