dataesr/scientific-paragraphs-categorization huggingface dataset:
This allows to evaluate whether the model is sophisticated enough and whether the observations can improve the model through assimilation or not. It then helps determining the most appropriate metrics to use in an objective function to be optimized which will depend on the aspect of the model under focus. In addition, most DA methods rely on Gaussian statistics. Therefore, a proper assessment of the satellite and model errors is needed, with potential bias correction, before any assimilation step. The ESA-CCI multi-instruments dataset provides the SM observations used in this study. Thanks to its large spatio-temporal coverage, the ESA-CCI product offers a good opportunity to benchmark LSMs and improve their parametrization with DA techniques  [32]. Combining several products from a broad range of instruments allows to get a large spatio-temporal coverage, which is suitable for global LSMs evaluations, and provides additional information at these larger scales compared to in situ observations in a global assimilation process.
Similarly, the satellite data suffer from temporal inhomogeneity because of instrument drift and lifespan issues. The sampling of the observations is not continuous and in the case of the ESA-CCI SM product where each independent dataset has been rescaled to be merged together, combining different instruments from different periods adds temporal inhomogeneity to the final dataset and thus complications for model evaluations. The quality of the ESA-CCI SM product has already been evaluated on a global scale by several studies with encouraging results. The merging of active and passive products has been shown to increase the number of observations and hence coverage, while keeping the same relative dynamics and minimally changing the accuracy  [23, 32, 34, 35]. By comparing the blended product to reanalysis and in situ measurements,  [36]  found that the product was able to capture the annual cycle of SM and its short-term variability.
The ORCHIDEE Land-Surface Model The ORCHIDEE LSM  [37]  allows the representation as explicit as possible of processes governing water, carbon, and energy budgets of the terrestrial biosphere. It has a high spatial resolution flexibility (based on the meteorological forcing spatial resolution) and a temporal resolution of 30 min. ORCHIDEE conceptually includes (i) a Soil Vegetation Atmosphere Transfer (SVAT) scheme, which calculates energy, water and carbon fluxes between soil, plant and atmosphere; (ii) a module describing carbon flow within ecosystems (plants and soils) and (iii) a sub-model describing the long-term dynamics of vegetation which is not activated in this study. We use the main version of ORCHIDEE (Trunk, revision 4783) that corresponds to the version used in the IPSL earth system model for the ongoing CMIP6 (Coupled Model Intercomparison Project Phase 6) climate simulations.
ESA-CCI SM Product The ESA-CCI SM project is part of the ESA (European Space Agency) Program on Global Monitoring of Essential Climate Variables (ECV), better known as the Climate Change Initiative (CCI) and initiated in 2010. A first version of the ESA-CCI SM product was released in June 2012 by the Vienna University of Technology  [42]. In this study, we use the new version of the global ESA-CCI SM product (v4. 2), released in January 2018 and freely available at http://esa-soilmoisture-cci. org/, that extends the data record from November 1978 to December 2016. ESA-CCI SM is based on a series of active and passive microwave satellite sensors. Three datasets are provided: a merged passive-only (produced from multi-frequency radiometers), a merged active-only (produced from C-band scatterometers) and a combined active and passive SM product.
To provide a global coverage, the active and passive retrievals were combined. Since data provided by the retrievals have different units, to merge them, the SM values need to be rescaled into a common climatology. Therefore, data were scaled by GLDAS-Noah surface soil moisture product (v1, soil depth 10 cm) by a direct CDF-matching of the collocated observations  [45]. The final combined ESA-CCI SM dataset is based on the spatial distribution of the temporal correlation between active-only and passive-only datasets. Over regions with correlation coefficients greater than 0. 65, a simple average between active and passive measurements is used. Outside these regions, the merged passive-only dataset is used over areas with sparse vegetation density and the merged active-only dataset is used over areas with moderate vegetation density.
The ESA-CCI SM product provides daily SM, with a spatial resolution of 0. 25 degrees, together with quality flags indicating the possible presence of dense vegetation, snow, or a temperature below 0 • C. For our study, we used only the final combined dataset which is provided in volumetric units (m 3 /m 3 ) and furnishes the largest spatio-temporal coverage of the three ESA-CCI SM datasets available.
Meteorological Forcing We use two different meteorological forcings to drive ORCHIDEE in this study. The first is the CERASAT forcing data, used by default in our simulations. This is a 3-hourly climate reanalysis dataset spanning 8 years between 2008 and 2016 generated by ECMWF within the ERACLIM2 project. It has been produced specifically for the satellite era  [47]. The second, used for a comparative run, is CRUNCEP, a 6-hourly 0. 5 • global forcing product  which is a combination of the annually updated CRU-TS monthly climate dataset  [48]  and NCEP climate reanalysis  [49]. Rainfall, cloudiness, relative humidity, and temperature are taken from the CRU while the other fields (pressure, longwave radiation, windspeed) were directly derived from NCEP. The forcings are interpolated to the half-hourly time step of the model.
In addition, Multi-Source Weighted-Ensemble Precipitation (MSWEP; [50]) data is used during the analysis step. The MSWEP data have a 0. 25 • spatial resolution and a 3-hourly temporal resolution. This forcing is independent from the two datasets used to drive the ORCHIDEE model, as well as the forcing data used to generate the GLDAS reanalysis, used in scaling the ESA-CCI product.
Land Cover ORCHIDEE uses land-cover maps to prescribe the distribution of vegetation used in the simulation. By default, we use the yearly land-cover maps created for the CMIP6 release of the model. These maps have been created through aggregation of the latest ESA-CCI land-cover map for 2010 (v2. 0. 7b) at 300 m resolution from 38 ESA land-cover classes into 15 ORCHIDEE PFTs at 0. 5 • resolution  [51, 52]. For producing historical PFT maps (1860-2010) the resulting map has been further merged with the LUH2 dataset (Land-Use Harmonization, http://luh. umd. edu/). The overall protocol of the ORCHIDEE PFT maps production is outlined here: https://orchidas. lsce. ipsl. fr/dev/lccci/. For our comparative run, an older version of the land-cover maps created for the CMIP5 LSM generation is used.
Performed Simulations The different simulations used in this study are outlined in Table  2. Since CERASAT only covers 2008-2016, our analysis will focus on this period. This is also the period for which the ESA-CCI SM combined product provides close to a complete spatio-temporal coverage  [32]. In this paper, we compare our default run (ORC_REF) to ORC_CRU in Section 3. 4 to assess the effect of meteorological forcing data on simulated SM values. Also in Section 3. 4, ORC_REF is compared to ORC_LC5 to evaluate the effects of different land-cover maps. ORC_REF is finally compared to ORC_NoRs in Section 3. 7 to discuss the effect different parameterizations can have on modelled SM.
Statistically Rescaling and Bias Correcting the Observations Cumulative density function (CDF)-matching schemes are used in this study to rescale the observed soil moisture y to the modelled soil moisture x (further explained in Section 3. 1). Two CDF-matching algorithms are implemented  [53]. The first is a linear CDF-matching which matches the mean and standard deviation in the following manner. Let x i,t=1. n and y i,t=1. n denote the time series of the two datasets for a given location i. Similarly, let σ x i and σ y i denote the standard deviation of each dataset respectively at location i. Then to obtain rescaled y * i,t at each grid point, we use the following equation y * i,t = σ x i (y i,t -y i ) σ y i + x i. (3) The second algorithm used a full CDF-matching which, in addition to the mean and standard deviation, matches higher moments of the distribution.
The CDFs function of y i is matched to the CDF y i such that y * i satisfies the following equation: cdf x i (y * i ) = cdf y i (y i ). (4) We use the python software (pytesmo package) to do this matching  [54]. In another approach to rescale data, it is also normalized in the following manner: x i,t = x i,t -min x i max x i -min x i. ( 5 ) When applied to SM data, this normalization translates values to what is known as the Soil Water/Wetness Index (SWI).
Correlation Scores We use the Pearson product-moment correlation coefficient, r, to assess the relative agreement of the temporal structures between two datasets: r = ∑ n t=1 (x i,t -x i )(y i,t -y i ) ∑ n t=1 (x i,t -x i ) 2 ∑ n t=1 (y i,t -y i ) 2. ( 8 ) We also consider spatial correlations. These are calculated using same formula but on single vector of locations at each time step.
MSD Decomposition Following  [56] , we can decompose the mean standard deviation (MSD = RMSD 2 ) into three informative components. Let x i,t=1. n and y i,t=1. n denote the two datasets, then MSD = (x i -y i ) 2 + (σ x i -σ y i ) 2 + 2σ x i σ y i (1 -r) (10) = bias 2 + VAR + LCS  (11)  where the bias = (x i - y i ), VAR = (σ x i -σ y i ) 2 and LCS = 2σ x i σ y i (1 -r). The first term of the decomposition is the squared bias. The second term, VAR, shows the difference in the magnitude of fluctuation between the simulation and measurements. The larger this term, the more the model fails to simulate the magnitude of the fluctuation among the n measurements. The last term of the decomposition is the Lack of Correlation weighted by the Standard deviations (LCS). This term is large when the model fails to simulate the pattern of fluctuation across the n measurements  [56].
Autocorrelation and Lag Time For a dataset x i,t=1. n , the lag-k autocorrelation coefficient can be expressed as: r k = ∑ n-k t=1 ((x i,t -x i ) -(x i,t+k -x i )) 2 ∑ n t=1 (x i,t -x i ) 2. ( 12 ) Following  [57] , we define the characteristic lag time (c t ) as the time at which the autocorrelation coefficient reduces to below e -1 ≈ 0. 37. This is also known as the e-folding time. This quantity determines a threshold after which the dataset is no longer considered as significantly autocorrelated. This can thus be seen as a way to quantify the inertia of the studied variable. This metrics is applied to the fast-varying component of the data.
Singular Value Decomposition Singular Value Decomposition (SVD) offers an adequate tool to analyze the temporal co-variability between two gridded datasets. A detailed description of the method can be found in  [58]. The spatio-temporal analysis will consider the total explained covariance of the two fields that is explained by the decomposition into pairs of optimally correlated observation and model patterns, as well as homogeneous correlation maps (depicting the geographic localization of the covarying parts of observation and model respectively) and observation heterogeneous correlation maps (reflecting how well observations are predicted by model patterns). Since the SVD analysis concerns itself with spatial patterns, the SVD algorithm is directly applied to the raw data instead of using the CDF-matched data.
The Effect of Meteorological and Land Cover Forcing Data The choice of forcing data heavily influences the simulation of SM in LSMs. In this section, we first compare our default ORCHIDEE run ORC_REF to one with a different meteorological forcing, CRUNCEP, introduced in Section 2. 3. From Figure  5  we can see that the ORC_REF run outperforms the ORC_CRU for the full data and the fast component. This is particularly notable in arid or desert regions (i. e. , Sahara, Asia, and Australia). ORC_REF shows stronger correlations than ORC_CRU for the fast component. This shows that the ORCHIDEE run using the CERASAT meteorological forcing not only captures the same seasonal patterns as the satellite data but also more of the sub-monthly behavior of SM. CERASAT is a new dataset of the ECMWF based on recent advances in data assimilation and uses satellite data in its creation.
Another reason for the CERASAT forcing data outperforming CRUNCEP may be due to the difference in temporal resolution between both products. CERSAT is 3-hourly whereas CRUNCEP is 6-hourly. This difference in frequency may affect how the precipitation is partitioned for each different time step and hence the response of SM in the model. The noticeable differences between the correlations for each run show that the choice of meteorological forcing is critical for the accurate simulation of SM. Many studies have in fact used SM to correct precipitation input  [63] [64] [65] [66] [67]. Conversely, to correct the SM parameters in ORCHIDEE, it is important to find a metric that is less sensitive to the meteorological forcing. Forcing data is not limited to the meteorological data used to drive the model.
It encompasses all external data used for a given model run. Another dataset used as forcing data in ORCHIDEE is the choice of land-cover map, which determines the distribution of the global vegetation. In this study, we only tested two slightly different land-cover maps, and the correlations between the ESA-CCI SM product and ORCHIDEE SM output remained very similar. This suggest that, in this case, the choice of land cover is not the primary source of variability.
Measuring the Covariance of Rainfall and Soil Moisture The chosen meteorological forcing data prescribes (among others) the precipitation in the model. Since precipitation, both the amount and intensity, has been shown to be a major driver to surface soil moisture dynamics  [68] , this dependency can be assessed through their covariance. Following  [69] , we use the SVD method described in Section 2. 4. 7 to identify and study coupled rainfall-SM covarying spatio-temporal patterns. To ensure that the comparison does not favor the model output and that therefore any differences found originate from the SM only  [69] , independent rainfall data is needed. For this experiment, it was provided by MSWEP data and the period 2008-2014 was considered because it overlaps all datasets. We focus our analysis over Australia and the Iberian Peninsula.
Australia was selected since the ESA-CCI SM product has shown a good performance over this country in terms of correlation with reanalysis and in situ datasets  [35, 36]. Also, passive, active, and combined products have all been used over this region  [70]  resulting in the fractions of days with valid observations to be very high  [35]. Therefore, we can expect a reliable quality of the product over this region. The Iberian Peninsula was selected to provide direct comparison with  [69]  who used satellite SM values from the ESA's Soil Moisture and Ocean Salinity product (SMOS-BEC). Considered in Table  3  are the first three covarying patterns representing approximately 95% and 99% of the covariance of SM and P for Australia and the Iberian Peninsula, respectively. The explained covariance is of the same proportion whether the ESA-CCI product is considered, or whether the different model runs are considered.
These processes are less important for the fast-varying PCs. In contrast, for Australia, we find that spatial correlations of each of the PCs show the same magnitude in each of the datasets. This may be due to the fact each of the PC1s for Australia is representative of different rainfall types. Overall these spatial correlations are stronger than those observed in  [69]. In  [69] , the SVD analysis was conducted using SMOS, a pure remote sensing estimate, whereas we are using a merged product where a model has been used. It is possible that the usage of the GLDAS-Noah model in the merging process has smoothed out a large part of the spatial variability associated with landscape features or measurement noise.
Conclusions Even though there have been many studies evaluating the usefulness of the ESA-CCI SM dataset, this study is unique in considering the product on a global scale using a cutting-edge terrestrial CMIP6 model. This global comparison has shown that both the model and the product are at a stage where useful information can be derived and assimilated. The strong temporal correlations seen between the ESA-CCI product and the model show that the dynamics in the model are developed enough to capture the main trends identified by the combined satellite product. This is especially the case when the CERASAT meteorological forcing data was used. Temporal autocorrelation was identified as a useful metric to investigate the discrepancies in the inertia of SM. Since it measures the response time of the model and in some cases is more intrinsically linked to the internal dynamics of the model, it gives us an opportunity to evaluate the structure of the model more precisely.
Acknowledgments: We would like to acknowledge the ORCHIDEE team for their provision of the latest ORCHIDEE version. Special thanks go to  Agnès Ducharne  for her documentation of the ORCHIDEE hydrological modules and development of the soil resistance parameterisation. We would also like to thank the anonymous referees for their thoughtful and constructive reviews. (LAI MAX ). For the soil textures, the 5 soil textures from the Zobler map are reduced to only three, which are then called the Coarse (holding class 1),  Medium  (gathering classes 2, 3, 4) and Fine (class 5) soil textures.
In order for methods combining ab-initio density-functional theory and many-body techniques to become routinely used, a flexible, fast, and easyto-use implementation is crucial. We present an implementation of a general charge self-consistent scheme based on projected localized orbitals in the Projector Augmented Wave framework in the Vienna Ab-Initio Simulation Package (vasp). We give a detailed description on how the projectors are optimally chosen and how the total energy is calculated. We benchmark our implementation in combination with dynamical mean-field theory: First we study the charge-transfer insulator NiO using a Hartree-Fock approach to solve the many-body Hamiltonian. We address the advantages of the optimized against non-optimized projectors and furthermore find that charge self-consistency decreases the dependence of the spectral function -especially the gap -on the double counting.
Acknowledgments We thank  Olivier Parcollet  and  Michel Ferrero  for discussions about the triqs interface. Funding by the  Austrian Science Fund (FWF)  within Project No. Y746  and within the  F41 (SFB ViCoM)  is gratefully acknowledged. O. P. is grateful to  A. Georges  for discussions on the details of charge self-consistency. O. P. acknowledges support from the  Swiss National Science Foundation NCCR MARVEL  and computing resources provided by the  Swiss National Supercomputing Centre (CSCS)  under projects s575 and mr17. The support from the  Austrian federal government  (in particular from  Bundesministerium für Verkehr, Innovation und Technologie and Bundesministerium für Wirtschaft, Familie und Jugend ) represented by  Österreichische Forschungsförderungsgesellschaft mbH and the Styrian and the Tyrolean provincial government , represented by  Steirische Wirtschaftsförderungsgesellschaft mbH and Standortagentur Tirol , within the framework of the  COMET Funding Programme  is also gratefully acknowledged.
In spite of these promising properties in the field of pharmacology, we still are at the dawn of a sound understanding of cyclic peptides, in particular for head-to-tail cyclization, which would open the road to in silico predictions of their 3D structures. Indeed, there are only a few tens of structures annotated as "cyclic peptide" in the Protein Data Bank (PDB) and in the Cambridge Structural Database, as far as the length is less than 50 amino acids. Thus, template-based homology modeling methods are not adequate and only ab initio modeling is feasible. Besides, most of the de novo methods for modeling proteins are not adapted to cyclic peptides. Recent efforts have been made to develop or to adapt structure prediction tools to cyclic peptides. However, few of them are able to treat small headto-tail cyclic peptides (with less than seven residues) involving chemical modifications.
Sampling algorithm The EGSCyP algorithm is summarized in Figure  2  for a pentapeptide. The exploration starts by the selection of two consecutive residues to be sampled, involving two pairs of φψ angles. By default, the selected residues are the first and the last ones in the PDB file given as input (see dataset paragraph). In the following, these two residues are named I and V , respectively. The effect of the choice of the two sampled residues has been evaluated by testing all the possible positions (see results section). The sampling of all possible combinations of the two pairs of φ -ψ dihedral angles is made with a constant step size of 10 • over the range from -180 • to 180 •. Then, for every combination, the five ω angles are randomly selected from a Gaussian distribution with a standard deviation of 10 • centered around 180 •.
This geometric filtering Side chains addition was not chosen too restrictive, because the final relaxation could avoid small collisions not filtered at this step. Then, in absence of collision, the side chains are positioned using one of the following methods: (1) the SCWRL4 software, 50 or (2) a variant of the Basin Hopping (BH) method. 51 Details about these methods are described below in the paragraph about side chains placement. Finally, a few steps of structural relaxation (detailed below) are performed using Sander from AmberTools 16. 52  This last stage allows releasing constraints imposed by the rigid geometry representation. The relaxation is made with the amberff96 force field  53  with a Generalized Born implicit solvent (all default parameters were set on). The choice of this force field and implicit solvent was made based on the reasonable performance of this combination on peptides compared to other force fields.
Side-chain placement Two methods were implemented and evaluated for the side-chain positioning. The first one was SCWRL4, 50 a widely-used and efficient tool for the prediction of side-chain conformations, based on a rotamer library. In our case, the applicability of this method is limited, because it cannot deal with the N-methylated residues (it replaces them by glycines). Damino acids are not represented in the rotamer library either. Moreover, SCWRL4 does not deal with cyclization: collisions usually happen between the side-chains of the first and the last residues. Within our multi-stage exploration approach, the subsequent all-atom relaxation (performed with Amber) can solve some of these problems. However, wrong side-chain conformations may remain. Finally, despite the fairly high speed of SCWRL4, its use as a third-party program requiring system calls slows down the overall computing time.
Dataset In this study, we considered three RGD cyclic pentapeptides selected from the work of the Kessler group. 40, 41 The sequences of these peptides are presented in Table  1. For both the REMD simulations and EGSCyP method, all the cyclic structures were generated using UCSF-Chimera. 71 This tool allows modeling non-natural amino acids, such as D-amino acids or N-methylated ones. The cyclization was made head-to-tail, i. e. , a peptidic bond was created between the N-terminus and the C-terminus amino acids. The D residue + N-methylated residue exhaustive exploration process performed by EGSCyP is independent of the initial conformation. The topology files were created with Tleap from AmberTools 16, 52 using the Amber ff96 force field. 53 The Amber topologies and partial charges of the N-methylated residues were computed with the RED-Server.