dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Datasets Two different approaches were followed in the example evaluations of real experimental data. For the experimental data on 2,4-D, the data for each soil were evaluated using current regulatory guidance for pesticide degradation kinetics in the EU. In addition to the error models currently in use (constant variance and variance by variable), the twocomponent error model was integrated in the procedure. Average modelling endpoints were then obtained by calculating geometric mean rate constants and arithmetic mean formation fractions. The degradation curves obtained in this way were then compared to the best nonlinear mixed-effects model fit selected based on the AIC. As the application of the FOCUS guidance frequently depends on expert judgement, a different approach was followed for the data on DMTA.
The intention was to provide a more direct comparison of separate and simultaneous evaluations. For the DMTA data, model selection for the data for each soil was performed by fitting all combinations of four degradation models and the three error models and by tabulating the resulting AIC values. For one of the combinations that showed the lowest AIC in the most cases, comparisons with the corresponding nonlinear mixed-effects models are presented. Additional model comparisons are shown in the Supplementary Materials. In these direct comparisons, average degradation curves were calculated from the separate fits by averaging the transformed parameters as found by fitting the reparameterised models. As log rate constants found in separate fits sometimes yield excessively small values very close to zero, which then dominate the average values and distort these mean curves, an alternative averaging procedure was used for plotting average curves for the real datasets, only considering log rate constants when the t-test for significant difference from zero (based on the natural degradation model, not the reparameterised model) shows that they can be distinguished from zero at a certain confidence level.
While both averaging procedures suffer from the problems of averaging parameters from separate evaluations, as described in the Introduction, the alternative averaging procedure was useful for defining improved starting values for the population parameters of the nonlinear mixed-effects model fits by avoiding numerical problems in ODE solutions that occured when using means of all transformed parameters. Nonlinear mixed-effects model fits for the two grouped experimental datasets were performed both with saemix and nlme. In general, the SAEM algorithm was favoured for theoretical reasons and due to practical difficulties to obtain convergence with nlme. However, variance by variable is not available in saemix and the fits are much more time consuming. Therefore, nlme was used to check this error model wherever possible as well as to confirm the model selection decisions based on saemix.
Synthetic Data for Simple Exponential Decline The evaluation of one of the 100 synthetic datasets generated for five virtual degradation experiments with half-lives centered around 15 days is shown in Figure  1. The residuals are standardised using the standard deviation predicted by the two-component error model at the respective magnitude of the predicted value. The corresponding fit using constant variance as well corresponding nonlinear mixed-effects model fits are shown in the Supplementary Materials. These look very similar, although in nonlinear mixed-effects models, the population mean curves are fitted first, the individual curves are estimated as empirical Bayes estimates in a second step and the standardised residuals are relative to those predictions. The residual plots in the right panels show a more random scatter when the two-component error model is used, as this is what was used to generate the data.
This is also apparent from the half-live statistics over the 100 results obtained from each of the six variants presented in Table  3. Error model selection based on the Akaike Information Criterion (AIC) favours the two-component error model for 489 out of 500 separate fits and for all 100 simultaneous fits to these datasets, which confirms the visual assessment of the residual plots. Synthetic datasets were also evaluated with half-lives centered at 120 days, 500 days and 800 days in order to explore the possibility to recover the known population half-life when the half-life is large compared to the last sampling time. The results for 120 days and 500 days are shown in the Supplementary Materials. Separate evaluations with constant variance for one of the synthetic datasets generated with half-lives centered at 800 days are shown in in the left panel of Figure  3.
Note that the example dataset in Figure  3  was selected as one of the 100 datasets where significant differences between the methods occur. In this evaluation, an almost horizontal degradation curve was found for one of the groups (Group 1). The population curve resulting from simple averaging of log rate constants is also almost horizontal, although some visible degradation was found in the other four groups. The reason for this is that the geometric mean rate constant obtained from from these fits is still very close to zero because of the excessively low rate constant found for Group 1, which corresponds to a half-life of around 60 million days. Using the two-component error model did not improve the fit for this type of datasets (Table  4 ). The mixed-effects model fit obtained with saemix for the same example dataset is shown on the right hand side of Figure  3.
Here, the population curve is not horizontal but shows a noticeable decline because it is determined by all data at the same time, so that ill-defined parameters in one of the groups cannot dominate the overall appearance of the curve. The boxplot for the population half-lives obtained for the input half-life of 800 days is shown in Figure  4. The number of successful fits and some statistics for the half-lives obtained by the different variants are shown in Table  4. For separate fits, the input half-life is often not found with reasonable accuracy but rather overestimated as in the example dataset shown in Figure  3. The deviations are less pronounced when the error model matches the one used in the data generation process. Population half-lives obtained using the nlme package show less overestimations.
However, the algorithm did not converge for many datasets, so no results were available. Where the results were available, they showed a certain tendency to underestimate the half-life. 4 ). Fits using the SAEM algorithm were still able to recover the input population half-life in most cases. In one out of the 100 datasets, singularities in the Fisher information matrix indicated overparameterisation, so the corresponding results were not included. The affected dataset was also the one that yielded the largest overestimation of the population half-life in the separate evaluations. In the datasets with an input half-life of 800 days, error model selection using the AIC would lead to selection of the constant variance fit in almost all cases for separate evaluations and in the majority of cases for the simultaneous fits (Table  4 ).
This can be explained by the fact that the residues hardly decrease from the first to the last sampling times so that almost the same variance is used for the random data generation and the two-component error model is overparameterised. In general, using the two-component error model only slightly increased the accuracy of the population mean half-lives in these SFO datasets, judged by the comparison of the statistics given in Table  4. The additional results for input half-lives of 120 days and 500 days shown in the Supplementary Materials show that the evaluation variants perform uniformly well for 120 days (with the exception of one case of non-convergence with nlme). In the case of 500 days for the input half-life, some cases of unrealistic population half-lives are obtained from separate fits and nlme shows a certain bias towards understimating half-lives, as the median as well as the first and third quartiles obtained in the fits that did converge are around five to ten percent lower than the known values.
Synthetic Data for Biphasic Decline with One Transformation Product To illustrate the synthetic DFOP-SFO data, the nonlinear mixed-effects model fit with the DFOP-SFO and the two-component error model to one of the datasets is shown in Figure  5. The boxplot for population parameters k2 (second rate constant of the DFOP model) and k_m1 (degradation rate constant of the transformation product) obtained for all 100 datasets using the DFOP-SFO degradation model and the two-component error model are shown in Figure  6. The initial concentration of the parent compound, the faster parent decline rate constant k1, the DFOP parameter g as well as the formation fraction of the transformation product were recovered by separate and simultaneous fits with similar accuracy and without clear indication of a bias.
To obtain the lowest AIC in each of the six soils, four different combinations of degradation models and error models were necessary. The separate fits for the DFOP-SFO3+ model with two-component error are shown in Figure  8. From this plot, it is clear that, for transformation product M23 as observed in the Borstel soil, no degradation can be determined in the separate evaluation. Therefore, the degradation rate constant determined for this fit is very close to zero and the t-test consequentially does not indicate significant difference from zero. Considering this rate constant in the calculation of a geometric mean degradation rate constant for M23 over all six soils results in a very small value. Similar problems occur in the separate fits of this model to the parent compound data. Since in some datasets, k1 or k2 are ill-determined, they can take on values that are very close to zero, which leads to nonsensical values when a geometric mean over all soils is calculated (a plot is shown in the Supplementary Materials).
While the FOCE algorithm implemented in the nlme package sometimes showed a certain bias in population parameters, this was not the case when using the SAEM algorithm as implemented in the saemix package. A more practical advantage can result from the fact that, for nonlinear mixed-effects models, model selection only has to be performed for the complete dataset and not for each individual dataset separately. While model selection also has to be performed for the parameter distribution models, these can be kept simple by assuming uncorrelated random effects, i. e. , that the random variations of the parameters across the population are independent of each other. The evaluation of experimental data also pointed to some practical limitations of the mixed-effects modelling approach tested in this work.
In several cases, the FOCE algorithm implemented in the nlme package did not converge, which limits the use of this software in practical applications. When using saemix, fitting more complex degradation models with transformation products takes more time (around 10 to 15 min in our case) and may therefore disrupt the workflow if no analytical solution of the model is implemented. While this is certainly still acceptable, a factor limiting the general use of the saemix package is that it currently does not allow for different variances of the observed variables (variance by variable). This could be overcome by using other available implementations of the SAEM algorithm, for instance nlmixr  [40]  or Monolix  [31, 32]. In this analysis, we chose parameter transformations based on the natural parameter boundaries, i.
e. , log transformation for positive real parameters such as degradation rate constants and logit transformation for fractions. For degradation rate constants, using the log transformation reflects common practice in pharmacokinetics and reflects the practice of computing geometric means from several individual soil degradation rate constants in the separate fit analysis. However, the transformations cannot be reliably tested given the small number of data available in this study. Using a larger number of individual datasets, we could explore the distribution of individual parameters, test the influence of covariates linked to soil characteristics such as pH or organic carbon content, or assess departure from log-normality using different transformations h. Petersson et al. [41]  suggested systematically testing transformations such as the logit distribution or the Box-Cox transformation to detect skewness, heavy-tailed distributions or even multimodalities in the parameters.
Conclusions Based on data generated with predefined statistical properties, it could be shown that, using nonlinear mixed-effects models, reasonable results can be obtained even in cases where degradation of the parent or a specific transformation product is very slow in some of the individual datasets. In addition to this exercise showing the advantages of the method in a controlled setting, the evaluations of experimental soil degradation data for two different pesticidal active substances showed that plausible results could also be obtained in cases where the conventional approach based on separate evaluations suffered from severe problems due to the lack of identifiability of certain parameters in specific individual datasets. An additional advantage of the use of nonlinear mixed-effects models is the simplification of model selection, as this does not have to be performed for each individual group of data but only once for the complete dataset.
These results may allow us to replace the current use of arbitrary default values for datasets giving ill-defined results. Furthermore, the shift in model selection from the individual datasets to the level of the complete data has the potential to significantly reduce the number of model comparisons while allowing us to assess the influence of covariates such as soil pH in the same step. The proposed use of likelihood-based model selection criteria such as the likelihood ratio test and the AIC also has the potential to overcome excessive and unpredictable discussions about visual acceptability assessments. While we cannot recommend the nlme package for the more complex degradation models as it often does not converge, the SAEM algorithm as implemented in the saemix package proved to be a reliable tool for fitting nonlinear mixed-effects models.
Acknowledgments:  Janina Wöltjen  and  Jana Schmidt  (Umweltbundesamt) already supervised a preceeding project on model selection criteria in regulatory degradation kinetics, which led to the idea and first applications of nonlinear mixed-effects models to regulatory degradation kinetics. The financial support for J. R. by the  German Environment Agency  is gratefully acknowledged. Thanks to  Martin Mächler  of the R core development team for reviewing the patch that introduces the two-component error model into the nlme package and to  Dirk Eddelbuettel  for reviewing changes in the inline package that made it possible to save and reload compiled ODE models with mkin.
A seamless mosaic has been constructed including a 3D terrain model at 50 m grid-spacing and a corresponding terrain-corrected orthoimage at 12. 5 m using a novel approach applied to ESA Mars Express High Resolution Stereo Camera orbital (HRSC) images of Mars. This method consists of blending and harmonising 3D models and normalising reflectance to a global albedo map. Eleven HRSC image sets were processed to Digital Terrain Models (DTM) based on an opensource stereo photogrammetric package called CASP-GO and merged with 71 published DTMs from the HRSC team. In order to achieve high quality and complete DTM coverage, a new method was developed to combine data derived from different stereo matching approaches to achieve a uniform outcome. This new approach was developed for high-accuracy data fusion of different DTMs at dissimilar grid-spacing and provenance which employs joint 3D and image co-registration, and B-spline fitting against the global Mars Orbiter Laser Altimeter (MOLA) standard reference.
A key component is to ensure that all DTMs have a common reference system, which in our case is the MOLA DTM. The UCL and DLR HRSC level 4 DTMs are co-aligned with the MOLA DTM to provide precise topographic information for future geological studies. The 3D reconstruction pipeline employed in our work is a hybrid version of CASP-GO, which was developed within the completed EU FP-7 iMars (http://www. i-mars. eu (accessed on 21 March 2021)) project for NASA Mars Reconnaissance Orbiter (MRO) Con-TeXt camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) stereo images. The UCL CASP-GO DTM system was previously used to produce planet-wide CTX DTMs  [39]. In this work, we improve the original CASP-GO CTX pipeline to produce optimised HRSC DTMs. Moreover, rather than employ bundle block adjustment, which requires specialist photogrammetry skills and processing of a bundle of data together, we employ a more brute force approach here given that a global reference 3D model and existing DLR level 4 products exist.
The rest of this paper is structured as follows. In Section 1. 1, we review the datasets employed and the relevant previous work follows in Section 1. 2. In Section 2. 1, details of the input and reference data are provided. From Sections 2. 2-2. 6, we describe technical details step-by-step for all the methods that are involved in this work, in the order of singlestrip HRSC DTM production, HRSC-to-HRSC and HRSC-to-MOLA 3D co-alignments and mosaicing, HRSC ORI production, and subsequent photometric correction and mosaicing of ORIs. In particular, Section 2. 2 reviews the CASP-GO pipelines which are used as backbone 3D mapping software in this work. In Section 2. 3, we introduce the hybrid HRSC DTM processing chain for single-strip HRSC DTM production, followed in Section 2. 4 , by an introduction into the joint 3D and image co-registration processing chain for HRSCto-HRSC and HRSC-to-MOLA co-alignment.
Datasets In this work, we use the MOLA areoid (https://pds-geosciences. wustl. edu/missions/ mgs/megdr. html (accessed on 21 March 2021)) data as our baseline reference system (both spatially and vertically). MOLA (from 1997 to 2001) was one of the five instruments onboard MGS that measured the time-of-flight of laser pulses at 1064 nm, over the distance from the spacecraft to the Martian surface. Individual MOLA laser tracks were interpolated and extrapolated, and corrected, by the MOLA team to produce a global DTM of Mars at 463 m grid spacing  [37] , which is available through the NASA Planetary Database System (PDS; https://pds-geosciences. wustl. edu/missions/mgs/megdr. html (accessed on 21 March 2021)). The HRSC stereo imaging instrument onboard the ESA's Mars Express (MeX) spacecraft is still orbiting Mars having acquired its first images in January 2004.
Previous Work In  [39] , we previously compared the quality and performance of CTX DTMs produced using different existing planetary 3D reconstruction software and introduced the CASP-GO system, which is based on the open-source NASA Ames Stereo Pipeline (ASP) [43], tie-point based multi-resolution image co-registration  [44] , the Gotcha  [45]  sub-pixel refinement method, and other optimisation algorithms. The CASP-GO system produces high quality DTMs in terms of improved completeness and accuracy, as well as minimising matching and interpolation artefacts. For HRSC data, DLR generate HRSC single-strip DTMs  [42]  in a sinusoidal projection system at 50-150 m grid-spacing for level-4 products. More recently, DLR have developed a level-5 processing chain to produce a large (>100 strip) HRSC DTM mosaics with the Leibniz University of Hanover and Free University of Berlin (FUB) for several United States Geological Survey (USGS) quadrangles (http://hrscteam.
The ASP and CASP-GO Auto-DTM Generation System The Ames Stereo Pipeline (hereafter referred to as ASP) software was developed by the Intelligent Robotics Group at the NASA Ames Research Center, Mountain View, CA  [43]. ASP is a suite of open-source automated geodesy and stereo-photogrammetry tools designed for processing planetary imagery captured either from planetary (robotic) orbiters or landers or from Earth observing satellites. The CASP-GO pipeline is based on the ASP framework with specific enhancements, dealing with matching artefacts, gaps, and co-alignment issues, from in-house software previously developed at the Imaging Group at UCL. CASP-GO uses the ASP I/O interface and takes USGS Integrated Software for Imagers and Spectrometers (USGS-ISIS) formatted stereo images (see Figure  2 ; see explanation of the USGS-ISIS Cube format in https://isis.
All HRSC single-strip DTMs (both UCL and DLR) are finally corrected against MOLA DTM, assuming there are no significant artefacts or topographical changes at the MOLA scale. Regarding the final HRSC DTM mosaic, overall quality has been visually checked and co-alignment accuracy has been quantified. However, the dataset is very large, and the stereo images are limited, so some local artefacts cannot be fully removed. There are known artefacts, including: tiling artefacts (seam lines), small interpolated-regions, smoothed area that were caused by using "foggy" HRSC stereo images (no other options were available as we have checked), occasional artefacts inside small craters, quilting artefacts at heavily shaded cliff areas, and the junction of lower resolution with higher resolution single-strip DTMs.
The Paramak uses CadQuery 2 to create the 3D CAD model. The Paramak framework is used to create a few example reactor configurations including: a spherical reactor, a regular large radius tokamak and a compact submersion tank reactor. Input parameters for the various reactors that the Paramak can generate generally fall into three categories: continuous ranges such as blanket thickness, integer ranges such as number of toroidal field coils and categorical parameters such as type of divertor. The Paramak facilitates parameter studies where users can investigate the impact of input design parameters on the reactor performance. The use of modern software practices allows the geometry to be continuously tested in analysis workflows to ensure it is fit for purpose. The generation of output metrics from input parameters lends itself to the use of data science and machine learning approaches in order to steer the design.
Fusion reactor design processes involve analysis being carried out and fed back into the creation of the next CAD model; this process is usually a manual GUI based operation. The interpretation of results from analysis and geometric modifications are necessarily decoupled in this mode of operation, which slows the speed of iteration. The situation can occur where the models are updated several times as different analysis streams feedback into the design. In the case where different analysis tasks take different amounts of time, there may be situations where the analysis streams therefore get different versions of geometry, before it is further modified for their respective analysis. The scripts that users create to generate the CAD model are compatible with version control and therefore the method of creating the CAD can be version controlled and traced across a design process.
CadQuery CadQuery 2 3 offers a potential solution for the creation of automated parametric CAD. CadQuery 2 is an open-source Python library that binds to OpenCascade (OCCT 7. 5 release)  4  and has some unique features among the possible open-source candidates. One such capability is the ability to search, filter and then operate on the CAD solids during construction. This allows components to be linked and built from each other without the user having to be concerned with redefining related solids when a linked solid is modified. This is already possible with proprietary CAD software but these capabilities are now emerging into the open-source area. CadQuery development is ongoing and the specific version used for this publication is 5.