dataesr/scientific-paragraphs-categorization huggingface dataset:
Exosphere processes may be important for the distribution of moderately volatile elements (e. g. ,  Peplowski et al. 2012). XRS identified clear correlations between the surface geochemistry and major geological features on the surface of the planet. However, of particular interest is a region of elevated Mg concentration (relative to Si) in the northern hemisphere that currently cannot be definitively explained in terms of geological features on the surface (for more detail see e. g. ,  Rothery et al. 2020  and references therein). BepiColombo's surface compositional data from MIXS and the Mercury Gamma ray and Neutron Spectrometer (MGNS;  Mitrofanov et al. 2010 Mitrofanov et al. , 2020) )  are expected to provide a versatile geochemical database on major and minor elements that will constrain the surface regolith, crustal, and mantle processes and reservoirs in new detail.
During periods of such intense background it is unlikely the data could be reliably calibrated in any case, meaning data loss of this type is unlikely to be a significant Fig. 11  Measurement of the voltage optimisation window in space and on ground for MIXS-C (A) and MIXS-T (B). The differences between measurements on the ground and in space are due to the different operating temperature in the two measurements. Note that the turning point of the curve near -2 V for MIXS-C in space was not found due to safety limits being set too tightly to allow the scan to complete (this will be re-measured at the next available opportunity) science constraint. As the readout is so fast, the probability of more than one photon hitting a given pixel per readout time (pulse pileup) is also typically very low.
A number of algorithms exist onboard to identify periods of intense background and prevent noisy data from entering the telemetry queue for return to Earth. These work by identifying periods where the highest energy channels have a grossly disproportionate number of events compared to the main part of the spectrum. In such a case, the threshold is adjusted to prevent saturating the telemetry queue with noisy data. After launch, and during the cruise to Mercury, the detector accumulates radiation damage. Radiation damage is caused by two main factors; ionizing radiation (total ionizing dose, TID) and non-ionizing radiation (total non-ionizing dose, TNID). The expected TID and TNID levels for the MIXS detectors, front end electronics and the electronic components in the MEB were calculated resulting in an equivalent shielding requirement of approximately 11 mm of aluminium over 90% of the solid angle.
e. after ∼5 half-lives. Each source will provide ∼1-5 counts per second during operations, meaning it is possible to get at least one reliable calibration measurement per orbit if needed. Before delivery of the instrument to the spacecraft, a dedicated test facility was used to uniformly illuminate the detectors with X-rays of different energies. Figure  12-A  shows the linearity of the MIXS detectors -the difference between the incident and measured value of the X-ray energy (precisely, the residual between measured X-ray energy and a linear fit to all measured X-ray energies). The linearity is better (i. e. , less) than 2% over the majority of the MIXS energy range. Figure  12-B  shows the energy resolution of the detector as a function of the energy of incident X-rays. The resulting data are compared to the fundamental limiting performance of a Si detector (the Fano limit -see for example  Scholze and Procop 2009)  shown in orange and fit with a curve including the total noise of the electronics chain.
Ground Calibration Summary Subsystem testing on the ground was performed as described above as well as dedicated calibrations that have been reported elsewhere (e. g. ,  Majewski et al. 2014 ). After integration  into the flight unit, MIXS ground calibration was achieved using a 28 m long X-ray beamline at the University of Leicester to validate the data taken at subassembly level. Good correlation was observed between subassembly calibration and data taken at instrument level; these results will be reported elsewhere in due course. In order to prepare for the scientific exploitation of MIXS data, a ground reference facility is being implemented at the time of writing (Fig. 14 ). This uses equipment left over from the development of MIXS along with new experimental facilities such as a vacuum chamber, precision rotation stages, an electron impact X-ray source (Moxtek TUB00146-RH3) and a customised electron gun (modified Henniker Scientific ES 40C1) to set up a laboratory experiment to simulate the observations that MIXS will perform in space.
g. ,  Parviainen et al. 2011; Näränen et al. 2009 ). We describe these models further below. The ground reference facility will be used to help to ensure that a robust and reliable measurement of Mercury's composition is derived from MIXS data by providing a "ground truth" verification of the data analysis methods to be used later at Mercury. Through this program of experimental work and related modelling on the properties of X-ray emission relevant to MIXS and Mercury, we have as an aim to determine absolute abundances and associated analytical uncertainties of some of the major elements during the science mapping phase of BepiColombo. However, at the time of writing the extent to which elemental ratios, as successfully used by MESSENGER XRS, or absolute abundances will be the main form of elemental data interpretation has not been determined.
As specified, this resolution is close to the Fano-limited resolution of a Si detector at this energy (119 eV, Scholze and Procop 2009). The corresponding centre and resolution for MIXS-C are 5897 eV and 142. 3 eV respectively. The significant peak spreading in uncalibrated data is due to the differences in gain between individual pixels; this effect disappears in calibrated data when these gains are accounted for. Figure  16  shows an overlay of the results from MIXS-T and MIXS-C. Note that MIXS-T and MIXS-C have different low-energy thresholds to remove the O Kα line from the MIXS-T data as a means to reduce telemetry volume. and the Si escape peak from Mn-Kα) is well-matched between both detectors and is close to the Fano limit. At low energy the MIXS-C spectrum shows significantly higher counts extending to a lower energy; this is the result of a different threshold setting on the detector Finally, the voltage optimisation procedure was tested, to verify its operation and to establish a baseline against which future measurements can be compared to assess radiation damage.
Precipitation is symmetric north-south about the magnetic equator, and strongly asymmetric dawn-dusk about midnight. Particles appear to be energised in the magnetotail by reconnection and/or dipolarisation. Characterisation of this phenomenon is of great interest for investigation of magnetospheric dynamics, as it allows direct remote sensing of particle interactions with the surface, which is otherwise not possible from orbit. MIXS will be able to observe X-ray fluorescence originating from particle events while its footprints are unlit. By careful comparison of MIXS and SIXS data (identifying the residual contribution of particles when the surface response to the observed SIXS spectrum is modelled), it may be possible to deconvolve solar-and particle-induced X-ray fluorescence events from the dayside of the planet, allowing global mapping and characterisation of precipitating particles.
M8 Monitor particle flux with SIXS-P and other particle instruments where possible * Metric M3 is not achieved under default instrument configuration due to the high data rate associated with telemetering the O-K line. The quantitative calibration of low energy lines is difficult due to the complexity of the solar spectrum in this soft X-ray band. Further work with the MIXS ground reference facility during BepiColombo's cruise is needed to assess MIXS's sensitivity to these species -particularly the C-K line highlighted in metric M3 and measurements of the three anions; oxygen, sulfur and chlorine, which would inform the oxidation state of metals in Mercury's surface. d. Determine whether the volatiles Na, K, S or Cl are involved in the hollow-forming process e. Investigate volatiles in the permanently shadowed regions f.
Characterise dawn and dusk particle precipitation phenomena and the asymmetries between them j. Determine whether particle sputtering is a source of the exosphere The associated performance requirements and metrics are summarised in Table  4. In order to optimise the data returned by MIXS, work is under way to model the behaviour of the instrument and chose the correct operating settings to maximise the scientific value in the telemetry. For instance, the default configuration of MIXS-T does not telemeter the lower part of the energy spectrum in order to avoid the large data rate increase associated with telemetering the intense O-Kα peak at 525 eV. Nevertheless, these settings can be changed in order to investigate lower energy peaks such as Fe-L, O, and ideally C in order to observe these species in low solar states.
Images shown in Fig. 4, Fig. 6 and Fig. 9 are adapted from drawings produced by  MagnaParva Ltd. under  ESA  contract number  20919/07/NL/FG. EJB is supported by a  Royal Society Wolfson Research Merit Award. The DEPFET X-ray detectors were contributed with the support of dedicated  Max Planck Society  grants by  MPG  and MPS as well as  ESA  contract number  4000108312. The Spanish contribution was funded by  MEC/MINECO/MICINN  grants;  AYA2008-03467/ESP ,  AYA2011-24780 ,  AYA2012-39362-C02-01 ,  ESP2014-59789-P  and  ESP2015-65712-C5-1-R. Finnish hardware contributions were funded by  Tekes (now "Business Finland ") during the era from 2004 to 2019. Research at the  University of Helsinki  is supported, in part, by the  Academy of Finland  (grant number  1325805 ). This research used the  ALICE and SPECTRE High Performance Computing Facilities  at the  University of Leicester.
Taking into account the small size of the system all determinations are made at the highest computational level i. e. CCSD(T). VCI calculations account for vibrational correlation effects and the use of PES as generated from the SURF program and a basis of VSCF modals in which all modals were generated from 16 distributed Gaussians. VCI calculations include single (S), double (D), triple (T) and quadruple (Q) excitations. All calculations are carried out in C2v symmetry. For the irreducible representation A1 27019 configurations (32 S, 504 D, 4820 T and 21662 Q) have been selected while 22387 configurations( 9S, 270 D, 3464 T, 18644 Q) are required to describe each irreducible representation B1 and B2. All calculations presented here have been performed with the MOLPRO suite of ab-initio programs  [35]  2-2 Computational details.
The CCSD(T) coupled-cluster electronic method is a standard tool to provide reliable values of molecular energies and properties as in our case Infra-red (IR) spectra. Associated at large number of orbital basis functions it gives chemically useful accuracy. To improve this method, explicitlycorrelated R12 approach that use additional two-particules basis functions depending explicitly on the inter-electronic distance has been proposed increasing the accuracy of reliable properties predictions  [36] [37]. This method including correlated germinal is known as the CCSD(T) R12 model  [37]  or the CCSD(T)-F12x model  [38]  in which the linear r12 correlation factor is replaced by a short range nonlinear function F12(r12) that leads to much better results and numerical stability. We will adopt this latter model in accordance with the MOLPRO software used in our calculations.
Description of CPlantBox CPlantBox is an extension of the model CRootBox  [73]. CRootBox is a fast and flexible FSPM focusing on root architecture and root-soil-interaction. We took advantage of the object-oriented structure of CRootBox to add new modules to represent the different shoot organs (fig. 3B ). The main extensions in CPlantBox are: -CPlantBox can simulate realistic plant shoots and roots as a single connected network. The output can be coupled with water and carbon flow simulations (fig. 3C, D ). -As we move from root simulation to a full plant simulation (fig. 3B ), more complex relationships between the different organs have been included in the model. For instance, roots can now grow from seed, roots or shoot organs (fig. 3B ). -The input parameter files are now XML-based (fig.
At the end of the simulation, output files can be interpreted and visualized by the framework. The current coupling is done by file exchange and command line automation. Simulating the carbon and water flow within a 300-segments plant for 100 hours growth time takes around 1 minute (dependent on parameter setting) on a laptop (CPU: Intel Core i5-6300U 2. 4GHz, RAM 8GB 2400MHz). We also created looped functions to run simulations in batch processes. Installation, preprocessing, post processing and visualization are exemplified in the following Jupyter notebook: github. com/Plant-Root-Soil-Interactions-Modelling/CPlantBox/blob/master/tutorial/jupyter/CPlantBox_PiafMunch_Tutorial_(include_i nstallation). ipynb.
Comparison with experimental results Recently, Knoblauch et al. experimentally tested the Münch theory on morning glory (Ipomoea nil)  [33]. To validate the functions of CPlantBox-PiafMuch and estimated carbon loading/unloading speed, we decided to perform a re-analysis of their experimental dataset (measurements are shown in Table  2 ). In particular, we used one set of morning glory experimental data (left column of 7. 5 m morning glory in Table  2 ) for calibration and another set (right column of 7. 5 m morning glory in Table  2 ) for validation of our modelling system. We choose to use these datasets for different reasons. Firstly, the experimental measurements match almost directly both the input and output of the CPlantBox-PiafMunch model (fig. 4 ). Secondly, the authors performed a variety of experimental treatments, allowing us to parametrize our model on one experiment and validate on the other.
CPlantBox Plants can display a variety of forms and architectures, both above and below ground. [76]. Stem branching patterns are important factors determining the above-ground architecture of plants. fig. 5A  shows an example of three branching patterns generated by CPlantBox using different parameter files. A second important determinant of the above-ground architecture is the arrangement of the leaves on the stems. fig. 5B  includes three leaf arrangements also created by only changing single input parameter. By combining different branching patterns and leaf arrangements, we extended existing CRootBox outputs  [73]  into full plant architectures (fig. 6 ). It is worth mentioning here that each unique structure is obtained solely by changing the input parameter files. The source code itself is not modified.
Finally, in the version of the models, carbon production is prescribed at the article level. Again, this what not an issue so far, as we wanted to explore the flow distribution only. However, in the near future, we plan to include leaf-level photosynthesis module  [99, 100] , to be able to better represent the dynamic response of the plant to its changing environment. Comment citer ce document : Zhou, X. -R. , Schnepf, A. , Vanderborght, J. , Leitner, D. , Lacointe, A. , Vereecken, H. , Lobet, G. (2019). CPlantBox, a whole plant modelling framework for the simulation of water and carbon related processes. BioRxiv, 19 p. , DOI : 10. 1101/810507.
Model and data availability -CPlantBox is open source under GPL 3. 0 license, available at https://github. com/Plant-Root-Soil-Interactions-Modelling/CPlantBox/tree/master/ and http://doi. org/10. 5281/zenodo. 3518068 -Model parameter files are available at: http://doi. org/10. 6084/m9. figshare. 9785396 -PiafMunch output of simulation example 2, 3, 4 can be found here: https://figshare. com/articles/Output_of_CPlantBox-PiafMunch_coupling/9971225 Comment citer ce document : Zhou, X. -R. , Schnepf, A. , Vanderborght, J. ,  Leitner.
Ecological knowledge available at the time of MPA creation was limited. Exhaustive benthic habitat maps were often unavailable and information on the mobility of the pelagic and adult stages of targeted fish species has only started to be available in the last 15 years. As new comprehensive datasets become available, such as the EMODnet Seabed Habitat Coverage, opportunities arise to conduct post-hoc assessments of the existing MPAs and propose necessary amendments (see  Abecasis et al. 2015). Although earlier works assessed the MPA coverage of infralittoral habitats in the Azores  (Amorim et al. 2015; Schmiing et al. 2014 Schmiing et al. , 2015)) , the level of representativeness of the full breadth of shallow to deep-water seabed habitats that characterize the region remains unassessed. This information is instrumental to guide regional, national and international actions contributing towards conservation goals such as the Aichi Biodiversity Target 11 (Convention on Biological Diversity 2010) of having 10% of coastal and marine areas protected by 2020, or the 'Promise of Sydney' recommendation of achieving 30% of protection by no-take zones  (Wenzel et al.
In addition, if the structure of the set of MPAs itself is considered, zoning was not designed specifically taking into account ecological criteria linked to connectivity and detailed information on species/habitat distribution, but rather more general ideas based on best available knowledge and frequently building on previously designated areas. The inclusion of threatened habitats (i. e. , endangered and vulnerable habitats with sufficient data in  Gubbay et al. 2016 ) in the MPAs is rather representative, with seven out of the nine Red List habitats receiving over 10% protection. However, data-deficient habitats are not considered (see above) and some rarer habitats are underrepresented in the MPAs. For example, there is no protection of deep circalittoral mud (A5. 37). This is probably also the case for maerl habitats, which are listed as threatened and/or in decline  (OSPAR 2008)  but still need to be properly mapped and included in EMODnet Seabed Habitats products.
Acknowledgements  Mara Schmiing  was supported by an  Individual Research Grant  from the  Fundo Regional para a Ciência e Tecnologia  ( M3. 1. a/F/044/2016 ). Barbara Horta e Costa was supported by national funds through  FCT: Foundation for Science and Technology, I. P. , in the scope of Norma Transitória  DL57/2016/CP1361/CT0038. Part of the seabed habitat data was obtained from the  European Marine Observation Data Network (EMODnet) Seabed Habitats Project  (www. emodnet-seabe dhabi tats. eu),funded by the  European Commission's Directorate-General for Maritime Affairs and Fisheries (DG MARE ). Thanks are due to Project  MESH-Atlantic  ( AA-10/1218525/BF ) for co-financing Patrícia Amorim's Contract. We acknowledge funds provided by the  Portuguese Science & Technology Foundation (FCT)  through individual contracts/grants to  Pedro Afonso  ( IF/01640/2015 ) and to  Fernando Tempera  ( Ref.
While there is ongoing research on how to make the currently predominant HPC programming model-namely MPI+X-scale well on such systems, the emerging and more high-level PGAS programming models have shown to deliver high productivity for users and certain types of codes  [10]. The JST-CREST funded postpetascale HPC project developed the XcalableMP (XMP) programming paradigm, which combines local and global view PGAS concepts. The multi-level programming paradigm FP3C  [13]  as described later in this paper is a solution for post-petascale systems targeting a huge number of processors and the attached acceleration devices. Programmers can express high-level parallelism in the YvetteML (YML) workflow language and employ parallel components written in SPMD programming paradigms like XMP or MPI.
Since YML drives and executes multiple SPMD tasks at the same time, this is characterized as mSPMD. The MYX project aims to combine the know-how and lessons learned of different areas to derive the input necessary to guide the development of future programming models and software engineering methods. Therefore we are developing correctness checking techniques for the XMP programming paradigm and make this analysis also available for the multi-level programming paradigm FP3C. The contributions of this work are: • Identify possible correctness issues of XMP applications, • define an XMP tools interface to provide runtime state and event information to runtime tools, • extend MUST by XMP specific runtime correctness analyses, • extend YML to soundly support innovative numeric techniques like UCGLE, and • provide a workflow to analyze YML+XMP applications driven by the FC2P framework.
The structure of the remaining paper is as follows. In Sect. 2 we introduce the concept of runtime correctness checking and provide an overview of the general implementation of MUST. In Sect. 3 we provide a brief overview of the concepts of the XMP programming paradigm based on an example code. In Sect. 4  we highlight potential correctness issues in XMP applications and what information is needed to analyze those errors. To perform such runtime analysis, a tool like MUST needs state and event information from the XMP runtime system. In Sect. 5 we, therefore, provide a brief overview of the tools interface that we proposed as an extension of XMP to the XMP specification consortium. In Sect. 6  we introduce the concepts of the YML workflow language based on an example code. The unite and conquer method described in Sect.
7 represents an example use case for an mSPMD program implemented with YML for the coarse-grained parallelism and XMP for the implementation of the individual YML tasks. To implement such a method, some extensions of YML are necessary, we also discuss the implications for correctness. In Sect. 8 we present the FP2C framework, which provides a YML+XMP implementation targeted to HPC systems. As MPI is basically the standard for distributed memory HPC systems and those systems also prefer fixedwidth jobs, i. e. , jobs with a fixed number of processes the FP2C framework is implemented with MPI and dynamically launches MPI processes to fill the requested number of process slots. Finally, in Sect. 9 we present the challenges and solutions to provide runtime correctness analysis in MUST for such a dynamic runtime system.
Runtime Correctness Analysis for Parallel Programs Other than serial programs, parallel programs are affected by non-determinism as an effect of the concurrent execution of multiple threads or processes. For defect programs, this non-determinism can manifest as data races or deadlocks which are not known in serial programming. Different approaches to identify and remove the defects in those programs include static code analysis, model checking, and runtime or post-mortem analysis. Here we want to discuss runtime correctness analysis, where the error detection is performed during the execution of the program. MUST performs runtime correctness checking for MPI parallel applications. The application developer executes the application under the control of MUST, which checks at execution time whether the usage of MPI is valid according to the MPI specification.