Adaptive training systems have the potential to revolutionize pilot training by dynamically adjusting simulation difficulty based on real-time assessments of individual needs and performance. This study investigates the relationship between physiological signals and varying difficulty levels in flight simulations, exploring the potential to assess workload. A publicly available dataset containing multimodal signals (electrocardiography, electrodermal activity, photoplethysmography, accelerometry, electromyography, oculometry, respiration, and flight data) was analyzed. The data originated from 35 pilots performing ILS approaches at four difficulty levels with progressively increasing wind, turbulence, and visibility restrictions. This study employed a rigorous data preparation and analysis pipeline. Preprocessing included time series validation and signal-specific filtering guided by established literature.