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Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively. 

Herein, we present a systematic investigation of the impact of silica nanoparticle (SiNP) size and surface chemistry on the nanoparticle dispersion state and the resulting morphology and vanadium ion permeability of the composite ionomer membranes. Specifically, Nafion containing a mass fraction of 5% silica particles, ranging in nominal diameters from 10 nm to >1 μm and with both sulfonic acid- and amine-functionalized surfaces, was fabricated. Most notably, an 80% reduction in vanadium ion permeability was observed for ionomer membranes containing amine-functionalized SiNPs at a nominal diameter of 200 nm. Further, these membranes exhibited an almost 400% increase in proton selectivity when compared to pristine Nafion. Trends in vanadium ion permeability within a particular nominal diameter were seen to be a function of the surface chemistry, where, for example, vanadyl ion permeability was observed to increase with increasing particle size for membranes containing unfunctionalized SiNPs, while it was seen to remain relatively constant for membranes containing amine-functionalized SiNPs. In general, the silica particles tended to exhibit a higher extent of aggregation as the size of the particles was increased. From small-angle neutron scattering experiments, an increase in the spacing of the hydrophobic domains was observed for all composite membranes, though particle size and surface chemistry were seen to have varying impacts on the spacing of the ionic domains of the ionomer.