dataesr/scientific-paragraphs-categorization huggingface dataset:
(2015b)  resulted in an average C values for vineyards across Europe of 0. 35. To our knowledge, no attempt has been made to evaluate RUSLE based on long-term soil loss measurements in vineyards, neither a calibration strategy based on a comprehensive analysis of vineyard conditions across Europe. Our hypothesis is that this evaluation can provide relevant information to orientate its future applications for local and regional studies, as well as to provide insight for adapting current soil conservation strategies in vineyards, best suited for the different environmental conditions in Europe. This manuscript presents a study with these objectives: 1 To present an Excel tool to facilitate calibration of RUSLE2 in vineyards in hillslope applications. 2 To evaluate the performance of several calibration strategies of RUSLE for permanent cover crop and bare soil by tillage using a long-term experimental record previously published.
Model description An EXCEL tool, ORUSCAL (Orchard RUsle CALibration), was built to allow the calibration of RUSLE2 by assuming the simplified situation of a hillslope with a regular slope profile for tree crops such as vines, olives, almonds or citrus (G omez Calero, Biddoccu, & Guzm an, 2020). The structure and main input data of the tool is shown in Fig. 1. Each of the factors of RUSLE is calculated in one of the Excel sheets, e. g soil erodibility (K), or in several ones from each of the corresponding subfactors, e. g. cover and management factor (C). Each factor is accompanied by a set of explanations and accompanying information to allow their determination, indicating, when necessary, sources of this information (for example rainfall erosivity) or data taken from the RUSLE2 database (e.
g. root biomass for different orchard management system). Within this approach, for the determination of the rainfall erosivity factor (R factor), there is the option of using the quarter erosivity for the location of interest which are usually available from several sources (e. g. ,  ICONA, 1988; Panagos et al. , 2015a) , calculating this from the correlation of daily rainfall and daily rainfall erosivity, which is also a common approach where these correlations have been developed (e. g. Domínguez  Romero, Ayuso Muñoz, & García Marín, 2007)  since daily rainfall is a commonly available data or upload the quarter rainfall erosivity calculated externally from high resolution rainfall data using software such as RIST (Rainfall Intensity Summarization Tool) (ARS-USDA, 2015). For the determination of soil erodibility (K factor), ORUSCAL uses the standard RUSLE equation based on the textural composition and organic matter of the soil, corrected by soil infiltration capacity and soil structural class.
The second considered subfactor is the canopy cover subfactor, C c which takes into account the reduction of soil erosion due to the provision of ground cover by living vegetation. It is calculated from the height and shape of this vegetation (which in the case of the vineyards can be the vines plants and the herbaceous vegetation in the lanes) and the ground cover provided by this vegetation in each quarter. The third subfactor is the surface roughness subfactor, S r , which includes the decrease of erosion with increasing roughness. This section provides values of the recommended roughness and disturbance values for the most common tillage operations and surface conditions from the RUSLE2 database and it calculates quarter S r values considering reduction due to rainfall after tillage. The soil biomass subfactor, S b , considers the reduction of erosion due to the presence of residues and root biomass within the top 25 cm of the soil.
Experimental data for evaluation The Italian soil erosion dataset was derived from the Cannona Data Base  (Biddoccu et al. , 2016)  collected in the Tenuta Cannona Erosion Plots. The monitored plots are part of a larger vineyard, within the Experimental Vine and Wine Centre of Agrion Foundation, which is located in the Alto Monferrato hilly area of Piemonte, North-West Italy. The climate is Hot-summer Mediterranean (Csa in the K€ oppen climate classification,  Kottek et al. , 2006). At the study site the average annual precipitation in the period 2000e2016 was 852 mm, mainly concentrated in October, November and March, with the driest month been July. The Cannona vineyards lies on Pleistocenic fluvial terraces in the Tertiary Piedmont Basin, including highly altered gravel, sand and silty-clay deposits, with red alteration products.
Evaluation of different calibration strategies One of the objectives of the study was to evaluate the performance of different calibration strategies of RUSLE among those that might be used. For this purpose, a subset of the Cannona dataset, which passed the requirements of having all the available information, was used in the calibration step of the model  (years 2004, 2005, 2007, 2008, 2010, 2011, 2012, 2013). The calibration was performed following four different calibration strategies described in Table  2 , which in short result from using a constant or variable Kfactor and considering, or not, the soil moisture subfactor, S m. The best calibration of the model was determined on the basis of both quarter and annual values, through the following statistics: the efficiency coefficient of Nash and Sutcliffe (NSE), root mean square error of residuals (RMSE) and the coefficient of determination of the linear regression (R 2 ), see  Nash and Sutcliffe (1970)  and  Moriasi et al.
Calibration of RUSLE for five wine growing regions across Europe Afterwards, ORUSCAL was fully calibrated for different kind of typical, or possible, soil management in five contrasting wine growing regions across Europe. This calibration was made combining field surveys to characterize vineyard conditions, publicly available data and information provided by wine growers. These regions were: S Spain, NW France, NW Italy, E Austria and Central Romania. In all cases each farm and management was simulated for 15 or 16 consecutive years to capture temporal variability in rainfall.
2  and 3 ). In Italy, the dataset includes 7 vineyards with low protective management and 5 vineyards with high protective management (Figs. 2  and 3 ). This resulted, as expected, in a multiplicity of soil management strategies that can be grouped in 5 classes: by decreasing intensity: 1-bare soil obtained by herbicide application and no tillage (NT); 2-bare soil using conventional tillage (CT); 3-partial soil cover, using temporary cover crops (TCC), defined as those grown during the rainy (fall and winter) season killed at the onset of the dry season period (early spring); 4-or partial cover using alternate cover crops (ACC) defined as permanent cover crop every two lanes with the intercalated lane having the CT typical of the area; 5-permanent cover crops (PCC), resulting in complete cover of the inter-rows.
Although the experimental dataset used in our analysis is small, this result suggests that this is point of concern in our current use of C values in vineyards across Europe, and should be further explored. Overall, these results show clearly the importance of using C-factor obtained taking into account local management and climate, especially rainfall, which allows better soil losses prediction than using literature values, referred to unknown or very different local and management conditions, namely for a land use that is characterized for such high variability. Also the need to introduce uncertainty in proposed C values for simulation analysis based on farm management classes and/or ground cover determined by remote sensing should be considered. For instance,  Baiamonte et al. (2019)  estimated the interand intra-annual variability of C-factor in vineyards from remote sensing data, with methods using NDVI.
The GIS map representing the C-factor assessment for EU28, at 100 m resolution  (Panagos et al. , 2015b) , represents a widely used source for this factor. The estimation of C-factors obtained in the present study shows a great variability (also vineyard to vineyard) for this factor and led to the identification of values that are not included in the range proposed in the European map for vineyards. The implementation of ORUSCAL in different study areas across Europe shows that it could be an effective strategy for the scientific community, for calibration and identification of site-specific values for C-factor. It could also be used for the evaluation of soil erosion risk under different intensities of soil management with limited datasets, with demonstrative and technical purposes. Indeed, ORUSCAL is proposed as a simple and effective tool to simulate at field scale the effect of implementation (or not) of soil conservation measures on soil losses, based on datasets that can be obtained by a collaborative approach involving farmers and technicians.
The output can assist in designing the most appropriate soil conservation measures for their commercial vineyards and into disseminating best management practices. Such approach would be crucial for increasing the participation and awareness of farmers towards soil conservation measures to implement effective soil conservation policies, as pointed by  Marques et al. (2015). Indeed, the advantage of using ORUSCAL is its ability to estimate soil losses, but also the RUSLE factors taking into account of the variability in soil, topographical, climate, management conditions by means of datasets that are low-cost, easily accessible (climate data, topographical) or that can be retrieved from field observations and farmers'/technicians' experience.
expected vegetation and residue cover. Soil management operations were used to determine the soil consolidation and soil roughness subfactors (S c and S r , subfactors of the C-factor). Data about residues and biomass, also needed to calculate the S b subfactor, were taken from RUSLE database. The soil moisture (S m ) subfactor was calculated internally by ORUSCAL using the simplified water balance model incorporated in the model using daily rainfall and ET, based on Allen et al. (1998).
Acknowledgments This study was funded by the  European BiodivERsA  project  VineDivers  (https://short. boku. ac. at/vinedivers) through the  Bio-divERsA/FACCE JPI  (2013e2014 joint call) for research proposals, with the national funders:  Austrian Science Fund  (grant numbers I  2044-/I 2043-/I 2042-B25 FWF ),  French National Research Agency (ANR) ,  Spanish Ministry of Economy and Competitiveness  ( PCIN-2014-098 ),  Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI)  and  Federal Ministry of Education and Research (BMBF/Germany). Also to the  CNR   Short Term Mobility Program 2016  for funding a stay at IAS-CSIC during which  M. Biddoccu  contributed to this study and the  SHui  project funded by the  European Commission  ( GA 773903 ), which supported the final steps of the analysis presented in this manuscript.
CC BY-NC -Creative Commons ( ) -( ) ( ) ( ) -( ) -( ) ( ) ( ) -( ) - ( ) N N N N V V V V M M M 1 2 1 2 1 2 1 2 1 2 1 0 0 0 0 0 0 ∆ ∆ ∆ ∆ ∆ ∆ ∆ ( ) ( ) ( )                                     = M K u u BBe 2 1 2 0 0 ( ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )      u u v v v v 1 2 1 2 1 2 1 2 1 2 0 0 0 0 ∆ ∆ ∆ ∆ ∆ ∆ q q q q                                 (6) The fundamental matrix of A, termed F A , is computed at x=0 and x=∆; using the SCILAB software, the associated command is "expm": F F A A x expm A x expm A = ( )= ( ) = ( )= ( )    0 0. ∆ ∆ (7) From these two 12*12 matrices, two matrices M' and N' are extracted. M' (N') is composed of the lines related to the nodal displacements (forces). For each, a first block of six lines and twelve rows comes from F A (x=0) and the second block of six lines and twelve rows comes from F A (x=∆), such that:   , , , , , ;  : M x x U A i j A i ' , , , , , , ; : , = ( )= j A i j x = = = = ( )            F ∆            (8) where i (j) indicates the line (row) number.
Acknowledgement The author affiliated to  Sogeti High Tech  gratefully acknowledges the engineers and the managers involved in the development of JoSAT (Joint Stress Analysis Tool), which is an internal research program. Both first authors indicate that this paper is a contribution to the initiative TACCOS; TACCOS stands for " Toulouse Adhésion Cohésion COllage Structural, de la Chimie à la Mécanique "  which means "Toulouse Adhesion Cohesion Structural Adhesive Bonding , from Chemistry to Mechanics" in English.
So far, this type of approach has not been applied to graphs. The intuition behind our proposed method, GT, is to leverage a similar partition in the vertices of a graph. Instead of using the similarity computation that we described previously, we chose to use the mass-based approach introduced by Ting et al. [28]  instead. The key property of their measure is that the dissimilarity between two instances in a dense region is higher than the same interpoint dissimilarity between two instances in a sparse region of the same space. One of the interesting aspects of this approach is that a dissimilarity is obtained without any post-processing. Let H ∈ H(D) be a hierarchical partitioning of the original space of a dataset D into non-overlapping and non-empty regions, and let R(x, y|H) be the smallest local region covering x and y with respect to H.
The mass-based dissimilarity m e estimated by a finite number t of models -here, random trees -is given by the following equation: m e (x, y|D) = 1 t t i=1 P (R(x, y|H i )) (1) where P (R) = 1 |D| z∈D 1(z ∈ R). Figure  1  presents an example of a hierarchical partition H of a dataset D containing 8 instances. These instances are vertices in our case. For the sake of the example, let us compute m e  (1, 4)  and m e  (1, 8). We have m e (1, 4) = 1  8 (2) = 0. 25, as the smallest region where instances 1 and 4 co-appear contains 2 instances. However, m e (1, 8) = 1 8 (8) = 1, since instances 1 and 8 only appear in one region of size 8, the original space. The same approach can be applied to graphs. Our proposed method is based on two steps: (i) obtain several partitions of the vertices using random trees, (ii) use the trees to obtain a relevant dissimilarity measure between the vertices.
The results are presented Table  3. We compared the mean NMI using the t-test, and checked that the differences between the obtained values are statistically significant. We observe that our approach is competitive with the two well-known methods we chose in the case of non-attributed graphs on the benchmark datasets. In one specific case, we even observe that Graph trees significantly outperforms state of the art results, on the graphs generated by the SBM model. Since the dissimilarity computation is based on the method proposed by  [28]  to find clusters in regions of varying densities, this may indicate that our approach performs particularly well in the case of clusters of different size.
Graph Trees on Attributed Graphs Now that we have tested GT on simple graphs, we can assess its performance on vertex-attributed graphs. The datasets that we used in this subsection are presented Table  4. WebKB represents relations between web pages of four universities, where each vertex label corresponds to the university and the attributes represent the words that appear in the page. The Parliament dataset is a graph where the vertices represent french parliament members, linked by an edge if they cosigned a bill. The vertex attributes indicate their constituency, and each vertex has a label that corresponds to their political party. The empirical setup is the following. We first compute the vertex-vertex dissimilarities using GT, and the vertex-vertex dissimilarities using UET. In this first step, a forest of trees on the structures and a forest of trees on the attributes of each vertex are constructed.
We then compute the average of the pairwise dissimilarities. Finally, we then apply t-SNE and use the k-means algorithm on the points in the embedded space. We set k to the number of clusters, since we have the ground truths. We repeat these steps 20 times and report the means and standard deviations. During our experiments, we found out that preprocessing the dissimilarities prior to the clustering phase may lead to better results, in particular with Scikit learn's  [22]  QuantileTransformer. This transformation tends to spread out the most frequent values and to reduce the impact of outliers. In our evaluations, we performed this quantile transformation prior to every clustering, with n quantile = 10. The NMI obtained after the clustering step are presented in Table  5. We observe that for two datasets, namely WebKB and HVR, considering both structural and attribute information leads to a significant improvement in NMI.
For the other dataset considered in this evaluation, while the attribute information does not improve the NMI, we observe that is does not decrease it either. Here, we give the same weight to structural and attribute information. In Fig. 3  we present the projection of the WebKB dataset, where we observe that the structure and attribute information both bring a different view of the data, each with a strong cluster structure. HVR and Parliament datasets are extracted from  [2]. Using their proposed approach, they obtain an NMI of 0. 89 and 0. 78, respectively. Although the NMI we obtained using our approach are not consistently better in this first assessment, the methods still seems to give similar results without any fine tuning.
g, feature vectors, it is then possible to compute pairwise dissimilarities between vertices in attributed graphs. Some aspects are still to be considered. First, the importance of the vertex attributes is dataset dependent and, in some cases, considering the attributes can add noise. Moreover, the aggregation method between the graph trees and the attribute trees can play an essential role. Indeed, in all our experiments, we gave the same importance to the attribute and structural dissimilarities. This choice implies that both the graph trees and the attribute trees have the same weight, which may not always be the case. Finally, we chose here a specific algorithm to compute the dissimilarity in the attribute space, namely, UET. The poor results we obtained for some datasets may be caused by some limitations of UET in these cases.
On the other hand, OCS could be the solution. Although also OCS has been only tentatively detected on ices  (Boogert et al. 2015) , astrochemical models interpreting molecular surveys towards protostellar shocks along the outflow driven by the L1157-mm Class 0 protostar (hereinafter L1157) suggest that OCS represents at least 50% of the sulfur reservoir in ice mantles  (Podio et al. 2014; Holdship et al. 2016). All these findings call for more comprehensive datasets to shed light on the sulfuretted grain/ice composition. A first step has been done by  Holdship et al. (2018) , who analysed the unbiased spectral survey towards again L1157 performed with the IRAM-30 m in the context of the Astrochemical Surveys At IRAM (ASAI) Large Program  (Lefloch et al. 2018 ): more than 100 lines have been associated with the following molecules CCS, H 2 CS, OCS, SO, SO 2 , and H 2 S (from  Holdship et al.
An atomic sulfur abundance of 1-4 × 10 -7 towards the south outflow of IRAS4A has been estimated by  Anderson et al. (2013)  from S[I] observations at 25 µm with Spitzer. We use interferometric observations taken in the context of the SOLIS  (Ceccarelli et al. 2017, Seeds  Of Life In Space) IRAM Northern Extended Millimeter Array (NOEMA) Large Program, supported by further interferometric datasets. The goal of this Large Program is to study the organic (and sulfuretted) molecular content in a sample of low-mass star-forming regions in different stages and environments. The spatial evolution of organic molecules along the outflows driven by IRAS4A is presented in a parallel and complementary study by De  Simone et al. (2019). The goal of the paper is twofold: (1) to derive for the first time the physical conditions of the IRAS4A outflows, so far observed only using individual transitions of CO, SO, and SiO, and to disentangle the red-shifted northern components; (2) to investigate the evolution of the chemical conditions, using also up-to-date physical/chemical models of shocked regions, along the two outflows driven by IRAS4A.
Observations We make use of various Plateau de Bure Interferometer (PdBI) and NOrthern Extended Millimeter Array (NOEMA) datasets from four observational programs in the 1. 3, 2, and 3 mm bands and at different configurations. The properties of the transitions targeted in this work are summarised in Table  1. For the SOLIS data, the IRAS4A binary system was observed in three different Setups (1, 3, and 5) with the IRAM NOEMA interferometer during several tracks in June and September 2016 (see Table  1  of  Ceccarelli et al. 2017). Setup 1, 3, and 5 cover the frequency ranges from 80. 8 to 84. 4 GHz, from 95. 7 to 99. 5 GHz, and between 204. 0 and 207. 6 GHz, respectively. For Setups 1 and 3, the array was used in both Notes. SOLIS-S1, -S3, and -S5 stand for Setups 1, 3, and 5 of the SOLIS data, respectively (see text for more details).
C07, P12, and T15 stand for  Choi et al. (2007) ,  Persson et al. (2012), and Taquet et al. (2015) , respectively. configurations D and C with baselines from 16 to 240 m for Setup 1 and from 15 to 304 m for Setup 3. For Setup 5, the array was used in configurations A and C. The phase centre is on the IRAS4A1 source, α(J2000) = 03 h 29 m 10. s 5, δ(J2000) = +31 • 13 30. 9. The bandpass was calibrated on 3C454. 3 and 3C84, the absolute flux was fixed by observing MWC349, LKHA101, 2013+370, and 2007+659, and 0333+321 was used to set the gains in phase and amplitude. The final uncertainty on the absolute flux scale is ≤15%. The phase rms was ≤50 • , the typical precipitable water vapour (pwv) was from 4 to 15 mm, and the system temperatures ranged typically between 50 and 200 K. The data were reduced using the packages CLIC and MAPPING of the GILDAS 1 software collection.
A continuum map was obtained by averaging line-free channels and self calibrating the data. The self calibration solutions were then applied to the spectral cube, which was subsequently cleaned. The resulting synthesised FWHM beam is 4. 5×3. 5 (PA = 27 • ) for Setup 1, 2. 2 × 1. 9 (PA = 96 • ) for Setup 3, and 0. 9 × 0. 7 (PA = 47 • ) for Setup 5. The half power primary beam is 61. 4, 59. 2, and 24. 5 for Setups 1, 3, and 5 respectively. We used the CALYPSO data observed with the IRAM-PdBI  (Maury et al. 2019)  to extract the interferometric maps from the SiO(5-4), SO(6 5 -5 4 ), and OCS(8-7) transitions at ∼218 GHz. These observations were carried out between February 2011 and February 2013 as part of the CALYPSO Large Program using the A and C configurations of the array. The synthesised FWHM beam at 1.
40 MHz (0. 8 km s -1 ) to obtain a better signal-to-noise. More details on the observational dataset can be found in  Taquet et al. (2015). PdBI observations at 225 GHz were performed on 27 and 28 November 2011, on 12, 15, 21, 27 March 2012, and on 2 April 2012 in the B and C configurations of the array. The synthesised FWHM beam is 1. 2×1. 0. A more detailed description of the observational setups is provided by  Persson et al. (2014). For all datasets, the amplitude calibration uncertainty is estimated to be ∼15-20%. Given we map relatively extended structures such as outflow lobes, we cleaned the images using the Hogbom method with natural weighting and with a mask containing all the molecular lobes to minimise artefacts in the cleaned image.