dataesr/scientific-paragraphs-categorization huggingface dataset:
This study aimed to complement the cadaveric studies  [25, 26]  by developing a subjectspecific musculoskeletal computational model based on one of the cadaveric specimens. Kinematic data for the intact and ACL-sectioned cadaveric knee were used for validation. This model was then modified to computationally assess the impact of the different reconstructions applied during the cadaveric study on the knee kinematics and ligament strains. We hypothesised that our computational model would: accurately capture the rotations and translations of the tibiofemoral joint for passive knee flexion; approximate the strain present in the knee ligaments throughout these movements; and correlate well with the results of the previous cadaveric study  [26]. Replicating these passive knee outcomes should provide an important step toward developing a computational model which can be used as a clinical tool for planning and optimising patient-specific augmented ACLR procedures.
Discussion This musculoskeletal modelling study developed an OpenSim model of the intact knee consisting of subject-specific tibiofemoral contact surfaces and 19 ligament bundles. The predicted FD model kinematics were validated against experimental IK results for the matching cadaveric knee in intact and ACL injured states. Following validation, the reconstructed knee model was created by modifying the intact model to reflect the reconstruction grafts. FD was used to calculate the knee kinematics and ligament strains, which were compared to the cadaveric study data to determine which procedure best restored the knee to intact kinematics. The results indicate that anterolateral augmentation moves the kinematics closer to the intact knee state; combined ACLR and ALLR provides the best outcome for this specimen.
Experimental data This study utilised data from a previous controlled laboratory experiment that assessed the knee kinematics and lateral compartment pressures of ten lower extremities cadaveric specimens  [25, 26]. For all knee states, knee kinematics and contact pressures were acquired for three cycles of passive knee flexion/extension combined with 5 Nm of internal rotation (IR) torque  [25, 26]  applied manually to the tibia. The test was repeated with 5 Nm of external rotation (ER)  [25, 26]. To track the knee kinematics, the limb was prepared with four bi-cortical pins (two for the femur and two for the tibia) with two retro-reflective markers fixed to each pin using a validated protocol  [62]. Knee kinematics were acquired using a 3D optoelectronic motion capturing system consisting of five Bonita cameras on tripods (Vicon, LA, USA).
Knee model development An intact, subject-specific musculoskeletal knee model consisting of a six-DoF tibiofemoral joint, a three-DoF patellofemoral joint, ligaments and capsular bundles was created in OpenSim (R3. 3, Simtk, USA)  [63]  (Fig. 6 ). The intact knee model was initially adapted from Xu et al. 's  [64]  and Schmitz et al. 's  [41]  models and then further developed by incorporating subject-specific contact surfaces (Fig. 6 ). Three-dimensional specimen-specific bone and cartilage geometries were reconstructed through the segmentation of CT and MR images. The contact surfaces were further amended using Fusion 360 software (R2020, Autodesk, USA) to guarantee medial and lateral contact over the entire natural knee rotation and match the experimentally measured kinematics for the intact and ACL-sectioned models  [26].
Data analysis For validation, the FD model outputs of a single cadaveric specimen were compared to the IK results for that same specimen. The model outputs were discretised using MAT-LAB into 1° increments for easier pointwise comparison. Since the number of specimens involved in the validation process was n = 1, a normalised root mean square error (NRMSE) criterion was used to compare FD results with corresponding IK results to address our hypothesis that the model accurately represents passive knee motions and ligament strains. RMSE was normalised by the range value observed for each variable. NRMSE is given by the following equation  [32] : where o is the observed value of the parameter, p is the predicted value according to the model, and N is the total number of observations in the validation dataset.
Commonly, an NRMSE of less than 10% is considered acceptable  [31, 32]. However, this criterion can vary depending on the application  [84]. In this study, cadaveric data are compared with a computational model very sensitive to various modelling parameters, i. e. contact surfaces, ligament material properties, slack lengths, and insertion points. As these parameters are determined in an ad hoc manner in this study, we consider NRMSE values < 0. 3 (30%) to indicate acceptable performance  [85, 86]. Adopting this lenient criterion suggests the potential for a full validation under stricter criteria in future studies which will use computational methods to drive these sensitive parameters towards an optimal solution. This was beyond the scope of this study. The ranges of FD tibiofemoral kinematics and ligament strains of our musculoskeletal models were compared to their corresponding IK results to assess the remaining reconstructed knee model outcomes.
We calculate total and differential cross sections for the pair production, at the Large Hadron Collider, of exotic leptons that could emerge from models with vectorlike leptons and in type-III seesaw scenarios. Our predictions include next-to-leading-order QCD corrections, and we subsequently match them with either parton showers, or threshold resummation at the next-to-next-to-leading logarithmic accuracy. Our results show an important increase of the cross sections relative to the leading-order predictions, exhibit a distortion of the shapes for various differential distributions, and feature a significant reduction of the scale uncertainties. Our predictions have been obtained from new FeynRules model implementations and associated Universal FeynRules Output (UFO) model libraries. This completes the set of next-to-leading-order implementations of new physics models featuring extra leptons that are publicly available on the FeynRules model database.
In order to allow for phenomenological studies of the model, we implement it in the FeynRules package  [63, 64] , starting from the SM implementation that is shipped with the program. We include the definitions of the gauge eigenstates of (1), together with the corresponding mass eigenstates appearing in the Lagrangian (2). Information on these fields, their names in the FeynRules conventions, and the Particle Data Group (PDG) identifiers that we have adopted for the physical fields, are provided in Tables I and II. These tables also include the symbols associated with the mass and width of the physical fields. All BSM couplings appearing in (2) have been implemented as threevectors in the flavor space, following the convention of Table  III. This table also includes information on the Les Houches block structure used to organize all model external parameters  [65] , as required by all high-energy physics programs relying on FeynRules for model implementation.
The latter can be defined from the neutrino oscillation parameters, namely the neutrino mixing angles θ ij (with i, j ¼ 1, 2, 3), the Dirac CP-violating phase φ CP and the two Majorana CP-violating phases φ 1 and φ 2. We implement the type-III model described above in the FeynRules package  [63, 64]  following the same method that has been used for the type II seesaw implementation  [70]. We begin with the implementation of the SM shipped with FeynRules, from which all lepton definitions and related Lagrangian terms have been modified. In practice, we have modified all lepton and neutrino definitions so that two-component left-handed Weyl fermions  [71]  are used instead for the gauge eigenstates of the model (E c R , L L ). Next, we add definitions for the fermionic triplets Σ k , still using left-handed Weyl fermions, and incorporate the mixing relations  (6)  for the definition of the four physical charged lepton states and the four physical neutrino states in terms of all gauge eigenstates.
C. From Lagrangian to events at the LHC In order to handle LHC simulations at NLO-QCD matched with PS, we make use of the two FeynRules model implementations detailed in Secs. II A and II B, and jointly use them with the MOGRE package (version 1. 1)  [72] , NLOCT (version 1. 0. 1) [73]  and FeynArts (version 3. 9)  [74]. This allows us to renormalize the bare Lagrangians (2) and (  5 ) relatively to Oðα s Þ QCD interactions, and generate UFO model files  [55]  including both tree-level interactions, UV counterterms, and the so-called R 2 Feynman rules required for the numerical evaluation of the numerators of one-loop integrands in a four-dimensional spacetime. Such UFO models can subsequently be used with MG5aMC  [56, 57]  for LO and NLO calculations in QCD, as well as by Herwig++  [75]  and Sherpa  [76]  at LO.
Before closing this subsection, we provide information on the MG5aMC framework  [56, 57]  which employ to carry out fixed-order (N)LO and ðNÞLO þ PS calculations. MG5aMC handles infrared singularities inherent to NLO calculations via the FKS method  [77, 78] , in an automated way through the MadFKS module  [79, 80]. The evaluation of UV-renormalized one-loop amplitudes is achieved by switching dynamically between several integralreduction techniques that work either at the integrand level (like the OPP method  [81]  or Laurent-series expansion  [82] ) or through tensor-integral reduction  [83] [84] [85]. This has been automated in the MadLoop module  [56, 86] , that exploits the public codes CutTools  [87] , Ninja  [88, 89]  and Collier  [90]. Moreover, one-loop computations have been optimized at the integrand level through an in-house procedure inspired by the idea of OpenLoops  [91].
ACKNOWLEDGMENTS The authors are grateful to  M. Cirelli  and  G. Soyez  for motivating discussions during the course of this project. We acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme  (Grant Agreement No. 824093 ,  STRONG-2020 ,  EU Virtual Access "NLOAccess") , the  European Research Council  ( ERC Grant  Agreement  ID 101041109 , "BOSON"), the  French ANR  (Grants  ANR-20-CE31-0015 , " PrecisOnium" , and  ANR-21-CE31-0013 , "DMwithLLPatLHC"), and the  CNRS IEA  (Grant No. 205210 , "GlueGraph"). FIG. 6. Same as Fig. 5 but for type-III seesaw leptons.
3. 4). In Sect. 2, we present our five targets, along with the data reduction process and the different steps that follow the Iraclis software. Then, we compare our reduced data sets with other reductions in the literature. In Sect. 3, we explain our methodology: the TauREx program and our TauREx set-up. We also describe how we established our different chemistry types and cloud parametrizations. We also talk about the effect of the sodium and potassium contributions. In Sect. 4, we review our results planet-by-planet. Then, in Sect. 5, we discuss the interpretation of the results, the limitations of our current models, and the possibilities for future works. Finally, we draw our conclusions in Sect. 6.
HST WFC3 data reduction We performed a re-analysis of the HST WFC3 data with the Iraclis tool (see  Tsiaras et al. 2016a Tsiaras et al. ,b, 2018)). Raw data are available on the Mikulski Archive for Space Telescopes (MAST). Iraclis is an open source pipeline 1 for data reduction and calibration of HST WFC3 data sets. It follows multiple steps, reduction and calibration of each image; extraction of the 1D spectra from each image; white light curves fitting; and spectral light curves fitting. We consider limb-darkening coefficients as constant because of degeneracies with other transit-shape parameters. These 1 https://github. com/ucl-exoplanets/Iraclis A51, page 2 of 18  Panek, E. , et al. : A&A, 677, A51 (2023)   degeneracies are especially important in our case because of the gaps that periodically occurs in HST data.
Before using Iraclis, we calculated these limb darkening coefficients for each star and in the wavelength band that interests us. We used ExoTETHyS  (Morello et al. 2020) , especially the Stellar Atmosphere Intensity Limb (SAIL) subpackage to account for this limb darkening effect. We choose a stellar model based on the ATLAS grid, and we adopted the Claret 4 coefficients limb-darkening law described in  Claret (2000). After calculating the limb-darkening coefficients, we used Iraclis  (Tsiaras et al. 2018 (Tsiaras et al. , 2016a,b),b). This is a pipeline developed to reduce the HST data of the WFC3 instrument for transiting exoplanets, when the scanning mode of the telescope is used. This method of reduction has been explained in detail in  Tsiaras et al. (2018) ,  Saba et al. (2022) ,  Mugnai et al.
(2021) ,  Guilluy et al. (2021). We propose here to make a short overview of the different steps of this method. First, we use Iraclis to apply the standard steps of data reduction: bias correction, zero-read correction, dark current subtraction, detector gain variation correction, flat field correction, and bad pixel correction, as well as cosmic rays corrections. Once these reduction and calibration steps have been performed, the next step is the extraction of the 1D spectra from each image, which gives the wavelength-dependent light curves. We extracted the white light curve, a broad band of wavelengths that includes most of the observed starlight, and the spectral light curves, with the light decomposed on several narrower wavelength bands. Afterwards we proceed to fit the light curves with a transitshape model using a Markov chain Monte Carlo (MCMC) method implemented in emcee  (Foreman-Mackey et al.
Comparisons with previous works We compared our reductions with data described in the literature to check whether they are consistent. For planets for which there is WFC3 data, we compared with  Sing et al. (2015). In addition, we also made comparisons with  Line et al. (2013) , A51, page 3 of 18    2022 ),  Alderson et al. (2022; WASP-17b) , and  Wakeford et al. (2017; WASP-39b). We obtained small offsets that can be explained by the differences in the reduction process. In the Iraclis pipeline, a sky-background subtraction step was added. It was also demonstrated that such offsets came from a difference in treatment in long term baseline  (Guo et al. 2020; Yip et al. 2020; Changeat et al. 2022). Cosmic ray and bad pixel correction are also calculated differently in Iraclis. For certain planets (WASP-17b, WASP-39b, HAT-P-12b), we had several visits and in consequence several data sets.
In those cases, we calculated a mean for our different radius ratio to have more signal and more data. We still observed an offset between the data sets of different instruments on WASP-6b. There is a gap between the STIS data set from  Sing et al. (2015)  and our own reduction of the WFC3 instrument. There is no WFC3 data for WASP-6b in the  Sing et al. (2015) , so there is no overlap in this wavelength band. To correct this inter-calibration effect between instruments, we did an interpolation on the Sing data and then removed the average difference between the two curves, the interpolation, and our spectrum. This offset is only present for WASP-6b, so we did not apply any correction for the other targets. Even after these corrections, we still noted small differences on our WASP-6b reduction and the reduction presented in  Carter et al.
To avoid inducing errors due to the use of data from several instruments, the analysis detailed in the following parts was carried out on three different data sets: a complete data set, a data A51, page 4 of 18 set from STIS, and a data set from WFC3 for each planet. This allows us to check the consistency of our results. Once we had our data sets complete, we used the TauREx program to help retrieve the characteristics of the atmosphere. The revisit of the previous HST observations of  Sing et al. (2015)  is justified by the addition of retrieval in the method, which is a statistical approach that allows us to make fewer assumptions than forward modeling (described below).
2022) , and H 2 S for WASP-39b (The JWST Transiting Exoplanet Community Early Release Science Team et al. 2023), but we nonetheless included these three molecules for every target. We first used two different equilibrium chemistry codes: ACE  (Agúndez et al. 2012;  A51, page 5 of 18  Panek, E. , et al. : A&A, 677, A51 (2023)  Table  3. Results of the reduction re-analysis: transit depths and errorbars for our five targets. (Stock et al. 2018) , which are described in more detail in Sect. 3. 2. These three computations were applied with and without the addition of clouds in the models, on our three data sets for each of the five targets, amounting to 30 models in total. We then ran every configuration on three different types of data sets: the complete data set, the STIS data set alone and the WFC3 data set alone, for a total of 90 models.
From that couple, the chemical composition that gives the lowest Gibbs energy to the system can be calculated. The ACE code is mainly focused on neutral species composed of C, H, O, and N and has been validated over a wide range of temperature-pressure conditions, up to 2500 K and 100 bar. It is therefore well suited to modeling hot Jupiter planets. The Fastchem code ranges down to 100 K and up to 1000 bar. It uses 396 neutral and 114 charge species sourced from the NIST-JANAF database  (Chase 1986 ). We decided to use this code because the ACE equilibrium chemistry does not take into account sodium and potassium (two species that we describe in more detail in Sect. 3. 4 ).
Planet-by-planet results After seeing the data reduction process and the method used, we go on to discuss the results of the study. These results are presented here planet by planet, with an additional section on the results concerning the effect of sodium and potassium contributions, which concern all five planets. To avoid any bias due to the assembling of data from several instruments, we decided to analyze the data from the two instruments separately and then together, namely, using three sets of data per planet. However, the data from the short wavelength STIS instrument presents fewer spectral signatures and will therefore be less reliable for the found abundances of the molecules. As for the WFC3 data, it will be less reliable in determining the presence or absence of clouds in the atmosphere because of the wavelength coverage starting further away.
Cut data sets We based each of the six models on HST STIS data sets alone and on HST WFC3 data sets alone to check that we did not have any bias due to the assembling of data from several instruments. It is much more difficult to conclude on these models: the log(evidence) are always quite similar between the models. The STIS data nevertheless show us a small increase in log(evidence) for the consideration with clouds, consistent for the three chemistry set-ups on the planets HAT-P-12b and HD 209458b. This result is consistent with the conclusions made in the following parts (Sects. 4. 2 and 4. 3). It is impossible to distinguish between the clear atmosphere hypothesis and the cloudy atmosphere hypothesis on the other three planets (WASP-6b, WASP-17b, and WASP-39b), which suggests a clear atmosphere.
On the WFC3 data alone, the log(evidence) is very similar, so this does not allow us to conclude on the presence or absence of clouds in the atmosphere of our targets, as expected. Nevertheless all best-fit models include the ACE equilibrium chemistry. This is quite consistent with the results presented on the entire data sets (see Table  4 ), but on the complete data, the best-fit fluctuates between ACE equilibrium chemistry and Fastchem equilibrium chemistry. For example, in Fig. 7 , we can see the differences between the models that has been retrieved on the three different types of data sets. We can see that models computed on the complete and WFC3 data sets are very similar, but the differences between the model with and without clouds are minimal and the log(evidence) are really close.
We cannot draw a conclusion on the absence or presence of clouds, however, we can conclude on the clouds on the model run on the full data set, thanks to the addition of the STIS data. We can also see that we have more optical absorbers and less water on the model calculated on STIS data set alone, which was expected due to the wavelength coverage and the position of the absorption features. Overall, the three models are consistent with one another and in agreement with our conclusions, developed in Sect. 6.
The red and green shadows correspond to the contributions of sodium and potassium, respectively. Notes. We defined the Bayesian factor as the difference between the minimum log(evidence) and the log(evidence) of the compared model, for a given planet. The best-fit for every planet is indicated in bold. A51, page 8 of 18 Panek, E. , et al. : A&A, 677, A51 (2023) Fig. 6. Model comparison for each planet. Details on parameters used and contributions that has been taken into account inside every model can be found in Sect. 3. 1. We can see that the WFC3 part of the spectra is most of the time well-fitted and we have discrepancies at shorter wavelength. Here, we use our whole data sets with Na and K contribution points. of HAT-P-12b is probably cloudy. We cannot however statistically distinguish between our three chemistry parametrization, as their log(evidence) is similar.
WASP-17b Sing et al. (  2015 ) concluded with sodium and water signatures as well as a clear atmosphere without hazes. Sedaghati et al. (2016)  agreed and also detected a potassium signature. Saba et al. (2022)  found strong H 2 O, TiH, and AlO absorption signatures, yet without completely refuting previous detections of Na and K. Alderson et al. (2022) , on the other hand, reported an H 2 O detection, as well as evidence for CO 2 , but no sodium nor potassium absorption. Pinhas et al. (2018)  showed that the model that best fit to his data has no stellar heterogeneity and no evidence for clouds or hazes. We decided to include the following species in our free model : H 2 O, CH 4 , NH 3 , HCN, CO, CO 2 , Na, K, H 2 S, TiH, and AlO. We also ran two equilibrium chemistry models and added clouds and hazes to both models.
, et al. : A&A, 677, A51 (2023)  Fig. 9. Abundances profiles for active molecules for the two forward models on HD 209458b comparing equilibrium chemistry (full lines) and disequilibrium chemistry (dashed lines). chemical retrieval programs and allowed us to measure the composition of the five exoplanets in the hypothesis of a chemical equilibrium. Further developments will address the question of out-of-equilibrium chemistry for data retrieval. Having models that take as many effects as possible into consideration is an important aspect from a scientific and computational point of view. However, the key question is whether the observations will be able to follow (and with sufficient precision and at a good resolution) to allow us to fit a complex model. Nevertheless, the spectral resolution in HST/WFC3 may not allow us to distinguish between a simpler model and a more realistic model.
Acknowledgements. E. Panek ,  J. -P. Beaulieu  and  P. Drossart  have been supported by  CNES  convention ( 6512/7493 ). O. V. acknowledges funding from the  ANR  project ' EXACT ' ( ANR-21-CE49-0008-01 ), from the  Centre National d'Études Spatiales (CNES) , and from the  CNRS/INSU Programme National de Planétologie (PNP).