dataesr/scientific-paragraphs-categorization huggingface dataset:
Key Engineering Materials In addition, the roll wear amplitudes predicted by the 3 wear formulations for the rolling validation dataset are illustrated by Figure  3. This figure shows that all the formulations tend to overestimate the roll wear measurements for wear amplitude values below 300 μm, while underestimated predictions tend to be observed above this wear amplitude value. It may be because the roll wear amount calculated for a given rolled strip is independent of the strip position in the rolling campaign schedule. In other words, the amount of wear generated by a same rolled strip (and same rolling conditions) at the beginning and at the end of the rolling campaign should be different due to the weakness of the thermo-mechanical roll properties. This effect is not taken into account by the wear model approaches presented here.
Thus, for larger wear amplitude values usually from longer rolling campaigns, the roll wear measurements tend to be underpredicted. Performance of the wear coefficient parametrizations. The different wear coefficient parametrization options (cf. Eqs. (  4 )-(  8 )) were investigated by using the Wang's mathematical formulation (cf. Eq. (  1 )) since this one led to better wear predictions. So, 3 Wang's model calibrations were made for each parametrization option by using the initial coefficients sets listed in Table  1. Note that these initial sets were adapted for each parametrization option in order to start the calibrations from the same initial cost function values. Best calibration results were obtained from initial set 1 and an overview of these results is given by Table  3. It can be seen from this table that the parametrization option 2 which is defined by 76 unknown wear coefficients enabled best calibration results (training dataset) since it reached smaller final cost function value and smaller averaged gap between wear measurements and predictions (≈43 µm).
But, concerning the validation dataset, Option 1 which is defined by 21 coefficients led to same averaged wear prediction (≈46 µm) than Option 2. Such wear predictions for the validation dataset are depicted by Figure  4. This figure allows for a qualitative analysis of the predicted results and it shows that similar (i) wear amplitude values and (ii) wear dispersion behaviour were obtained from the 3 coefficient parametrization options. Therefore, in terms of model performance, the parametrization option 1 was identified as the most suitable one due to the better compromise between number of unknown coefficients and wear predictions. Table  3. Calibration and validation results obtained by the 3 wear coefficient parametrization options. Furthermore, validation results listed in Table  3  also show that the averaged wear calculations using the parametrization option 3 were just ≈4 µm worse than the ones obtained by option 1.
This result reveals that the effect of not considering the strip grade factor on the wear coefficients parametrization led to a deterioration of the wear amplitude predictions around 8% for this validation dataset. Note that the parametrization option 3 is a simplified version of the option 1 by disregarding the strip factor and then is just defined by 12 wear coefficients. So, this smaller parametrization option may be more interesting to predict roll wear in hot strip mills only producing similar kind of strip family grades.
Artificial Neural Network vs. Classical Сalibration An Artificial Neural Network (ANN) approach was also developed and applied with the purpose of comparing the roll wear predictions calculated from this approach with the ones obtained by the classical calibration approach used above. The ANN, which was coded in Python, computed the corresponding wear for each coil by accounting for roll type/supplier and strip grade factors as well as rolling process parameters, such as hardness, forces, temperatures, speeds, thickness reduction, etc. First ANN trials to predict wear were not very conclusive, mostly due to the lack of learning data. To improve performances, the Wang's wear model (cf. Eq. (  1 )) was exploited and the ANN was then used to estimate the wear parameter K for each coil. This approach provided much better results, which can be explained as the ANN did not have to learn, from a reduced database, the impact of The ANN uses (i) 2 hidden layers of neurons defined by a total of 20 neurons, using selu and sigmoid activation functions, and (ii) recurrent architecture to account for the cumulative wear calculated from previous strips as input data.
Recurrence supplies information about the current wear of the roll to the ANN, providing the ability to modulate the individual wear based on the state of the roll. This ANN structure was determined by trials, where the small size of the dataset led to a small network size too. The ANN was trained for each roll stand by using the training dataset and was then tested on the validation dataset. Figure  6  shows the wear predictions obtained by the Artificial Neural Network for the validation dataset. In addition, roll wear predictions from the classical calibration (CC) using the Wang's model and the coefficients parametrization option 1 were also included in this figure. From a qualitative point of view, it appears that similar wear prediction trends were obtained by both ANN and classical calibration approaches.
628 Achievements and Trends in Material Forming Table  4  lists the averaged wear gap between wear amplitude measurements and predictions for both training and validation datasets. It can be seen from this table that the ANN led to a much better performance (+25%) than the classical calibration on the roll wear prediction of the training dataset. However, quite similar roll wear predictions were obtained by both approaches for the validation dataset since ANN was just 7. 6% better than the classical calibration approach. While the classical calibration approach led to the same performance on both training and validation sets, the ANN approach revealed some overfitting for the training dataset. Nevertheless, this work is a first shot at ANN wear models and additional data could greatly improve their performances, by reducing the overfitting for instance.
The results from the validation dataset show that both approaches had almost equivalent wear prediction performance for this rolling database. On one side, the classical calibration approach has the advantage of the wear coefficients are known once calibration is made and so, a relationship between these coefficients can be established. It is particularly interesting when plant purchases work-rolls from new suppliers, since wear coefficients for these rolls could be easily assigned after a few rolling campaigns. On the other side, ANN have important potential for bigger training database and additional data which, assorted with bigger ANN structure, could lead to much better roll wear predictions. The ANN described here shall not be taken as an overall statement on their performances for such wear applications.
Conclusion In this work, the performance of 3 roll wear models as well as 3 different options to parametrize the unknown wear coefficients of these models were compared by using an industrial rolling database for finishing stands F6 and F7. The roll wear models consisted of contact-based mechanic equations while the wear coefficient parametrization options consisted of distinct parameter sets relating (i) roll stand, (ii) roll type/supplier and (iii) strip grade factors. The performance analysis revealed that: • averaged wear amplitude gap in-between 46-51 μm were achieved by the 3 wear models • all the wear models led to underestimated predictions for roll wear amplitudes above 300 μm • Wang's wear model with simplest formulation (since defined by less process parameters) was able to give similar or even better results than the 2 other wear models evaluated here • wear coefficients parametrization distinguishing strip grades could improve roll wear predictions in about 8% In addition, roll wear predictions obtained from ANN approach were also compared with the ones obtained from a classical calibration approach to identify the most performant one.
22 mrad/pixel  (Malin et al. , 2017). Meanwhile, the right Mastcam (M-100) has a 100 mm focal length, a FOV of 6. 8°×5. 1°, and a 0. 074 mrad/pixel scale of sampling  (Malin et al. , 2017). Data from this instrument were used to identify large-scale features, such as crossbedding or alteration halos, and to locate the ChemCam targets with respect to the stratigraphic units. MAHLI is situated on the turret at the end of the Curiosity rover's arm and uses a 2-megapixel color CCD camera to analyze the textures, structures, and morphologies present at the finest grain size possible for Curiosity's cameras, 0. 0042 mm (silt)  (Edgett et al. , 2012; Mangold et al. , 2017). Data from this instrument were used to provide additional, high resolution, color data to support the classifications of the black and white images from the ChemCam RMI.
, 2018; Bedford et al. , 2020a; Potter-McIntyre et al. , 2014; Potter et al. , 2011). A previous analysis of concretions in the Stimson formation sandstone by both the Alpha Particle X-ray Spectrometer (APXS; e. g. ,  Gellert & Clark, 2015)  and ChemCam instruments showed that they have the same geochemical composition to non-concretionary Stimson  (Bedford et al. , 2020a; Siebach et al. , 2017)  and are hypothesized to relate to closedsystem diagenesis that preserves the bulk rock geochemistry  (Banham et al. , 2018; Bedford et al. , 2020a; Siebach et al. , 2017). This is also true for the Greenheugh pediment capping unit for concretions smaller than 5 mm (Appendix A); as such they are included in the bedrock dataset. However, larger concretions that are greater than 5 mm (Fig. 4 ), with multiple ChemCam observation points directly on the concretions, and which occur closer to the unconformity have been isolated from the bedrock dataset of all Stimson localities for a more in-depth analysis on the diagenesis that formed these features.
As the Stimson formation across all localities has a relatively uniform grain size  (Banham et al. , 2018; 2021;  this issue), we have not classified bedrock targets according to grain size. Bedrock targets do not include analyses of drill tailings and dump piles as these samples have been modified by the rover. Furthermore, bedrock analyses have been restricted to only include observation point data with oxides that sum to between 90-105 wt% to minimize the influence of volatile elements (S, Cl, and H), or elements such as Ni which are not nominally quantified in ChemCam data. Removing the potential impact of volatile elements is important for our bedrock dataset as these elements may have been emplaced due to open-system alteration. Restricting the total sum of oxides can further remove any targets that have been contaminated with Martian soil, as these deposits also have higher concentrations of volatiles  (Bish et al.
, 2013; Ehlmann et al. , 2017; Lasue et al. , 2018; Meslin et al. , 2013; O'Connell-Cooper et al. , 2017). ChemCam analyses that have targeted secondary diagenetic features in the Stimson sandstone have been placed into an alteration feature dataset and subclassified according to "vein", and "halo" categories. Vein alteration targets relate to light-toned mineral veins that infill hydrofractures in the Stimson formation  (Bedford et al. , 2020a; Kronyak et al. , 2019a). Halos relate to the light-toned halos that extend into the Stimson bedrock ~20 cm either side of large fractures (Fig. 4 )  (Bedford et al. , 2020a; Frydenvang et al. , 2017; Yen et al. , 2017). These secondary diagenetic features have been separated from the bulk rock dataset to isolate the effects of later diagenetic alteration from geochemical trends that may relate to the sediment source characteristics of the Stimson formation across all localities, in addition to prelithification mineral sorting processes, and the formation of the sandstone cement.
Statistical methods and data interpretation The ChemCam LIBS laser sampling footprint is 350-750 μm for distances 3-7 m from the rover mast  (Maurice et al. , 2012) , which is a similar size to the average grain size of the Stimson formation (336-634 μm at the Greenheugh pediment,  Banham et al. , this issue). As such, target analyses are not likely to be representative of whole rock compositions as individual grains are more likely to be sampled  (Cousin et al. , 2017). However, the bedrock Stimson formation ChemCam dataset to date (August, 2021) consists of 741 data points which permits the use of a statistical approach to both estimate a bulk rock composition and detect geochemical trends that may relate to mineralogical variations in the relatively alteration-free bedrock dataset. For each locality and stratigraphic interval of the Stimson formation, we give the basic statistics (mean, median, range, and standard deviations), followed by density contour plots which will be used to estimate the bulk rock composition.
Density contours display the data across two variables and represent the density of smoothed data within each pixel on the bivariate plot defined according to the bin size  (Bedford et al. , 2019a; Eilers et al. , 2004). Data densities are smoothed according to  Eilers and Goeman (2004) , and contours are generated using MATLAB (2019a). This method is used to estimate the bulk rock composition which is in turn used as a comparison to the geochemical variation associated with the alteration feature datasets. By comparing the estimated whole rock composition to alteration features, we can constrain how these features formed. After the density contour analysis, sandstone components (the minerals and amorphous materials that comprise the sand and cement) are delineated using a hierarchical, agglomerative cluster algorithm on the bedrock dataset with Minitab 19 statistical software (2020), following the methods of  Bedford et al.
(2020a)  and  Bedford (2019a). In this study, we use the complete linkage measure of association for Euclidean distances with all major oxides, now including MnO, and the total sum of oxides, as the variables. Complete linkages and Euclidean distances were used as these methods provided the best fit for the dataset showing the most defined clusters and was successfully used in a previous investigation of the Stimson formation  (Bedford et al. , 2020a). Clustering methods have also been successfully employed in other studies of Gale crater sediments  (Rammelkamp et al. , 2021; Gasnault et al. , 2019 Gasnault et al. , , 2013)). Variables were standardized in order to minimize the effect of scale differences.
Distribution of the classified data across the Stimson formation In total, 430 ChemCam observation points were acquired during the Greenheugh pediment mini campaign that were analyzed <4 m from the rover and were of good data quality. This included 324 bedrock analyses, two analyses of calcium sulfate veins, 10 soil analyses, 10 float analyses, 20 analyses that targeted large (>5 mm) nodules, and 15 analyses of the Edinburgh drill hole wall and tailings (Fig. 5 ). This is in contrast to the distribution of observation point classifications for the Emerson and Naukluft plateaus which has a much larger proportion of observation points (50% at Emerson plateau, 65% at Naukluft plateaus) that targeted alteration features such as halos, concretions, and calcium sulfate veins (Fig. 5 ). These manuscript submitted to JGR: Planets 13 proportions are not a result of the true distribution of these features relative to bedrock as targeting bias to investigate certain features has played a role.
4 ). Concretions are also present across all sites. All the large and small concretions at the Greenheugh locality were observed in the lowermost Gleann Beag interval in the ChemCam data and Mastcam images (Fig. 3B  and 4 ). Consequently, there is a similar number of large concretion analyses in the Stimson formation at the Greenheugh pediment (20 points over 4 targets) and the Naukluft plateau (29 points over 6 targets). Only three observation points in the targets Dakota and Heath sampled large concretions in the Stimson formation at the Emerson plateau. Of the 324 bedrock analyses, 220 constituted data within the 90-105 wt% total sum of oxide range. This suggests that 104 observation points on the Greenheugh capping unit contains a higher abundance of volatile elements (S, H, or Cl) or other elements which are not routinely quantified, 82% of which are within the Edinburgh interval.
In comparison, the Naukluft plateau's dominant peak is at 3. 1 wt%, with a greater proportion of data points situated over 4 wt% Na 2 O. The mean compositions of SiO 2 , Al 2 O 3 , MgO, CaO, and MnO across the Stimson formation localities are all situated within 1 standard deviation of each other (Table  1 ), however, variations in the distribution of the data suggests that there may be statistically significant differences, supported by equivalence test results (Table  S. 2). The Emerson plateau and Greenheugh pediment capping unit both share unimodal SiO 2 distributions with peaks at 45 wt% (Fig. 6 ). However, the distribution of SiO 2 at the Emerson plateau shows a right skew to higher SiO 2 concentrations up to 57. 1 wt%, whereas the Greenheugh pediment capping unit shows a skew to the left with a greater proportion of data points <44 wt% SiO 2 causing the nonequivalence between the Emerson plateau and Greenheugh pediment capping unit.
The additional peak of the Greenheugh capping unit at 8 wt% has resulted in lower mean (10. 3 ± 0. 2 wt%) and median (9. 6 wt%) compositions compared to the rest of the Stimson formation localities with average compositions of 11. 3 ± 0. 2 wt% and 12. 1 ± 0. 2 wt% for the Emerson and Naukluft plateaus, respectively. Both the Emerson and Naukluft plateaus have a unimodal distribution for MgO, with a peak at ~7. 5 wt%, while the Greenheugh pediment capping unit has a bimodal distribution with peaks at 7 wt% and 9 wt%. All localities have a right tail to MgO concentrations of >17 wt%, generating a slight right skew in the datasets (Fig. 6 ). The distribution in CaO is also the same between the Emerson and Naukluft plateaus in that both are unimodal with a dominant peak at 6. 4 wt%. Meanwhile, the Greenheugh pediment capping unit has a bimodal distribution with the 6.
4 wt% peak shared with the other localities, in addition to a different, more dominant peak at 4. 0 wt%. MnO has a unimodal distribution for all localities with a strong peak at 0. 2 wt%, however, outliers exist to high concentrations at the Emerson plateau (up to 2. 6 wt%) and Greenheugh capping unit (4. 0 wt% in target Aegis_post_2730). The outlying MnO values at the Greenheugh pediment capping unit are more numerous than those of the Emerson plateau, and all occur within the Edinburgh interval bedrock targets, generating a mean concentration that is ~1. 6x that of the Emerson and Naukluft plateaus. To summarize, the Stimson formation at the Greenheugh pediment capping unit is situated within the compositional range of earlier Stimson units, according to the standard deviation of the dataset.
However, statistically significant variations in the geochemical distribution of the dataset exist, as is evident in the mean, equivalence test results, and the histograms of major element oxides. The Greenheugh pediment capping unit has a higher abundance of MgO and MnO, and lower abundances of SiO 2 , Al 2 O 3 , and CaO relative to the Stimson formation at the Emerson and Naukluft plateaus that may relate to different abundances of sandstone components such as mafic minerals, felsic minerals, or sandstone cement.
The distribution of halogens across the Stimson formation. For the bulk rock dataset, all Stimson formation localities have a unimodal distribution of H peak area about 0. 004, however, the Stimson formation at the Greenheugh pediment has a bimodal distribution with a second modal peak of H peak abundances at a value of 0. 008 (Fig. 6 ). This contrasts significantly with the right-skewed unimodal distributions of the Emerson and Naukluft plateaus. Looking at the distribution of the H data in relation to the stratigraphic intervals at the Greenheugh pediment (Fig. 8A ), these peak area values are divided between the Gleann Beag interval that contains concretions and is situated close to the unconformity, and the Ladder and Edinburgh intervals that do not contain notable concretions and are higher in the stratigraphy (Fig.
Stimson bedrock cluster analysis results The results of the cluster analysis show that the Stimson formation bedrock dataset can be divided into 5 clusters (Fig. 9 ). Cluster 1 is defined as the cluster with the highest mean MgO content (14. 7 ± 1. 1 wt%), higher than average MnO (0. 7 ± 0. 2 wt%) and the lowest Al 2 O 3 (6. 9 ± 0. 4 wt%), CaO (3. 6 ± 0. 4 wt%), Na 2 O (2. 3 ± 0. 1 wt%), and K 2 O (0. 3 ± 0. 1 wt%) of all clusters (Table  2 ). Nineteen data points have a Cluster 1 membership from across the Stimson formation with eight at the Greenheugh pediment (Fig. 9 , 10). Cluster 1 is most similar to Cluster 2 and is differentiated by a similarity level of 36. 3 (see  Bedford (2019)  for the methods to calculate similarity level). Cluster 2 has a moderately high abundance of MgO (8. 6 ± 0.
1 ± 0. 1 wt%), CaO (6. 0 ± 1. 8 wt%), and lower abundances of FeO T (18. 7 ± 0. 1 wt%) and MgO (6. 5 ± 0. 1 wt%). Cluster 3 has a total of 330 observation points with the majority situated on the Naukluft plateau (Fig. 10 ). Clusters 2 and 3 are the largest clusters in the dataset and as such are situated within the density contours of the Stimson bulk rock (Fig. 12 ). Similar to the results of  Bedford et al. (2020a)  for the main clusters 2 and 3, the standard deviations are higher than for the other clusters, which could indicate that some of the data points are not well bound to these clusters. However, the bimodal distribution of SiO 2 and Na 2 O of the Naukluft plateau (Fig. 6 ), which contains the majority of Cluster 3 memberships, shows peak abundances of 45 wt% and 48 wt% SiO 2 , and 2.
7 wt% and 3. 4 wt% Na 2 O that align with the Clusters 2 and 3 averages, respectively. The variation of Al 2 O 3 between the Emerson plateau and Greenheugh pediment localities that share a peak of 10 wt% Al 2 O 3 also differs from the main peak composition for Al 2 O 3 of the more Cluster 3-rich Naukluft plateau (13 wt%), suggesting a more bimodal distribution between all Stimson localities for this major element oxide. As such, this indicates that Clusters 2 and 3 are an inherent part of the dataset and accurately represent the most common sandstone components across all localities, despite the overlap of some points. Furthermore, as Clusters 2 and 3 derive from a total of 702 points, the overlap of <10 % of datapoints does not significantly skew the analysis of these two clusters. The two most distinctive clusters are Clusters 4 and 5 (Fig.
9 ). Cluster 4 consists of 15 data points, distributed relatively equally between localities (four at Emerson, five at Naukluft, and six at Greenheugh, Fig. 10 ). Cluster 4 is defined as having the highest abundances of SiO 2 (54. 50 ± 0. 84 wt%), Al 2 O 3 (17. 97 ± 0. 61 wt%), Na 2 O (5. 74 ± 0. 34 wt%), and higher than average K 2 O for the Stimson formation (0. 97 ± 0. 16 wt%). Cluster 5 only has five data points, one at the Emerson plateau and 4 in the Edinburgh interval of the Greenheugh capping unit and is defined by its high MnO concentration (2. 92 ± 0. 19 wt%) and high K 2 O concentration similar to Cluster 4 (1. 0 ± 0. 3 wt%). Aside from the MnO and K 2 O concentration of Cluster 5, it is very similar in composition to the MgO, FeO T , and TiO 2 -rich Clusters 1 and 2.