dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the simulation generated a database of 200 heterogeneous strain fields (Figure  3c ). The force resultant from the upper boundary was also extracted. The viscous mechanisms due to the presence of the second term in Equation (  27 ) can be observed as the load relaxes with time (Figure  3d ). It should be noted that the time steps and the duration of the test have been arbitrarily chosen because significant viscoelasticity happens for this time scale and the considered Prony series coefficients. More generally, the time step and the duration of the test define the lower and upper limit of the observable time scale and would be defined accordingly to the test set-up and loading conditions.
In order for the tangent modulus to converge to the analytical values given by Equation (  27 ), the time step ∆t should be chosen such that only a purely elastic behavior happens within the first time interval. An illustrative example using a onedimensional analytical model is given in Appendix D. In practice, this problem is well-known and one must define a time scale for which the viscoelastic behavior can be accurately observed. This time scale is impacted by two factors: the full-field measurement system framerate, and the temperature considered for the test since the time-temperature equivalence states that increasing or lowering the temperature of the sample is equivalent to respectively increase and decrease the lower limit of the  [15, 33]. Parameter K 0 [MPa] G 0 [MPa] ν 0 [ ] E 0 [ If a large dataset was considered (i.
This difference is lower for G since slower relaxations mechanisms are used for the description of this parameter (Table  1 ). The same observation is made for E. For the time interval of [0. 2 s, 20 s] the MAPE displays an error with an order of ±0. 01% for each viscoelastic function. These results validate the proposed methodology, and show that the viscoelastic properties can be directly identified without any a priori parametrization of their time-dependance. Nevertheless, care must be taken for the timescale definition. If fast relaxation mechanisms happen for the considered material, the time resolution should be high enough to capture the viscoelastic properties with high accuracy. It should however be noted that this limitation is not specific to the L-VFM. It is just that the information is not present in the dataset.
This study develops a lightning data assimilation (LDA) scheme for the regional, convection-permitting numerical weather prediction (NWP) model AROME-France. The LDA scheme intends to assimilate total lightning, i. e. , cloud-to-ground (CG) and inter-and intra-cloud (IC), of the future Meteosat Third Generation (MTG) Lightning Imager (LI; MTG-LI). MTG-LI proxy data are created, and flash extent density (FED) fields are derived. An FED forward observation operator (FFO) is trained based on modeled, columnintegrated graupel mass from 24 storm days in 2018. The FFO is successfully verified for 2 independent storm days. With the FFO, the LDA adapts a 1-dimensional Bayesian (1DBay) retrieval followed by a 3-dimensional variational (3DVar) assimilation approach that is currently run operationally in AROME-France for radar reflectivity data.
The 1DBay retrieval derives relative humidity profiles from the background by comparing the FED observations to the FED inferred from the background. Retrieved relative humidity profiles are assimilated as sounding data. The evaluation of the LDA comprises different LDA experiments and four case studies. It is found that all LDA experiments can increase the background integrated water vapor (IWV) in regions where the observed FED exceeds the FED inferred from AROME-France outputs. In addition, IWV can be reduced where spurious FED is modeled. A qualitative analysis of 6 h accumulated rainfall fields reveals that the LDA is capable of locating and initiating some local precipitation fields better than a radar data assimilation (RDA) experiment. However, the LDA also leads to rainfall accumulations that are too high at some locations.
However, the use of WWLLN as a GLM proxy is debatable as WWLLN mainly detects CG flashes, whereas GLM detects total lightning. A 3DVar LDA was applied by Y. Wang et al. (2017)  using the relationship given by  Fierro et al. (2012)  and assimilated pseudo-RH profiles in the form of sounding data. They noted increased forecast skills in general but also that the method still needs improvement to suppress spurious convection. Fierro et al. (2016)  replaced their previous nudging by a 3DVar LDA with 10 min FED. They found that the radar data assimilation (RDA) yielded better forecasts of convective cells during the first 30 min of the forecast, while the LDA gave better storm structures 1 h into the forecast. The combination of both RDA and LDA provided the highest forecast skill. Fierro et al.
FED at 10 km pixel resolution is assimilated using both graupel mass and graupel volume-based observation operators, with positive results on the convection forecasts. The LDA of Chinese satellite Fengyun-4 (FY4) lightning data was realized by  Liu et al. (2020)  through creating pseudo-RH profiles and by  Chen et al. (2020)  through retrieving maximum proxyreflectivity and finally pseudo-reflectivity profiles from the lightning data. The main goal of this work is to develop a 3DVar LDA for MTG-LI data to improve analyses and forecasts, especially in convective situations. Pseudo-observations of the MTG-LI are generated to form the lightning database (Sect. 3). A novel LDA scheme is developed for the regional, operational, convective-scale model AROME-France of Météo-France. Various approaches in which FED is assimilated together with or without radar reflectivity and Doppler wind are compared to assess the added value of the developed LDA.
5 and 8. 3 kA for Meteorage and NLDN, respectively). In all cases, flashes with a large extent, long duration, or a high number of ISS-LIS events or LF strokes and/or pulses were more likely detected by both LLSs than the small, shortduration flashes. Overall, Meteorage was validated as a suit-able input to the GEO lightning pseudo-observation generator. Pseudo-observations of MTG-LI are then generated on a regular latitude-longitude grid with an average pixel resolution of 7 km, which approximates the expected MTG-LI resolution over France  (Bartolomeo Viticchiè, personal communication, 2020). FEDs are calculated per 5 min interval that can then be summed as needed for the assimilation. This work uses a 10 min interval of FED data in the LDA that is centered at the time of the analysis (as done by  Fierro et al.
Our observation operator uses a simple linear regression between observed FED and simulated column graupel mass. It is unbiased by definition. Our approach optimizes both the slope factor and the y intercept of the regression, whereas, e. g. ,  McCaul et al. (2009)  only used a proportionality between FED and a proxy. Analyzing the training data, the linear relationship fits the majority of the data well. A discrepancy is identified for the largest graupel mass and FED values (Fig. 3 ). Here, the observation operator tends to underestimate the FED for a given graupel mass. The high values (of both FED and graupel mass) are rare relative to the lower values as indicated by the colored pixels in Fig. 3a. Hence, this observed discrepancy has little effect on the Pearson correlation coefficient.
It should still be considered that high FED values are systematically underestimated. It is further noted that the y intercept is negative, meaning that statistically a certain mass of graupel is required to initiate lightning. This result is well in accordance with the widely accepted non-inductive charging as the main electrification process in extratropical storms. The validation data (Fig. 3b ) roughly follow the regression line inferred from the training data. One can see, however, that the slope is smaller than that of the black regression line; i. e. , observed values of FED are always lower for a given m g than the training data imply. The number of FED-m g pairs is significantly lower for the 48 h validation case than for the 5 The 1DBay plus 3DVar assimilation method FED cannot be assimilated directly since lightning is not predicted by AROME-France.
The previously described observation operator relates the FED observations to the prognostic variables of the NWP model and allows for comparing background and observations. The final step prior to the assimilation creates pseudo-observations that can be ingested in the 3DVar system. Unlike  Fierro et al. (2019) ,  Hu et al. (2020) , and others who used an empirical method to adjust moisture in thunderstorms, the expected water vapor PO is retrieved for each model grid column (applying Bayes' theorem) in a 1DBay approach. This allows us to (i) replace the humidity field in spurious convection areas with that of their convection-free environment and thus in principle abolish the wet bias that results from data assimilation techniques that only consider the occurrence of lightning (e. g. ,  Fierro et al.
Equations (  1 ) and (  2 ) can be used with different model variables or diagnostics; e. g. , the sensitivity test for σ o as described in the following used x FED i rather than x RH i. Each RH PO is the best estimation from a weighted linear combination of RH profiles taken from the model background in the vicinity of the FED observation. The method was proposed by  Caumont et al. (2010) , used in the operational model AROME-France  (Wattrelot et al. , 2014) , and applied by  Borderies et al. (2019)  for radar reflectivity data and  Duruisseau et al. (2019)  for microwave radiances. Here, weights are calculated based on differences between observed and simulated FED. The 1DBay retrieves the best estimate of RH at the center of each observed FED pixel from the background using Bayes' theorem.
It is expected that the model can predict a quantity similar to the observations within a certain area in most cases. The vicinity is initially fixed to a square of 160 km × 160 km centered at the observation. This size follows the suggestion of  Borderies et al. (2018 Borderies et al. ( , 2019)). However, the FED pixels (about 7 km) are significantly larger than the radar reflectivity pixels (about 1 km), and a given vicinity contains less FED than radar data points to retrieve the expected profile. Hence, the vicinity is adapted to the specifications of the FED data. The 1DBay vicinities of 160, 320, and 500 km were tested regarding the RMSE between observed FED and retrieved FED (not shown). It was found that larger vicinities give lower overall RMSE as there are more background profiles to feed the retrieval and to find grid points that are similar to the FED observation.
In consequence, all LDA experiments presented in the following use a 1DBay vicinity of 500 km. The database comprises model forecasts at the observation time and in the vicinity of the observation location. Since FED has no vertical dimension, the covariance matrix is reduced to a single value of variance, σ 2 o , which is assumed to be constant. A small σ o means that the retrieval favors columns with values close to the FED observation. This can produce accurate retrievals; however, no retrieval can be created when the difference between all simulated FEDs in the vicinity and the observed FED is large relative to σ o (see Eq. 2 where weights approach 0 in that case). Large values of σ o cause smoothing over all grid points in the vicinity of the observation. In that case, the likelihood to retrieve POs is high, at the cost of a less accurate retrieval potentially independent of the observation.
Conclusions The objective of this work is to design an assimilation technique for the upcoming MTG-LI data for the regional, convection-permitting model AROME-France. To date, AROME-France applies a 3DVar assimilation system. A tailored 1DBay plus 3DVar assimilation technique  (Caumont et al. , 2010)  is used to assimilate pseudo-MTG-LI flash extent density (FED) in AROME-France. A similar assimilation technique is currently used operationally for radar reflectivity data assimilation in AROME-France but has not yet been tested for LDA. This work first generated MTG-LI data that are used to create the FED observations  (Erdmann et al. , 2022). Then, an observation operator for FED is developed based on a linear, climatological relationship between observed FED and the column-integrated AROME-France graupel mass, m g , above the -5 • C isotherm.
The noCloud filter is a first approach trying to overcome this issue by keeping the background humidity constant when lightning was correctly simulated but not observed. The authors encourage further research on how the specifications of FED data can best be addressed by the LDA scheme, i. e. , through correlations between lightning locations and cloud cover. In addition, this study found that the thinning of the FED observations (on a grid of resolution of 7 km) was necessary to avoid the effect of correlations between the observation errors that violate the assumptions of a diagonal observation error matrix in the AROME-France 3DVar data assimilation system (not shown). Finally, forecasts of 6 h rain accumulations are evaluated through FSS analysis. The developed LDA scheme can in general compete with the established RDA, an encouraging result for the further testing and development of the LDA in AROME-France.
Positive effects on the forecast of rainfall by both RDA and LDA are found mainly for the highprecipitation threshold and during the first 9 to 12 h of the forecast. Longer forecast times show a small spread in FSSs between the control run, RDA, and LDA for the three RA thresholds, indicating that the assimilated radar and FED data do not significantly affect the model forecast after 12 h. The FSSs of the combination of RDA and LDA indicate in most cases skill between the RDA and the LDA. The combined RDA-LDA approach provides the best trade-off in a general sense and with respect to the assimilated observations. The case of 2 February 2018 was unique in that neither RDA nor LDA improved the FSS of the control run. This case was significantly influenced by weather phenomena over the Atlantic Ocean, with limited radar and Meteorage coverage.
Real MTG-LI data will provide more accurate lightning records over the Atlantic than the synthetic MTG-LI data based on Meteorage observations used in this study, and the LDA performance might improve in such situations. It could be further studied whether the radar reflectivity and LDA can be coupled. A coupled approach may help to overcome some issues explained for zero FED at cloudy locations in general or at least for precipitating clouds and, at the same time, benefit from the successful promotion and suppression of convection by both RDA and LDA. As the LDA explored here is based on satellite observations, such a coupled approach will also benefit forecasts over regions with limited radar coverage. The specific improvement over regions such as oceans and mountain ranges has not been studied here since our pseudo-MTG-LI observations are based on ground-based Meteorage records.
Acknowledgements. This work was a part of  Felix Erdmann 's PhD and as such was funded by  CNES  and  Météo-France. The authors thank two anonymous reviewers for their valuable feedback that helped to improve the article. The authors also thank  Laurent Labatut  and the  Météo-France IT department  for providing remote access to the  Météo-France  servers that was needed for the revision and creating the new figures.
Code availability. Python 3. 4 coding was used, with standard libraries and Matplotlib for the figures. The code was mainly developed during Felix Erdmann's PhD and as such is the property of the funders CNES and Météo-France. Python code that is subject to active research and further studies cannot be made available. Parts of the code (Python scripts) to create the figures are available from the corresponding author upon request. Data availability. This work uses pseudo-MTG-LI data that are generated as described in  Erdmann et al. (2022). These data are not publicly available as they are created based on commercial Météorage lightning observations. Météorage data are provided by and are the property of Météorage as a company. AROME model fields are available in real time at https://donneespubliques.
Events of interest are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors, selects events at a rate of around 100 kHz  [40]. The second level, known as the high-level trigger, further reduces the event rate to around 1 kHz before data storage  [41]. W ± q q 0 n `± W ± q 0 q n `± q q n n q 0 W ± W ± Z `± W ± W ± `± q 0 q q n q 0 q 0 n W ± `± q 0 g q 0 W ± `± Charged leptons are required to originate from the primary vertex (PV) to avoid contributions from additional pp interactions in the same and nearby bunch crossings (pileup). Electrons are identified using a multivariate analysis (MVA) discriminant that combines observables sensitive to the matching of charged-particle tracks in the tracker to the energy deposits in the ECAL and the amount of bremsstrahlung photons emitted along their trajectory  [42, 53].
4. 2 (2. 6. 5) generator  [66]  using the FxFx jet merging scheme  [67]  for the 2016 (2017 and 2018) data-taking period. The MADGRAPH5 aMC@NLO generator is also used to simulate Vγ, ttV, and triboson (VVV) production. The POWHEG BOX (v2. 0) code  [68] [69] [70]  is used to simulate SPS W ± W ± , ZZ, and Wγ * production processes. The simulated samples of backgrounds for the 2016 data-taking period use the NNPDF 3. 0  [71, 72]  PDF set whereas the NNPDF 3. 1 set is used for 2017 and 2018. Background simulations and the dShower event generator are interfaced with PYTHIA8 for modeling of the parton showering, hadronization, and underlying event processes, which have similar versions and tunes as the signal process. The CMS detector response is modeled using GEANT4  [73] , and the simulated events are reconstructed with the same algorithms used for the data.
Acknowledgments We congratulate our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the  Worldwide LHC Computing Grid  and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the  LHC , the CMS detector, and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ; CAS, MoST, and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES and CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT and ERDF (Estonia) ;  Academy of Finland, MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3 (France) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIH (Hungary) ;  DAE and DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP and NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE and UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand) ;  PAEC (Pakistan) ;  MES and NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI and PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI and NSTDA (Thailand) ;  TUBITAK and TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC (United Kingdom) ;  DOE  and  NSF (USA).
Girchenko et al. (2012)  studied numerically the interaction of a nonlinear pseudoplastic fluid, with a helical shell. They combined the commercial software FlowVision (finite volumes) and Simulia ABAQUS (finite elements) for showing the  differences between Newtonian and NN flows in terms of stresses on the helical structure. Another study regarding FSI and NN fluids was focused on arterial bypass  (Kabinejadiana and Ghista, 2012) , where the effects of wall elasticity and NN fluid rheology were investigated numerically through the commercial software ANSYS. The experimental study on the rheology and processing of solvent-free core shell ''polymer opals'', given in  Wong et al. (2014) , analyzed an elastic shell grafted to hard colloidal polymer core particles in order to study the optical properties under deformation.
Experimental design Participants completed an 'Apple Gathering' task (Fig. 1 ) previously used to investigate motivated behaviour in patients with Parkinson's and small vessel cerebrovascular disease  30, 31, 33. This was designed in psychtoolbox (psychtoolbox. org) using MATLAB and administered on a mounted lap top  31, 33. Participants were offered monetary reward in return for physical effort which involved squeezing a hand-held force dynamometer (SS25LA, BIOPAC Systems)  33. Reward magnitude was speci ed by the number virtual apples on a tree (1,4,7,10, or 13), and effort by the height of a yellow bar on the tree trunk (Fig. 1A )  33. The force required was derived from each participant's own maximum voluntary contraction (10,36,48,64, or 80%), computed in a 'calibration phase'. During the experiment, offers could be accepted/rejected by pressing the left/right buttons on the lap top keyboard.
Thirdly, as they used pooled questionnaire data from previous studies, negative symptoms were measured using three different questionnaires. Two of these, the scale for the assessment of negative symptoms (SANS)  55  and the negative component of the PANSS  49  , were developed more than thirty years ago and do not re ect the current understanding of the negative syndrome  9, 56. Nevertheless, the authors were also able to demonstrate this association in a sub-group of their patients using a 'nextgeneration' negative symptom questionnaire known as the Clinical Assessment Interview for Negative Symptoms (CAINS)  29. A recent comparison between this and the BNSS questionnaire, which we used, showed that while both demonstrated good psychometric properties, there was low to moderate convergence between the two questionnaires on motivation and pleasure questions  57.
The major elements were determined by Thermo iCap6500 ICP-OES. The analytical precision of the studied samples was better than 10%. The REE concentrations were normalized relative to the Post Archean Australian Shale (PAAS) values  (Taylor and McLennan 1985). La, Ce, and Eu anomalies were calculated as geo metric aver ages  (Lawrence et al. 2006). The whole-rock mineralogy was obtained from powdered, unoriented samples by means of X-ray diffraction (XRD). The XRD patterns were measured using a Bruker D8 Advance diffractometer in the range of 2-70° 2θ. The quantitative mineralogical compositions were interpreted and model ed using the Rietveld algorithm-based program Topas 4 by Bruker. The relative error for major mineral components (> 5 wt%) was ~10%, and ~20% for minor mineral components (< 5 wt%).
Instead,  Algeo and Li (2020)  and  Bennett and Canfield (2020)  have suggested that only Mo, U, Re as well as transition metal V are the meaningful elements for redox interpretation analysis with reasonably well differentiated redox thresholds for distinct redox conditions and depositional environments. For the analyzed dataset, it is evident that the hydrogenous redox proxies (e. g. Ce anomaly) cannot be assessed even in the purest carbonate samples with the calcite or dolomite content of > 95 wt%, where the trace element and REE signature is carried in the terrigenous fraction of the sediment. This is due to low REE concentrations in the carbonate phases masked by detrital phases and/or overprinting due to diagenetic processes (e. g. LREE recycling from the dissolution of Fe-Mn oxyhydroxides), which significantly mutes the seawater signatures  (Caetano-Filho et al.