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.

Abstract
Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario. © 2021 IEEE.

Abstract
The Real-Time Monitoring and Performance Management suite tool, known as UIL (User Interface Layer), was developed in the FASTEN project, a R&D initiative financed by the innovation and research program H2020 within a bilateral Europe-Brazil call. UIL was conceived and deployed in the IIoT architecture of the project. The goal was to provide a usercentered assistance to the human operator for both decision-responsibility and control loop, in a continuously updating information fashion, related to system's state. In order to have experimental results, a qualitative assessment was conducted in an industrial environment. The architecture proposed was based on the adoption of a Knowledge Engineering User Interface to support Operator 4.0. Our empirical experiments point out to a successful set of results. Copyright (C) 2021 The Authors.

Abstract

Abstract
To identify the most frequently developed topics in the area of Big Data and Digital Marketing, a quantitative analysis was developed in December 2020. This analysis was focused on 750 publications and later on 67 publications on Big Data and Digital Marketing from the Scopus database. A bibliometric analysis was developed using the VOSviewer software and the technique of term co-occurrence and author co-authorship. Clusters were found for each of the analyzed situations. © 2021 AISTI.

Abstract
The need for organizations to cooperate, especially SMEs, is currently considered an appropriate way to gain competitive capacity, and ICT are an enabler to support the creation of interorganizational cooperation networks. However, in rural regions, investment in technology is scarce and often seen as an unbearable cost. In order to respond to this reality, this work presents a conceptual technological model capable of leveraging and supporting the creation of cooperation networks, with the contextualization of the business in rural regions being previously done, exploring the fundamentals of cloud computing and cooperation networks.