dataesr/scientific-paragraphs-categorization huggingface dataset:
Sun Photometer Dataset. In addition to the Lidar dataset, the Sun Photometer measurements performed with the CE-318 Cimel Sun Photometer on Lille observation platform were used for analyzing the atmosphere and aerosol. This instrument was integrated into the Aerosol Robotic Network (AERONET) and maintained by Lille University. The standard CE-318 Cimel Sun Photometer consists of bands with center wavelengths of 340, 380, 440, 500, 675, 870, 936, 1020, and 1640 nm  [30]. The Aerosol Optical Depth (AOD) and Ångström exponent can be obtained from the ground-based measurements of direct solar irradiance. For consistency purposes with the Lidar wavelength, AOD were interpolated at 532 nm from 440 and 870 nm channels.
Determination of PBLH by Ground-Based Lidar. In order to validate the PBLH results derived from space-based Lidar, the PBLH results obtained from ground-based Lille Lidar station were used. Although the detection techniques for determining the PBLH by ground-based Lidar are already mature, several important instrumental aspects, such as nonlinearity of the detectors and the overlap function, still need to be considered. Besides, the attenuated backscattered signals were averaged over a time period of 20 minutes and were normalized for data preprocessing. In addition, spectral filtering which allows reducing the noise associated with high frequencies vertical variation was applied to the measurements. Clouds conditions were also removed to the PBLH retrieval thanks to filtering based on the vertical variability of the backscatter profiles.
The required thresholds are determined from climatologic values, the whole Lille database covering more than 10 years of continuous measurements. The description of the cloud detection method applied on the profiles of backscatter was based on the work of Mortier et al. [35]. Consequently, two different layer heights (i. e. , the PBL height and top layer (TL) height) were detected for each profile. The TL refers to the top of the highest aerosol layer if the related vertical gradient is significant enough. This latter retrieval is of particular interest when aerosol layers are transported above the PBL. The principle of the detection method is available in Section 3. 1. 1, and more details can be found in the work of Mortier  [36]. Besides, the detection of TL height was based on Léon et al.
Conclusions In this paper, an improved maximum variance method was developed, and an integrated algorithm by considering both advantages of the wavelet covariance transform and the improved maximum variance method was developed, which  aims to obtain the reliable PBLHs from the CALIOP observations. Besides, an aerosol fraction threshold was applied to the integrated algorithm considering the applicability of these two methods, in order to solve the problem of determining PBLH under different aerosol conditions (e. g. , weak aerosol layer or another aerosol layer exists). Due to the effects of clouds (especially the thick clouds) on the backscattered signals acquired from space-borne CALIOP, the cloud elimination based on VFM data was adopted. In addition, a height threshold was given to identify the aloft aerosol layer.
The validation of PBLH estimated by CALIOP has been performed using nine years of synchronous observations based on the data from Lille Lidar site. The main conclusions are shown as follows: (1) An integrated algorithm by considering both advantages of the wavelet covariance transform and the improved maximum variance method was developed, in order to solve the problem of determining PBLH under different aerosol conditions (e. g. , weak aerosol layer or another aerosol layer exists). PBLHs derived by CALIOP and ground-based Lidar show good agreement ( ≥ 0. 79) under clear sky conditions. The mean absolute differences of PBLHs are, respectively, of 206 m and 106 m before and after the removal of the aloft aerosol layer. According to the analysis above, it is implied that the integrated algorithm can effectively estimate the PBLH under clear sky conditions.
Acknowledgments This work was supported by the  National High Technology Research and Development Program of China  (no. 2011AA12A104-2 ), the  STS Project of Chinese Academy of Sciences , the  Instrument Developing Project of the Chinese Academy of Sciences  (no. YZ201664 ), and the  National Natural Science Foundation of China  (no. 41671367 ). The authors would like to thank the whole  CALIPSO  science team for providing CALIOP data, and they are very grateful to AERONET and PHOTONS for providing AOD and Ångström exponent data.
25 × 10 -2. (49) Figure  3:  A plot of the scalar potential given by eq. (  43 ) for the parameters in eqs. (48). It is important to emphasise that neither the choice of function F in eq. (  44 ), nor the choice for the particular values of the parameters is unique. Different choices of F , or even different choices for the parameters α, ξ and B can lead to similar CMB results as the ones presented in Table  1. Figure  5  shows that our predictions for n s and r are within 2σ C. L. of Planck '15 contours with the number of e-folds N = 57. 98. On the other hand, the gravitino mass is given by m 3/2 = 5. 96 × 10 7 TeV and the Hubble scale is 6. 80×10 8 TeV. No other parameter set consistent with the CMB data were found with a Hubble scale below 10 8 TeV. Moreover, the tensor-to-scalar ratio satisfies r > 10 -6.
Acknowledgements This work is supported in part by  Franco-Thai Cooperation Program in Higher Education and Research 'PHE SIAM 2016 ', in part by the "CUniverse" research promotion project by  Chulalongkorn University  (grant reference CUAASC). H. I. would like to thank  Toshifumi Noumi  for important suggestions. The authors would like to thank  C. Pongkitivanichkul  for stimulating discussions concerning Section 5.
Several instruments were placed along the mooring line. A MS5-Hydrolab sensor (OTT Hydromet, Kempten, Germany) providing temperature and salinity measurements (at 60 min intervals) was fixed 1. 50 m below the sea surface. An upward looking acoustic Doppler profiler (Acoustic Wave And Current profiler, 1000 KHz, Nortek AS, Vangkroken 2, Norway; hereafter referred to as AWAC) was fixed on a structure placed near the seabed to record the backscattered acoustic intensity (at 30 min intervals), the intensity and direction of the currents (1 min of time-integrated data at 30 min intervals), water elevation (at 30 min intervals), as well as wave height, period, and direction (at 60 min intervals). The return echo was sampled over the entire water column with 25 cm-thick cells. In this configuration, the first sampling cell of the AWAC profiler was located 1.
Forcing Source Initial & boundary conditions (3D velocities, temperature, salinity) GLORYS global ocean reanalysis  [34]  Wave (Significant height, peak period, bottom excursion and orbital velocities) WaveWatch III hindcast  [35]  Meteorological conditions (Atmospheric pressure, wind, temperature, relative humidity, cloud cover) ARPEGE model  [36]  Tide (14 components) FES2004 solution  [37]  River discharge (flow and SSC) Daily runoff data (French freshwater office) Regarding wave forcing, a wave hindcast database built with the WaveWatchIII (WWIII) model (realistic and validated configuration of Boudière et al. [35] ) enabled the computation of bottom wave-induced shear stresses (τ w in N•m -2 ). The wave-induced shear stress was computed according to the formulation of Jonsson  [38] , with a wave-induced friction factor determined following Soulsby et al.
The latter can be written as: C bulk = C rel mud 1 + f s • C rel mud ρ s -1 (5) For large sand fraction (above 90%), the given relative concentration of 550 kg•m -3 becomes unlikely, so that C bulk is maximised by C max , which corresponds to a dense sediment (C sort or C mix depending on the properties of the sediment mixture, i. e. , well-sorted or mixed sediments). This formulation could be validated by comparing with recent data in two sectors of the BoBCS  [48] : bulk densities of 1540 kg•m -3 and 1380 kg•m -3 were obtained for 85% and 75% of sand, respectively, while the application of Equation (  5 ) with sand fractions f s of 0. 85 and 0. 75 led to C bulk values of 1668 kg•m -3 and 1346 kg•m -3 in fair agreement. Sediment concentrations linked to each particle class were then deduced according to their respective fraction.
This review analyses the currently available (Q)SAR models for regulatory assessment of nanomaterials risks. The survey first analyzes and discusses the regulatory relevance and reliability of models within the context of the 5 OECD (Q)SAR validation criteria. Then, the information required to apply the models is outlined, focusing especially on descriptor development. The most important mechanisms of toxicity, knowledge gaps that impede the development of structure-activity models, as well as the domains where (Q)SAR models are expected to convey fundamental contributions, such as in tiered testing schemes and analysis of functional assays' results, are also discussed. It is concluded that the development of (Q)SAR models for nanomaterials requires both the development of nano-specific descriptors and curated experimental datasets.
In the field of toxicology, (Q)SAR methods are typically applied towards the more elusive goal of predicting potential toxicity outcomes for in vitro cell cultures or in vivo animal test systems, where the toxicity endpoint (e. g. , mutagenicity, developmental toxicity, cancer) tends to be less well understood, and is likely to encompass multiple mechanisms and pathways to adverse outcome, i. e. , where no single interaction mechanism is known or anticipated  (Cherkasov et al. , 2014). For conventional chemicals, it is generally accepted that (Q)SAR success in modelling toxicity is more likely when one or more of the following conditions are met: (i) compounds within the training set are structurally similar (i. e. , congeneric); (ii) the toxicity endpoint being modelled is either non-target specific or subject to relatively well-understood chemical reactivity principles; (iii) the toxicity endpoint is linked to a well-defined molecular target; or (iv) toxicity data are available for a sufficiently large number of diverse chemicals to capture all or most of the possible structure-activity associations, representing multiple possible adverse outcome pathways (AOP) within the same dataset  (Cherkasov et al.
, 2014). Hence, a defining challenge for (Q)SAR applied to toxicology is that of balancing the highest possible endpoint resolution with the need for sufficient statistical representation. An area of active (Q)SAR expansion is in the use of predictive models for regulatory purposes by government agencies, where a growing number of specialized regulatory tools and databases are being developed and validated (U. S. EPA, 2015; JRC, 2010). The U. S. Food and Drug Administration (FDA) has expressed interest in several SAR approaches for food contact chemicals; they include DEREK,  MultiC-ASE, and OncoLogic (FDA, 2015). The REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) regulation, in Annex XI, par. 1. 3, lists the following conditions for application of (Q)SARs instead of experimental testing  (ECHA, 2008) : (i) "Results are derived from a (Q)SAR model whose scientific validity has been established; (ii) the substance falls within the applicability domain of the (Q)SAR model; (iii) results are adequate for the purpose of classification and labelling and/or risk assessment, and (iii) adequate and reliable documentation of the applied method is provided.
Defined endpoint Some studies analysed in this report focus on the development of (Q)SAR models for human toxicity, others are based on ecotoxicity data -although E. coli is a faecal bacterium and not an organism which plays a role for determining ecotoxicity (see Table  1 ). While only one study focusses on in vivo endpoints  (Burello, 2017) , the rest of the literature concentrates on in vitro effects. All studies considered are based on defined endpoints: this means that relevant information on how the toxicological tests are performed is described in detail (or references to original work are cited). It is therefore possible to replicate the experimental measurements reported in these studies and/or complement the datasets of tested samples with additional nanomaterials (tested under similar experimental conditions) in order to obtain larger training datasets for modelling.
Unambiguous algorithm Almost all studies report the (Q)SAR regression equations or the classification conditions that characterize the tree structures, and accurately describe the statistical methods employed to obtain the regression equations/classification tree,s as well as the methodology deployed to calculate the descriptors. Some exploratory algorithms (e. g. ,  Fourches et al. , 2010; Chau and Yap, 2012; Liu et al. , 2013a Liu et al. , , 2013b) )  used to understand variation in datasets are inherently ambiguous and cannot be recommended for regulatory applications (e. g. , the architecture of neural network models does not generally correspond in any obvious way with underlying mechanisms of action, OECD, 2007): these regression methods are not transparent and therefore not acceptable for regulatory use.
Defined domain of applicability The majority of (Q)SAR studies employ metal oxides as test compounds. Studies that report the applicability domain of the (Q)SAR models are  Mu et al. , 2016 , Kar et al. , 2014a , 2014b , Jagiello et al. , 2016 , Gajewicz et al. , 2015 , Puzyn et al. , 2011 , and Fourches et al. , 2010 ; the leverage approach is used to determine the model's applicability domain. In all other (Q)SAR models, the applicability domain should be calculated from published raw data of the (Q)SAR models, where possible.
g. , nano-bio interactions or toxicity mechanism (i. e. , at the molecular initiating event). The calculation of descriptors for carbonaceous nanomaterials is less problematic than that for metal and metal oxide structures, because fullerenes, nanotubes, graphene and the like are based on light atoms. In this case, there are numerous codes and programmes based on classical mechanics principles that use parametrized force fields to calculate the potential energy of a system: from this energy minimized structure the desired descriptors are obtained. Some (Q)SAR models listed in Table  1  that consider metal oxides  (Puzyn et al. , 2011; Gajewicz et al. , 2015; Mu et al. , 2016) , require material/molecular modelling software such as MOPAC 2009  (Stewart Computational Chemistry, 2009) , which uses PM6 semi-empirical methods to energy minimize the structure of nanomaterials and calculate their descriptors.
In these cases, it is suggested that, besides the need to compare calculations with measured values, there should be an effort to verify if some codes like, for example, VASP (a computer program for atomic scale materials modelling from first principles,  Kresse, 2010) , are capable of delivering descriptor values which are comparable to the experimental measurement. Although the requirement to verify whether a descriptor is capable of reproducing its corresponding experimental measurement is not included in the five OECD principles for (Q)SAR validation, it is important that descriptors reflect the measured physicochemical characteristics of the nanomaterials. Indeed, the fact whether a descriptor is capable of reproducing an experimental value has profound consequences on the construction and validity of a (Q)SAR model.
Datasets used for the development of (Q)SAR models Datasets which are useful for (Q)SAR modelling should contain a sufficient number of diverse sampled nanomaterials: these two conditions (diversity and sufficiently high number of samples) are currently hard to come by, except when high-throughput screening (HTS) methods are used (e. g. ,  Zhang et al. , 2012; Fourches et al. , 2010; Kaweeteerawat et al. , 2015). These three studies provide enough data for (Q)SAR modelling and read-across. In principle, one could collect information from different experimental studies to obtain larger groups of sampled nanomaterials; however, this is possible only if the test conditions are the same across different studies. Currently the standardization of test conditions for nanomaterials is still under development, so large datasets are still rare.
Recently, efforts from EU projects such as eNANOMAPPER PROJECT, 2006 and NANOPUZZLES PROJECT, 2013 have been focussing on data curation, collection and ontology definition for the development of databases that store information on the physicochemical characteristics (both intrinsic and extrinsic properties) and endpoint measurements of nanomaterials. Some of this information is publicly available, but other sources have still restricted access; the aim of these projects is to create large databases of nanomaterials which contain curated information that will allow read-across and (Q)SAR modelling. In this way, by looking at larger datasets, the (Q)SAR models developed in the future will be more robust.
Mechanisms of toxicity The majority of the published (Q)SAR studies is currently focusing on the same datasets and therefore the same in vitro endpoints, namely metal oxides and oxidative stress  (Burello and Worth, 2011a , 2011b , 2015 , Zhang et al. , 2012 , Gajewicz et al. , 2015 , Mu et al. , 2016). In these studies, bioreactivity and ROS generation are the dominant mechanisms of action that emerge from the (Q)SAR models (see Fig. 4  and  Auffan et al. , 2009 , Burello and Worth, 2011a , 2011b). For these nanomaterial classes, redox potentials, energy band levels and dissolution emerge as the most relevant properties to account for bioreactivity, ROS generation, oxidative stress and finally inflammation. In this context, it seems that particle reactivity (of metal oxides) can be predicted using the conduction band energy level (Ec), whereas dissolution (of metal oxides) can be represented by the formal charge of the cation (Z) 1  or by the enthalpy of formation of a gaseous cation  (Puzyn et al.
, 2013 and in Groso et al. , 2016). In addition, since other (nano)materials such as quantum dots or perovskites are semiconductors, it is expected that also for this class of nanomaterials the Ec descriptor, and the valence band energy level, could be potentially useful in describing bioreactivity and functionality. The study from  Fourches et al. (2010)  analyses surface properties of nanoparticles that affect cellular uptake, as an important contributor to the overall toxicity. In this study, the nanomaterials are metal oxide nanoparticles with the same core material but different surface coatings; the relevant descriptors playing a role in the uptake process are related to the hydrophobicity of the coating materials. Other studies employing the same dataset of  Fourches et al. (2010)  but different descriptors and model algorithms (see Table  1 ) reach analogous conclusions.
g. for the development of QSPR models to predict the PZC (point of zero charge) of metal oxides see  Burello, 2015). Also, reliable experimental data on properties like wettability, which is expected to play a significant role in nano-bio interactions and fate, is currently lacking. Another major knowledge gap is the lack of curated datasets that can be readily used to develop the models. In this sense, there is a need for data obtained from standardized testing protocols and complemented with metadata. In addition, the ongoing development of reporting formats and ontologies is highly required to enable interoperability. Repositories with well-characterized nanomaterials will help to get reliable and comparable results. Another important topic concerns the modelling of the nano-bio interactions, which includes all the interactions of nanomaterials with different components of a cell (e.
Conclusions Overall, given that the majority of the (Q)SAR models here analysed do not fully comply with all the five OECD validation principles and/or do not consider relevant endpoints, the use of these models for regulatory purposes can be proposed, at the moment, only in a weight of evidence approach and therefore in conjunction with other in vitro and in vivo information. To improve this condition, (Q)SAR modellers need good datasets to develop their models. These datasets should be ideally collected from experiments performed under standardized conditions and should contain metadata as well. One crucial knowledge gap is the lack of nano-specific descriptors (e. g. , description accounting for surface properties): currently, this information is difficult to obtain due to the size, structural complexity and lack of experimental measurements of nanomaterials.
Computational methodologies are an essential tool, because ultimately we want to use in silico approaches to complement and substitute the experiments. The (Q)SAR techniques can be applied for readacross, for analyzing the outcome of functional assays (e. g. with or without cells and possibly in high-throughput screening methods), for outlining Adverse Outcome Pathways and Modes of Action and for supporting the design of safe nanomaterials during the R & D phase (i. e. , by combining information on functionality and safety/toxicity). In order to become fully operational in these areas, the (Q)SAR methodology requires more theoretical insights and stronger collaborations between experimentalists and modellers to yield useful datasets.
Event selection Collisions at three different center of mass energies (0. 9, 7, and 8 TeV) are analyzed. The data used for the analysis were collected at low beam currents and low pileup during three data taking periods: the 0. 9 and 7 TeV samples were acquired in 2010, while the 8 TeV sample was collected in 2012. The last sample is the most affected by the pileup contamination. For this reason we selected few specific runs with low contamination from pileup events for this energy, and used data taken with interaction rate not exceeding 1 kHz. A pileup is defined as more than one collision occurring during the readout time of the detector (300 ns, for the SPD, and 2 µs sampling time, for the FMD). Such events produce a bias towards larger multiplicity that enhance mostly the tail of the multiplicity distribution.
The response of the detector is determined by the matrix R mt , which corresponds to the probability that an event with true multiplicity t and measured multiplicity m occurs. This matrix is obtained using PYTHIA ATLAS-CSC flat tune  [21]  simulations in which the generated particles are transported through the experimental setup using the GEANT3  [22]  software package. The same reconstruction algorithm is used for simulations of real data. Experimental conditions and detector settings at the time of data-taking at a center-of-mass energy of √ s = 0. 9, 7, and 8 TeV are simulated when evaluating the response matrices. Figure  1  shows two different response matrices for different pseudorapidity ranges. The left panel of Fig. 1  shows the response matrix obtained for the |η| < 2. 0. In this range, the unfolding increases the multiplicity on average because of the acceptance gaps in the SPD.
9 TeV (left plot) and 7 TeV (right plot). At 0. 9 TeV, PHOJET, PYTHIA 8 Monash tune and EPOS LHC cannot reproduce the tails, and the lowest values of the multiplicity distributions, while PYTHIA 6 Perugia 0 tune does not reproduce the data at all. At 7 TeV, both PHOJET and PYTHIA 6 strongly underestimate the tails of the multiplicity distributions. PYTHIA 8, with the Monash tune that uses LHC data, reproduces the tails for the wider pseudorapidity range, but shows an enhancement in the peak region. EPOS LHC models the distributions well, both in the first bins, which are dominated by diffractive events, and in the tails. The evolution of the multiplicity distributions with the center-of-mass energy √ s can be studied using the KNO variable N ch / N ch  [27]. KNO scaling violation is observed if the tails of the distributions increase with increasing energy.