dataesr/scientific-paragraphs-categorization huggingface dataset:
The name of each file includes the approximated (integer) solar longitude and MY of the first and last available sols within the file. The format for the file names is as follows: mgs-tes-reanalysis mars MY Ã Ls Ã MY Ã Ls Ã v1À0:nc; where the asterisks correspond to the values of MY and solar longitude of the first and last sols. Each Net-CDF file contains the same header with detailed information about the variable dimensions, a short description of all the variables that are present in the file (including units and CF standard names), and general information about the dataset (i. e. global attributes of the NetCDF file). The 63 data files, each about 295 MB in size, have been added to the BADC archive, where they are freely available for download following the procedure explained in Section 5.
One-dimensional temporal variables The three temporal variables are reported in Table  3. Our main continuous time variable in the dataset is the Martian mean solar day (sol), which does not reset to zero at the beginning of a new year. The solar longitude value, instead, resets to zero each time Mars crosses the position of the northern hemisphere spring equinox, thus defining the beginning of a new year. The integer part of each time value defines the sol, and the decimal part defines the fraction of the sol, from which one can calculate the corresponding Mars Universal Time (MUT), i. e. the local time at the Prime Meridian. The time origin in the dataset (sol = 0. 0) is not yet included in the standard CF variable list, there is no corresponding unit related to a standard Martian calendar.
In the dataset, we use the standard time unit for the Earth calendar referred to a reference date of 0000-00-0 00:00:00, mainly for ease of use of software that automatically recognizes time variable units. The values of the time variable, though, are intended as 'sols since 0. 0' where 0. 0 is the time reference in the dataset as mentioned above. The scientific community primarily uses a combination of MY and solar longitude when referring to the Martian calendar. This combination, though, is not satisfactory in a reanalysis dataset, because of the requirement to keep track of the fraction of a sol at the beginning of each new year, when the solar longitude resets to zero at sol 668. 6. There is, therefore, the need to standardize a Martian calendar based on MYs, months and sols, which can be used for precise temporal determination in reanalyses datasets.
This convention simplifies the Martian calendar, but is not ideal to keep track of the fractions of sol at the end of each year in multiannual reanalyses. At the time of writing, the CF community is engaged in discussions to standardize the Martian calendar and introduce the variable 'Sol' in the CF standard variable list. The 'Martian year' is not a standard CF variable either, and could be simply calculated from the time and solar longitude variables. We decided to explicitly include it for ease of use of the dataset. Future releases of the MACDA reanalysis may be able to be fully CF-compliant in relation to the Martian calendar.
Three-dimensional variables The four variables that provide time-evolving surface data are described in Table  4. Note that variable dimensions in a NetCDF file are usually reported in reverse order, e. g. when interrogating the file with the command ncdump. The proper order of the dimensions in every dataset array is the one indicated in Tables  4  and 5. The 'surface carbon dioxide ice' variable refers to the mass per square metre of carbon dioxide depos- ited as ice on the ground at polar latitudes, after having condensed from the atmosphere in the winter seasons. Surface pressure and surface temperature reflect the seasonal condensation and sublimation of carbon dioxide in the Martian atmosphere. In particular, in the presence of carbon dioxide ice on the ground, the surface temperature assumes the value equal to the carbon dioxide condensation temperature at the corresponding surface pressure.
The surface pressure at each grid point shows a seasonal behaviour compatible with the cycle of condensation and sublimation of the carbon dioxide, which is the main component (about 95%) of the Martian atmosphere. The 'total dust optical depth' variable is related to how much radiation (at average visible wavelengths of about 670 nm) would be removed from a beam during its path through the entire atmosphere by absorption and scattering due to airborne mineral dust. The total dust optical depth at each horizontal grid point in the dataset can be referred to a specific pressure level (e. g. 610 Pa) by dividing its value by the surface pressure and multiplying by the value of the reference pressure (assuming that dust is well mixed and the dust properties are consistent throughout). This interpolation/extrapolation eliminates the effects of topographical inhomogeneity.
It should be noted that surface temperature, surface pressure, surface carbon dioxide amount, as well as the horizontal wind components, although not directly modified by observations, are indirectly adjusted by the data assimilation procedure, which dynamically modifies these fields according to the analysis of atmospheric temperature and the update of the model column-integrated dust optical depth. Because we have not used the dust lifting, transport, and sedimentation model available in the MGCM to carry out the complete assimilation of dust observations, the 'total dust optical depth' variable should not be considered as a fully analysed variable. We include it in the dataset to specify the dust distribution which has been used in the MGCM. We provide the total dust optical depth variable also at those times and locations where there is no information from TES total dust optical depth observations (see Figure  2 ).
Dataset Visualization We have developed a web visualization tool (the 'MAC-DA Plotter'), coded in Python, that allows exploration of the variables included in the MACDA dataset. Figure  3  shows the graphical user interface which is displayed when the web interface (version 1. 0) is opened, as explained in Section 5. This version of the tool does not perform interpolation in time or space. The variables included in the dataset can only be displayed as maps at the times and sigma levels provided, which coincide with those described in Section 3. When a specific value of solar The user can specify the desired range of longitudes and latitudes, as well as several parameters related to the appearance of the plot. Note that warning messages appear when the user chooses unacceptable values for the parameters.
Access to the Dataset, the Documentation and the Visualization Tool The MACDA reanalysis dataset for MGS/TES v1. 0 is archived at the British Atmospheric Data Centre (BADC, http://badc. nerc. ac. uk), Harwell Campus, Didcot (UK). This data centre is based in the Centre for Environmental Data Archival (CEDA) group. The CF-NetCDF data files are freely available to all registered BADC users, as this allows the BADC to monitor the use of these data. The registration to obtain a BADC username and password can be requested at http://badc. nerc. ac. uk/reg/user_register_info. html. BADC MACDA webpage. This webpage can be accessed at http://dx. doi. org/10. 5285/78114093-E2BD-4601-8AE5-3551E62AEF2B (or, alternatively, at the shortened URL: http://bit. ly/165Ulxd). It provides information about the dataset and its use (including copyright and disclaimer issues), references and documentation, as well as the hyperlinks to the data file archive, the visualization tool, and other related items (scroll down the webpage to the section 'Online References' to find the hyperlinks).
BADC MACDA Archive. Click on the 'MACDA: MGS/TES v1. 0 data directory' hyperlink in the 'Online References' section on the webpage to access the archive and download the data files. BADC username and password will be required at this stage. The user can download a single data file or multiple files. The 'Download multiple files' box at the top right of the screen allows the user to select a set of files for downloading as a single gzipped tar file, but there is a limitation to 1 GB at a time. To download multiple files it may be easier to directly use the CEDA FTP service. If you want to use this option, connect to ftp://ftp. ceda. ac. uk/badc/mgs/data/macda/v1-0/ using your BADC username and password. There is no size limitation via the ftp server. MACDA Visualisation tool. Click on the 'Data Visualisation' hyperlink in the 'Online References' section to access the web visualization tool, which does not require BADC username and password.
The MACDA Plotter can also be directly accessed at http://macdap. physics. ox. ac. uk. For any data access problems, please contact the BADC helpdesk directly (http://badc. nerc. ac. uk/help/ contact. html) or send an email to the corresponding author of this paper. Scientific use of the data included in the MACDA dataset is freely allowed provided that the origin of the data is appropriately acknowledged in any publications. The correct reference to the dataset is provided at the beginning of this article, in the Section 'Dataset', or at the end in the Section 'References'  (Montabone et al. , 2011). The authors provide no warranties regarding the reliability, validity or accuracy of the data, and bear no responsibility for any use made of such data.
Future versions Updated versions of the dataset will become available in future. People interested in the most recent version are encouraged to contact the corresponding author of this article to ascertain the status of the work in progress. Future improvements of the MACDA reanalysis will include the following: 1. Update of the MGCM to the latest available version  (Forget et al. , 2011). 2. Full assimilation of dust observations using the lifting, transport and sedimentation model for dust particles available in the MGCM. 3. Parameterization of the carbon dioxide condensation under supersaturation conditions, to allow the assimilation of supersaturated temperature profiles. 4. Extension of the reanalysis period to cover observations from the Mars Reconnaissance Orbiter/Mars Climate Sounder radiometer (2006 to date, i.
Acknowledgements MGS/TES retrievals of thermal profiles and infrared total dust optical depths have been provided by  Michael D. Smith  ( NASA Goddard Space Flight Center, Greenbelt, MD, USA ) to the University of Oxford in 2005. The MACDA reanalysis has been processed by the  University of Oxford, Department of Physics , and  The Open University, Department of Physical Sciences , Figure 4. The MACDA Plotter graphical user interface: example of a plot (surface pressure). in the UK. The reanalysis data files were provided to the  British Atmospheric Data Centre  in August 2011. L. Montabone wishes to thank  Russell Jones  at the  University of Oxford  for his invaluable help in setting up the MACDA web plotter tool, as well as  Giacomo Correnti  for useful tips in html and python programming.
The authors are extremely grateful to  J. -B. Madeleine  and to another anonymous reviewer for their very useful comments, which helped to improve the paper. L. Montabone  and  S. R. Lewis  wish also to acknowledge support from the  UK Science and Technology Facilities Council  and the  UK Space Agency  during the course of this work (Grant Refs. ST/I001948/1 ,  ST/K00106X/1 ,  ST/J001597/1 ,  ST/I003096/1 ). Last but not the least; special thanks are due to BADC for supporting the MACDA v1. 0 dataset.
This article was funded by a grants from  STFC , grant numbers  ST/I001948/1 ,  ST/K00106X/1  and  ST/J001597/1 , and  UKSA/ STFC , grant number  ST/I003096/1. The Mars Analysis Correction Data Assimilation (MACDA) dataset version 1. period of about three Martian years (a Martian year is about 1. 88 terrestrial years). This has been produced by data assimilation of observations from  NASA's Mars Global Surveyor (MGS)  spacecraft during its science mapping phase (February 1999-August 2004). In particular, we have used retrieved thermal profiles and total dust optical depths from the  Thermal Emission Spectrometer (TES)  on board MGS. Data have been assimilated into a Mars global climate model (MGCM) using the Analysis Correction scheme developed at the UK Meteorological Office. The MGCM used is the UK spectral version of the.
This paper presents a novel way for a structural dynamic simulation analysis on a three-dimensional (3-D) finite element (FE) model of a 6-DOF Manipulator using ANSYS Workbench 13. 0 that allows integrated optimization. The load between driving and driven gear is delivered by elastic frictional contact, which leads to some non-liner contact problems, and the contact problems are solved according to the FE parametric programming method. This study particularly focused on investigating static, dynamic, and fatigue behaviors of the gear system in a 6-DOF robot mechanism, which is modeled using SolidWorks software. Moreover, the ANSYS Workbench was used to determine the stress distribution, deformation and fatigue behaviors of the mechanism, and finally to carry out the optimization simulation analysis of its materials together with structural geometry.
Fatigue Analysis of the Gear System Using ANSYS Workbench All components have a finite life, and it is estimated that 50~90% of structural failure is due to fatigue  [2] , typically resulted from stress, wear and failures caused by flaws in the material, poor or inconsistent manufacturing processes, complex geometries, etc. [3]. The FEA software of ANSYS Workbench was used to obtain the fatigue life, factor of safety and damage distribution. 9 ) shows the safety factor within a fatigue failure at a given design life. This value varies between the minimum safe zones (0) to maximum safe zones (15). As it can be seen from this figure, minimum safety of the design (5. 3057) designates the critical zones, which are the contact zones of gears.
CONCLUSION In this work, an optimal design based on Kriging interpolation algorithm for a robotic arm is developed, and being implemented parallel with the dynamics simulating model  [11]. The structure is tested under evaluation criteria of maximum strain(or stress), useful life and safety factor. The results generated by integrated finite element analyses completely meet the technological requirements. Thus, this paper presents an efficient Kriging interpolation algorithm-based methodology for structural dynamic analysis of a 6-DOF robot arm using ANSYS workbench 13 simulation software, which allows visualization of the distribution of stress or strain from each work-position under different safety-factors and applied moments, and enables integrated optimization of the robot arm's mechanical structure in terms of materials and shape.
Field pea can be considered to be an orphan species considering its limited genomic resources. Its genome covers 4. 3 Gb, which is around 10 times larger than the genome of the model species M. truncatula  [36] , including repeats mostly based on transposon-based sequences  [37]. To date no full genome sequence and only poor EST resources  (18, 576  EST sequences in Genbank in June 2013) are available. Recent reports show that large new sequencing resources are under development  [15, 16, 37]  and that a consortium for pea genome sequencing is being built (http://www. coolseasonfoodlegume. org/pea_genome). However, these efforts have not yet reached the development of large numbers of new molecular markers to saturate pea maps and improve QTL mapping both towards research and breeding objectives.
Even though the two studies gave rise to a significant enrichment in EST contigs and resources, they did not provide a large marker resource. Our objective, to complement the existing resources and to better fit with research and breeding demand for markers, was to develop a comprehensive SNP database in pea with extended validation in breeding and genetic mapping positions. For this purpose, we deeply sequenced eight genotypes representing the genetic diversity present in modern breeding material, and developed a dedicated bioinformatics pipeline for assembly and SNP identification.
The eight normalized cDNA samples, one for each cultivar, were subjected to 454 sequencing and data assembly. From half a sequencing run dedicated to each sample, we generated 365,255 to 591,513 raw reads per sample, reaching a total of 1,369 Mb from 3,826,797 reads. Median read length per genotype ranged from 361 to 420 bp and 68% to 78% of the read lengths were between 300 and 600 bp depending on the sample. After data cleaning for small/ long reads, PCR duplicates and low complexity sequences, we kept 78% of available sequences. The last cleaning steps consisted in masking repeated sequences and removing chloroplast derived sequences: 1,068 Mb of high quality sequences were eventually used for de novo assembly (Table  1 ). Eighty percent of the data could be assembled (2,466,808 reads) in 68,850 contigs, representing a cumulated length of 58 Mb.
In addition, we found little difference in the distribution of pea contig homologs along M. truncatula chromosomes between the two data sets, contigs from both data sets showing even distributions along the chromosomes (Figure  1 ) except for a few M. truncatula chromosomal regions that remained uncovered both by the whole dataset and by the 1920 SNP subset, the largest on chromosomes 1, 3, 6 and 7. The 1920 subset therefore constituted an unbiased sampling of the whole SNP resource generated. From 1920 SNPs selected for the GoldenGate assay, 1620 (84. 5%) were successfully genotyped (Additional file 8: Table  S4 ) on either a diversity panel of pea accessions, or on one or more of four pea RIL populations. The remaining 300 SNPs (15. 5%) failed due to missing or noninterpretable signal. Genotyped SNPs were classified between A and H quality levels (Additional file 9: Table  S5 ), most of them (1250) having the highest quality (A).
Finally, seven accessions, mostly lying in Subcluster 1-1, and mainly corresponding to marrowfat pea cultivars, showed intermediate positions between G1 and G3. A classification of 1538 genotyped SNPs was performed using a transposed matrix through a Ward hierarchical clustering using the genotyping data of the 92 accessions of the diversity panel. Forty-eight sub-clusters of SNPs were defined (Additional file 8: Table  S4 ), within which two to ten SNPs were chosen based on SNP quality level, and position on the consensus map to select a 297 SNP subset. This defined 297 SNP subset (Additional file 8: Table  S4 ) classified the 92 pea accessions of the diversity panel as well as the 1538 SNP set into the same five clusters (Additional file 11: Figure  S6 ). Fifty-nine accessions were conserved identically on the Maximum Agreement sub-Tree (data not shown) resulting in the comparison of the two Ward hierarchical clustering.
Data assembly and SNP calling Since at the time this project was initiated very little sequence data was available for pea (5,004 nucleotide entries and 18,552 EST sequences in genbank in 2010), the chosen strategy was de novo sequencing and assembly. Like for many other non-model plants transcriptome sequencing projects  [42] , we chose to use the Roche/454 platform with the GS-FLX Titanium chemistry which provides long read lengths (400 bp in average) which is critical for de novo assembly. While many different assembly strategies combining different tools have been tested in previous transcriptome studies  [42] , we chose to use the MIRA assembler, which was present in the top three assemblers used in recent 454 transcriptome projects  [43]  and has proven to work well on pea  [16]  and on other complex species like rapeseed  [20]  or wheat  [44].
Previous pea transcriptome assemblies using the Roche 454 technology reported an average contig length of 324 bp from 250 bp read length  [16] , or an average contig length of 719 bp  [15]. We obtained a longer average contig length (842 bp) and N50 (956 bp), closer to this last report. Furthermore, a comparison of our assembly to previous ones showed that it covers them well with 70,337 contigs (86%) out of 81,449 from Franssen et al. [16]  and 12,776 (95%) out of 13,445 from Kaur et al. [15]  having a hit against our assembly (megablast with e-value lower than 1e-5 and option -p 95). Reciprocally from 68,850 contigs from this study, 49,235 (71%) and 39,868 (58%) had a hit against Franssen et al. [16]  and against Kaur et al. [15]  assemblies respectively. The nearly 20,000 contigs from our assembly that show no similarity with previous datasets may be due to a higher sequencing effort in this study (3.
8 M reads compared to 720 K reads in Kaur et al. [15] ). We can anticipate that new sequencing technologies, such as the Illumina Miseq that now generates 2×300 bp at a lower cost will help to design new sequencing projects with both long reads and deep coverage. The objectives of the earlier studies mentioned above aimed at SSR development and validation  [15] , or exhaustive representation of expressed genes  [16] , whereas this study clearly focused its experimental design and bioinformatics analysis on identification of SNP markers easy to genotype with high throughput technologies. Although they all contributed to generating pea cDNA contig sequences, it appears that these three recent studies could be complementary in many points to better characterize the pea transcriptome. We also developed a dedicated script for SNP calling adapted to the data we generated.
Library preparation and sequencing Sequencing library preparation was performed using Roche 454 GS-FLX kits according to the manufacturer's recommendations. For each cultivar, we started with 1 μg (ds) cDNA that was submitted to fragmentation using a nebulization method (Roche). An average size of 700 pb was obtained for each sample, as verified by capillar electrophoresis (Agilent Bioanalyzer). Libraries were sequenced on a 454 GS-FLX sequencer (Roche) with the Titanium chemistry (400 bp read length). Each cultivar was sequenced on half a PicoTiterPlate (PTP). A total of four PTP, each generating in average 400 Mb sequences (1 million reads, 400 pb length), was necessary to sequence the eight cultivars. Raw data were produced as sff files.
Sequence cleaning Raw data were first processed through different cleaning steps. Pyrocleaner v1. 0  [57]  was used to remove sequences with a length outside a given range (mean read length +/-2 × standard deviation), as well as potential PCR duplicates, and low complexity sequences. Repeat-Masker v3. 2. 9  [58]  was used to identify and mask known repeats using the Medicago repeat library from TIGR Plant Repeats (ftp://ftp. plantbiology. msu. edu/pub/data/ TIGR_Plant_Repeats/TIGR_Medicago_Repeats. v2). SmartScreener  [59]  and SeqClean  [60]  (http://compbio. dfci. harvard. edu/tgi/software/) were used in order to remove remaining PCR oligos introduced during the cDNA normalization protocol. Finally Seqclean was also used to screen sequences for chloroplast contamination using the Pea chloroplast genome sequence (NCBI RefSeq NC_014057.
SNP discovery To our knowledge, at the time the data was produced and analyzed, although different tools were being developed for SNP discovery, none of them were specifically designed or well established for calling SNPs from 454 data on homozygous diploid crop lines. Therefore SNP discovery was conducted using a custom perl script which we have used with success on other projects in diploid and polyploid crop cultivars  [20, 44, 62]. This script can directly process MIRA's assembly ACE output format by going through each contig alignment, looking for variant positions and then filtering these positions according to default thresholds and/or user-controlled parameters: minimum base quality, NQS (Neighbor Quality Standard) and coverage criteria. Here, we set the parameters to use a "20/15 NQS criterion" for a 11-base window as initially described by  [63]  in order to define high quality bases.