dataesr/scientific-paragraphs-categorization huggingface dataset:
(3) PDEs of the type FE or FD or hybrid solutions (FE = Finite Element, FD = Finite Difference). (  4 ) Tensor calculus and mathematical logic or hybrid algorithms (based on vectors and matrices). For all these applications, we assume that the optimal distribution of transistors on the chip, over the involved computing resources, should be as it was given in Table. 1. This means that the flexible FPGA-based Data Flow dominates in the distribution of transistors over the paradigms implemented on the supercomputer-on-a-chip. Of course, for applications of interest, data are provided either from the internal Memory System or from an external Internet Protocol or another stream. In a number of applications, data are provided via IoT (Internet of Things) and/or WSNs (Wireless Sensor Networks).
Also, further development of machine learning algorithms and sample preparation requires combined knowledge in the field of earthquake engineering, construction, and programming. Carefully designed curricula that contain theoretical basis with the application through practical examples and case studies, is necessary for preparing the students for this problem in practice. Employment of existing datasets from previous earthquakes, containing huge amounts of data, would be of great importance for successful training and development of knowledge and skills within the students. Enthusiasm and effort of students to reach the positive results in the application of the machine learning algorithms employed for this purpose give additional motivation to professors, but also to the next generation of students.
Conclusion This course tunes the students of civil engineering and geosciences to the potentials coming from the synergistic interaction of four different computing paradigms. This course is discussed in the context of civil engineering and geosciences but could be easily ported to other engineering disciplines. This course prepares students for complex educational or research missions, both in academia and in industry, for research and/or production. This course (the approach advocated in this article) assumes the existence of a supercomputer on a chip that includes some of the existing computing paradigms; namely, those which are used more frequently, and effectively interface to the other paradigms, which are used less frequently! Researchers without ICT backgrounds sometimes claim that software engineers do not aim to comprehend the essence of the universe, and their only goal is to embed it all in a software algorithm or an Excel Spreadsheet.
In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper DLBench+, a benchmark to evaluate and compare data lake implementations that support textual and/or tabular contents. More concretely, we propose a data model made of both textual and CSV documents, a workload model composed of a set of various tasks, as well as a set of performance-based metrics, all relevant to the context of data lakes. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience.
Although there are several benchmarks for big data systems in the literature, none of them considers the wide range of analyses that are possible in data lakes. To fill in this gap, we introduced the Data Lake Benchmark (DLBench)  [4]  to provide a quantitative performance evaluation of data lake systems managing textual and tabular contents. However, sheer quantitative performance is not enough. Another key issue is to make data accessible and valuable to users  [5]. This is especially true for business users who are not expected to have sufficient skills to extract information from the lake themselves. Thus, there is also a need for qualitative performance evaluation related to user experience. In this paper, we propose an enhanced version of DLBench, called DLBench+, which incorporates both quantitative and qualitative features to evaluate data lake systems in a more comprehensive way.
DLBench+ remains technology-agnostic, i. e. , it focuses on a data management objective, regardless of the underlying technologies  [6]. The quantitative side of DLBench+ is designed with Gray's criteria for a ''good'' benchmark in mind, namely relevance, portability, simplicity and scalability  [7]. The qualitative side of DLBench+ is based on the measure of user experience. More concretely, DLBench+ features in its quantitative part a data model that generates textual and tabular documents, i. e. , spreadsheet or Comma Separated Value (CSV) files whose integration and querying is a common issue in data lakes. A scale factor parameter  allows to vary data size in predetermined proportions. DLBench+ also features a workload model, i. e. , a set of analytical operations relevant to the context of data lakes with textual and/or tabular content.
Finally, we propose a set of performance-based metrics to evaluate such data lake implementations, as well as an execution protocol to execute the workload model on the data model and compute the quantitative metrics. The qualitative part of DLBench+ features a set of metrics to measure the user experience and a survey-based protocol to measure them. The metrics we propose thus allow to evaluate the usability, the time-saving and the usefulness provided by the system from the users view. The measure of the usability is based on the System Usability Scale (SUS) methodology  [8]  which is widely used to evaluate software tools. The time-saving and usefulness measure are more specific to data lake systems. Eventually, DLBench+ mostly focuses on the analysis capabilities of data lake systems. Evaluating the preprocessing phase would indeed presumably require a full benchmark on its own, i.
e. , a benchmark very similar to ETL (Extract, Transform, Load -even though data lakes usually adopt an ELT strategy) benchmarks that do already exist  [9, 10]. Yet, we still provide a simple way to evaluate preprocessing, by measuring preprocessing time (i. e. , data integration and metadata extraction) and storage space overhead. The remainder of this paper is organized as follows. In Section 2, we survey existing benchmarks and show how DLBench+ differs from them, as well as user experience evaluation. In Sections 3 and 4, we provide DLBench+'s specifications from the quantitative and qualitative point of view, respectively. In Section 5, we exemplify how DLBench+ works and the insights it provides by benchmarking the AUDAL data lake  [11]. Finally, in Section 6, we conclude this paper and present research perspectives.
Textual benchmarks In this category, we consider big data benchmarks with a consequent part of text on one hand, and purely textual benchmarks on the other hand. BigDataBench  [22, 23]  is a good representative of the first category. It indeed includes a textual dataset made of Wikipedia 3  pages, as well as classical information retrieval workloads such as Sort, Grep and Wordcount operations. The PRIMEBALL benchmark  [24]  also belongs to this category with a workload and a dataset related to news articles management in cloud systems. One of the latest purely textual benchmarks is TextBenDS  [25] , which aims to evaluate performance of text analysis and processing systems. For this purpose, TextBenDS proposes a tweet-based data model and two types of workloads, namely Top-K keywords and Top-K documents operations.
The LSHTC benchmark  [26]  addresses more specifically the evaluation of text classification systems. It features a dataset including Web pages and a set of relevant metrics such as system accuracy, precision, recall and F 1 score. Similarly, Reagan et al. 's benchmark is dedicated to sentiment analysis tasks  [27]. Eventually, other purely textual benchmarks focus on language analysis tasks, e. g. , Chinese  [28]  and Portuguese  [29]  text recognition, respectively.
Discussion None of the aforementioned benchmarks proposes a workload sufficiently extensive to reflect all relevant operations in data lakes. In the case of structured data, most benchmark workloads only consider SQL operations (TPC-H, TPC-DS, HiBench). More sophisticated machine learning operations remain marginal, while they are common analyses in data lakes. Moreover, the task of finding related data (e. g. , joinable tables) is purely missing, while it is a key feature of data lakes. Existing textual workloads are also insufficient. Admittedly, BigDataBench's Grep and TextBenDS's Top-K documents operation are relevant for data search. Similarly, Top-K keywords and WordCount are relevant to assess documents aggregation  [19, 25]. However, other operations such as finding most similar documents or clustering documents should also be considered.
Thus, DLBench+ stands out, with a broader workload that features both data retrieval and data content analysis operations. DLBench+'s data model also differs from most big data benchmarks as it provides raw tabular files, inducing an additional data integration challenge. Moreover, DLBench+ includes a set of long textual documents that induces a different challenge than short texts such as tweets  [25]  and Wikipedia articles  [22]. Finally, DLBench+ is technology-agnostic, unlike big data benchmarks that focus on a particular technology, e. g. , TPCx-HS and HiBench.
Tabular data are reused from an existing benchmark  [30]. It is actually a set of 5000 synthetic tabular data files generated from an open dataset stored inside a SQLite 6  database. Many of the columns in the tables contain similar data and can therefore be linked, making this dataset suitable to assess structured data integration as performed in data lakes. We apply on this original dataset 7  a script that extracts either all or a part of the tables, in the form of CSV files. As for textual documents, this second script also generates a metadata catalog. The script as well as guidelines are available online. 5.
However, since AUDAL is the only data lake system that supports both textual and tabular data, to the best of our knowledge, we cannot compare it to any equivalent system. Thence, at this time, we resort to propose alternative implementations of AUDAL, e. g. , re-implementing AUDAL with Cassandra instead of MongoDB, Apache Solr instead of Elasticsearch, Allegrograph instead of Neo4J and MariaDB instead of SQLite, and all combinations of these software components. Another lead is, since unstructured data are currently seldom included in data lakes, to focus on the structured part and user-experience evaluation to compare data lake systems such as Aurum  [38] , Mami et al. 's  [42]  or Bogatu et al. 's systems  [43]. Another enhancement of DLBench+ could consists in providing an overview of value distributions in generated data.
That would allow a further analysis of the evaluation results, especially for keyword-related tasks. For example, we could analyze how the system reacts to a query with very frequent or uncommon terms. The results of these in-depth analyses could be used to improve the indexing strategy underlying the system. Finally, we also envisage an extension of the structured part of DLBench+'s data model with an alternative, larger dataset. In this way, we would better reflect the challenge related to data volume in the structured part of our benchmark.
For this reason, this framework includes a nonlinear structural solver interfaced with steady and unsteady VLM modules. Again, the wing structure is represented by a beam model. More precisely, this is done through a geometrically-nonlinear structural mechanics solver, which correctly models the nonlinear deformations, but retains the linearity of the constitutive relationships. The chosen structural dynamics software is the GEBT program (standing for Geometrically-Exact Beam Theory), freely available at  [38]  and documented in  [39, 40]. The software, developed in Fortran, has already been successfully employed on several aeroelastic applications, such as in  [41, 42]. It is a general-purpose tool designed to address the challenging analysis of highly-flexible, slender structures. The main feature of the approach is that displacements, forces and moments are transferred between the deformed and undeformed beam frames according to exact, nonlinear kinematic relationships involving all the translation and rotation degrees of freedom in space.
) through a series of sizing loops involving modular analyses for the key disciplines. These include basic flight mechanics, aerodynamics, structures, propulsion, weight and balance. The original approach is based on a point mass approximation together with semi-empirical equations for performance, weight and aerodynamic predictions. This allows high computational efficiency and accuracy to be achieved as long as traditional concepts are treated. The propulsion module can be based either on a pre-specified dataset, or on an analytical model that provides thrust and fuel consumption as functions of altitude and flight speed  [51]. The performance module gathers all the information from the disciplinary modules and performs a time marching simulation of the full mission. Sizing and positioning of components are iteratively updated during the design loops through dedicated geometry, weight and balance modules.
Uncertainty Quantification Early aircraft analysis is inherently affected by aleatory and epistemic uncertainties. With our attention directed to aeroelastic performance estimation at conceptual design level, we include and propagate the most relevant uncertainties onto the desired output performance indicators. We consider the investigation into static sizing loads, flutter speed and dynamic gust loads at wing level, and therefore the uncertainty herein considered is that arising from the approximate methods used during the sizing process to estimate wing structural characteristics. The off-the-shelf Uncertainpy toolbox  [57]  was selected for uncertainty quantification and sensitivity analysis and interfaced with the aeroelastic analysis modules. It was a natural choice following its successful use on a previous robust aircraft MDAO application, based on an earlier version of the present framework  [45].
We hypothesize that the reports of MDPI editor reviews will be relatively shorter, given that referees did not have much time to produce them. Thus, we used data from the Publons platform to compare the average size of reviewer reports for MDPI and that of other publishers. To ensure that we are comparing what is comparable, we constructed a control group of publications sharing the same characteristics as those of MDPI (discipline, type of journal, open access, etc. ) from the WoS database. [1] https://unimibox. unimi. it/index. php/s/86RPXTPqBrcdrap [2] https://twitter. com/GemmaDerrick/status/1636719477042692096.
Data and method Publons Data Publons is an online platform that tracks, veri es, and showcases researchers' peer review contributions and activities. The platform was founded in 2013 with the aim of providing researchers with visibility and recognition to their peer review activities and achievements. Publons allows researchers to see a full history of their peer review activities, including those performed anonymously, and to receive recognition and credit for their contributions to the scienti c community (Teixeira da Silva and Nazarovets, 2022). The platform provides a secure and con dential environment for researchers to manage their activities, making it easier for them to track their contributions and receive recognition. The platform also provides insights and data to publishers, institutions, and funding bodies to help them make informed decisions about research and funding.
Publons is now part of Clarivate Analytics, a global company that provides insights and technology solutions for various industries, including academic and research publishing (Web of Science -WoS database). Publons, as a platform for managing and showcasing peer review contributions, has become a valuable asset for Clarivate Analytics in its mission to support the research community and improve the scienti c publishing process. The acquisition by Clarivate Analyics in 2017 has allowed Publons to expand its services and reach a wider audience, while still maintaining its commitment to transparency and recognition for peer reviewers. Publons also made available more than 300,000 items designating the characteristics of referee evaluation reports in journals: average word count, country of referees and journals metrics.
Web of Science data The data about citations scores and disciplinary assignation of publications has been extracted from the French "Observatoire des Sciences et Techniques" (OST) in-house database. It includes ve indexes of WoS available from Clarivate Analytics (SCIE, SSCI, AHCI, CPCI-SSH and CPCI-S. for more information see: https://clarivate. com/webofsciencegroup/solutions/webofscience-platform/) and corresponds to WoS content indexed through the end of November 2020. We have limited the analysis only to the original contributions; i. e. , the following documents types: "Article", "Conference proceedings" and "Review".
Method We extracted publications from the MDPI publisher from the Publons data, in order to compare the length of the reviewers' reports concerning them. Since the length of the reviewers' reports can strongly depend on the intrinsic characteristics of publication (for example the discipline), we extracted a set of publications sharing exactly the same characteristics as those of MDPI (see Fig. 1 ). We also identi ed publications that are not part of either MDPI or the control group. Thus, in this study, we compared the length of reviewers' reports for these three datasets (MDPI, control group and Publons outside MDPI).
Results In this section, we rst present some descriptive statistics of the Publons database: distribution of publications by country, distribution of reviewers by country, evolution of the average number of words in reviewer reports over time, and average number of words by discipline. Second, we compare the average number of words in reviewer reports in the Publons database, those of MDPI journals, and those of a control sample sharing the same characteristics as MDPI (e. g. , disciplinary distribution).
Descriptive statistics Figure  2  below shows the geographical distribution of the publications in the Publons database. Figure  2  shows that the distribution of countries in the Publons database is strongly similar to that of the WoS database as a whole (see appendix). Thus, the signi cant weight of high-income countries such as the United States, China, the United Kingdom, and Germany is observed in this database. However, it should be noted that some countries are overrepresented in the Publons database, mainly some countries of northern Europe, Australia, and Iran. This result is to be expected, especially for countries like Norway, Sweden, or Finland where open peer review practices are highly present. On the contrary, several African countries are not represented in the Publons database.
In other words, the metadata of the reviewer reports is not available for the publications from these countries. Figure  3  shows the distribution of reviewers based on their country of a liation in Publons database. It is important to note here that the data provided by Publons does not allow for disambiguation of authors, as their names are not available. Therefore, for example, if an American reviewer evaluated ve papers and uploaded their reports to Publons, they will be counted 5 times for the USA. This means that Fig. 3  simply represents the number of reviews performed by the country of a liation of the reviewer and not the number of distinct reviewers by country. This means there are two factors that can impact the number of reviewers by country: 1) the extent to which scienti c journals solicit researchers from that country to review papers, and 2) the degree of voluntarism of these researchers to upload the metadata of their reports to Publons (or allow the journal to do so on their behalf when they agree to review a paper).
Another striking result (besides the 23. 73% from the US and 9. 72% from the UK) is the strong presence of researchers from certain countries, which are relatively lower ranked in terms of publication numbers, such as Italy in third position and Australia in fourth. This is also the case for Portugal, Sweden, Switzerland, and the Netherlands. However, these results should be approached with caution as the data also depends on the commitment of researchers to open peer review and, more broadly, open science. Table  1  shows the year-wise distribution of reports and the median, mean, and standard deviation of the number of words in the Publons platform. It can be observed that 95% of the available reports in our database pertain to publications between 2011 and 2020. This is due in part to the creation date of this database (2013), so few reviewers publish their reports retrospectively.
) as those published by MDPI, consists of 1163 publications. Finally, the other publications present in Publons and which are not in the rst or second dataset make up the vast majority, i. e. 52,478 publications. We divided the data into two periods: 2013-2016 and 2017-2020. The goal is to analyze whether the length of reviewers' reports evolves over time. The Fig. 6  shows the average number of words in reviewers' reports, according to the dataset and period. The most striking result, contrary to our hypothesis, is that the average number of words for MDPI reviewers is not lower than that of the control group or even Publons (excluding MDPI). The average length is around 500 words for the rst period in all three datasets. For the second period, there is a signi cant decrease in the average number of words for the control group and Publons excluding MDPI.
The average number of words in MDPI reports remained stable over the period. Given the signi cant variability in the length of reviewers' reports, we prefer to be cautious about interpreting the observed decrease in 2017-2020. In any case, the results clearly indicate that MDPI reviewers do not write less than those of other publishers, at least in the Publons dataset. This result should also be taken with caution, given the representativeness of the Publons database (see the section on study limitations).
Conclusion In this paper, we aimed to verify if the short review times applied by MDPI negatively impact the depth of peer review, measured by the average number of words in reviewer reports. To do so, we used data provided by Publons, consisting of a dataset of 61,197 publications and 86,628 reviewer reports. We also examined the structure and disciplinary/geographical composition of this dataset. Several insights can be drawn from this analysis, most notably that the length of MDPI reviewer reports is not lower than that of reviewers in other journals, contrary to our initial hypothesis. Even we acknowledge that MDPI reviewers may also review publications for other publishers, our ndings show that they write, on average, the same number of words in their reports and that shorter review times do not seem to affect the quality (or at least the depth) of their evaluation.