dataesr/scientific-paragraphs-categorization huggingface dataset:
Observational data on auroral emissions at these altitudes can give information on particle fluxes at the top of the atmosphere which is very important for understanding of space weather conditions affecting polar Low Earth Orbits (LEO). The altitude range associated with aurora is too high for balloons, which can reach altitudes up to 50 km, and too low for satellites, which cannot survive for a long period of time at altitudes lower than 300 km. This means that no long-term in situ measurements can be made regarding these particle precipitations. The currently available in situ measurements have been obtained using sounding rockets. Almost all other available data were collected by remote sensing of the ionosphere at these altitudes. Experimental techniques that target optical emissions are particularly powerful since these emissions are mainly related to the excitation processes associated with suprathermal particles.
The lunar reflectance is unmatched in its stability, which is better than 10 ppb over one year  (Kieffer, 1997). Its radiance is similar to sunlit landmasses, but can be known with much higher precision (<1%) and accuracy (<8%)  (Stone & Kieffer, 2004). The Moon is frequently used as a photometric calibration source  (Wagner et al. , 2015) , a task that is greatly facilitated by the availability of an accurate dataset and modeling of lunar observations as a function of the phase angle known as ROLO for Robotic Lunar Observatory. The French space agency created a similar tool called POLO for Pleiades Orbital Lunar Observations  (Lacherade et al. , 2014; Xiong et al. , 2014). The precision achievable with the ROLO and POLO models is of the order of 10% in absolute calibration and 1% in relative calibration  (Goguen et al.
The interferogram will then belong to the spectrum comprised of the three red lines, belonging to the O 1 D -O 3 P triplet (at 630, 636, and 639 nm), which exhibit a constant intensity ratio only controlled by the Einstein coefficients. The 639 nm line is very faint and will normally be hardly visible, if at all. 4 Imaging: AMICal Sat and ATISE imager 4. 1 ATISE imager It will be fundamental to interpret the spectrometric data, to know what kind of auroral structure the spectrometer is looking at. Since some auroral arcs can be very structured, one of the lines of sight can be in and the others out of such an arc. In order to have information on the scene being observed it is thus necessary to use an imager in addition to the spectrometer. Moreover, an imager with a better vertical resolution than the ATISE instrument is a way to interpolate the data within each line of sight of the spectrometer, which will enable increasing the vertical resolution of the entire mission.
However the ADCS system that will be used in the AMICal 2U CubeSat, is much more basic. This will necessarily lead to image degradation (blurring) due to both satellite speed and pointing errors. The first source of image blurring is well known, and can be corrected for, provided the SNR is sufficiently high. AMICal Sat will also test the lunar calibration procedure using the Moon as a reference source that should lead to absolute photometric data with 10% accuracy and 1% precision. AMICal Sat is expected to be launched during the first trimester of 2019 for a 1-year mission, extendable to 3 years. Despite the fact that the sun synchronous orbit (SSO) is not totally satisfactory since there is no shift in local hour, this orbit is more easily accessible due to a larger number of launch opportunities.
All SSOs with a local hour equal to 12 h ± 4 h will be acceptable, with a preference for the 10h30-22h30 local hour. The final choice will be determined by the launch opportunities. AMICal Sat measurements can be synchronized with those provided by the Russian Meteor M2 satellite  (Asmus et al. , 2014; Kalegaev et al. , 2018). The orbit of Meteor M2 is an SSO at an altitude of about 800 km altitude. It has on-board meteorological instruments, including detectors developed at MSU. One of the detectors measures fluxes of keV charged particles. Comparison of AMICal Sat and Meteor M2 measurements in magneto-conjugate regions will enable obtaining more information on mechanisms of auroral particle precipitations. AMICal Sat will use the S-band for data transmission, meaning it will necessarily take fewer pictures than ATISE.
As for ATISE, recording will be limited to the auroral oval only. The extent of the auroral oval will be estimated based on K p forecast values. To limit the number of pictures and therewith the data rate, images registration frequency will be lower than in the ATISE case. A superposition of 1/3 of each photo, regarding the previous one, is then planned in nadir mode. This will allow to both set positioning of the satellite during images recording and to limit the number of images. In limb mode, one picture every 30 s is planned instead of one every 2 s. Data production is then limited to around 250 Mbits per day.
Interpretation of the data Altitude resolved spectra can be interpreted using kinetic codes that calculate the light emissions based on an M. Barthelemy et al. : J. Space Weather Space Clim. 2018, 8, A44  atmospheric model and the energetic inputs like solar UV flux and particle precipitations. Several of these models exist, some with bi-directional flux calculation (up and down fluxes), others with multi stream calculations. The model to be used with the ATISE and AMICal data will be the Transsolo code  (Lummerzheim & Lilensten, 1994; Lilensten & Blelly, 2002)  developed over the last 25 years to calculate the vertical transport of the particles through the thermosphere and the associated light emissions. The code has recently been used to calculate the NO emissions in auroral regions  (Vialatte et al.
, 2017). The Transsolo code is a multi-stream kinetic code that allows from inputs such as the solar EUV flux and the electron precipitation to calculate the emissions. It is a 1D code meaning that all calculations are done along an axis which is often the magnetic field line. A schematic overview of the Transsolo flow is presented in Figure  5. This code takes the atmospheric model as an input. In our case, two models are useable: NLRMSISE 2000  (Picone et al. , 2002)  or IRI  (Bilitza et al. , 2012).
Spectroscopic data It is first clear that the recorded intensities for each line of sight do not only comprise the intensities of the auroral emissions at the tangent point of the line of sight, but actually integrate all emissions, also those from higher altitude atmospheric layers since the satellite will fly at 650 km. This means that some O I red line emissions will be visible, even for the lowest line of sight. Concerning the interpretation of the data with the Transsolo code this means that several simulations corresponding to different geographical positions will be required, especially for the lowest line of sight. Since the Transsolo code is a 1D code, the only way to accomplish this is by running it several times using the hypothesis of negligible horizontal transport. The advantage of a large band spectrometer is to enlarge the number of emission lines and bands accessible; enabling a more complete characterization of the system.
). In a first stage, by using multiple direct runs of the code, a positioning of the measured results in the input parameter space will be feasible, and will allow for the interpretation of the data in a reasonable amount of time. However, a study of a proper inversion procedure of the data is ongoing and will be finalized before the ATISE satellite launch in 2021. Strategies like those used by Simon  Wedlund et al. (2013)  based on the multiplicative algebraic reconstruction technique or the maximum entropy method could be adapted since the forward model is similar. A first version will be tested on an AMICal Sat dataset.
Imaging data The limb image data by themselves will be interesting for space weather studies, but the amount of information that they can provide is limited. In a first processing step, only the total intensities of the vertical profile will be used. In a second step, the sparse RGB information will be processed. This means that if interpretation of the limb data of AMICal Sat will be carried out using the Transsolo code, it is likely that the solution is ill-determined, especially when not using the RGB information in the first processing step. However, since Space Weather is a multi-instrument and multi-measurement science, some coordinated measurements will be possible with some ground based instruments such as the EISCAT radars, which give the state of the ionosphere along the radar line of sight, or with optical instruments such as the ALIS cameras.
Before the launch of the satellites, several additional ground based campaigns will be performed with both the imagers and the spectrometer. The ATISE spectrometer will be tested again during a campaign planned for the 2019 winter. Data on particle precipitations obtained during the ATISE flight campaign will be compared with measurements of low-energy electron fluxes made by Meteor-M2 or similar LEO satellites. We also plan to make sure that the ATISE mission will coincide with measurement campaigns of particle instruments, such as the Dynagrad developed by ONERA, or similar auroral electron detectors included in the ''UniverSat Socrat'' mission currently developed by the Moscow State University  (Panasyuk et al. , 2017). The two satellites will be included in this large multisatellite mission dedicated to Space Situational Awareness (SSA).
Acknowledgements. The AMICal Sat and ATISE missions are being developed by the  CSUG  and  MSU/SINP , in collaboration with the CSUT and SMC TC. CSUG is funded by  Air Liquide Advance Technologies ,  Teledyne E2 V, Nicomatic, Sofradir  and  Gorgy Timing  through the  UGA Foundation. The  MSU/SINP  team was supported by  RFBR  grant  16-05-00760. Educational aspects have been partly funded by the  IDEX in Grenoble. The ATISE prototype has been funded by the  Auvergne Rhone Alpes region. This work was partly supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES  and by  CNES. The editor thanks  Johan De Keyser  and an anonymous referee for their assistance in evaluating this paper.
We describe and demonstrate algorithms for treating cohesive and mixed sediment that have been added to the Regional Ocean Modeling System (ROMS version 3. 6), as implemented in the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST Subversion repository revision 1234). These include the following: floc dynamics (aggregation and disaggregation in the water column); changes in floc characteristics in the seabed; erosion and deposition of cohesive and mixed (combination of cohesive and non-cohesive) sediment; and biodiffusive mixing of bed sediment. These routines supplement existing noncohesive sediment modules, thereby increasing our ability to model fine-grained and mixed-sediment environments. Additionally, we describe changes to the sediment bed layering scheme that improve the fidelity of the modeled stratigraphic record.
The paper presents results of model runs that test and demonstrate these new features and shows their application to realworld systems. The authors encourage the coastal modeling community to use, evaluate, and improve upon the new routines. Code and data availability. The algorithms described here have been implemented in ROMS (version 3. 6) distributed with the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST, Subversion repository revision number 1234). COAWST is an open-source community modeling system with a subversion source-control system maintained by John C. Warner (jcwarner@usgs. gov) and distributed under the MIT/X License  (Warner et al. , 2010). The COAWST distribution files contain source code derived from ROMS, WRF, SWAN, MCT, and SCRIP, along with MATLAB code, examples, and a user manual.
Acknowledgements. The authors thank  Jeremy Spearman ,  Alexis Beudin ,  Julia Moriarity , and five anonymous reviewers (two from  Ocean Dynamics  and three from Geoscientific Model Development) for helpful comments on earlier drafts of this paper. This work was supported by the  US Geological Survey, Coastal and Marine Geology Program  and the  National Ocean Partnership Program. Courtney K. Harris  was supported by the  NSF  ( OCE-1459708 ,  OCE-1061781 , and  OCE-0536572 ). This paper is contribution number  3741  of the  Virginia Institute of Marine Science, College of William & Mary. Bénédicte Ferré  is affiliated with the  Centre of Excellence: Arctic Gas hydrate, Environment and Climate (CAGE)  funded by the  Norwegian Research Council  (grant no. 223259 ). The model code is implemented in ROMS version 3.
COURLIS is a 1D sedimentology module coupled with MASCARET, 1D hydraulic code of the TELEMAC-MASCARET open source system. The code has been developed for more than 10 years, mainly for suspension sediment transport. Recently, the need of a 1D bedload code has been identified to model the long term evolution of rivers and reservoirs (several decades). New numerical schemes were implemented, some improvements were done in the geometry evolution algorithms. In terms of performance and robustness, the best scheme implemented is a finite volume upwind/downwind scheme. Several solutions are implemented to reduce calculation time. This new version of COURLIS for bedload transport was validated successfully on test-cases (Soni and Newton experiments). A real case has been simulated during an 11 year period.
Introduction Modeling sediment is crucial to deal with environmental and safety issues such as saving biodiversity and flood risks. Even now, with the increase of computational capacity and development of 2D and 3D CFD codes, one dimensional sediment transport codes are efficient engineering tools to predict reservoir and river bed evolution. They are more efficient in terms of computational times and generally, 1D models are simpler to build and require less field data. COURLIS has been developed for more than 10 years. The first objective of this code was to understand the processes of fine sediments in reservoirs, i. e. deposition, erosion and advection-diffusion of fine sands and mud. The code has been used to prepare the emptying of several French reservoirs  [1, 2]. The main particularity of COURLIS as 1D code is that output results include not only bed evolutions on the river longitudinal profile but also on initial cross-sections, preserving real geometry of the river.
Recently, due to the emergence of bedload continuity issues, a 1D bedload code, which would be robust and with fast computing time, is needed. It should be able to model long term evolution of rivers and reservoirs (several decades). This tool must help engineers to better understand and predict sediment deficit or deposit, in order to improve sediment management in hydroelectricity reservoirs and in rivers impacted by dams. Based on a benchmark of several 1D codes (commercial and research, including HEC-RAS), it was decided to develop a bedload option in COURLIS. The Exner equation has been implemented in the code and, an efficient and well-adapted numerical scheme has been used to solve it. In the followings, COURLIS suspension (which stands for suspension option) will be described and some applications will be mentioned; then, COURLIS bedload (which stands for bedload option) new developments and validation test cases are presented; finally, some perspectives and future developments are given.
COURLIS Suspension COURLIS suspension is coupled with MASCARET  [3] , the hydraulic 1D software of the opensource TELEMAC-MASCARET system (www. opentelemac. org). Both codes are internally coupled. That is to say, during one time step, first MASCARET solves the 1D shallow water equations with finite volume scheme  [3] , then, with variables calculated by the hydraulic model, COURLIS calculates sediment transport processes and bottom evolutions. (1) Where is the cross sectional flow area, the suspended concentration, the flow rate, the dispersion coefficient, and respectively the Erosion and Deposition linear rates. Deposition and erosion are calculated according to the type of sediment:  For cohesive sediment, empirical formulae are used : Krone (1962) formula (deposition)  [4] : 𝐷𝐷 = 𝑤𝑤 𝑠𝑠 𝐶𝐶 (1 - 𝜏𝜏 𝜏𝜏 𝑐𝑐𝑐𝑐 ) (2) Partheniades (1965) formula (erosion)  [5] : 𝐸𝐸 = 𝑀𝑀 ( 𝜏𝜏 𝜏𝜏 𝑐𝑐𝑐𝑐 -1) (3) Where is the erosion rate coefficient,   the settling velocity,   and   deposition and erosion critical shear stresses.
Two meshes are used: a fine mesh, with one cross section every 20 meters, and a coarse mesh, with one cross section every 100 m. The geometric and physical parameters of the model are presented in Table  5. Laboratory test cases  (Soni and Newton experiments)  show that COURLIS bedload gives results in a fairly good agreement with measurements. Moreover, several numerical schemes have been tested and the uncentered scheme showed good properties (stability, robustness, efficiency, etc. ). Some developments have been done to reduce calculation times. A real test case shows that developments carried out COURLIS allow the software to simulate long term evolution of gravel rivers with reasonable calculation time. Future work will focus on -Implementation of other bedload formulas, -Integration of an extended grain-size model, -Management of the transition from a rocky bottom to an alluvial bottom (adaptation of the friction law).
Introduction Xenacoelomorpha unites acoel, nemertodermatid, and Xenoturbella species. Most recent phylogenetic analyses place this group of animals at the base of bilaterians  (Hejnol et al. 2009; Cannon et al. 2016; Rouse et al. 2016 ). Previously, there has been considerable debate about the placement of representatives of this group  (Hejnol et al. 2009; Philippe et al. 2011)  but the new molecular data corroborate a scenario that was already favored by previous studies  (Westblad 1949; Hyman 1951; Ruiz-Trillo et al. 1999; Achatz et al. 2013). To gain further insight into the evolutionary history of bilaterians Xenacoelomorpha serve as an important group to investigate, because a subset of their features might represent ancestral character states of bilaterians. Currently, there are no available genome assemblies for any of these species and very few are cultivatable in the laboratory.
Sequence Assembly, Homeodomain and Family Membership Identification We sequenced the poly-adenylated transcriptome (mRNA) of I. pulchra, S. roscoffensis and X. bocki on an Illumina HiSeq 3000. We generated a total of 84 930 312, 61 857 037, and 116 117 207 paired-end 150 bp long reads for I. pulchra, S. roscoffensis, and X. bocki, respectively, and these data sets have been uploaded to NCBI (accession numbers for Ip: SAMN07276911, Sr: SAMN07276888, and Xb: SAMN07276887). In addition, we downloaded the publicly available data set for Hofstenia miamia sampling expressed RNA from mixed stages SAMN02690669  (Srivastava et al. 2014) , which represented a total of 87 544 989 paired-end 80 bp long reads as well as the Nemertoderma westbladi data set, part of the PRJNA295688 Xenacoelomorpha project  (Cannon et al.
2016). Next, we performed a trimming of low quality bases where leading or trailing bases with a quality score lower than three were removed as well as a 4 bp sliding window search for low quality bases where reads were cut if the average quality score dropped below 20. The trimmed reads were then used to perform a de novo transcriptome assembly using Trinity  (Grabherr et al. 2011). Open Reading Frames were identified based on the TransDecoder tool and searched for the "Homeobox" term from the PFAM database  (Finn et al. 2016). Sequences of all homeodomain containing Open Reading Frame's (ORF's) can be found in supplementary data S6, Supplementary Material online. To avoid false positive homeodomain identification, we applied the following selection criteria: A P-value below 0. 001 and an initial length of at least 40 amino acids.
We then performed a manual curation of identified homeodomain containing proteins and supplemented this list with additional, manually retrieved, homeodomain sequences of partial ORF's. Next we performed BLAST searches on NCBI and the HomeoDB2 database to identify affiliations to known homeobox gene classes and families. A gene was placed into a particular family if all of the top ten hits of the blast results against HomeoDB2 identified the same family. Genes not fulfilling this criterion are indicated as ambiguous in supplementary figure  S3 , Supplementary Material online. In addition, we searched the ORFs for additional PFAM protein domains to support the class identification (supplementary fig. S3 , Supplementary Material online). As described, and in selected cases only, BLAST searches were also conducted for other species to check if other genomes harbor genes of interest.
elegans, D. melanogaster, Branchiostoma floridae, C. gigas, and Nematostella vectensis both for ANTP as well as for the rest of the homeodomain classes (supplementary table  S11 , Supplementary Material online). All trees show similar within-family groupings of acoel or Xenacoelomorpha sequences, whereas the position of families within a class can change. To independently assess orthology relationships we additionally performed reciprocal BLAST analysis for a subset of genes confirming our classification (data not shown).
S3 and S4, Supplementary Material online). In addition, we performed reciprocal BLAST analyses that confirmed the results from such searches (data not shown). As done in previous studies, we then grouped the sequences in ANTP and non-ANTP homeoboxes, aligned them, and initially generated phylogenetic trees of only the acoel sequences alone (figs. 1 and 2, see Material and Methods). Of the 61 families predicted by the BLAST analysis for acoels, 43 are supported by a bootstrap value over 70%, confirming the close relatedness of individual family members (orange nodes in figs. 1 and 2). Furthermore, these analyses identified only 3 families (Meox, Nk4 and Pou6) that we found in only a single acoel species while 4 families contained members of 2 species (antHox, Nk1, Emx, and Shox) and 54 families have at least one member of all three acoel species, suggesting a good coverage of the expressed homeodomain containing sequences in our acoel data set (figs.
PRD Class Many PRD class genes encode a PAIRED domain  (Bopp et al. 1986 ) while some additional homeobox genes without the PAIRED domain (sometimes called PRD-like) also group with the PRD class phylogenetically and are therefore included in this class  (Holland et al. 2007). Some proteins containing a PRD domain, like Pax1/9 orthologs, do not contain a homeodomain and are therefore not included in this analysis. PRD class genes generally contain either a Q, K or S at position 50 in their homeodomain  (Galliot et al. 1999 ) and we identify members of all of these subclasses in our data set. The PRD class is the second largest class of homeodomain proteins with 31 human families and another 4 families not found in humans  (Zhong and Holland 2011b). Our search of Xenacoelomorpha homeodomain sequences recovered putative members of 14 specific PRD families.
Nematostella vectensis contains four LIM families  (Ryan et al. 2006 ) while sponges contain only three  (Larroux et al. 2008). Ctenophores and a placozoan each contain four of the families, although not the same four  (Srivastava et al. 2008; Ryan et al. 2010 ). Together, these analyses suggest that different LIM families were lost in different distantly related animal groups. In addition our data suggest that some LIM families were specifically multiplied in some Xenacoelomorpha species like N. westbladi or D. longitubus (supplementary tables S1 and S2, Supplementary Material online).