dataesr/scientific-paragraphs-categorization huggingface dataset:
VALIDATION DATABASE FOR DYNAMIC KRUEGER FLAP MOTION A major objective of the UHURA project is the creation of a database with aerodynamic flow data for the unsteady flow properties of a fast-moving Krueger flap high-lift device. by combining the different models and tunnels, further influence factors shall be evaluated that cannot be easily done with a single entry. By using two models with same shape at different scale, the Reynolds number effect can be differentiated from the effect of wind speed and Mach number. By comparing full-span and part span Krueger flaps, effects of the limited span during motion and its effect on the dynamic behaviour is accessible. Further, the effect of wing sweep is evaluated by the different setups. In order to be comparable in all these data sets, it is important to establish a common basis and a common reference.
From the drive system the angular position of the drive shaft was transiently recorded. 1  lists the test conditions where data has been obtained. First, for the two given wind speeds static data points have been acquired by an angle of attack sweep from zero incidence up to 5 degrees beyond maximum lift coefficient. From this the reference angle of attack of 6 degrees was established by comparison to the target pressure distribution for all further measurements with deployed Krueger flap. Second, at this incidence, statically deployed Krueger positions at 5 steps from fully retracted to fully deflected have been measured. For the turbulent statistics up to 2500 snapshots have been sampled. Figure  8  exemplarily shows the time averaged relative axial velocity and the turbulent in-plane kinetic energy content.
The major time of the test was spent with the recording of unsteady transient flow data by pressure and PIV. PIV image acquisition was possible at a rate of 5 Hz. In order to get a sufficient angular resolution of the motion, the PIV image acquisition at high deployment speeds were repeated with a phase shift so that at least 20 snapshots per deployment cycle were obtained. This results in an angular resolution of about 7 degrees Krueger deployment. Each phase angle of a motion cycle was recorded at least by 300 snapshots for a statistical evaluation of phase averaged data. Thus, for the shortest deflection time of 1 second, in total 1200 deployment and retraction cycles have been sampled. For the longest deflection time of 4 seconds the acquisition frequency was high enough to avoid the phase shift and repetition.
Tests at DNW-LLF wind tunnel For the third test campaign, the larger DLR-F15LS-LLE model was mounted in the open 6x8 m² test section in the Large Low-speed Facility DNW-LLF in Marknese, The Netherlands. The campaign was conducted in two entries. In April 2021 the data acquisition mainly concentrated on pressure, position and deformation measurements using SPR and discrete accelerometers on the Krueger flap were performed, while the second entry in April 2022 concentrated on acquiring flow field information by PIV. The larger model DLR-F15LS-LLE is a 2:1 upscale of the model used in ONERA L1 and DNW-NWB. Thus, at the same flow condition the double Reynolds number is obtained. The Kreuger flap in this model spans 3 m and is like for the cantilever wing in DNW-NWB a partspan arrangement. Due to the larger size, the Krueger is now driven by two synchronized motors from both sides.
CONCLUSION The project UHURA aimed at validating numerical simulation methods for the aerodynamics of moving high-lift devices. These validate methods shall further on help mitigating critical flow states at innovative high-lift systems, especially Krueger flaps to be implemented at laminar technology wings. The project created a unique and comprehensive database for such flows by conducting 5 wind tunnel campaigns in three different tunnels using two different models of same shape but different scale. It cannot be emphasized enough that this was a very special journey since the COVID-19 pandemic mostly prevented travelling and thus all the tests performed in a cooperative way had to be conducted with remote support only. In total, 34 GigaByte of time resolved data have been collected , not yet including the PIV and SPR images.
left) and the DNW-NWB wind tunnel as a cantilever wing with wing sweeps of 0 deg and 23 deg (Figure6middle). The larger variant for swept installation DLR-F15LS is a 2:1 upscale with 1. 2 m chord and 7 m span and was mounted at a sweep angle of 30 deg in the DNW-LLF wind tunnel (Figure6right). With this wind tunnel strategy, the project was able to create a consistent database on the aerodynamics of a moving Krueger device including the effects of Reynolds number and wing sweep.
a b s t r a c t This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the ScienceDirect website. This dataset includes two sets of data: manual table annotation results and automatic data extraction results. The tables were first annotated by one annotator and cross-curated by three different annotators. The annotation task aimed to identify all table data dealing with packaging permeability measurements and compositions. An Ontological and Terminological Resource (OTR) was used for the annotation process. The annotation guidelines were drawn up through a collective iterative approach involving the annotators, and they may be accessed alongside the data.
This dataset of n-Ary relations can be used in natural language processing (NLP) approaches implemented in experimental fields, especially for n-Ary relation extraction research. It can also be useful for training or evaluation of methods for the extraction of experimental data from tables and text in scientific documents, especially in experimental domains such as food packaging. © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Subject Data Science: Data Mining and Statistical Analysis Specific subject area Food packaging permeability and component Type of data. csv Table ,. py source codes and annotated. html files How data were acquired These data are the result of manual and automatic annotations. Manual annotation was performed by one annotator before being cross-curated by three different annotators. Automatic annotation reproduces the method described in  [1, 4]. Ten html files of scientific articles from different journals were manually downloaded from ScienceDirect and reprocessed. The pre-treatment programs are available within the dataset. The annotations were performed according to the annotation guidelines, which may also be accessed alongside the data (see Data accessibility). Only the 31 annotated tables extracted from the 10 articles are available with their respective captions and titles, while the full texts are not presented for copyright reasons.
Data Description The dataset  [9]  primarily consists of two data archives (. targz). The manual annotation and automatic annotation archives contain the annotated tables in html format, which are grouped in directories by document names. A result table (. csv) file is also present in each archive and represents the annotated data in terms of partial n-Ary relations. An annotation manual (. pdf) provides instructions for annotators and explains the choices. An archive (. targz) contains the python codes to make document modifications required for the annotation process and the result tables in the archives. Each result file contains the partial n-Ary relation instances present in a table (an example of a n-Ary relation instance row is given in Table  1  ). Each data entry is described as a partial n-Ary relation with a set of features:  Caption the textual content of the table caption; Segment the names of the section and sub-section in which the table is present.
Experimental Design, Materials and Methods The dataset consists of manual and automatic annotations of the scientific article tables. All pre-processing codes used are available in the dataset. This dataset is complementary to the work presented in  [6]  focused on the annotation of argument instances in document texts. This previous dataset was used to evaluate approaches dealing with the extraction of argument instances from scientific documents  [7, 8]. The work presented here includes a subset of documents associated with this previous work. This data paper is not devoted to argument instance annotation in texts but rather to the annotation of partial n-Ary relation instances in the document tables. These n-Ary relations are composed of several argument instances that are gathered in a relation concept instance representing the information on a more complex level.
Data (input) The dataset includes 31 tables from 10 articles which were manually downloaded in html format from different journals on the ScienceDirect website. We discarded all other textual content so as to comply with the article copyrights (not all papers are from open access journals). Two features, i. e. DOIs and document names, are also provided to enable recovery of the original documents. TRANSMAT  [2, 3]  Ontological and Terminological Resource (OTR) represents concepts in the food packaging domain and the relations between them. This OTR is structured in two parts, a core ontology and a domain ontology (see Fig. 1  and  [1]  for further details). The up-core ontology is the representation of the n-Ary relations structure, defined as a relation concept linked to the arguments composing the relation.
This dataset enhances resources available for table extraction and NLP in specialized fields, especially in the new generation and bio-sourced food packaging domain. • This dataset can be useful for computer and data scientists conducting NLP and data mining tasks. • This dataset can be used in the evaluation or training of methods for different tasks: data table extraction, named entity extraction in a specialized domain, including terminological variations, measurement units, complex numerical values and relation extraction (binary or n-Ary).
Value of the Data • This dataset enhances resources available for table extraction and NLP in specialized fields, especially in the new generation and bio-sourced food packaging domain. • This dataset can be useful for computer and data scientists conducting NLP and data mining tasks. • This dataset can be used in the evaluation or training of methods for different tasks: data table extraction, named entity extraction in a specialized domain, including terminological variations, measurement units, complex numerical values and relation extraction (binary or n-Ary). specific Competency Questions (CQs): CQ1: What are the food packaging constituents and associated quantities in the packaging composition? CQ2: What are the O2/CO2/H2O permeability values and units associated with the different food packagings studied in the article? CQ3: What controlled parameter values and units are associated with the O2/CO2/H2O permeability measurements? Description of data collection The files were selected from the ScienceDirect website according to the competency questions.
They were manually downloaded in html format. The tables were automatically preprocessed and manually verified to standardize the table layouts in order to be compatible with the automatic extraction process. Data source location The data are available on the CIRAD Dataverse portal. The data have beenwere manually collected by the UMR TETIS joint research unit (Univ. Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France) and manually and automatically annotated by the UMR IATE joint research unit (Univ. Montpellier, INRAE, Institut Agro, Montpellier, France). Data accessibility Repository name: TRANSMAT data tables Data identification number: 10. 18167/DVN1/GCZBC9 Direct URL to data and annotation guidelines: https: //dataverse. cirad. fr/dataset. xhtml?persistentId=doi:10. 18167/DVN1/GCZBC9 1 https://ico.
2021). We also refine our method in several ways: (i) we retrieve the rotation curve using different techniques, thus testing the robustness of the curve and of the methods used to obtain it; (ii) we include in a much more consistent way the contribution of the pressure gradient in the disc, as a function of the height of the relevant gas emitting layer; (iii) we explore the dependence of our results on additional structural parameters, such as the disc pressure scale-height. The paper is organized as follows. In section 2 we describe the two sources we use for our analysis and the relevant data sets and parameters. In Section 3 we describe our method to extract and model the rotation curve. In Section 4 we present and discuss our results on the disc mass of IM Lup and GM Aur. In Section 5 we draw our conclusions.
Basic system parameters The general problem of fitting kinematical data to a model involves a large number of parameters, including geometrical parameters (e. g. distance, inclination, position angle of the source. ), physical parameters (e. g temperature and pressure scale height, disc and stellar mass, etc. ) and radiative parameters (location of the surface from where the different lines emit), depending on the sophistication of the model (see  Pinte et al. 2022  for a recent review). Rather than constructing a single fitting procedure to fit for all of the unknown parameters (which would typically imply a large degree of degeneracy if the dataset is limited), here we assume some of the system parameters from the literature (e. g. , geometry, height of the emitting layers,. ) and fit for the stellar and disc masses based on an observed rotation curve.
CONCLUSIONS In this paper we have analysed the rotation curves of two protostellar discs, IM Lup and GM Aur, for which high quality kinematical data are available from the MAPS program. In particular, we have refined our technique to infer dynamically the disc mass, by fitting the observed rotation curve to a model curve that includes the disc self-gravity. We had previously used the same method to determine the mass of the disc in Elias 2-27  (Veronesi et al. 2021 ) and here we thus extend the same analysis to two additional sources. With respect to our results for Elias 2-27  (Veronesi et al. 2021) , we have improved the analysis in several respects: (i) We retrieve the rotation curve by using the tool  (Izquierdo et al. 2021). (ii) We have tested the robustness of the rotation curve retrieval by comparing the results of to those obtained from the package (Teague 2019).
ACKNOWLEDGEMENTS This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 01055. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO , and  NAOJ. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  823823  ( RISE DUSTBUSTERS  project). We used the emcee algorithm (Foreman-Mackey et al. 2013), the  corner package Foreman-Mackey  ( 2016 ) to produce corner plots and python-based MATPLOTLIB package (Hunter 2007) for all the other figures.
Data Collection As shown in Figure  1b , Curiosity embarked on a science campaign to investigate the pediment capping unit between Sols 2618 (18 December 2019), and 2780 (1 June 2020). Images of the pediment capping unit were collected from a vantage point at Western butte before the rover traversed onto the pediment around Sol 2695. Geochemical data pertaining to the capping unit were collected using the ChemCam and APXS instrument as the rover traversed the pediment capping unit. Drill tailings from the Edinburgh drill site, (Sol 2702 end-of-drive location) were analyzed by the SAM instrument suite. These geochemical data are described in detail by  Bedford, Banham, et al. (2020 ), Bedford, Schwenzer, et al. (2020)  and  Thomson et al. (2011).
Visual estimation of cross-bed dip azimuth was performed as part of this study, particularly for outlying outcrops beyond the limits where digital outcrop models (DOMs) can be reliably constructed (>40 m), or where stereo Mastcam data was not available. Details of this method can be found in the SI material, and in  Banham et al. (2021) ;  Banham et al. (2018). Textured three-dimensional DOMs were generated using Mastcam stereo images using the Planetary Robotics Vision Processing tool (PRoViP)  (Paar et al. , 2012) , and were interpreted using Planetary Robotics 3D viewer (PRo3D)  (Paar & Consortium, 2016; Traxler et al. , 2018). Where DOMs exist, this software was used to measure geometric attributes of the sedimentary architecture, including set thickness, and dip-azimuth of cross-laminations, and subset bounding surfaces.
Grainsize and Sorting Analysis Using MAHLI Data Grain-size analysis was performed on 9 MAHLI images, where sufficient grains could be identified uniformly across the image. Analysis was conducted using the same methodology as  Banham et al. (2018)  at Emerson plateau. In total 1950 grains were measured across all the samples, with analysis being performed on all measured grains as well as grains from individual images. The whole sample set indicates that the pediment consists of a bimodal, moderately well-sorted, medium-grained sandstone, with a geometric mean grain size of 486 μm, a sorting of 1. 499, with a symmetric skewness. The geometric mean grain size for individual samples range between 335 μm for Galloway Hills, and 634 μm at Assynt Window. Details for each target are provided in Text S1 in Supporting Information S1.
Digital Outcrop Models (DOMs) In total, 574 dip-azimuth measurements were taken across 20 digital surfaces rendered from stereo images. These were used to constrain the palaeotransport directions and aided defining the three key intervals observed. These data were plotted in stereonets, and then displayed spatially with their associated interval on an outcrop model generated using OnSight  (Abercrombie et al. , 2017) : Figure  12  highlights the distinct palaeotransport direction for each interval, and the mapped spatial extent of the stratigraphic intervals. For the data collected using DOMs, the vector mean and vector magnitude of the cross-lamination dip azimuth was calculated using a trigonometric method described by  Lindholm (1987). The vector mean and vector magnitudes for each interval respectively are: Gleann Beag, vector 024.
6. The Stimson formation is unlikely to represent a complete record of activity within the dune field. A major, prolonged reversal of sediment transport, recorded by the Ladder interval is coupled with a laterally extensive bounding surface, which may represent a deflationary supersurface. Reversing of the wind is a large-scale allogenic event and should be recorded across the Stimson dune field deposits. That it is only observed at the Greenheugh pediment indicates that the northern Stimson outcrops are incomplete records, where the top of the succession is missing. T001755/1, ST/W002280/1). Gwénaël Caravaca was supported by the French space agency CNES under convention CNES 180027. VRVis are acknowledged for ongoing support and development of the PRo3D software. Joanneum Research are acknowledged for processing of key data products which made this research possible.
VRVis and JOANNEUM RESEARCH activities were funded by ESA PRODEX contracts 4000105568 and 4000117520, as well as FFG ASAP Contract 882828. Data presented in this study are archived with the NASA Planetary Data System (pds. nasa. gov) and readily accessible through the MSL Curiosity Analyst's Notebook (https:// an. rsl. wustl. edu/msl). Parts of this study were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Reviewers Ryan Ewing and Charlie Bristow are acknowledged and thanked for their constructive comments during the review process: this manuscript has been improved by their input.
Acknowledgments The authors acknowledge the enormous efforts made by the  NASA Mars Science Laboratory (MSL)  Project's engineering, science, and management teams in making this work possible. The authors are also grateful to the many MSL team members who participated in tactical and strategic operations during the  Glen Torridon campaign. Special thanks go to the  Malin Space Science Systems  operations team for their hard work in acquiring the extensive image data and software team for production of the Mastcam mosaic products used herein. Steven G. Banham ,  Sanjeev Gupta , and  Robert Barnes  acknowledge funding from the  UK Space Agency (UKSA)  (Grant numbers:  ST/N000579/1 ;  ST/S001492/1 ;  ST/S001506/1 ; ST/.
Data Availability Statement Data presented in this study are archived with the NASA Planetary Data System (pds. nasa. gov) and readily accessible through the MSL Curiosity Analyst's Notebook (https://an. rsl. wustl. edu/msl). Images used can be found in a data table in Text S1 in Supporting Information S1 slide pack. Data (interpretations) generated during the course of this study are available in Text S1 in Supporting Information S1.
Numerical modelling The optical and mechanical modes observed experimentally were identified using COMSOL, a 3D finite element model software. We also used a multiphysics model to establish the impact of a sessile droplet on the optical and mechanical resonance frequencies of the resonator with a no-slip boundary condition at the interface between the liquid sessile droplet and the disk. The results are used to retrieve the geometry parameters (contact angle, base radius, volume) of the droplet from the experimental data. disk slice where the droplet did land. The inplane vibrations of the disk induce shear-waves in the droplet that are attenuated along the vertical direction, while the no-slip condition is imposed at the solid-liquid interface. For the sake of illustrating the situation at t=0, the droplet contact angle was artificially increased from 12 • to 35 •.
The Zemax optical design software does not offer predefined Fresnel GRIN surfaces in its surface library. However, the user can specify a specific surface by using the "User Define Surface" function. Thus, we created a GRIN Fresnel surface for which the refractive index profile is defined by Equation (4). This surface was then implemented in the Zemax software and we defined the base index of the material (75(GeS 2 ) -15(In 2 S 3 ) -10(CsCl)) at n = 2. 08. The GRIN Fresnel lens was then optimized to meet the specifications of the camera with a spectral bandwidth of [3. 2; 3. 4] µm. A GRIN lens works similarly to a conventional lens, and the radial index variation corresponds to the radial variation of the sag surface for a conventional lens. The surface sag (z Fresnel ) of a lens is defined by its curvature and its conicity.
The Zemax optical design software does not offer predefined Fresnel GRIN surfaces in its surface library. However, the user can specify a specific surface by using the "User Define Surface" function. Thus, we created a GRIN Fresnel surface for which the refractive index profile is defined by Equation (4). This surface was then implemented in the Zemax software and we defined the base index of the material (75(GeS 2 ) -15(In 2 S 3 ) -10(CsCl)) at n = 2. 08. The GRIN Fresnel lens was then optimized to meet the specifications of the camera with a spectral bandwidth of [3. 2; 3. 4] µm. We obtained a GRIN lens with a diameter of 3. 8 mm that was composed of 38 rings, with the outer ring having a width of approximately 24. 8 µm. This 100 µm thick GRIN Fresnel lens had a refractive index variation coefficient of n r ∼ -0.