dataesr/scientific-paragraphs-categorization huggingface dataset:
2nd derivative The second derivative can remove both the linear baseline slope variations and additive effects. The derivative in the spectral domain is done with the help of a polynomial fitting within a window of chosen size (to have fair comparison in the present work a default of 15 was used for all datasets) and then the differential of the polynomial is calculated. The algorithm most often used for carrying out this operation is the Savitzky-Golay (SAVGOL) algorithm. In the present work, the SAVGOL algorithm was implemented using the MATLAB codes explained in literature  (Roger et al. , 2020b).
SPORT approach to pre-processing Sequential pre-processing through orthogonalization (SPORT) is a recent approach to combine multiple pre-processing techniques to extract complementary information  (Roger et al. , 2020a). The complementary information from each pre-processed dataset is extracted and combined to improve model accuracies. The main core of the SPORT pre-processing is sequential orthogonalized partial-least square (SOPLS). SOPLS belongs to the family of multi-block PLS methods where, in addition to the PLS regression, an orthogonalization step is applied while calibrating the models with different data matrices  (Biancolillo et al. , 2019). SOPLS involves extraction of information sequentially from each measurement block used to build the calibration model, i. e. the aim is to incorporate blocks of data one at a time and to assess the incremental and additional contributions.
In the case of the fruits, the number of LVs ranged from 4 to 7. The R 2 p was greater than 0. 90 in the case of the grape and olive dataset. In the case of apple, olive fruit and apricot, the R 2 p range was 0. 69-0. 81. The SNV+2nd derivative showed an improvement in model performance compared to just 2nd derivative as presented in Fig. 4. For the apple dataset, R 2 pred increased by 2 % and RMSEP decreased by 2. 8 %. For the grape dataset, R 2 pred was increased by 3 % and the RMSEP decreased by 23 %. To olive oils dataset, R 2 pred increased by 2 % and the RMSEP decreased by 21 %. For olive fruit dataset, R 2 pred increased by 7 % and the RMSEP decreased by 19 %. For the apricot dataset, no change in R 2 pred and RMSEP was observed.
Modelling with SPORT pre-processing Fig. 5  shows the modeling results from the SPORT pre-processing fusion for the same datasets presented in Figs. 3  and 4. A summary of improvements in model performance is presented in Table  2. NIRS prediction modeling of ingredients (soluble solids, organic acids, dry matter, fatty acids) with the SPORT approach for all the food materials resulted in an increase in R 2 pred and a decrease in RMSEP. In comparison to the SNV+2nd derivative, the improvement with the SPORT pre-processing approach was limited to the samples measured in reflection mode i. e. the apple, olive fruit and apricot datasets. In the case of transmission measurements i. e. grapes and olive oils, the improvements were limited. This was because in the presented use of the SPORT pre-processing, the aim was to eliminate the scatter from the data which in the case of transmission is low compared to reflection.
The improvement in model performance was obtained because the SPORT approach used information from multiple pre-processing techniques (Table  4 ). This was possible through the sequential extraction of latent variables from different pre-processing treatments considered as different blocks. In the case of the apple dataset, the SPORT approach used a fusion of MSC and VSN pre-processing. In the case of grapes, a fusion of raw data with VSN and 2nd derivative  preprocessing was performed. In the case of olive oils, a fusion of VSN, SNV and 2nd derivative was identified. In the case of olive fruits, a combination of VSN and 2nd derivative was optimal. In comparison to the primary work related to olive fruits, the combination of 2nd derivative and VSN was found optimal by  (Sun, Subedi et al. 2020 ).
). Fig. 5. SPORT preprocessed models. (A). Brix prediction in apples, (B). Brix prediction in grapes, (C). Linoleic acid fat prediction in olive oils, (D). dry matter prediction in olive fruits, and (E). citric acid prediction in Apricot. The blue dots explains the calibration set (70 %) and the red dots explains the test set (30 %) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ). vectors from each bock are presented in Fig. 6 -10. To facilitate the comparison the regression vectors are plotted in arbitrary scales. In the case of the apple dataset, the SPORT selected the VSN and the MSC as optimal pre-processing techniques. The VSN pre-processing highlighted extra wavelengths corresponding to CH 2 and CH bonds which were missed during the MSC pre-processing (Fig.
6 ). CH 2 and CH bonds could be linked to the sugar components in the fruits which are linked with the Brix  (Osborne, 2006). In the case of the grapes dataset, the SPORT identified raw, VSN and 2nd derivative as optimal pre-processing techniques. Both the MSC and the VSN (Fig. 7 ) were dominated by the color information whereas the 2nd derivative captured the CH2, ArOH and H 2 O wavelength regions  (Osborne, 2006). In the case of the olive oil dataset, the SPORT identified the VSN, SNV and 2nd derivative as optimal pre-processing techniques. Information around 1700 nm (Fig. 8 ) was captured by all the three preprocessing techniques and can be explained as the first overtones of the CH 3 , CH 2 and CH bonds in the triglycerides  (Osborne, 2006). However, SNV captured some extra information around 2200 nm which also corresponds to the CH 3 combination band region  (Osborne, 2006).
The complementary role was the fusion of information of the first overtones and the combination bonds. In the case of the olive fruits dataset, the SPORT identified the VSN and the 2nd derivative. The 2nd derivative captured the information about the OH bond whereas the VSN extracted information mainly about ArOH, meaning that apart from moisture there is also some ArOH compound that are correlated with the final dry matter of fruits (Fig. 9 ). In the case of citric acid there were a number of peaks present in the regression vector from the different pre-processing (Fig. 10 ). In case of SNV, there are peaks at 1000 nm and 1700 nm which were not captured by the MSC and the 2nd derivative. The 2nd derivative captured something distinct at 1900 nm.
, 2020b). Different pre-processing techniques can provide complementary information which can be used in a sequential approach to improving models  (Roger et al. , 2020a). In the present work, the complementary fusion of differently pre-processed data with the SPORT approach showed model improvements for all the food materials. In the case of apple dataset, the MSC and the VSN were able to capture complementary peaks related to the sugar components. In the cases of grape dataset, the 2nd derivative revealed the main peaks related to sugar    components whereas the VSN was mainly limited to the color information. In the case of the olive oil dataset, the SNV was able to capture also the combination bonds information in the regression vector compared to other pre-processing. In the case of the olive fruit dataset, the VSN and the 2nd derivative captured different peaks, highlighting that different pre-processing algorithms are not capturing the same information and rather capturing different things.
The reflection is performed in a non-contact way and is infamous to   have huge scattering (in the case of fruits) accompanied due to interaction of the light and the multiple internal reflections when it penetrates the surface of material before being reflected. Further, the fruit peel and flesh attenuate these effects. In the present work, fusion of different scatter correction algorithms improved the model performance for the samples measured in reflection mode. There was little improvement in the case of transmission datasets of grapes and olive oils as, apparently, scattering is less of a problem in these samples. For NIR reflection measurements of fruit quality aspects, optimal fusion of pre-processing algorithms with SPORT pre-processing may be recommended to improve prediction power.
Morphological data set For the phylogenetic analyses in this study, we use the largest data set of floral traits currently available, compiled from the PROTEUS database  (Sauquet, 2019). The original data set was assembled within the eFLOWER initiative, an international collaborative project aimed at answering key questions on flower evolution  (Sauquet et al. , 2017). The data set contains floral trait data for 792 species (exemplar approach), which were sampled to match the taxa of a recent dated phylogeny of angiosperms  (Magallón et al. , 2015) , representing 63 orders (all but one of the orders recognized in APG IV, 2016) and 372 families (86%). As in  Sauquet et al. (2017) , we transformed all characters, as they are scored in PROTEUS (primary characters), into secondary characters for analysis by converting continuous characters into discrete characters and by reducing the number of character states of discrete characters (see Appendix S1 for the full data set analyzed in this study, and Appendix S2 for details on scoring philosophy).
Analyses of the same data set with the original secondary character yielded results remarkably similar and congruent to those presented here, but we believe the new secondary character is more suited for future analyses of this dataset. Our philosophy for scoring morphological data remains the same as in our earlier paper using the original version of the dataset  (Sauquet et al. , 2017). In connection with this, detailed information on how we dealt with issues such as polymorphic data, missing data, and continuous (quantitative) vs. discrete (qualitative) characters is given in Appendix S2, which also contains adapted/complemented definitions and explanations for all characters used in this study.
, 2017). These trees differ mainly in the relationships among the main lineages of angiosperms, which remain in flux according to data and analytical methods (e. g. , One Thousand Plant Transcriptomes Initiative, 2019), but also in some finer details of relationships among families and orders that remain weakly supported. Hence, they provide a reasonable initial assessment of the sensitivity of our approach to future improvements in angiosperm phylogenetics. Although all of our backbone trees are a Only families mentioned in APG IV; CG = crown group, SG = stem group (i. e. , the stem node and all of its descendants exclusive of the crown group). b PA = studies including a restricted phylogenetic analysis. dated and drawn as such (chronogram), branch lengths were not used in the analyses presented here.
All analyses were performed in R (R Core Team, 2019) using a custom script relying on the package "phangorn" version 2. 0. 2  (Schliep, 2011)  to compute unordered (Fitch) parsimony scores. Briefly, for each fossil, we performed the following steps. The total number of possible branching positions for any fossil on the extant phylogeny is 1582 (792 terminal branches + 790 internal branches). Each fossil taxon was successively grafted onto each of the 1582 possible branches to obtain a parsimony score for each of these locations (based on the 30 characters in the dataset). To visualize the most parsimonious (MP) positions, the entire tree was then colored according to a gradient based on the parsimony score, with a high contrast for the lowest scores: black for MP positions, green for positions within one parsimony step of the MP position.
g. , assuming that all characters are linked and evolve according to the same symmetrical rate regime;  Wright et al. , 2016). In addition to the data set and matrix (Appendix S1), we provide the R script and electronic data files (including the backbone trees) in an online public repository (https://github. com/eflow erpro ject/ paleo eflower). This will allow anyone to replicate the analyses we did for this study or to test the phylogenetic position of any other fossil flower by simply adding its floral characters to the data set and running the backbone analysis.
g. ,  Doyle et al. , 2008)  or of alternative subsets of characters  (e. g. , von Balthazar et al. , 2008)  on phylogenetic analyses. Likewise, the discovery of additional specimens of a fossil taxon or alternative interpretations based on new investigations (e. g. , D. cordiformis;  Manchester et al. , 2018)  might make it desirable to adapt earlier scorings and to rerun phylogenetic analyses at a later stage. Our approach, with a flexible data set and database (PROTEUS), in which each data record is unequivocally referenced  (Sauquet et al. , 2017; Sauquet, 2019) , makes it easy to replicate and verify any part of the analyses. This open and transparent approach to which the eFLOWER initiative is committed has already proven fruitful in allowing constructive discussion and further improvement of the data set  (Sauquet et al.
Clade-specific analyses based on the Paleo-eFLOWER data set Once an initial angiosperm-wide phyloscan analysis has been conducted and has helped identify a particular angiosperm subclade (an order or a clade with several orders) as the most likely phylogenetic "home" for a given fossil, it might be advisable to build a clade-specific data set (or use an existing one) with denser taxon sampling and additional, clade-specific characters, with the goal of placing the fossil more accurately within that clade. Furthermore, while such clade-focused data sets may be built entirely independently from the angiosperm-wide data set presented here, we propose that using the PROTEUS and eFLOWER framework to develop such data sets would not only allow one to build on existing data (where taxa and characters overlap), but, in turn, would also contribute toward a much more densely sampled angiosperm-wide data set that will allow improved future phyloscan analyses.
ACKNOWLEDGMENTS We thank  P. Crane , president of the  Oak Spring Garden Foundation  ( OSGF, Virginia, USA ) and the participants of the second eFLOWER  Summer School , held at OSGF during September 2018, for valuable discussions on our Paleo-eFLOWER initiative. We thank  U. Schachner  and  M. Lachmayer  for help with figures, tables, and references during manuscript preparation;  R. Buchner  for help with administering the eFLOWER server; B. Crepet,  J. Doyle ,  P. Endress , and  E. M. Friis  for critical input and helpful discussions during the early stages of this project; and two anonymous reviewers for their constructive and valuable comments on our study.
The probe geometry is simpler as no guard ring is employed. The corrosion rate calculation is based on three-dimensional numerical simulations that take several geometrical parameters into account (concrete cover and resistivity, rebar diameter and spacing). In addition, an iterative method is developed to evaluate the corrosion rate as this parameter also modifies the distribution of current lines in the system. The present study focusses on corrosion rate assessment. The concrete cover resistivity measurement method was described in detail in  [8]. Here the DIAMOND probe for on-site measurements will be presented first and will be used to identify the corrosion state of rebars embedded in concrete slabs that are presented later. The measurement methodology will then be introduced, with numerical modelling of the measurement using COMSOL software and presentation of the numerical results.
To perform the measurement, the probe is then electrically connected to the reinforcement network that constitutes the working electrode (WE). The probe is linked to a portable tablet with a software developed by the authors on Excel. The software controls the test, provides the signal required for polarization and registers the potential on the RE. After the end of the test, the potential values measured on the RE are automatically used to calculate the concrete cover resistivity and the rebar corrosion rate. The concrete cover resistivity calculation was detailed in  [8]  and the corrosion rate evaluation method is detailed in this article (part 5). A typical polarization curve recorded during on-site measurement is presented in Figure  2. Before the polarization, a galvanostatic pulse current of 10 µA is applied.
Depending on the response, the galvanostatic current (from 1 to 25 µA) is automatically calculated for the transient polarization. The galvanostatic polarization is then applied. If the potential measured is not stabilized (less than 0. 1 mV of variation during 20 seconds) after 60 seconds (which is always the case for passive rebar) curve fitting is performed using is a typical exponential charging RC circuit (Randles model) and the potential is estimated at 300 seconds  [41]. The data recorded are then converted into the output data of interest to quantify the corrosion state of the reinforced concrete structure under inspection: corrosion potential, concrete cover resistivity and corrosion rate of the rebar. The instantaneous ohmic drop is converted into concrete cover resistivity as presented in  [8]  and the steady-state response is used to determine the rebar corrosion rate.
2003; Kishimoto et al. 2009 Kishimoto et al. , 2011a) )  as well as the VLT Interferometer instrument AMBER  (Weigelt et al. 2012 ) have measured compact sizes associated with partially resolved sources. The size measurements are consistent with an origin at the sublimation radius as found from reverberation. The second generation VLTI instrument GRAVITY has vastly improved sensitivity and coverage as a result of combining light from all 4 UT telescopes, resulting in a 6 baseline array  (Gravity Collaboration et al. 2017). We use data from our ongoing AGN observing program to measure sizes for a sample of 8 of the brightest type 1 AGN, nearly doubling the sample for which NIR interferometry is available. We describe the data acquisition and selection procedure (section 2) and fitting methods used to measure sizes from both continuum and differential visibilities (section 3).
Observations The main science goal of our GRAVITY AGN observing program is to spatially resolve the broad emission line region, which has recently been achieved with observations of the quasar 3C 273  (Gravity Collaboration et al. 2018 ). Targets are selected as the brightest Type 1 AGN on the sky visible from the VLTI. For BLR science we require deep integrations and repeated observations over many nights, with less emphasis on observing calibrators. In the past 2 years we have successfully observed 8 Type 1 AGN over 26 nights 1. Details of the targets and observations are given in Table  1. For each observation, we first close the loop of the MACAO visible AO systems on the visible AGN source with each of the 4 UT telescopes. The AGN is then acquired on the GRAVITY acquisition camera and we place both the GRAV-ITY fringe tracking (FT) and science channel (SC) fibers on the AGN (observing on-axis), and split the light between the two detectors.
The correlation of visibility squared with Strehl ratio also vanishes. Although there are other effects possibly still keeping the visibility of the shortest baseline (UT3-UT2) below unity, the data selection based on the group delay apparently helps to improve the data. For NGC 1365 on 2018-01-08, the V 2 approaches 1 at short baselines. For IRAS 09149-6206 on 2018-11-20, the short baselines remain significantly below V 2 = 1. The black lines are the best fitting 1D Gaussian models for each epoch. UT4-3 UT4-2 UT4-1 UT3-2 UT3-1 UT2-1.
Continuum visibility fitting Sample FT continuum V 2 data for two sources are shown in Figure  2 , color-coded by baseline. In both cases the source is partially resolved, e. g. the visibilities fall with increasing baseline length. For NGC 1365, the visibility at zero baseline reaches unity, as it should for a model of a single compact source. In IRAS 09149-6206, it reaches only V 2 (0) 0. 8. The zero baseline visibility varies between sources but also between exposures and nights for the same target. We attribute this to a likely coherence loss, although some fraction could also result from extended nuclear K band continuum emission. To deal with this effect, we fit for the zero baseline visibility along with the source size in each exposure for each object over all nights. The function fit is then, V 2 = V 2 0 exp (-2π 2 r 2 uv σ 2 ), (1) for zero baseline visibility V 0 , size parameter σ in radians, and r uv the baseline length in units of the observed wavelength λ.
The differential amplitude v at the line depends on the ratio V l /V c. In the partially resolved limit, that in turn measures the quadrature size difference between the line and continuum  (Waisberg et al. 2017). For a larger (smaller) spectral line emission region, |v| decreases (increases) at the line. We used these data previously to show conclusively that the hot dust emission size is larger than that of the Pa α BLR in 3C 273  (Gravity Collaboration et al. 2018). The continuum visibility is V c V l = f v(1 + f ) -1. (3) We measure continuum visibilities V c from the data (v, f ) by assuming V l = 1, e. g. that the broad emission lines are unresolved. The values f are taken from the photometric flux spectra, flattened to remove the instrument profile and averaged over the 4 UT telescopes and all exposures.
They are then normalized to the continuum level. For 3C 273, we have found a BLR size from modeling differential phase data of R BLR = 46 ± 10µas  (Gravity Collaboration et al. 2018). That size corresponds to a visibility V l 0. 996 at a long VLTI baseline of 120m, justifying our approximation. We note that in general the BLR is found from reverberation to be a factor of 2 -5 smaller than the hot dust continuum (e. g. ,  Koshida et al. 2014). In that case, our approximation will tend to overestimate continuum visibilities, leading to a 10 -20% underestimate of the continuum size. These measurements require deep integrations, but do not suffer from systematic errors related to calibration or coherence loss. We measure V c from this method in 4 sources (3C 273, PDS 456, NGC 3783, IRAS 09149-6206).
The hot dust is more extended than the BLR In all sources where we detect a differential amplitude signature, the amplitude increases at the line, showing robustly that the broad emission line region is more compact than the hot dust continuum. In the remaining targets we were limited by sensitivity, e. g. the size difference is not constraining. We confirm that the hot dust emission region is much larger than the BLR, as inferred from past reverberation and spectral measurements (e. g. , Netzer 2015, and references therein). In 3C 273 we also have an interferometric BLR radius measured from kinematic modeling of the detected velocity gradient in differential phase data (Gravity Collaboration et al. 2018), which is a factor 3 smaller than that of the hot dust.
g. ,  Koshida et al. 2014). Leaving the slope free, we find a best fit with a flatter luminositydependence, R ∼ L 0. 40±0. 04 bol. The individual data points are plotted as blue x's. Combining the Seyfert galaxies in our sample (L bol 10 46 erg s -1 ) with past KI/AMBER results, we find a similar best fitting radius-luminosity relation from NIR interferometry as from reverberation. The two luminous quasars (L bol > 10 46 erg s -1 , PDS 456 and especially 3C 273) show smaller hot dust angular sizes than the other sources. Those sizes are robust, obtained with both SC and FT data, and precise (uncertainties 20%). The measurement for 3C 273 agrees with that reported by  Kishimoto et al. (2009). We generally find that GRAVITY Collaboration: Resolved size of hot dust near AGN interferometry sizes are larger than those from reverberation at comparable bolometric luminosity, in agreement with past work (e.