dataesr/scientific-paragraphs-categorization huggingface dataset:
2 and in Section 4. 3 on Dataset 1. The reference methods known in literature, i. e. , the branch and bound algorithms used to solve formulations [V S -L] and [V S -L2], are compared to algorithm [U BA]: they all provide the same upper bound to the utility of the first player in a Nash equilibrium. We recall that the proposed algorithms [BN E] and [W N E] provide the tightest (respectively, upper and lower) bounds to the utility of every Nash equilibrium. Table2reports the computation time for every instance of each algorithm as well as the number of iterations of the three iterative algorithms[U BA], [BN E] and [W N E]. Comparison [U BA] vs [V S -L] vs [V S -L2]. First and main result is that the algorithm [U BA] overcomes the methods relying on [V S -L] and [V S -L2] by some orders of magnitude.
The 15 groups are built alternating 3 different structures {R3 * 729, C3 * 729, U 5 * 729} and 5 different utilities ′ * ′ ∈ {R, D, Z, A, F }. The utility E has been discarded, as we show next that it does not need further analyses. The analysis performed on Dataset 3 allows to understand the variability of the results obtained for Dataset 2.
Data Collection Data collection for this study involved the distribution of offline questionnaires to the Tunisian population. The questionnaire consisted of 13 constructs adapted from previous studies and related to the extended UTAUT2 model. Each construct was measured using multiple items on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). In addition to measuring the constructs, the questionnaire collected demographic information, such as age, gender, education, occupation, and frequency of use. Prior to the data collection, a pretest was conducted with expert researchers and potential participants to ensure the clarity and understandability of the survey. The data collection period spanned from November to December 2022 and covered all regions of Tunisia, including 12 governorates.
Data and software repositories such as Zenodo or Pangea complement platforms that provide access to Earth Science manuscripts before (preprints) or after (postprints) peer-review at no cost, such as EarthArXiv  (Narock et al. , 2019)  and ES-SOAr (https://essopenarchive. org/). Together, these repositories and archives cover the entire publishing cycle, providing Digital Object Identifiers (DOI) for all research outputs produced. DOAJs further enhance the accessibility and impact of research in the Earth Sciences by including editorial handling, peer-review processes, and formal typesetting/formatting. Among these, there are well-established international DOAJs like Palaeontologia Electronica (1998), The Sedimentary Record  (2003 ), and Volcanica (2017 : Farquharson and Wadsworth, 2018) , as well as new journals such as Seismica (2022:  Rowe et al.
The form is meant to guide unambiguous comments by reviewers, facilitate revision and response by the authors, and allow swift and fair decisions by the editors. The review form is designed to dissociate scientific content from manuscript structure and includes a section for an overall evaluation, general comments and summary. It also includes a series of subsections (title, abstract, introduction, datasets and methods, results, discussion and conclusions, and figures, tables, and citations) for assessing the manuscript's form, evaluating its scientific merit and relevance, and facilitating authors' responses to reviewers' comments. Finally, it has sections for comments and feedback on the review process that are being implemented into a new review form to improve the review process at Tektonika.
Additionally, we are facing issues related to DOI activation and Google Scholar discoverability of our published articles. We are also exploring ways of facilitating and promoting post-publication discussions on published articles. We are working on resolving these issues, which may involve migrating to another manuscript management system. Resolving these challenges will ensure the sustainability and scalability of the journal in the short term. Further integration and interoperability with other repositories should follow in order to enable indexation in bibliographic databases, such as SCI (Science Citation Index) of the Web of Science and Scopus, which should increase the discoverability of Tektonika articles and their citations. Tektonika aims to continuously improve and adapt its systems with the goal of enhancing the experience of both authors and readers within its open access vision.
Long-Term Aspirations The geoscience diversity crisis. The geosciences are at the forefront of the Global North academia diversity crisis as the least diverse discipline at all academic levels  (Bernard and Cooperdock, 2018). The discipline is disproportionally white due to an institutional culture that has not yet escaped its history of colonialism  (Dowey et al. , 2021). Gender inequities are reported where data is available  (Handley et al. , 2020; Hori, 2020; Kamm et al. , 2020; Kavanagh et al. , 2022) , and the ethnic and racial imbalance is alarming, and shows no sign of improvement over time   (Bernard and Cooperdock, 2018; Dowey et al. , 2021). The minimal diversity gain over time exposes that current solutions to remove barriers and biases are unsuccessful. At Tektonika, we are firmly convinced that diversity strengthens science.
Acknowledgements First and foremost, we would like to thank the tectonics and structural geology community for embracing this initiative from the start. Their feedback, enthusiasm, and passion about DOA were essential to the launch of Tektonika. The success of Tektonika would not be possible without our Associate Editors, who volunteered their time to support the editorial process, the authors, who trusted us with their research, and the reviewers, who agreed to provide their invaluable peer-review. These three pillars of the publishing system made the publication of this first issue possible. We would like to thank the  University of Aberdeen , especially the  Department of Geology and Geophysics in the School of Geosciences , for supporting Tektonika financially and morally. We are also grateful to Volcanica and its team for leading the way and sharing with us their know-how to set up a community-led DOAJ.
This cell was modelled in the software LS-DYNA version 9. 3. 1. The basic concept is shown in Figure  2A. It is based on the model described by  Raffler et al. (2017)  where beam elements also play a central role  (Raffler et al. , 2017). However, instead of a cylindrical cell, a cell in pouch format is modelled here. The basic concept can be divided into four essential components, which are highlighted in Figure  2A. The model was calibrated to fit the experimental data described by  Raffler et al. (2022) ;  Schmid et al. (2022). The macroscopic model is shown in Figure  2B. The outer layers ① represent the pouch shell. This is modelled by shell elements. An isotropic elastic-plastic material model (*MAT_PLASTICITY_COMPRESSION_TENSION) is used for this. The required parameters can be determined by tensile tests of samples of the pouch.
However, the impactor velocity is 3 m/s, which is significantly higher than the quasi-static level (1 mm/s). (Remark: Clearly, this could have been modelled through rate-dependent dampening of the beam elements, too. However, it turned out, that the stable time-step chosen by the solver drops massively once dampening is enabled in the non-linear 6 DoF beam material model). To summarize, the macroscopic model is calibrated through tensile tests of the four components anode, cathode, separator and pouch. In addition, the data from two quasi-static and one dynamic cell tests are used to calibrate the model. The quasi-static experiments were performed with a hydraulic press. The system had a maximum force of 420 kN and allowed displacements of up to 400 mm. The possible load velocities were between 0.
These unknown coefficients are found by solving the minimization problem iteratively by means of the Alternating Direction Strategy (ADS), which conducts to the solution of small algebraic systems in each direction s k  (Kisters et al. , 2017). An important point regarding the method is the collection of data points. The algorithm is called sparse PGD because it fits the data from a sparse sampling. In general, the Design of Experiments for the sPGD is built from a Latin Hypercube Sampling. The implementation of the sPGD is performed in Python 3. 8.
f′ s ( ) f s ( ) -f 0 - d i 1 f i s i (11) Also, for the ANOVA case, the generalization to vector-valued functions is done following  (Champaney et al. , 2022a). In addition, the implementation of the ANOVA-PGD is performed in Python 3. 8 and integrated in some beta versions of AdMoRe (being one of the latest developed algorithms).
This does not require a-priori knowledge of the system itself, as it is a method of data analysis. This is shown in Eq. 13, where f (x,t) is the collected data and f (x,t) its approximation. The data was measured at N points. The approximation is calculated as a finite sum of the temporal coefficients α i and the spatial modes φ i (x). Therefore, only those P eigenmodes with the biggest energetic proportion are used  (Liang et al. , 2002). f x,t k ( ) ≈ f x,t k ( ) P i 1 α k i φ i x ( ) k ∈ 1, N [ ] (13) There are different methods for calculating the coefficients and modes. The snapshot method is very well known  (Sirovich, 1987). As shown in Eq. 14, the spatial modes φ i (x) consist of a linear combination of the temporal coefficients α i and the snapshots f (x,t k ). φ i x ( ) N i 1 α k i f x,t k ( ) (14) Thus, the temporal coefficients α i are the last remaining unknowns.
Data generation To create the database, 100 simulations were carried out with different parameter combinations. The results are shown in Figure  5. The left subplot shows the contact force between impactor and cell over the penetration depth. The maximum force reached around 340 kN. The respective parameter combination also gives the largest penetration depth of around 2. 8 mm. It can be seen that all forcedisplacement curves have a similar characteristic shape. The force increases up to the maximum and then the contact force decreases. The right subplot of the figure shows the kinetic energy of the impactor over time. The time range that was simulated was the same for all 100 runs. The termination time was 5 m. However, the initial value varies between the individual parameter combinations.
Data prediction The database consists of 100 runs for both methods, sPGD and ANOVA-PGD. Of these, 80 are used for the training phase. The remaining 20 runs are available for validation. Here, five comparisons between HD model and meta-model are discussed as examples. This is the case for the prediction of the forcedisplacement curve, the kinetic energy over time and the contour  plot. These combinations were picked randomly out of the validation data of the sPGD DoE. The corresponding parameter combinations are listed in Table  2.
Acknowledgments This work was conducted during the realisation of the project  SafeLIB  (Grant agreement No. 882506 ) with data from the previous project SafeBattery as a collaboration of  Vehicle Safety Institute of Graz University of Technology and Arts et Métiers ParisTech (Paris Campus, PIMM Lab). SafeBattery was funded within the framework of  COMET-Competence Centers for Excellent Technologies  and by the  Province of Styria , as well as the  Styrian Business Promotion Agency SFG. The follow up SafeLIB has additionally been funded by the  Province of Upper Austria. Both projects are administered by the  Austrian Research Promotion Agency (FFG). The authors would like to thank the mentioned agencies and the cooperating companies. Additionally, the authors acknowledge the support of the  ESI Group  through its research chair CREATE-ID at  ENSAM ParisTech.
A previous game that terminated in its tth period specifies the pair of actions taken in that game in each of its periods 0 through t. A player uses the record to estimate the probability that her opponent in her current interaction will cooperate, given that the current interaction has reached some period t with history h t. In principle, the player might aspire to attach a different probability to each history, just as the strategies in a standard repeated game can attach different probabilities to different histories. However, the number of such histories is (countably) infinite, putting the estimation of a probability for each beyond the reach of any plausible data set, regardless of recent advances in big data. Hence, the player classifies histories into analogy classes, and then calculates the empirical frequency of C and D actions in the record for each analogy class.
The solution uses as input a textual document and extricates the named entities identified therein while associating them with the correct coordinates. SNE disambiguation is designed to support the process of fast exploration of spatiotemporal data analysis, most often for event tracking. The proposed approach was tested on 1360 SNEs extracted from the GeoVirus dataset. The results show that SNEToolkit outperformed the baseline, the standard Geonames geocoder, with a recall value of 0. 911 against a recall value of 0. 871 for the baseline. A flexible Python package is provided for end users.
We distinguish three common cases of ambiguity: • Case No. 1 1 : a spatially named entity can be shared by different places (e. g. , Montpellier from France and Montpellier from Canada). The perfect example is San Jose, which matches 1716 place names in the world. 2   • Case No. 2 3 : a spatially named entity can designate both a place and a nonplace or generally refers to places named after people (e. g. , Washington); • Case No. 3 4 : the same place can be designated by several names or several appellations (e. g. , United Kingdom and Great Britain, Paris and Panama). In this paper, we present a Python module named SNEToolkit that assists in overcoming several forms of ambiguity related to spatial named entities mentioned in textual documents. In the literature, we can distinguish four main approaches for spatial named entity disambiguation.
(i) Approaches based on rules and criteria, such as the Geonames 5 database, for native disambiguation that uses geographic level and population size. In  [2] , the authors include a similarity measure of the basics rules of Geonames by using the Levenshtein distance measure to compute the similarity between the extracted place name in the text and the others proposed by the Geonames database. The authors in  [3]  also applied fuzzy logic techniques to resolve spatial ambiguities. Some approaches, such as  [4] , use knowledge graphs or gazetteers  [5]  to disambiguate named entities. (ii) The second approach uses Machine Learning methods  [6]  to disambiguate spatial named entities within the textual data. (iii) We also note Deep Learning approaches such as  [7]  that attempt to disambiguate spatial named entities.
The paper compares the proposed approach with 20 latest and commonly-used approaches based on 12 public datasets, including several highly challenging datasets. The detailed evaluation results can inform future methodological developments and guide the selection of proper approaches based on application needs. We note that spatial named entity recognition and disambiguation has been notably applied in various domains, including the analysis of news articles, research papers, as well as in the geolocation of brief messages sourced from social media platforms like Tweeter. The nature of these applications, however, is considerably distinct, each posing unique context disambiguation challenges. In the case of news articles, there is not typically an implicit assumption that mentioned spatial named entities are in close proximity to each other.
Conversely, social media texts, due to their brevity and personal context, often contain only a single named place, necessitating a different approach for effective resolution. Nonetheless, it is important to acknowledge that hybrid methodologies, akin to the one put forth in Ref. [15] , have been found to be capable of delivering superior performance across diverse types of documents. Such an approach consolidates the strengths of various techniques, thereby enhancing the effectiveness of spatial named entity recognition and resolution irrespective of the document context. Among these strategies, we designed and added SNEToolkit, which is based on (i) rules that combine fuzzy matching, (ii) alias resolving and (iii) popular country code (based on context) approaches with gazetteers that address ambiguous spatial named entities at the country level.
All these sets of rules are ranked based on population size. SNEToolkit offers two access modes: a Python package for advanced users and a graphical interface based on Streamlit for all users. The baseline of this study is the Geonames standard geocoder on which we have implemented SNEToolkit, adding the disambiguation approaches as our main contribution. SNEToolkit is applied as a postprocessing step to the output of the Geonames geocoder. SNEToolkit is primarily implemented for on WikiNews data, which does not guarantee its effectiveness with other types of data, particularly social network data such as tweets and other short messages. Its performance may also be uncertain in instances where spatial named entities are employed metaphorically.
Architecture SNEToolkit is written in Python 3. The SNE extraction process is supported by spaCy  [16] , and the geocoding step is supported by the Geonames API. Most techniques also depend on NumPy. All the output is standardized so that it is compatible with data processing packages such as Pandas. The frontend is implemented using Streamlit to establish the interface to the backend. Users have the flexibility to use their desired step, including SNE extraction, SNE geocoding, and SNE disambiguation. A tutorial with an illustrative example that uses the user interface is available on the GitHub repository. Fig. 1  summarizes the SNE disambiguator processing steps. Textual data are passed as input for SNE extraction with spaCy. The set of SNEs is geocoded with Geonames, where each SNE has a set of candidates (listOfCand).
Spatial named entity geocoding This step consists of associating the geographic coordinates with each spatial named entity. We use Geonames,  6  a geographical database that contains a large volume of places and location names associated with their coordinates. The Geonames database is enriched and populated by external databases and sources. 7 For a given spatial named entity, Geonames returns a set of candidates with the associated coordinates that may suit the input. The native Geonames API incorporates a static disambiguation approach (as it does not take semantic context into account), and unfortunately, it cannot resolve context issues. Without additional attributes such as feature class or country code, Geonames will always return the candidate that has the largest number of inhabitants or a large area by default.
In complex situations, we need to add more techniques and criteria that consider the context in which the spatial named entity is mentioned. To address the limitations found in the default geocoding of the Geonames database, our approach aims to refine the extracted Spatial Named Entity (SNE) from the output provide by Geonames. This refinement is not integrated within Geonames but implemented as a post-processing measure where we introduce additional selection criteria. Specifically, the context is carefully considered with a scoring strategy which takes into account nonambiguous SNEs as well as population size. We also utilize a fuzzy matching technique to effectively correct any syntactic or spelling inaccuracies that may have emerged in the initial Geonames results. In the following subsection, we describe the entire process utilized to select the final candidate.
Spatial named entities disambiguation • splitAmbiguousAndNonAmnbiguous 1: This first step consists of splitting the set of geocoded SNEs with corresponding candidates into two types: ambiguous and -non_ambiguous. We assume that by querying the geographic databases in the geocoding step, we have the possibility of returning one or more candidates that can be completely associated with different countries. Thus, the same place name can be associated with several unique locations, where the same name is identified by several coordinates (Case No. 2) and the same name can have several names (Case No. 3). Generally, Case No. 2 is resolved by spaCy but has weaknesses in some cases  [20]. We assume that a spatial named entity is nonambiguous only if the SNE has one and only one candidate in the geographic database, all the candidates have the same name (spelling) as the input SNE and if and only if all the candidates are from the same country (identified by the country code).
The country is associated with its country code. We assume that a spatial named entity is ambiguous only if the SNE has more than one candidate from different countries in the geographic database. We can distinguish two categories of ambiguous SNE, ambiguous named entities whose candidates have the same name (or spelling) as the main or input SNE that we note AsSNE and those whose candidates have a different name (or spelling) than the main or input SNE that we note NotAsSNE. Depending on the category, the adopted disambiguation approach will differ from other ambiguous SNEs. • resolveByFuzzyMatch 3 : Fuzzy Matching (also referred to as approximate string matching) is a technique that helps identify two elements of text, strings, or entries that are approximately similar  [21]. Similar to  [3] , we introduced these techniques as new strategies with the Geonames API to address the NotAsSNE set to solve problems such as typing problems (e.
Illustrative examples To illustrate the functionality of the proposed approach for spatial named entity disambiguation, we utilized the GeoVirus  [24]  corpora. Geovirus contains 229 articles from WikiNews 8  related to global disease outbreaks and epidemics. Place name mentions are manually tagged and assigned Wikipedia page URLs with their global coordinates. The GeoVirus dataset provides 1516 unique toponyms. For evaluation purposes, there were no duplicated toponyms in the same article. We filtered the GeoVirus corpus based on the steps illustrated in Fig. 3. 1360 of the 1362 SNEs are utilized for the experiments, as the two SNEs (Karo and Soviet Union) do not match any country code in Geonames although they have some coordinates. Users can apply disambiguation from textual documents. In Fig.
The green dots correspond to the locations that are correctly solved with both approaches. To evaluate the proposed approach, experiments are conducted on the GeoVirus dataset. Table  1  summarizes the different scores for each step. We note that the number of disambiguated SNEs keeps increasing from the FuzzyMatch step to the Score-WithPop step (1144 to 1204). As the ground truth data were annotated by experts, each label is considered true (all locations are true), and the precision is always equal to 1. 0. The main goal in this context is to improve the value of the recall, which considers the predicted values (disambiguated or default geocoding value). By considering the remaining SNEs after each step as errors, the recall and F-score increase after each step, from 0. 806 to 0. 850. On the other hand, when the remaining SNEs are resolved or completed by using default geocoding, we also notice a high value of recall with a small variation, from 0.