Background Tetrahydrocannabinol (THC) vaping has become increasingly prevalent, yet available empirical data on puffing behavior during THC vape use remain limited. This study aimed to characterize the puff duration of people who use THC vapes by analyzing publicly available YouTube videos. Additional observations included device type, liquid composition, and notable user behaviors. Methods YouTube was systematically searched using predefined search terms related to THC vaping. Videos published between 2018 and 2024 were screened for inclusion based on clear visibility of puffing events. A total of 100 videos were included in the analysis. Puff initiation and termination were defined by visible facial muscle contraction and relaxation, respectively. Puff duration was determined using frame-by-frame analysis. Results The mean puff duration obtained from 456 analyzed puffs was 3.68 seconds with no significant differences between cartridge and disposable devices ( p =0.1). Behavioral observations included engagement in extended puffing, coughing, and subjective description of aerosol harshness and flavor. Conclusions The observed puff durations during THC vape use closely aligned with those previously reported for electronic nicotine delivery systems (ENDS), suggesting that ENDS puffing regimens may be appropriate for preliminary emissions testing of THC vape products. However, distinct user behaviors reported in this study highlight the need for THC vape specific protocols that reflect real-word use patterns.

Global Navigation Satellite Systems (GNSS)-denied environments pose significant challenges for autonomous drone navigation, requiring robust visual localization systems capable of real-time performance. Existing approaches either sacrifice accuracy for speed or fail to adapt to varying flight altitudes and orientations, limiting their practical deployment. We present Real-Time Elevation and Orientation-Aware Localization Architecture (REOLA), a visual localization system that combines similarity-driven autonomous window sizing, element-wise correlation-based orientation detection, and reinforcement learning with human feedback (RLHF) enhancement for publicly available satellite imagery. On desktop hardware (i7-10700K + RTX 3070), the REOLA achieved approximately 59 FPS performance with sub-5-m accuracy across diverse flight conditions through intelligent similarity-based matching, combined with efficient MobileNet-V3 embeddings and FAISS similarity search. For embedded deployment on NVIDIA Jetson Orin Nano, the system achieved 22.5 FPS, meeting real-time requirements for autonomous drone localization. The system autonomously selects optimal window sizes corresponding to the current elevation and determines drone orientation through element-wise correlation scoring across discrete rotation angles. Enhanced through RLHF, the REOLA achieved a $97.1\%$ success rate (sub-5-m localization) while processing frames in 17 milliseconds on desktop hardware (44.4 ms on embedded hardware), providing a substantial margin over real-time requirements. The approach demonstrates particular superiority over traditional keypoint-based methods in challenging environments with repetitive patterns such as agricultural fields, rocky mountains, dense forests, and grasslands, where conventional keypoint detection struggles. We explicitly identify featureless sand dune deserts and open-sea or coastal water flights as out of scope, since the reference satellite imagery in those regimes does not contain stable landmarks.