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The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.

Dataset recorded in front of the University of Luxembourg Belval Campus for the aim of drone visual localization algorithms evaluation. Included files: 5 fullHD video sequences numbered from 3 to 7 were sampled at 30FPS Calibration sequence and camera parameter file Poses files, i.e. extrinsic parameters of the cameras for each image Sparse reconstruction with GPS coordinates alignment Part 1 of this dataset is available at: 10.5281/zenodo.7242900

Dataset recorded in front of the University of Luxembourg Belval Campus for the aim of drone visual localization algorithms evaluation. Included files: 5 fullHD video sequences numbered from 3 to 7 were sampled at 30FPS Calibration sequence and camera parameter file Poses files, i.e. extrinsic parameters of the cameras for each image Sparse reconstruction with GPS coordinates alignment The Part 2 of this dataset is available at: 10.5281/zenodo.7243492

These are plot data from fifteen 40 m by 40 m sample plots established in Oban Division of Cross River National Park, Nigeria, between 23rd August 2019 and 9th September 2019. We have also included data summaries and RStudio codes used for analysis and generating results for the manuscript entitled: "Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest", submitted for publication as an original research article in Biotropica. All data and R code required to generate the results as shown in the manuscript have been included. https://doi.org/10.5061/dryad.kh189328z

Purpose: This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features. Design/methodology/approach: In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices. Findings: The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback. Originality/value: This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

Project TISPLALI studied the use of drone-based thermal infrared imaging using known plastic targets deployed at sea. The published study can be found at: https://doi.org/10.3390/rs14133179.

Project TISPLALI studied the use of drone-based thermal infrared imaging using known plastic targets deployed at sea. The published study can be found at: https://doi.org/10.3390/rs14133179. Created via Ocean Scan.Campaign ID: ee6a58ca-af2c-4813-a7ba-f2a099544c9a.

In order to reduce the risk posed by flooding, areas of woody vegetation have been removed along the riverbank of Elbe river in order to expedite the inflow and outflow of water from the main channel, and thus contribute to flattening the peak hydrographic response. Maintaining the effectiveness of this clearing requires that there is little or no regrowth of this woody vegetation. The NBS that have been implemented in OAL-Germany sees the use of various animals to graze these areas. NBS is devoted to prevent the re-growth of woody vegetation after an intervention which took place over a period from autumn 2014 to February 2015, when woody vegetation along the riverbank was cut back. Monitoring the effectiveness of the NBS is being performed by means of high spatial resolution remotely sensed data , i.e. Rapideye at 5 m spatial resolution. The preliminary analysis of this experiment consisted in the monitoring of the fractional vegetation cover over four of the seven NBS sites. The green fractional abundance (fc) was calculated by an algorithm based on scaling NDVI in-between the maximum and minimum NDVI values. A semi-empirical method based on the use of NDVI was used following Zeng et al, (2000), to calculate fc. The dataset contains layer stack of fractional abundance calculated for the images calculated by Rapideye images acquired on 18 April 2013, 15 April 2015, 17 March 2016, 9 April 2019. Data provided by European Space Agency

Dataset related to publication "Active hyperspectral sensing (AHS) applications for mineral deposit exploration"