dataesr/scientific-paragraphs-categorization huggingface dataset:
When pressure from GDAS is not available, the algorithm uses a standard pressure calculated with the site elevation, based on the US Standard Atmosphere. A flag indicates if the current AOD value is calculated with a standard pressure or using GDAS pressure. This strategy to obtain pressure for all network sites is similar to the one followed by AERONET using NCEP/NCAR reanalysis data  (Giles et al. , 2019). Figure  3a  presents the scatter plot between the pressure from CAELIS and AERONET, where the range of pressure values spans from 660hPa at Teide site (3570m a. s. l. ) up to 1030hPa at sea level sites. More than 180 thousand pressure values used for AOD observations are compared in this plot, showing a high correlation between both databases. The differences between local pressure calculated by CAELIS and AERONET are in general below 2hPa as shown in Figure  3b.
Temperature Correction The Cimel photometers are not stabilized in temperature during operation. In turn, the sensor head is equipped with a temperature sensor that allows correcting the measured signals with respect to a reference temperature of 25°C. The correction is based on a laboratory characterization in a thermal chamber. Whenever a hardware element is changed in the photometer head (filter, detector, electronic card) a new thermal characterization is run for the instrument. The AERONET procedure for temperature characterization of the Cimel photometers is described in detail in  Giles et al. (2019). The information produced during these characterization, i. e. the temperature correction coefficients for each wavelength above 400nm, is stored in the corresponding table of CAELIS database.
CC BY 4. 0 License. Krotkov et al. , 2017)  data between 2005 and 2017. An example of global NO 2 for the month of August of this climatology can be seen in figure  4. For the O 3 climatology we use Multi Sensor Reanalysis from  GOME-2, OMI and SCIAMACHY sensors between 1978 and 2008 (van der A et al. , 2010). An example, in this case the global values O 3 for the month of May, can be seen in figure  5 , where higher ozone values are observed in the Northern hemisphere, as expected in spring. The comparison between the climatology tables used in CAELIS and AERONET for NO 2 and O 3 are shown in figure  6  by means of frequency distributions of the differences. For NO 2 , the determination coefficient between CAELIS and AERONET is high (R 2 =0. 978), and the mean of all differences (-0. 04 DU) points out a little underestimation of CAELIS database to AERONET climatology values with a standard deviation around 0.
The voltage signal (V ) has a temperature correction following the equation 2. C 1 and C 2 are the coefficients for the thermal characterization that are stored in the database and T is the temperature given by the sensor head during the measurement. V = V /(1 + C 1 (T -25) + C 2 (T -25) 2 ) (2) An absolute calibration (given by V 0 ) is required for AOD retrieval. In order to obtain the top of the atmosphere instrument signal the Langley plot method can be applied  (Shaw, 1983; Toledano et al. , 2018)  or the calibration be transferred from a reference instrument by side-to-side comparison  (Holben et al. , 1998). This calibration is supposed to be constant over time Different air mass factors m are taken into account for the various species; the reason behind is the different vertical distribution of the gases (O 3 is mainly stratospheric, CO 2 is uniformly mixed, etc.
Recently, AERONET improved the cloud screening algorithm with several significant changes, including the addition of aureole radiance checks for detection of thin cirrus clouds  (Giles et al. , 2019). A cloud screening procedure is therefore needed in CAELIS. Given the extensive tests with large data sets performed by  Giles et al. (2019)  and the improvements shown with respect to the previous algorithm, we have tried to reproduce this algorithm for Cimel photometers as a first step for CAELIS. The first step of the algorithm is to determine whether the Sun triplet collected is a valid measurement for AOD computation. In this sense, a minimum signal must be achieved in the measurement in order to warranty that photometer is pointing to the Sun (or Moon), i. e. more than 100 counts in the infrared channels (870 and 1020nm).
, 2019; Smirnov et al. , 2000). The label applied in this case is 'restoration' and it is equivalent to 'cloud-free', although very few data in our subset fulfill this conditions. All flags mentioned above result in the measurement point not to be considered 'cloud_free', and allow us to identify in the database the reason for the rejection. Thus we can query the database for cloud-free data in a certain site and period; but we can also analyze the cloud screened data and discriminate for any specific check. The full scheme as it has been described, is applied to solar AOD data. For lunar observations, we have maintained the same analysis and thresholds, except for the aureole radiance check, that cannot be performed at night. Further testing is needed to possibly refine the cloud-screening algorithm for night-time.
Another important aspect of the subset is the variety of Cimel photometer types. We have covered all generations of Cimel instruments (analog, digital and triple) and multiple versions (see Table  1 ). This feature involved considerable work to ensure a flexible enough algorithm and adequate database construction, so that all data can be consistently processed. In turn, we expect this experience will be of help in the addition of new photometer types to CAELIS. Thus, we have analyzed a large amount of data to have statistical strength in the comparison and cover multiple situations. The data set is used for validation of the daytime (solar) algorithm. This data set will also be used for cloud screening comparison (section 6. 4), in which a variety of climate conditions is also crucial.
Note that the level 1. 0 (unscreened data) in AERONET does not include all the measurements attempted by the photometers, because many observations with too low or too variable raw signals do not qualify for AOD level 1. 0 computation  (Giles et al. , 2019). For those data that passed this first requirement, the AERONET cloud screening will select the cloud free cases and include them in the level 1. 5 database 2. According to the flagging system of our cloud screening algorithm in CAELIS, we have compared the 'cloud-free' or 'restoration' flags with the AERONET level 1. 5 database for all the investigated sites and time periods (Table  1 ). The cloud screening comparator links all the photometer observations (full triplet) from CAELIS with its correspondent in AERONET and stores the output in two different arrays, one for CAELIS and another one for AERONET.
The value of 1 will be stored to each database if the observation is 'cloud-free', and 0 if is not cloud-free. With those two arrays the confusion matrix has been generated and it can be seen in Figure  9 , where we indicate the number of observations and the corresponding relative numbers in percent. In the confusion matrix the first row represents all the values that are cloud-free in AERONET, and the second row is for the cloud contaminated data in AERONET. In the same way, the first column represents the cloud contaminated data in CAELIS, and the second column includes the cloud-free data in CAELIS. More than 250 thousand observations have been analyzed here. The results are clearly satisfactory, with more than 99. 8% agreement in the classification. The number of points outside the main diagonal of the confusion matrix is marginal.
Conclusions The CAELIS software tool was primarily designed to assist in the management of the calibration facility of Cimel photometers at University of Valladolid, associated to AERONET. It provides access to meta data information to the users and intends to facilitate the daily operation of the photometers on site, with the final aim of improving data quality. CAELIS already provides to users processing of sky radiances and a set of flags to monitor the instrument performance in real time. The AOD product now complements this tool. Moreover, the AOD is needed for exploiting remote sensing data with the application of inversion algorithms, like GRASP  (Dubovik et al. , 2014; Torres et al. , 2017). These are the reasons behind the development of an operational aerosol optical depth product as well as the necessary cloud screening algorithm.
Acknowledgements. AP thank the  Centre National d'Études Spatiales (CNES)  and the  DIM-ACAV+ program  from the  Île-de-France region  for funding his PhD work. AV aknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement number  757561 ). The authors join to thank the reviewer for the constructive remarks provided.
(2017)  presented deep X-ray observations of the galaxy cluster Abell 2744, the analysis of which suggests a gas fraction of 5% to 15% for the filaments that surround the cluster and a plasma temperature of 1-2 × 10 7 K. Therefore, the characterization of these structures through observables like X-ray emission or Sunyaev-Zel'dovich effect is still challenging due to the low density and temperature of the WHIM. An alternative is to analyze the galaxy distribution at large scales. Recently, with the availability of large sky area databases such as the Two Degree Field Redshift Survey (2dFRS,  Colless et al. , 2001) , the 2MASS Redshift Survey (2MRS,  Huchra et al. , 2012)  and the Sloan Digital Sky Survey (SDSS,  Albareti et al. , 2017) , the development of accurate structure detection algorithms has become an even more important concern for astronomy.
Another approach presented by  Aragón-Calvo et al. (2010)  makes use of a watershed segmentation techniques to trace the spines of the filaments. Also,  Cautun et al. (2013)  propose an algorithm that takes into account the density, tidal field, velocity divergence and velocity shear of the galaxies, called NEXUS. Other examples are the algorithms by  González & Padilla (2010) , which uses the binding energy for selecting the filament members; and the DisPerSE algorithm, by  Sousbie (2011) , based on the Morse theory -both utilize Delaunay-Voronoi tessellation based on density estimations. On the other hand, several attempts were made to trace the distribution of the real cosmic web using the SDSS database. For example, the algorithm by  Bond et al. (2010) , called "smoothed Hessian major axis filament finder" (SHMAFF), was applied to the SDSS-DR6 after removing the "finger-of-God" (FoG) effect.
The SDSS galaxies The main galaxy sample of SDSS-DR13  (Albareti et al. , 2017)  is a suitable database to search for filamentary structures on the LSS since: (i) it covers a large sky area (14,555 square degrees), containing various MSCC superclusters; (ii) it contains homogeneous photometric and spectroscopic data for galaxies with an astrometric precision of 0. 1 arcsec rms and uncertainty in radial velocities of about 30 km s -1  (Bolton et al. , 2012) ; (iii) it is roughly complete to the magnitude limit of the main galaxy sample (r Pet = 17. 77), which corresponds to an average z ∼ 0. 1, going (inhomogeneously) deeper for data releases after DR7  (Abazajian et al. , 2009) ; (iv) at the limit of our sample, z = 0. 15, the SDSS spectra are complete for galaxies brighter than M r ∼ -21.
SDSS-DR7 joins the SDSS-I/II spectra for one million galaxies and quasars. It has ∼6% incompleteness due to fiber collisions  (Strauss et al. , 2002)  and another ∼7% incompleteness attributed to pipeline misclassification  (Rines et al. , 2007). These spectra are included in the final data release of the SDSS-III  (Alam et al. , 2015). The Baryon Oscillation Spectroscopic Survey (BOSS) is part of the SDSS-III observations and has obtained spectra for another 1. 4 million galaxies. The BOSS observations are divided in two main samples, LOWZ (z < 0. 4) and CMASS (0. 4 < z < 0. 7). The SDSS-DR13  (Albareti et al. , 2017)  includes spectra for more than 2. 6 million galaxies and quasars. Although photometric redshifts are available for SDSS galaxies, for this work we have selected those objects listed on the SpecObj sample with spectroscopic redshifts available (downloaded from the SkyServer web service) and denoting an extragalactic object (that is, galaxies and low-z quasars).
Detection of high density regions We first searched for the high relative density regions, clusters and groups of galaxies (which we will refer generically as galaxy systems), inside the studied superclusters, since these systems are the natural nodes for filaments. This was also necessary for correcting the FoG effect and having the data prepared for the application of the filaments-finding algorithm (see next Section). Furthermore, the detection of galaxy systems allows the identification of new ones possibly not known before (especially poorer galaxy groups), and the improvement of the membership estimation of the superclusters themselves. A description of the algorithm, including the strategy we used for optimizing its parameters using simulated mock volumes, is presented in  Santiago-Bautista et al.
(2018) , for the GAMA spectroscopic survey. The GSyF and GFiF algorithms can be used to search for this kind of structures in different surveys, using spectroscopic or photometric redshifts. We plan to apply them to other galaxy databases, like the ones that are becoming available for the southern celestial hemisphere, and also, to galaxy surveys that reach deeper redshifts. Both algorithms and catalogs can be obtained electronically upon request.
Acknowledgements. I. S. -B. thanks  CONACyT and DAIP  for funding this research. This project was partially financed by DAIP funding  CIIC 205/2019. The authors are grateful to the anonymous referee for the important comments that helped to improve the paper. All the pattern recognition computing, statistics and graphics have been made using MATLAB c. Part of this work was carried out with the computational facility TITAN at the  Institut de Recherche en Astrophysique et Planétologie, Toulouse, France. This work has made use of  NASA's Astrophysics Data System Bibliographic Services. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The  SDSS-III  web site is http://www.
Chronology The chronology for the current analyses (1. 36 to 0 Ma ago) was published previously  (13)  and uses tephrochronological data as first-order tie points and tuning of climate-sensitive proxy data against orbital parameters as second-order tie points. Tie points were cross-evaluated by two paleomagnetic age constraints (base of the Jaramillo sub-Chron and Matuyama/Brunhes boundary).
Indicator time series Grain size analysis was performed on 64-cm-spaced samples following the procedure described previously  (29). Data processing was carried out by using the GRADISTATv8 program  (30). Grain size variations in undisturbed hemipelagic sediment successions can be used to directly infer changes in transport energy, with coarser grain sizes representing higher transport energy levels. Grain size data are available as data S2. Oxygen isotopes ( 18 O) were measured on calcite every 16 cm through zones of higher (>0. 5%) total inorganic carbon (TIC) and on siderite within zones of overall low TIC. The dataset was supplemented with results from the Lini site in Lake Ohrid (core Co1262)  (31). Carbonate species identification, mineralogical assessment, and analyses were carried out as described previously  (32).
 18 O lakewater data are available as data S3. All other indicator time series from Lake Ohrid and surrounding areas [percentages of arboreal pollen excluding Pinus pollen, percentage of pollen from deciduous oaks, TIC concentrations, total organic carbon concentrations, potassium (K) and calcium (Ca) counts from x-ray fluorescence scanning, and relative sedimentary quartz content] were taken from a previously published study of the Lake Ohrid drill core (fig. S3 )  (13). Global indicator time series, that is, Medstack  18 O planktonic Foraminifera isotope ratios  (33)  and LR04 benthic Foraminifera  18 O stack isotope ratios  (34) , were obtained from the literature. Northern Hemisphere summer insolation at the latitude of Lake Ohrid (41° N) was calculated as described previously  (35).
Trends in all univariate local and regional indicator time series (that is, the signal separated from stochastic noise) were recovered by following the modeling protocol for paleoecological time series  (36). Because of the size of the dataset, the "bam" implementation of the recommended generalized additive models (GAM) was applied using the package "mgcv" 1. 8-22  (37)  for the R 3. 6-1 statistical programming language  (38). The autoregressive option accounted for the temporal autocorrelation in the indicators' time series. Because of the multivariate nature of the grain size composition, a redundancy analysis (RDA) was performed with polynomials of time as predictors of isometric logarithmic ratio transformed values by using the R package "composition" 1. 40. 1  (39)  and "vegan" 2. 4.
96 m c. d. , and every 64 cm (around 2000 years) between 406. 96 and 447. 28 m c. d. The higher sampling frequency in the early phase of Lake Ohrid was chosen because this period was environmentally very dynamic  (13, 17). For comparison, we also analyzed 124 samples from the phase before 1. 36 Ma ago (447. 28 to 83. 92 m c. d. ). Sediment samples were acid cleaned as described previously  (42). Residues were mounted with Naphrax (Brunel Microscopes Ltd. ) on glass slides and analyzed by transmitted light microscopy. With a random subset of samples, a rarefaction analysis was performed with the R package "vegan" to determine the number of individuals per slide to be counted for a robust species richness estimation of endemic and nonendemic species (i. e. , 300 individuals). To account for very rare endemic species in the diversification analyses, the remaining valves on the slides were screened for endemic taxa only.
Statistical analysis A total of 201 endemic diatom species have been documented from Lake Ohrid, 169 extant and 32 extinct species (see data S1). Of these, 152 (120 extant and 32 extinct) species were obtained from the DEEP sediment succession. With a retrieval rate of 75. 6%, the fossil record used for the current analyses is representative. It covers all taxonomic groups and lifestyles (benthic and planktonic species; data S1). In addition to the 152 endemic species, 218 nonendemic species were found in the sediment succession, 16 before and 202 after lake formation. The latter information was used for the modeling of diversity dependence and the equilibrium diversity analysis. To take into account the temporal uncertainty of speciation and extinction events due to preservation biases, we used the software package PyRate  (54)  to obtain a set of potential times for the speciation and extinction of each endemic species.
Moreover, we extended the PyRateDES implementation by adding a Gamma-distributed heterogeneity in sampling (mirroring the model we used in the BDS analyses), continuous time-variable dispersal and local extinction rates, and parameter estimation via maximum likelihood (available on https:// github. com/dsilvestro/PyRate). The same shifts in sampling rate as for the endemic species and an exponential relationship of dispersal and extinction rates with time were specified. Using the maximum likelihood estimates as a starting parameter for a Bayesian inference, we were able to determine the uncertainty in dispersal, local extinction, and sampling rates after sampling every 100 of 50,000 MCMC iterations and omitting the first 1000 iterations as burn-in. We then inferred the average nonendemic richness through time by running 1000 stochastic diversity simulations based on the estimated dispersal, local extinction, and sampling parameters, using the R package "simDES" 0.
1  (57). To test whether some taxonomic groups of diatoms are more likely to go extinct or to speciate, the strength and significance of a phylogenetic signal in the binary trait extinct versus extant were calculated. The D-statistic  (18)  indicates a phylogenetic signal if D is significantly smaller than 1 but significantly greater than 0. The hierarchical taxonomic classification of the endemic diatoms and the D implementation of the R package "caper" 1. 0. 1 (58) with 10,000 permutations were used to examine the existence, strength, and significance of the phylogenetic signal in extinction. Moreover, to test whether extinction and speciation through time altered the hierarchical taxonomic structure of the endemic diatom assemblages, the taxonomic diversity of the endemic species assemblage  (59)  was calculated as a richness measure, and mean pairwise distances (60) provide a measure of taxonomic clustering.
Standardized effect sizes of both indices were obtained by comparing the observed values with their expected distribution according to a null model, which was constructed by shuffling the tip labels in the taxonomic tree to realize random assemblages with the same species richness as our empirical dataset. Ten random sets out of the speciation and extinction times inferred by each of the 100 BDS analyses were extracted, and the R package "picante" 1. 7 (61) was used for the index calculation and to obtain 999 realizations of the null model for each of these 10 × 100 sets. Per-lineage speciation () and per-lineage extinction () rates were constructed from the generations of the BDS analyses using equal-area binning (62) with three events per bin. Expected species longevity was calculated as the reciprocal of the per-lineage extinction rate  (19).
We used a relative change in probabilities of first appearance or speciation rate through time from -400 to +400% in steps of 200% to simulate 100 replicates of the missing taxa for each step and added each of these replicates to one random pick of the 300 datasets of speciation and extinction times. We then inferred shifts in speciation and extinction rates with the birth-death model and the colonization-speciation-death model in PyRate and calculated the difference between the age of the shifts inferred from the observed data and the ages obtained from the same data augmented by the simulated appearance of the missing taxa (fig. S5 ). Using the same 3 × 100 random sets of origination and extinction times, we inferred whether species richness and climate and environmental conditions influenced speciation and extinction rates and whether the effects differ among time strata.
The use of the judicious cohesive model is crucial since it can lead to compliance difficulty and wrong results. DCB test as described in  [16]  has been numerically simulated in order to identify values of the interfacial stiffness and strength. A 3D model was therefore built via ABAQUS/Explicit software using an explicit time integration rule. The developed numerical model consists of an assembly of two half-beams of 165 × 25 × 1,7. These beams are meshed with 6000 C3D8R elements separated by a very small distance (0. 001 mm) as cohesive zone. A cohesive behaviour was then introduced as an interaction between the nodes of the two opposite surfaces while guaranteeing the pre-crack length of u " = 40. All the parameters used in the cohesive zone model are summarized in Table  4. The elastic properties of the orthotropic plies were computed from the second step of the multiscale approach detailed in  [16].
• Geometry At the mesoscale, a realistic geometry of the laminate composite plates has been created thanks to Texgen software. This software can highlight the architecture of the yarns embedded in the acrylic matrix (Fig. 4 ). To this end, some tomographic analysis of the laminate as reported by Fig. 1  were analysed. The section shape of fibres tow are considered elliptic with a major diameter and minor diameter of 3. 47 mm and 0. 22 mm respectively (Fig. 5 ). A single layer is reproduced using Texgen and duplicated to obtain a 4-layers laminate samples.