dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, the database of displacement evolution with time at the impact vicinity and at distance is considered. The displacement data used in this study is plotted in Figure  3  for both impact and distant axes. A secondary y-axis is added for the distant axis albeit to the relatively low displacement amplitude compared to the impact axis. Measures along the impact axis are derived from video records while measures from cable extensometers are considered along the distant axis. Due to the absence of this later data for the 520 kJ impact test at point C, the evolution obtained from simulations presented in  [10]  are considered. This exception is thought to have a minor influence due to the very small amplitude in displacement observed in this specific case. The dynamic response of the structure is clearly visible through the relative initiation time lag between observations corresponding to impact and distant axes.
Simulation database The structure model is impacted at the wall center height and in the middle of its footprint length. The model wall response is computed for a one-second duration with a 0. 25 ms time step for which the simulation duration of about 20 minutes is reported. A demonstrative post-impact deformed structural Penetration stopper (4) -Sphere (7) 0 0 Impacting the wall Block (0,1) -Projectile  (8)  µ cc e ine conformation is presented in Figure  10. Here, the initial (pre-impact) configuration is also presented (similar to Figure  4 ) where transparent blocks reveal the underlying connector model characteristics.
This upper limit is selected based on multiple simulations such that the rebound effect does not cause excessive inter-blocks displacement. As a first step, the overall response of the NSCD model is assessed through a sample set of different combinations of the five input parameters. This sample set is statistically generated using sampling methods based on Monte-Carlo (MC) and quasi MC (such as Sobol', Halton)  [37]  implemented in the UQlab input module  [38]. The Sobol' sampling method is used to define a set of 300 combinations of these five input parameters, as presented in Figure  11. This sampling method provides a random selection of the input parameters within their predefined ranges such that the input space is well-filled. Besides, without any prior bias to the magnitude of the five model parameters, uniform distributions are assigned for all.
Meta-model creation and accuracy assessment A meta-model is a mathematical modelling tool that bridges the gap between the current version of the numerical model and the requisite large number of computations for stochastic analysis. Here, the small statistically created input sample of the model parameters (presented in Figure  11 ) is used to define the mathematical expression incorporating the model behaviour with the desired accuracy. A polynomial chaos expansion (PCE) based meta-modelling technique implemented in the framework of UQ[py]Lab (a python library of UQLab  [39] ) is used in this work (see Appendix B for details). A total of 24 meta-models are created from the displacement response at three time instances -for four different points -for two different energies (i. e. , 3 × 4 × 2 = 24).
For each meta-model creation, a mathematical relation (as presented in Equation B. 2), is established between the 300 distinct sets of input parameters and the corresponding displacement output set. The mathematical relation here can be analogically referred to as obtaining a regression for a 2D database. The accuracy of a meta-model is accessed through the leave-one-out (LOO) error (Equation B. 5) in the order of 10 -2 to 10 -1 for all the computational cases. Thereby, as the NSCD model is highly nonlinear and non-smooth, we accept the meta-model accuracy and the underlying uncertainty. Such a level of accuracy with the added advantage of a single model computation within micro-seconds is deemed sufficient for the upcoming Bayesian interface based calibration method. Further, the accuracy of the meta-models is assessed by comparing the meta-models predictions with the NSCD model computations for the 520 kJ and 1020 kJ impact tests (Figures  14a  and 14b ).
The UQlab sensitivity analysis module  [43]  is used for such computations. The total Sobol indices for the three time instances, at all considered points and for both impact tests are presented in Figure  15. The Sobol indices are observed to significantly differ from one parameter to the other. The higher values are observed for the vertical play (v p ) and concrete-to-soil friction coefficient (µ cs ) e. g. , at points  influence cannot be neglected. Besides, we observe that the Sobol index for a given parameter varies over time and is different from one impact energy to the other, e. g. , 'v p ' ranges from 0. 01 to 0. 7, and 'e' ranges from 0. 04 to 0. 22. Overall, these results aid in understanding the structure response highlighting the mechanisms at work with time, complementing the insights to the experimental data discussed in Section 2.
Bayesian interface for model calibration The Bayesian interface is inspired by the Bayes' theorem  [45]  -a representation of the changing beliefs -simply demonstrating that probability of a 'hypothesis' being correct becomes more reliable with supporting 'evidence'. In our work, the 'hypothesis' states that the 300 distinct sets of input parameters give a correct range of displacement output. The idea of reliability increase in on our hypothesis, given the experimental database as 'evidence', means that there exist at least one set of input parameters such that the recorded evidence is reproduced, in other words, the model is calibrated. Hence, our goal is to find the optimal values of the input parameters that allow one to best fit the model predictions to the observations. A brief description of the Bayesian interface is presented in Appendix C.
Implementation The Bayesian interface module in UQLab  [49]  is implemented to obtain a set of input parameters considering three approaches referred to as 'point', 'energy', and 'all'. These three approaches are inspired by the scope of the user's interest to reproduce the experimental outcome which ranges from a particular time instance calibration to overall model behavior calibration in both space and time. The first approach amounts to giving priority to a given point, meaning that the model is calibrated to provide precise predictions over time for this specific point courtesy of the multiple model output feature (demonstrated in Equation C. 8). The second approach, 'energy' is in line with strategies consisting in calibrating a model at a given impact energy before using it for another impact energy.
Benefits of the calibration approach This research proposed a complex approach making use of Bayesian interface accelerated with PCEbased meta-models for calibrating the developed NSCD model. The model calibration concerned five input parameters whose values could not be precisely measured or determined a priori. The proposed stochastic analysis required an investment of 300 NSCD model runs to retrieve the essential database. By comparison, a simple calibration method, for example based on Monte Carlo simulations, would require a much larger number of simulations for obtaining a reliable set of model parameters. The combination of the meta-model and Bayesian interface thus presented a practical route to surpass the induced computational time constraint. On that subject, it is worth highlighting that the average computational time for the simulation of one structure impact response is of about 20 minutes with the developed NSCD model.
This is in line with the basic idea that the intended use of the model should be taken into account when defining the model parameters calibration strategy. By contrast, the 'all' calibration approach appeared to result in displacement predictions at a specific point as good as the prediction by the model calibrated for this specific point according to the 'point' approach. This demonstrates that calibrating the model considering a large number of data is not at the expense of a reduction in prediction capacity as compared to models calibrated for a more specific purpose. In the end, the significance of using all spatio-temporal database for calibrating the model is.
Conclusions and perspectives In this work, a numerical model of a structure consisting of concrete blocks connected one to the others via metallic components and designed to intercept rockfall is presented and successfully calibrated. The model is kept simple and developed in Siconos software package based on NSCD approach mainly for the computation time-saving purpose. NSCD revealed efficiency for modelling such a complex structure where In the calibration process different calibration approaches, ranging from the local point to the global structure's response are presented. It is evidenced that the model parameters value significantly depended on the calibration strategy, meaning on the number and variety of data used in this purpose. The final set of parameters value is reported to nearly mimic the spatial-temporal response of the real structure for both impact tests.
Institutional and academic contexts Handwritten text recognition (HTR) and its upstream task layout segmentation (LS) have become two important topics in the context of Digital Humanities and digital approaches to cultural heritage collections in the GLAM 1 domain. Its growth over the past three to four years in digital projects can easily be linked to the emergence of user interfaces (UI) allowing for the annotation of ground truths (GTs, data that will be used for training), training new models (for the transcription and, lately, for the segmentation) and for the automatic transcription of the users' own data. At first, only Transkribus (Kahle, Colutto, Hackl, & Mühlberger, 2017) provided such a service through the READ project without fees or infrastructure requirements. 2 At the end of the 2020 European Union funding, Transkribus became a paid service accelerating the interest growth of at least one alternative, namely eScriptorium (Kiessling, Tissot, Stokes, & Ezra, 2019) at the EPHE-Scripta-PSL.
Unlike the former, the latter is completely open source, at the cost of not offering a centralized server. In this context, the Consortium pour la Reconnaissance d'Écritures Manuscrites des Matériaux Anciens (CREMMA) project was created to fund a regional server. Its aims are to support students' training and to provide local researchers with a free solution. The CREMMA funding consisted of a grant for the initial cost of the infrastructure as well as an evaluation grant for providing base models for the community of CREMMA's users. The latter was divided into two main languages: French and Latin, from the 9 th to the 21 st century. A postdoctoral position, CREMMAlab provided the infrastructure with complementary time for building a dataset (CREMMA Medieval) and expertise around transcribing medieval manuscripts.
As the CREMMA project was being drafted,  Chagué and Clérice (2020)  provided a solution for facilitating the FAIR principles in an HTR context and providing machine-actionable metadata for datasets. HTR-United, both a catalog of open source HTR ground truths and a toolkit to strengthen the control of documentation and validity of HTR data, records, as of late October 2022, 56 datasets composed of 41. 5 million characters, 725,862 lines in over 13 languages and 6 scripts. HTR-United's catalog provides a useful overview to build new datasets which can complement previous ones.
HTR for Latin and Old French Handwriting in the Middle Ages can, in a simplistic way, be divided into two big writing systems: cursive and calligraphy  (Bischoff, 1985, pp. 58 sqq. ). They reflect two complementary practices, namely cursive hands (écritures d'usage), which are more common to everyday and administrative documents such as accounting books and letters, and book hands. 3 While cursive represents a harder challenge due to the variability of handwriting styles, both families have the potential to be highly abbreviated, depending on the expected audience of the document: literary classics, such as Cicero or Vergilius, might be less abbreviated than pharmaceutical recipes, scholastic works, or accounting books. This situation resulted in mainly two different kinds of strategies for creating HTR ground truth datasets: (1) datasets that would resolve abbreviations directly in the transcription (a practice found mostly used by historians, and quite common for cursive, specifically in France) and (2) datasets that would keep a diplomatic approach to transcription.
Our dataset builds on the experience of Ariane Pinche, specifically her work on the CREMMA Medieval dataset, which treats different variations of Old French from the 13 th to the 15 th century, with a heavy focus on the first section of the period. As the first recipient of the CREMMALab post-doctoral funding, Pinche co-organized a research seminar around the formalization of transcription guidelines for graphemic transcription of Old French  (Pinche, 2022c). Based on her recommendations, a few datasets emerged around the École nationale des chartes and the CREMMA project. Notably, the Gallic(orpor)a corpora  (Gabay, Pinche, Leroy, & Christensen, 2022)  and the course project DecameronFR  (Biay, Boby, Konstantinova, & Cappe, 2022)  provided two additions for Old French and Middle French data, centered around the end of the middle ages.
Document sampling strategy From the three registers making up the construction of a qualitative corpus according to  Bauer and Aarts (2000) , namely channel, domain, and function, only the first parameter is constant in our case: the sample represents exclusively the written Latin language while giving room to texts of multiple functions addressed to different audiences belonging to various genres (while not aiming at exhaustiveness at this stage). The corpus construction can be regarded as a cyclical process: it has not been entirely determined a priori but rather evolved, bearing in mind the logic of complementarity regarding the already existing  datasets. Estimated abbreviation rate and use of specific characters, known genres and scripts were implemented to compensate for what was thought to be missing from the network of the corpus and the corpus itself in order to make it as "representative" as possible.
SegmOnto was born out of the need for a small/restricted common ontology based on existing standards for the description and analysis of document layout, ranging from content categorization to text recognition, mainly addressing the case of manuscripts and early printed books. SegmOnto has already been implemented in several projects led by Pinche and connected to the CREMMALab project such as  Gabay et al. (2022) , resulting in segmentation models mainly for late medieval manuscripts and early prints. 7 As per the CREMMA Medii Aevi dataset, the documents present two kinds of layout: multi-columns and singular columns, for which lines are most often long, except for the Psalms and Book of Hours. SegmOnto offers multiple levels of description, of which only the first is completely standardized, as the second is intended for custom refinement and the third for local and document-based differentiation.
For the purposes of the project, only the first level of SegmOnto has been utilized, such as MainZone for columns and MargintextZone for marginalia. Pinche's Transcription Guidelines  Pinche (2022c)  stressed that HTR was an answer to the need for scientific projects to acquire textual data either to undertake editions or to constitute large corpora. Her guidelines address the need to establish principles common to projects dealing with the transcription of manuscripts in order to: • build shareable, reusable, and durable ground truth data sets; • produce robust generic models, reusable in "out-of-domain" manuscripts; • minimize the collective cost, including that of training people; • build GT that seeks to optimize the learning space of HTR models. Pinche has privileged a graphemic transcription, which reproduces graphemes, i.
e. a canonical form for each character, instead of a graphetic one, which tries to reproduce each variation of a letter (such as ſ and s). 8 Pushing the imitation too far through a graphetic approach induces a risk of making the transcription harder to complete (as it requires technical skills to recognize differentiated shapes of characters), harder to make uniform (specifically as more annotators are to participate in a dataset) and potentially unusable for HTR (as it might introduce more characters and ultimately noise for HTR engine to learn). Therefore, in cases where functional signs have more than one graphetic manifestation but essentially the same function, they could be represented by the same sign: for example, for every manifestation of the paragraph sign, we opt for the pilcrow sign " ¶" (U+00B6) on every occasion, instead of several variations such as "" (U+F1E1).
9 In the context of the guidelines, we set up a list of allowed characters and a list of common and rare cases (such as Table  2  and 3 ). On the topic of abbreviations, resolving them produces specific difficulties for HTR engines, as it leads them to learn more about the language than they originally intended. 10 Abbreviations 7 More information and case studies can be found here: https://segmonto. github. io/. 8 See  Pinche, Duval, and Camps (2022). On this particular topic,  Gueville and Wrisley (2022)  and Pinche took different paths. However, while graphemic transcription cannot be turned and reused for graphetic model training, graphetic transcriptions can be turned easily into graphemic GT, at the cost of establishing a "translation" table for each character.
Journal of Open Humanities Data DOI: 10. 5334/johd. 97 are not resolved in our dataset, as this constitutes rather an interpretative act linked to the specificity of each document. It is not the same as a textual prediction and it could prove to be detrimental to the extension of an HTR model in the long term. Pinche's graphemic approach without abbreviation resolution simplifies the interpretation step of the text, and in turn, the reduction of characters diversity ultimately smooths both the human transcriber and the HTR engine's learning curves. In order to ensure the rigorous application of these guidelines and the homogeneity of the data produced, we introduced quality control to the production and publication workflow. Each manuscript transcription was passed through ChocoMufin  (Clérice & Pinche, 2021) , using project-provided character translation and control tables.
This software, alongside these tables, allows for each dataset to be both controlled at the character level and adapted to guideline specifications and modifications. It also allows for project-specific transcription guidelines to be translated to a more common one such as CREMMALab's (Pinche & Camps, 2022). 11 This process has been largely used in the first months of the CREMMA Medieval project, as the guidelines were still being drafted. It allowed Pinche to produce or align datasets first and harmonize later, as long as the harmonization was from an upper level of details (closer to graphetic) to a lower level (closer to graphemic).
TRANSCRIPTION GUIDELINES FOR THE CREMMA MEDII AEVI The section that follows aims to guide the reader through the transcription norms followed for the Medii Aevi dataset, illustrating the process and the more common and complex cases, especially where new characters have been introduced compared to the CREMMA Medieval dataset. The project adheres to the general principles laid out by Pinche  (Pinche, 2022c, Tables pp. 4 -15) concerning the base cases (punctuation, word separation, functional signs, superscript letters, abbreviations, ligatures, and roman numerals). Using the project-provided character conversion table, ChocoMufin controls the transcription and corrects any anticipated error by transforming the character automatically so it conforms to the pre-defined guidelines (data should be used in their post-ChocoMufin converted state as it sometimes corrected mistranscription).
However, where the guidelines were not directly addressing the situation (new characters, new types of abbreviations), we positioned ourselves and interpreted the guidelines in light of the situation. Each decision was discussed with the original guidelines' author. 12 n general, the main differences that we isolated between the CREMMA Medieval and Medii Aevi datasets, stemming from the language as well as the genre's own characteristics, are: 1. the dataset bears no accentuated vowels like in the Old French texts (a rare event though for the corpus); 13  2. no normalization or distinction of u and v was provided, nor of i and j; 3. two variations of con are found, namely the antisigma and the 9-shaped form; 4. a higher diversity of abbreviating character usage and signification; 5. Arabic numerals alongside roman, mostly in scholastic and medical treatises.
In our corpus, in cases where a macron is extended to more than one letter due to the cursivity of the script, this trait has been reproduced in the transcription, as well as in the case of stacked diacritics, usually in later medieval manuscripts (cf. Table  4 ), as long as it was a semantic feature and not a decorative one. Abbreviation marks significant in themselves: "Standard" Abbreviations signs have been preserved as such, like pr(a)e -p ̃ (p + combining tilde, p + U+0303), pro -ꝓ (U+A753), hocħ (U+0127), ẜ (s with diagonal stroke, U+1E9C) for secundum or ser-, ꝯ for 9 shaped con/cum (U+A76F), Tironian sign ꝰ for the desinence -us (U+A770), ᷑ for (t)ur (U+1DD1), and Ꝙ / ꝙ for quod. Absent from the CREMMA Medieval but present in Medii Aevi, the truncated ending -is is transcribed using the character ꝭ (U+A76D).
The original insular abbreviation has a simple vertical tilde next to the letter "q". Rare characters and Numerals Referring to corpus construction practices for balanced corpora,  Maniaci (1993)  stresses that "sporadically attested variables will therefore be preferred to those that appear in all -or almost all -the individuals that are part of the corpus. " Rare characters, a subset of freestanding abbreviation signs, specifically occurring in the Medii Aevi dataset are therefore given special attention (cf. Table  5 ). In two of the manuscripts, both of medical content, some occurrences of graphemes for the denotation of the metric values ounce and semuncia were encountered. For their transcription, ℥ (U+2125) and  (U+10192) were used. "Barred O" is represented by ∅ (U+2205) and is widely used to transcribe the word instans instead of ꝋ (U+A74B) that, according to MUFI documentation stands for the abbreviation of obi(i)t  (Coulson & Babcock, 2020, p.
CHARACTER(S) UNICODE RESOLUTION EXAMPLES ⁊ U+204A Et ⁊+◌̃U+204A + U+0303 Etiam ꝭ U+A76D -is đ U+0111 d + Last but not least, in addition to roman numerals, often preceded and followed by dots such as ". ii. ", Arabic numerals are also comprised in the dataset, mainly due to the medical treatises (see Figures  3  and 4 ).
Production pipeline The data was built using eScriptorium and Kraken for both segmentation of zones and lines (specifically the BLLA model). Manuscripts were annotated successively. First, the manuscript is automatically segmented, then its segmentation is manually corrected, and finally the text is transcribed. Once each sample is entirely annotated, its use of characters is controlled via the ChocoMufin software, while its conformity to the segmentation classification vocabulary is controlled by HTRVX. Finally, data are released on Github. 16 All the combining and abbreviation signs suggested for use by the present adaptation of Pinche's guidelines can be also found on a custom-made eScriptorium keyboard configuration, in order to facilitate reuse and compatibility with the guidelines. 17.