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Abstract
Previous works have shown the great potential of Virtual Reality (VR) in the area of Education. This paper studies if users can learn a second language when using a gamified VR application through an English learning test and how learning influences user satisfaction, sense of presence, cybersickness, and quality of experience through questionnaires. For this purpose, the VirtualeaRn game was developed. 20 Portuguese participants were exposed to the application, and the learning test was used before and after using the application. Result analysis shows an increase in learning results after using the VR gamified application, indicating the technology's efficacy in learning a second language. A positive user satisfaction, sense of presence, and quality of experience were also found. Some cases of cybersickness were reported. The outcomes are promising and provide enough information to show the potential of the gamification of VR technology for the area of learning a second language.

Abstract
Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.