Safety and Airworthiness Design of Ultra-Light and Very Light Amphibious Aircrafts

AbstractUltra-light and very light amphibious aircrafts are the special kinds of low-speed general aircrafts. They are low weighted and small sized but can takeoff and land either on land or water without changing the structure of any parts. These characteristics result in the distinctive configuration and structure design, and meanwhile bring about significant features of safety and airworthiness design. These problems are investigated by developing the ultra-light amphibious aircraft “Frigate bird” and analyzing the other aircrafts’ design. This paper mainly discusses the preliminary design about structure, aerodynamics, power effect, flying qualities, dynamics and statics on water. Some analysis methodologies and design parameters which are different from the conventional general aircrafts’ are also represented

Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens

Vision-language models (VLMs) seamlessly integrate visual and textual data to perform tasks such as image classification, caption generation, and visual question answering. However, adversarial images often struggle to deceive all prompts effectively in the context of cross-prompt migration attacks, as the probability distribution of the tokens in these images tends to favor the semantics of the original image rather than the target tokens. To address this challenge, we propose a Contextual-Injection Attack (CIA) that employs gradient-based perturbation to inject target tokens into both visual and textual contexts, thereby improving the probability distribution of the target tokens. By shifting the contextual semantics towards the target tokens instead of the original image semantics, CIA enhances the cross-prompt transferability of adversarial images.Extensive experiments on the BLIP2, InstructBLIP, and LLaVA models show that CIA outperforms existing methods in cross-prompt transferability, demonstrating its potential for more effective adversarial strategies in VLMs.Comment: 13 page