Sequential two-column electro-Fenton-photolytic reactor for the treatment of winery wastewater
In: Environmental science and pollution research: ESPR, Band 24, Heft 2, S. 1137-1151
ISSN: 1614-7499
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In: Environmental science and pollution research: ESPR, Band 24, Heft 2, S. 1137-1151
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 19, Heft 5, S. 1738-1746
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 20, Heft 4, S. 2252-2261
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 19, Heft 5, S. 1800-1808
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 20, Heft 10, S. 7348-7354
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 20, Heft 9, S. 5983-5993
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 20, Heft 4, S. 2172-2183
ISSN: 1614-7499
This is the final version. Available on open access from MDPI via the DOI in this record. ; Data Availability Statement: All relevant material has been made available as Supplementary Materials ; This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available. ; European Union Horizon 2020 ; Spanish Ministry of Science, Innovation and Universities ; Fundación BBVA
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We describe a three axis vector magnet system for cryogenic scanning probe microscopy measurements. We discuss the magnet support system and the power supply, consisting of a compact three way 100 A current source. We obtain tilted magnetic fields in all directions with maximum value of 5T along z-axis and of 1.2T for XY-plane magnetic fields. We describe a scanning tunneling microscopy-spectroscopy (STM-STS) set-up, operating in a dilution refrigerator, which includes a new high voltage ultralow noise piezodrive electronics and discuss the noise level due to vibrations. STM images and STS maps show atomic resolution and the tilted vortex lattice at 150 mK in the superconductor β-Bi2Pd. We observe a strongly elongated hexagonal lattice, which corresponds to the projection of the tilted hexagonal vortex lattice on the surface. We also discuss Magnetic Force Microscopy images in a variable temperature insert ; This work was supported by Convocatoria Doctorados en el Exterior 568-2012 COLCIENCIAS, the Spanish MINECO (FIS2011-23488, MAT2011-27470-C02-02, CSD2009-00013), by the Comunidad de Madrid through program Nanofrontmag-CM (S2013/MIT-2850) and by Marie-Curie actions under the project FP7-PEOPLE-2013- CIG-618321. The research leading to these results has received funding from the European Union Seventh Framework Programme under Grant Agreement No. 604391 Graphene Flagship. We also acknowledge Banco Santander, COST MP1201. J.A. and C.M. acknowledge the FPI (BES- 2012-058600) and Juan de la Cierva (JCI-2011-08815) programs, respectively
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