dataesr/scientific-paragraphs-categorization huggingface dataset:
▪ Surface roughness is estimated pointwise for local regions, as the distance between each point to the best fitting plane computed on its nearest neighbours. The total roughness value is the mean value of all points' roughness in that section. Here we measure the roughness for two sphere radii, to achieve a more robust measure. ▪ Plane orientation is defined if a plane divides 3D space into two half spaces. Because the surface normal is perpendicular to the plane, it always points to one half space. Thus, the normal vector distinguishes the two areas, by always pointing to exactly one of them. The external measures of each point-cloud are evaluated based on a reference point-cloud, which is considered the most reliable of the available datasets: ▪ Point-to-point distance is defined as the average distance between the analysed point to the reference.
Study site & data acquisition A rockfall close to the village of Obergurgl, Austria (46°51'59"N, 11°00'60"E, Figure  1 ) has been chosen to evaluate the quality of photogrammetric based point-clouds. This rockfall is defined by an area of approximate ~110 m height and ~60m width. It is formed in Triassic and Cambrian metamorphic rocks (meta-schists and gneiss formations). Figure  1. Schematic view of the investigation site in Obergurgl, Austria. Images were acquired on two succeeding days sharing similar lighting conditions with a DSLR camera (Canon EOS 6D) equipped with a zoom lens and a full-frame CMOS sensor (36 x 24mm) with 6240 x 4160 pixels (Figure  2 ). Due to the steep terrain and the difficult accessibility of the site, the images were taken ~100 m away from the modelled surface on a path facing the rockslide, over its middle section.
Photogrammetric processing To acquire comparable datasets, we produced multiple pointclouds from the same collected images by means of two photogrammetric software products: Agisoft Metashape Professional v1. 5, , a proprietary stand-alone software  (Agisoft, 2019)  and a combination OpenMVG  (Moulon et al, 2016)  and OpenMVS  (Cernea, 2015)  open-source libraries. Agisoft Metashape is one of the most commonly used photogrammetric software for geomorphological surveys  (Eltner et al. , 2016; James et al. , 2017). Detailed descriptions of photogrammetric workflows for the production of point-clouds using Agisoft Metashape Pro can be found e. g. , in Turner et al. (  2015 ) and  James et al. (2017). OpenMVG provides methods to solve camera orientation and calibration problems, by implementing keypoints matching across images and subsequent bundle adjustment.
OpenMVS is used afterwards to produce depth maps and dense point-cloud via a patch matching approach  (Bleyer et al, 2011). 1 ). In total, six datasets were created. Following their creation (Figure  5 ), all point-clouds were registered to the TLS data using the semi-automatic registration tool in Cloud Compare (CloudCompare, 2019). Note that dataset no. 6 represents only of a small region within the entire rockfall and it was analysed accordingly. Figure  5. Schematic representation shows the flow of the proposed methodology.
Results To assess the quality between different photogrammetry-based point-clouds, and as well as assess the suitability of such pointclouds for geomorphological usage. Generally, we applied the proposed workflow on the photogrammetric datasets, while using the TLS data as reference. Table  2  describes quality measures for one sub region of the rockfall (upper section). The radius in which the analyses were made is 5cm. Note that also the reference dataset has been analysed, to provide comparable information. It can be seen that the point density of dataset no. 6 is ~2. 5-fold than that derived from the TLS, and almost 11 times larger than the one acquired by 30 images with focal of 24mm and processed with the opensource software. Notably, the proprietary software did not produce much better results, with 6.
200 point per square-metre, as opposed to 5. 740. This is also reflected by the number of neighbours in a 5cm sphere. Initially, individual point cloud metrics have been assessed such as the total number of points (Table  1 ), and the pointcloud density. Then, pointclouds were assessed among each other by comparing with the TLS reference. Thirdly, a geometrical approach assessment has been carried out by implementing a cross section among the models to evaluate their respective points scattering. The latter theoretically should remain stable between all datasets. In Figure  6  is shown a comparison of the point-clouds with their respective neighbour density in 5cm radius. A small region common for all the models, has been selected and cropped for further analysis using above describe quality metrics.
Different metrics are presented in Figure  7 , Figure  8  and Figure  9  for the respective 3D pointclouds. Moreover, a geomorphological cross section (Figure  10 ) has been designed and overlaid on the multiple resulted 3D models for detailed inspection. In the current section, we computed a cross section as the intersection between the produced mesh model and a specific plane surface. The latter comprises a characteristic rockfall scene as it represents the active source zone. It can be seen that in individual parts of the scene the 3D models show a multivariate scattering dependent on the selected configuration compared with the reference TLS. For instance, regarding the software used for SfM, Agisoft Metashape showed an increased point scattering from the reference when compared with the open source one.
DNA Sequencing and Analysis To identify Cryptosporidium species/genotypes, secondary PCR products were purified using the NucleoFast ® 96 PCR kit (Macherey Nagel, GmbH & Co KG, Düren, Germany). Purified PCR products were sequenced in both directions, using the secondary PCR primers (Genoscreen, Pasteur Institute of Lille, Lille, France). Obtained nucleotide sequences were aligned using the BioEdit v. 7. 0. 1 package, and compared with available DNA sequences of Cryptosporidium in GenBank data base using the NCBI BLAST basic local alignment search tool (http://www. ncbi. nlm. nih. gov/BLAST/). For longer 18S rDNA and actin amplicons, secondary PCR products were purified using a filter tip method  [23]  and sequenced in both directions using an ABI Prism™ Dye Terminator Cycle Sequencing kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions at 58 • C.
Phylogenetic Analysis at the Actin Locus Sanger sequencing chromatogram files were imported into Geneious Pro 10. 2. 6  [24] , analysed and aligned with reference sequences from GenBank (C. huwi (AY524772), C. molnari (HM365220), C. molnari (HM365219), C. bollandi (MT160193) and C. scopthtalmi (KR340589)) using Clustal W (http://www. clustalw. genome. jp). Distance and Maximum Likelihood (ML) trees were constructed using MEGA v. 7, after first identification of the most appropriate nucleotide substitution model  [25]. Bootstrap support based on 1000 replications was included.
Detection of Cryptosporidium by Immunofluorescence A conjugated anti-Cryptosporidium spp. (Sporoglo, Waterborne, New Orleans, LA, USA) was used in direct fluorescent-antibody staining assay according to the manufacturers' instructions. DAPI (4 ,6-diamidino-2-phenylindole) was used for nuclei identification. Slides were examined in a LSM880 Confocal Microscope (Zeiss, Oberkochen, Germany) equiped with a UV laser for the detection of DAPI (excitation at 355 nm and emission at 405 nm) and VIS (visible light) laser for the detection of sporoglo (excitation wavelength at 555 nm and emission at 561 nm). The images were taken using Ziess Axiocam digital camera. The images were processed using Carl Zeiss Zen software.
Detection of Cryptosporidium by Immunofluorescence A conjugated anti-Cryptosporidium spp. (Sporoglo, Waterborne, New Orleans, LA, USA) was used in direct fluorescent-antibody staining assay according to the manufacturers' instructions. DAPI (4′,6-diamidino-2-phenylindole) was used for nuclei identification. Slides were examined in a LSM880 Confocal Microscope (Zeiss, Oberkochen, Germany) equiped with a UV laser for the detection of DAPI (excitation at 355 nm and emission at 405 nm) and VIS (visible light) laser for the detection of sporoglo (excitation wavelength at 555 nm and emission at 561 nm). The images were taken using Ziess Axiocam digital camera. The images were processed using Carl Zeiss Zen software.
Funding: This work was supported by the  French National Research Agency  (Grant No. ANR 2010 ALIA 004-01 ), the  Conseil Regional Hauts-de-France  ( Concerted Research Actions of Regional Initiative ,  ARCir 13 ABC FISH No. 13003283 ), the regional competitiveness center AQUIMER (Boulogne s/mer, France), the  Institut Pasteur of Lille , the  University of Lille , the  CHU of Lille , the  Institut National de la Santé et de la Recherche Médicale  and the  Centre National de la Recherche Scientifique. M. S. was funded by a  PhD fellowship  from the  University of Lille.
1. The global land parameter data record (LPDR)  [83] : This dataset is produced using calibrated T B acquisitions from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) onboard the National Aeronautics and Space Agency (NASA) EOS Aqua satellite (05/2002-10/2011), and from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Water1 (GCOM-W1) satellite (from 05/2012). VOD is retrieved from T B acquired by AMSR-E and AMSR2 at X band (10. 7 GHz) at H and V polarizations using τ-ω model. T B are preprocessed to minimize the effects of RFI (radio frequency interference), precipitation, and permanent ice and snow cover using the flags defined in  [84]  and the freeze thaw status under frozen conditions  [85].
Besides, the VOD is expressed as a function of the land-water emissivity slope index α as follows  [86] : τ = -log        -B -B 2 -4AC 2A        (20) with:            A = (1 -ω) r dry (θ, V) -αr dry (θ, H) B = αe dry (θ, H) -e dry (θ, V) + (1 -ω) αr dry (θ, H) -r dry (θ, V) + 1 -α C = (1 -ω)(α -1) + e water (θ, V) -αe water (θ, H) (21) α = e(θ, V)e water (θ, V) e(θ, H)e water (θ, H) where e dry , e, e water are the dry bare soil, land, and open water emissivities, r dry is the bare soil reflectivity. All these parameters have predefined values except e  [86]. ω is a constant set to 0. 06  [87]. This dataset is made available by the University of Montana at  [88].
Land Parameter Retrieval Model (LPRM) [89]: This dataset is obtained from T B acquired by the: Using the τ-ω model  [90]. Only nighttime T B are used as they are supposed to be less affected by lower temperature-related error because the soil and vegetation media are closer to equilibrium at night than during the day  [90]. A threshold approach was developed to filter out data exhibiting differences between observed and simulated T B higher than 0. 25 K. These data correspond to grid points with frozen soil conditions, snow cover or excessive vegetation  [91]. VOD is expressed as a function of the microwave polarization difference index (MPDI), which is frequently used to remove the temperature dependence of T B , as follows  [92] : • Scanning Multichannel τ = C 1 ln(C 2 MPDI + C 3 ) (23) with: MPDI = T B (θ, V) -T B (θ, H) T B (θ, V) + T B (θ, H) (24) The coefficients C i are polynomial functions absolute value of the soil dielectric constant  [92].
3. Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA)  [94] : VODCA is an approach developed to combine multiple VOD datasets, derived from multiple sensors (SSM/I, TMI, AMSR-E, Windsat, and AMSR-2), that have in common to have been processed using LPRM  [90, 92]. The merging approach is composed of three steps )  [94] : • a preprocessing to identify and remove potentially noisy data using the Radio Frequency Interference (RFI) flag proposed by  [95]   This dataset is retrieved from T B acquired by SMOS at L band (1. 4 GHz) at H and V polarizations, from 12/2009, using the L-MEB model inversion approach in the τ-ω  [99]  and 2S  [72]  versions. RFI are filtered using multiple thresholds on daily annual T B RSME and time variations of the VOD on each pixel. Surface conditions flags are also applied to detect the presence of strong topography, water bodies, urban areas, ice and frozen conditions.
SMOS Level 2 [104]: This dataset is obtained through the inversion the T B acquired by Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency (ESA) SMOS mission at L band (1. 4 GHz) at H and V polarizations, from 12/2009, using the τ-ω model. Thresholds and 2-sigma criteria are applied on the T B to filter out invalid data (see  [105]  for a detailed description of these criterion). A RFI flag, based on a modelling approach of these interferences, is applied to mask the contaminated pixels  [106]. Corrections of the direct solar radiations are also applied  [107]. Due to the coarse resolution of the SMOS data (between 25 and 60 km), soil and vegetation are not uniform inside a pixel. Each pixel T B is the sum of the emission of each vegetation and soil type class weighted by its intra-pixel cover fraction.
SMOS Level 3 [105]: The SMOS Level 3 retrieval is based on the same physical approach as SMOS Level 2 but taking into account several orbits to benefit from the longer temporal autocorrelation length of the VOD compared to SM  [105]. The same preprocessing as Level 2 is performed (see SMOS Level 2 description). Retrievals are performed considering three revisits over a 7-day temporal window minimizing the following cost function expressed as: F = T B obs (p, θ) -T B model (p, θ) p cov(T B ) -1 T B obs (p, θ) -T B model (p, θ) + τ -τ 0 p cov(τ) -1 τ -τ 0 + SSM -SSM 0 p cov(SSM) -1 SSM -SSM 0 (29) with:                      cov(T B ) = σ 2 (T B ) cov(SSM) = σ 2 SSM 0 I 3 cov(τ) = σ 2 τ 0           1 ρ(t c , t i ) ρ(t f , t i ) ρ(t i , t c ) 1 (t f , t c ) (t i , t f ) (t c , t f ) 1           (30) where X 0 is the initial value of the parameter X, cov(T B ) is the error covariance matrix of T B data assuming no temporal autocorrelation, σ 2 (X 0 ) is the variance of X 0 , I 3 is the 3 × 3 identity matrix, t i , t c and t f are the initial, central and final times respectively in the 7-day temporal window, ρ is the correlation function modeled assuming a Gaussian distribution of the autocorrelation: ρ(t 1 , t 2 ) = ρ max (t 1 , t 2 ) exp       - (t 1 , t 2 ) 2 T c 2       (31) where ρ max is the maximum of the correlation function between the instants t 1 and t 2 , and T c is the VOD characteristic correlation time.
7. SMAP Level 2 Passive Soil Moisture Product (L2_SM_P)  [109]  and SMAP Level 2 Enhanced Passive Product (L2_SM_P_E)  [110] : These datasets are retrieved from the T B acquired by the NASA Soil Moisture Active Passive (SMAP) satellite at L band (1. 41 GHz) at H and V polarizations from 04/2015 using the τ-ω model. The high quality T B acquisitions are RFI-filtered using the approach presented in  [111]. Data are checked for the presence of water, urban, precipitation, frozen ground, snow and ice fractions, as well as Digital Elevation Model (DEM) statistics  [109, 110]. The same processing is used for both products but in the case of L2_SM_P applied to the SMAP T B at 36 km of spatial resolution and in the case of L2_SM_EP Backus-Gilbert enhanced T B  [110]  at 9 km of spatial resolution.
8. Multi-temporal dual-channel retrieval algorithm (MT-DCA)  [116] : This dataset is retrieved from T B acquired by the NASA SMAP satellite at L band (1. 41 GHz) at H and V polarizations from 04/2015 using the τ-ω model. MT-DCA is applied to SMAP Backus-Gilbert enhanced T B  [110]  to derive soil moisture and VOD as well as effective scattering albedo (ω)  [117]. The three parameters are obtained using a combination of T B from two consecutive overpasses, assuming VOD is constant during the corresponding time period, and minimizing the following cost function: F(X) X=SM 1 , 1 VOD = p=V p=H        N i=1 T B obs (p, θ) -T B model (p, θ) 2        (37) This approach can be applied to N overpasses as long as the assumption of constant VOD remains valid. Two overpasses are used to increase the liability of the constant VOD assumption  [116].
9. Metop-A ASCAT VOD from Technische Universität (TU) Wien  [119] : This dataset is obtained through the inversion of σ 0 acquired at C band (5. 255 GHz) at V polarization by the Advanced Scatterometer (ASCAT) sensors onboard the EUMETSAT MetOp-A and B satellites launched in September 2006 and October 2012 respectively, using the WCM model. VOD is given by  [119] : τ(θ) = cos(θ) 2 ln ∆σ 0 s ∆σ 0 ( 38 ) where ∆σ 0 s represents the maximum range in backscatter values over bare soils due to a change in soil moisture, assumed to be constant through time. And ∆σ 0 is the difference between the wet and dry references which are calculated from the historically wettest and driest backscatter measurements at θ equals 40 •  [120]. Temporal variations of the VOD were introduced using a second-order Taylor expansion for the dry reference for incidence angles not equal to 40 • [119]: σ 0 (θ, t) = σ 0 (40, t) + σ (40, DOY)(θ -40) π 180 + 1 2 σ (40, DOY)(θ -40) 2 π 180 2 ( 39 ) where σ and σ" are the slope and the curvature, respectively average every day of the year (DOY) over several years.
VOD and Carbon Balance VOD was found to be highly correlated to AGB. As AGB datasets are generally statics, many studies used spatial correlation of VOD against AGB to assess interannual variations and trends of AGB. L-VOD from the SMOS-IC product was used to quantify annual AGB carbon (AGC) changes over many of the major bioclimatic areas. Decreases in vegetation carbon stocks of 0. 05 and 0. 02 Pg. year -1  were estimated between 2010 and 2016 in the drylands (53% and 47% of the study area) and wet areas of Sub-Saharan Africa  [150]. The AGC budget was found to be almost in balance in the tropics over 2010-2017 with gross gains and losses of -2. 97 and -2. 86 Pg. C. yr -1. Large spatio-temporal variations were observed in the tropics during the wet La Niña event of 2011 and the extremely warm and dry and warm 2015-2016 El Niño event.
Comparisons between different L-band VOD from SMOS (i. e. , SMOS Level 2, SMOS Level 3 and SMOS-IC v105) and three different AGB datasets, as well as three height NDVI and EVI also exhibited very good agreement between the different parameters. Among the L-VOD products, SMOS-IC v105 obtained the higher correlations and lower dispersion against all the evaluated VOD datasets  [144]. The major drawbacks of the current VOD products from radiometers and scatterometers are their sensitivity to RFI, the loss of accuracy in presence of water bodies in the sensor footprint, and their coarse spatial resolutions. Concerning the important issue of the RFI, preprocessing approaches are increasingly improving to filter out the data impacted by these perturbations. For example, in the version 2 of the SMOS-IC data, available at  [103] , a special attention was devoted to the prefiltering step allowing to improve the quality of the VOD retrievals compared to v105  [187].
[98]used for Climate Change Initiative (CCI) SM products[96], land surface temperature (LST), derived from T B acquired at Ka band[97]by the same sensors, below 0 • C corresponding to a frozen soil and negative VOD. • a co-calibration based on matching of a cumulative distribution function (CDF) on a per-pixel basis using AMSR-E VOD as the scaling reference for the different frequencies, similarly to what was done for ESCA CCI SM [96]. • an aggregation of the datasets averaging the temporally overlapping observations of the scaled data. This dataset is made available by the Technical University of Wien and Vandersat at[98].
[98]used for Climate Change Initiative (CCI) SM products[96], land surface temperature (LST), derived from T B acquired at Ka band[97]by the same sensors, below 0 • C corresponding to a frozen soil and negative VOD. • a co-calibration based on matching of a cumulative distribution function (CDF) on a per-pixel basis using AMSR-E VOD as the scaling reference for the different frequencies, similarly to what was done for ESCA CCI SM [96]. • an aggregation of the datasets averaging the temporally overlapping observations of the scaled data. This dataset is made available by the Technical University of Wien and Vandersat at[98]. 4. Soil Moisture and Ocean Salinity (SMOS)-INRA-CESBIO (IC) [99]:.
CanBas4 validation The ability of CanBas4 to reproduce the dynamical and biogeochemical characteristics of the CanC system is presented. The physical model is assessed by comparing the simulated annual sea surface temperature (SST) with annually averaged satellite SST observations from the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua; NASA 2014) sensor. An extended validation of the hydrodynamical parent solution is available in  Mason et al. (2011). A general evaluation of the phytoplankton biomass is carried out by comparing seasonal means of sea surface chlorophyll (SChl) with corresponding satellite means. In particular, modeled SChl is compared with 4-km resolution data from MODIS-Aqua accessible through http://oceancolor. gsfc. nasa. gov/cgi/l3. The general distribution of SST in CanBas4 shows a similar pattern to that of the satellite data (Fig.
3 ). However, as previously observed in other numerical studies in the region (e. g. ,  Marchesiello and Estrade, 2009; Lachkar and Gruber, 2011; Auger et al. , 2015) , the model has a narrower cold upwelling band along the coast. This deviation is possibly related to a warm bias in the SST forcing dataset as observed by  Dufois et al. (2012)  in a number of modeling studies over the main EBUS where high SST gradients exist (e. g. ,  Machu et al. , 2009; Veitch et al. , 2010; Albert et al. , 2010; Echevin et al. , 2011). The figure also shows the inability of CanBas4 to reproduce the cool waters found in the inner shelf southward of ∼24 • N. This bias may be attributed to uncertainties in the model nearshore wind structure (e. g. ,  Veitch et al. , 2010; Colas et al. , 2012). The wind structure along coastal upwelling regions is not well constrained by surface wind products derived from scatterometers  (Albert et al.
g. ,  Arístegui et al. , 2009; Cropper et al. , 2014)  may thus be poorly represented by our wind forcing dataset. Moreover, the warm bias along the coast observed in the annual SST comparison (see Fig. 3 ) will reduce the width and inten-sity of the upwelling. This may imply that the retention of phytoplankton cells, nutrients, and organic carbon near the coast may equally be reduced by a weak cross-shore gradient between upwelled and non-upwelled waters, leading to a lower SChl signal (see Fig. 4 ). In addition, picophytoplankton (< 2 µm), which is the most abundant size fraction for photosynthetic organisms in the Canary Island area (e. g. ,  Montero, 1993)  is not specifically diagnosed by the PISCES formulations. The limited accuracy of satellite products, on the other hand, is a source of error; the presence of chromophoric dissolved organic matter, errors in the SChl algorithms in coastal regions  (Le Fouest et al.
Acknowledgements This research was supported by projects CAIBEX (CTM2007-66408-CO2-02) and FLUXES (CTM2015-69392-C3-1-R) funded by the 'Spanish Plan Nacional de I + D', and by projects SUMMER (AMD-817806-5) and TRI-ATLAS (AMD-817578-5) financed by the 'European Commission' to JA. EM acknowledges support from the Spanish Research Agency and the European Regional Development Fund (Award no. CTM2016-78607-P).