dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we used the NIPALS algorithm implemented in the scikit-learn 1. 0. 2 package (Python 3. 7. 4), which aims to linearize models that have non-linear parameters  [92, 96]. The optimal number of components was selected using both the coefficient of determination (R 2 ) and the RMSE of a 5-fold cross-validation (RMSECV) and relying on the grid search method (Table  A2  in Appendix B). This method allows models parameters to be adjusted by searching a grid of all possible combinations and evaluating them using cross-validation (Figure  3 )  [97, 98].
Like other machine-lea gorithms, RF is increasingly used to predict soil properties. It has been shown to prediction performance due to its excellent non-linear learning ability and flexi this study, optimal hyperparameters (max_features and max_depth correspondi number of features to consider when looking for the best split and the maximum the tree, respectively) were adjusted based on the RMSECV using the "R izedSearchCV" and "RandomForestRegressor" functions implemented in the sci 1. 0. 2 package (Python 3. 7. 4) (Tables  A2  and A5  in Appendix B). Randomized sea alternative method to the grid search method and it performs a random search perparameters within a predefined grid. A fixed number of iterations is perform at each iteration the algorithm selects a combination of hyperparameters, tunes th and evaluates its performance using RMSECV  [97].
A higher number of samples used in each node helps to prevent overfitting by avoiding more specific trees  [102]. Like other machine-learning algorithms, RF is increasingly used to predict soil properties. It has been shown to increase prediction performance due to its excellent non-linear learning ability and flexibility. In this study, optimal hyperparameters (max_features and max_depth corresponding to the number of features to consider when looking for the best split and the maximum depth of the tree, respectively) were adjusted based on the RMSECV using the "RandomizedSearchCV" and "RandomForestRegressor" functions implemented in the scikit-learn 1. 0. 2 package (Python 3. 7. 4) (Tables  A2  and A5  in Appendix B). Randomized search is an alternative method to the grid search method and it performs a random search for hyperparameters within a predefined grid.
It was better suited to our cas study than a CNN, which is more adapted to data with spatial structure and most com monly used in remote sensing  [107]. Thus, the abundant cloud cover over our study are led to a loss of spatial and temporal information. The rectified linear unit (ReLU) activa tion function was used to establish relationships between input and output neurons i each hidden layer. The optimal number of hidden layers and neurons in each layer wa selected based on the RSME using the "tuner. search" framework implemented in th keras_tuner 1. 1. 3 package (Python 3. 8. 16) (Tables A2 and A5 in Appendix B), which is scalable hyperparameter optimization framework. It is based on Bayesian optimization Hyperband, and random search algorithms  [108]. The random search strategy was use in our case.
It was better suited to our case study than a CNN, which is more adapted to data with spatial structure and most commonly used in remote sensing  [107]. Thus, the abundant cloud cover over our study area led to a loss of spatial and temporal information. The rectified linear unit (ReLU) activation function was used to establish relationships between input and output neurons in each hidden layer. The optimal number of hidden layers and neurons in each layer was selected based on the RSME using the "tuner. search" framework implemented in the keras_tuner 1. 1. 3 package (Python 3. 8. 16) (Tables A2 and A5 in Appendix B), which is a scalable hyperparameter optimization framework. It is based on Bayesian optimization, Hyperband, and random search algorithms  [108]. The random search strategy was used in our case.
Descriptive Statistics of Measured SOC Content The measured SOC contents of the 58 sampling points that were in bare soil, ranged from 15. 2-49. 4 g•kg -1 , with a mean of 22. 3 g•kg -1 (Table  3 ). They lay within the range of SOC content usually observed in the Naizin watershed and were consistent with those measured in a previous study  [111]. The descriptive statistics of the calibration and validation datasets generated for the three iterations using the S2 time series are presented in Table  4. The mean reflectance of soil samples was lower for Sentinel-2 spectra than for laboratory spectra (Figure  5a ). Some mean S2-indices were lower than mean Lab-indices, while others were higher or nearly the same (Figure  5b ). As expected, the former also varied more, since surface conditions, especially soil moisture, varied among sampling points and dates.
Vaudour et al. [38]  found that temporal mosaicking approaches increased the predicted area but did not predict SOC content better than single-date models. This is because soil conditions, such as soil moisture, soil roughness, and vegetation significantly influence prediction performance by disturbing soil reflectance  [31]. Considering Sentinel-2 images acquired on different dates allows one to assess the ability of models to represent different soil conditions and, thus, increase the number and composition of training sample datasets.
Performance of Calibrated Models Using Only Bands: DNN vs. PLS and RF When using only Sentinel-2 bands, calibrated DNN models outperformed PLS and RF models for all three iterations. These results are consistent with previous studies that found that DNN models are more suitable for predicting SOC due to their ability to capture complex relationships through multiple hidden layers and neurons  [61, 105, 107 ]. In addition, DNN models can model complex data. For example, Odebiri et al. [105]  found that a DNN model improved the prediction of SOC at the national scale, even when using a dataset with high spatial uncertainty (i. e. , multiple scales of variation, different sampling sources, and different acquisition dates). Moreover, DNN models in the present study were less sensitive to high-leverage samples (Figure  8 ) than PLS and RF models were.
Effects of Additional Information on Model Calibration and Validation Including additional information, in particular S2-indices, S1-indices, and S1-soil moisture, was intended to increase the models' predictive performance by providing new variables that distinguish the varying soil conditions better on different acquisition dates. This additional information had more influence during calibration than validation and on the DNN models than on the RF models (e. g. , when including S2-indices, mean RPIQ improved from 2. 02 (±0. 15) to 2. 59 (±1. 30) when calibrating the DNN model and from 1. 50 (±0. 13) to 1. 57 (±0. 07) when calibrating the RF model) (Figure  9 ). It likely had more influence during calibration than validation because the validation dataset contained spectral information acquired on dates when soil surface conditions of the sampling points in the training dataset were unknown.
Thus, the algorithm used influenced the performance of models calibrated with Sentinel-2 data. By using all of the complementary information simultaneously, the DNN algorithm was able to extract the most appropriate information for predicting SOC content; thus, this approach provides models with more features, which increases their learning capacity. Wang et al. [27]  tested different combinations of Landsat TM-and PALSAR-derived data and found that using six TM bands, six TM-derived indices, and four PALSAR polarizations yielded the best model. In addition, Nguyen et al. [51]  found that combining Sentinel-1 C-band dual-polarimetric SAR data and Sentinel-2 optical datasets increased prediction performance.
Effects of Including Laboratory Spectral Indices Due to the complex nature of soils, their Vis-NIR spectra aggregate multiple soil chromophore spectral signals  [122]. Influenced by many other confounding factors, Sentinel-2 soil reflectance predicted SOC less well than spectra acquired under laboratory conditions  [28, 33, 61]. Thus, as suggested by Ben-Dor et al. [10]  and confirmed by Vaudour et al. [32] , the predictability of a given soil property using remotely sensed data does not depend solely on the property itself or its spectral behavior as a chromophore. When compar-ing the performance of Sentinel-2A-derived models for two contrasting agroecosystems, Vaudour et al. [32]  found that loading values of the Sentinel-2 bands for a given soil property varied among study areas. Consequently, we included laboratory-acquired soil spectral information, assuming that doing so would provide models with more spectral information about soil properties themselves.
With only two indices, we achieved good performance for both RF and DNN models (mean R 2 = 0. 70 (±0. 15) and 0. 91 (±0. 04) and mean RPIQ = 2. 01 (±1. 13), and 1. 77 (±0. 23), respectively). However, the results were less stable for RF than for DNN, even when including five indices. This is consistent with the idea that RF classifiers can be used to classify multisource remote sensing data, mainly due to their computational speed  [115] , but the fact that they optimize models using only the input datasets identified as important made them unstable in our study. These results suggest that considering the same spectral information acquired in the laboratory for all dates instead of the Sentinel-2 spectral information, which varies as a function of surface conditions, minimizes the effects of surface conditions and highlights the spectral behavior of the soil property itself.
Funding: This study was supported by the  European Union 's  Horizon H2020 research and innovation European Joint Programme Cofund on Agricultural Soil Management  ( EJP-SOIL  grant no. 862695 ) and was carried out in the framework of the  STEROPES  project of  EJP-SOIL. It was also supported by the  MELICERTES  project ( ANR-22-PEAE-0010 ) of the  French National Research Agency , under the  France2030 program  and the national  PEPR "agroécologie et numérique" program , and the  French-Tunisian project PHC -Utique IPASS  ( 44324WB/20G093 ). The lead author was awarded a Ph. D. grant by the  Tunisian Republic.
Complex 1 is even stable for weeks as a solid under air (<6% area of new signals observed in  31  P NMR spectra after one month). Thus, precatalysts 1-6 can be handled and used without the need for a glovebox. In fact, all catalytic evaluations were carried out by weighing the precatalysts and preparing solutions under air (vide infra). To conduct an initial assessment of the performance of precatalysts 1-6 in diverse asymmetric allylic alkylations, we compared them against standard catalyst systems for a series of benchmark reactions. Representative comparisons at identical Pd loading are given in Figure  5 ; additional comparator data under alternative reaction conditions is available in Tables S5-S8. We chose five criteria to evaluate each catalyst system, visualized in a radar plot analysis, including isolated yield, enantioselectivity, reaction time, the Pd to chiral ligand ratio, and any additional steps required to generate the active catalyst ("Practicality" in Figures  5 6 7 ).
ACKNOWLEDGMENTS J. H. , J. L. , and D. C. L. acknowledge with respect the Lekwungen peoples, on whose traditional territory the  University of Victoria (UVic)  stands, and the Songhees, Esquimalt, and WSÁNEĆ peoples whose historical relationships with the land continue today. They also thank  Prof. Irina Paci  at  UVic  for fruitful discussions regarding computational studies, and  UVic ,  NSERC ,  CFI , and  BCKDF  for general operating and equipment funds. S. A. and T. K. thank  Dr. Rodolphe Tamion  and  Dr. Jean Fournier  for fruitful discussions, as well as  Dr Lucile Vaysse-Ludot  from  Oril Industrie affiliated  to " Les Laboratoires Servier"  and  Queen Mary University of London  for financial support. S. E. J. thanks  Dr. William A. Sabbers ,  Dr. Taylor M. Keller , and  Prof. Michael J.
Statistical genetics analyses VCFtools was also used to: i) calculate the heterozygosity and inbreeding coe cient (F) for each individual. ii) infer the degree of relationship/kinship between individuals, using the method developed by In the R program (R Core Team, 2020), we used the packages "VCFR"  45  , "poppr version 2. 8. 6" 46 , "ape"  47  , "RcolorBrewer" 48 , "ggrepel" 49 , "adegenet" 50 , "reshape2" 51 and "ggplot2" 52 to perform: i) Multivariate analyses represented in two-dimensional graphs using Principal Component Analysis (PCA) to show inter-individual relationships through genetic distance data between individuals from different populations; ii) Discriminative Principal Component Analysis (DAPC) to identify groups of genetically related individuals; iii) Compoplot that represents in a bar graph the probability of population association for each sample of the predetermined localities.
iv) a heatmap with pairwise kinship data normalized by Pearson's correlation matrix, in which the highest relationship level was assigned the value 1 and the lowest relationship level was assigned the value - 1. The phylogenetic reconstruction using Bayesian Inference (BI) among individuals was performed with the Beast program  53  using the SNAPP package  54  , which collected the SNPs for each individual that was designated as a separate OTU. The run was executed with a chain length of 200 million, convergence was checked with Tracer version 1. 7  55  , and the maximum clade credibility tree was calculated using TreeAnnotator. The number of possible population groups (k) and their geographic boundaries was estimated by Geneland v. 3. 2. 2 56 , using three independent runs with 30 million iterations each, in a range of 1 to 10 possible clusters, and the uncorrelated allele frequency model, which assumes that allele frequencies between populations can be different.
The runs were performed with a model of false null alleles and thinning of 300,000 with results visualized after a burn-in of 100 times, where the convergence of each model was evaluated. The number of population groups without geographic correlation was estimated through the STRUCTURE v. 2. 3. 4 program 57 , using a model that predicts the possibility of gene ow between populations (admixture model), with 1,000,000 MCMC randomizations and a burn-in of 250,000. Population number parameters (K) from 1 to 10 were evaluated with 10 independent runs for each K. The choice of the appropriate K was made with Structure Harvester 58 and the nal graph was generated with Clumpak  59. Measures of differentiation between geographic populations was estimated through pairwise FST analyses, Analysis of Molecular Variance, and Mantel tests that were done using the Arlequin 3.
Acknowledgements Davidson P. Campos  thanks the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) of Brazil  for his PhD fellowship in the  Program of Genetics of UFMG (Belo Horizonte, Brazil). FRS thanks  Conselho Nacional do Desenvolvimento Cientí co e Tecnológico (CNPq) of Brazil  for his research fellowship. We thank  Terra Brasilis ,  Funatura ,  Cer Vivo ,  Naturatins ,  Projeto Mergus da Chapada dos Veadeiros , and  Zooparque Itatiba  for merganser samples used in this study, which are part of the collection of the Brazilian Action Plan for Conservation of  ICMBio (PAN pato mergulhão ). This project received funds from  Fundação o Boticário ,  CNPq  and  FAPEMIG (Brazil). Data sets were processed in Sagarana HPC cluster,  CPAD-ICB-UFMG. Data Accessibility and t-Sharing Sequence les for ddRAD SNPs are deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number  PRJNA962330.
In time averages of the volumetric tip vortex visualizations, the leapfrogging position can be identified as the location of maximum radial separation between the inner and outer wake envelopes, as seen in Figure  4a. Cross-section visualization images were used to obtain a more precise leapfrogging location by tracking the positions of the vortex centers over time (Figure  4b ). The vortex tracks were obtained using motion-based multiple object tracking in MATLAB. Figures  4c  and d  show example time series of the axial and radial positions, respectively, of the average positions of the vortices shed from the three rotor blades. Time (t) is normalized by the vortex spacing and circulation, such that t * = tΓ/(2h 2 ), where the tip vortex circulation Γ is measured in the wake of the baseline rotor (Section 3.
In addition to vortex displacement, Figure  9d  presents the circulation of the vortices shed from all three blades in both configurations. Note that there are fewer data points than in the circulation plot in Figure  5e , as fewer phases of rotation were measured for the asymmetric cases than the symmetric case. The vortex shed from blade W (in the blade 0 position in the left plot of Figure  9d ) exhibits significantly higher circulation values than the vortices shed from the other two blades. This circulation increase is related to the length change of the blade. In all the configurations tested, a radial extension causes the tip vortex circulation to increase, while a radial shortening reduces the tip vortex circulation. In general, tip vortex circulation is determined by the chord length, the incident velocity magnitude, and the angle of attack experienced by the blade.
Direct imaging of exoplanets and circumstellar disks at optical and infrared wavelengths requires reaching high contrasts at short angular separations. This can only be achieved through the synergy of advanced instrumentation, such as adaptive optics and coronagraphy, with a relevant combination of observing strategy and post-processing algorithms to model and subtract residual starlight. In this context, VIP is a Python package providing the tools to reduce, post-process and analyze high-contrast imaging datasets, enabling the detection and characterization of directly imaged exoplanets, circumstellar disks, and stellar environments.
, 2015; Flasseur et al. , 2018; Gomez Gonzalez et al. , 2016 , 2017; Ruffio et al. , 2017). Furthermore, as opposed to VIP, pyklip does not offer any preprocessing options (e. g. PCA-based sky subtraction, image centering or bad frame trimming). pynpoint was originally developed as a PCA-based PSF-subtraction mini-package  (Amara et al. , 2015) , which was later significantly expanded into an end-to-end processing pipeline including similar options as VIP regarding preprocessing  (Stolker et al. , 2019). Nonetheless, the PCA implementation in VIP offers a much wider diversity of options, such as the possibility to carry it out in concentric annuli, and to consider a parallactic angle threshold when creating the PCA library. Depending on the high-contrast imaging dataset at hand, different post-processing methods and reduction parameters can lead to better speckle suppression, helping with the detection of fainter companions  (Dahlqvist et al.
, 2021). In that regard, VIP is thus better equipped than other existing toolkits. It is also worth mentioning that FFT-based methods are implemented in VIP (default option) for all image operations (rotation, shift and rescaling) as these outperform interpolation-based methods in terms of flux conservation  (Larkin et al. , 1997). To our knowledge, FFT-based methods for image operations had only been implemented in the non-public high-contrast imaging pipeline graphic to date  (Hagelberg et al. , 2016). The VIP package follows a modular architecture, such that its routines cover a wide diversity of tasks, including: • image pre-processing, such as sky subtraction, bad pixel correction, bad frame removal, or image alignment and star centering (preproc module); • modeling and subtracting the stellar point spread function (PSF) using state-of-the-art algorithms that leverage observing strategies such as angular differential imaging (ADI), spectral differential imaging (SDI) or reference star differential imaging  (Marois et al.
, 2006; Ruane et al. , 2019; Sparks & Ford, 2002) , which induce diversity between speckle and authentic astrophysical signals (psfsub module); • characterizing point sources and extended circumstellar signals through forward modeling (fm module); • detecting and characterizing point sources through inverse approaches (invprob module); • assessing the achieved contrast in PSF-subtracted images, automatically detecting point sources, and estimating their significance (metrics module). The features implemented in VIP as of 2017 are described in Gomez Gonzalez et al. (  2017 ). Since then, the package has been widely used by the high-contrast imaging community for the discovery of low-mass companions  (Hirsch et al. , 2019; Milli, Hibon, et al. , 2017; Ubeira-Gabellini et al. , 2020) , their characterization  (Christiaens et al.
3. 5). At a structural level, VIP underwent a major change since version v1. 1. 0, which migrated it towards a more streamlined and easy-to-use architecture. The package now revolves around five major modules (fm, invprob, metrics, preproc and psfsub, as described above) complemented by four additional modules containing various utility functions (config, fits, stats and var). New Dataset and Frame classes have also been implemented, enabling an object-oriented approach for processing high-contrast imaging datasets and analyzing final images, respectively. Similarly, a HCIPostProcAlgo class and different subclasses inheriting from it have been defined to facilitate an object-oriented use of VIP routines. Some of the major changes in each module of VIP are summarized below: • fm: new routines were added to create parametrizable scattered-light disk models and extended signals in ADI cubes, in order to forward-model the effect of ADI post-processing  (Christiaens et al.
, 2019; Milli et al. , 2012) ; -the log-likelihood expression used in the negative fake companion (NEGFC) technique was updated, and the default convergence criterion for the NEGFC-MCMC method is now based on auto-correlation  (Christiaens et al. , 2021) ; the NEGFC methods are now fully compatible with integral field spectrograph (IFS) input datacubes. • invprob: a Python implementation of the ANDROMEDA algorithm  (Cantalloube et al. , 2015)  is now available as part of VIP; -the KLIP-FMMF and LOCI-FMMF algorithms  (Dahlqvist et al. , 2021; Pueyo, 2016; Ruffio et al. , 2017)  are now also available in the invprob module. -a Python implementation of the PACO algorithm  (Flasseur et al. , 2018 ) is now also available, including both the planet detection and flux estimation algorithms. • metrics: calculation of standardized trajectory maps (STIM) is now available  (Pairet et al.
Finally, a total of nine jupyter notebook tutorials covering most of the available features in VIP were implemented. These tutorials illustrate how to (i) load and post-process an ADI dataset (quick-start tutorial); (ii) pre-process ADI and IFS datasets; (iii) model and subtract the stellar halo with ADI-based algorithms; (iv) calculate metrics such as the S/N ratio  (Mawet et al. , 2014) , STIM maps  (Pairet et al. , 2019).
Acknowledgements An up-to-date list of contributors to VIP is available here. VC acknowledges financial support from the  Belgian F. R. S. -FNRS. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  FP7 and Horizon 2020 research and innovation  programmes (grant agreements No  337569  and  819155 ), and from the  Wallonia-Brussels Federation  (grant for Concerted Research Actions).
Acknowledgments: Authors thank the participation of  Tomas J. Madera-Santana  from  CIAD  ( Centro de Investigación en Alimentación y Desarrollo ) and  Rodrigo Melendrez-Amavizca  from  UNISON (University of Sonora ) for the irradiation process. Authors also thank  Nayeli Camacho-Tapia  for advising on the ZnO particle preparation and  Perla Alcántara  for thermal characterization support.