Reentrancy attacks target smart contracts of Decentralized Finance systems that contain coding errors caused by developers. This type of attacks caused, in the past 5 years, the loss of over 400 million USD. Several countermeasures were developed that use patterns to detect reentrancy attacks on smart contracts before deployment on the Ethereum blockchain. However, the smart contracts are by default public and immutable once deployed on the blockchain. That is why the research question is: How can we protect smart contracts of DeFi systems deployed on the Ethereum blockchain that are known to be vulnerable to reentrancy attacks? A solution that detects reentrancy attacks on smart contracts after their deployment is presented in this paper. It flags transactions when a difference is found between the users' funds on both the application and protocol layers before and after each transaction using special made smart wallets. A proof of concept shows that the proposed solution can detect reentrancy attempts and stop them during the execution phase of smart contracts.

Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in photo-realistic 3D reconstruction. NeRFs often take as input posed images where the camera poses come from either off-the-shelf S extit{f}M or online optimization together with NeRFs. However, we find that both strategies yield suboptimal results in recovering camera poses from images when encountering texture-less and repetitive patterns, particularly in aircraft engine inspection. To reconstruct photo-realistic 3D engine blades from images, we propose BladeNeRF, a new variant of NeRF model that incorporates camera constraints into learning and enables accurate pose learning. In addition, we propose to separate the blades in the foreground from the constant background, eliminating background artefacts and enhancing depth estimation accuracy. Experimental evaluations on synthetic data demonstrate the advantage of our model in precise camera pose estimation and high-fidelity 3D scene reconstruction compared to other NeRF variants.