dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the remainder of the discussion will focus on OSSE 10 in comparison to OSSEs 1 and 3. OSSE 10 provides comparable results to OSSE 9 and is in good agreement with OSSE 3 while using a lower percentage of data for training. Figures  3  and 4  are summarised in Fig. S1  of the Supplement. The maximum in absolute value from four outputs for each OSSE FFNN is shown (Eq. 4). There is a large improvement in the Southern Hemisphere for OSSEs 3 (Fig. 5b ) and 10 (Fig. 5c ) compared to OSSE 1 (Fig. 5a ): the difference varies mostly between -3 and 3 µatm for OSSEs 3 and 10, and between -15 and 15 µatm for OSSE 1 (Fig. 5 ). However, the average values of the mean over biomes are not always better for OSSE 3 (Table  5 ): in biome 13, OSSE 1 shows a small positive difference of 0. 11 µatm, while for OSSE 3 a negative difference of -0.
32 µatm is computed, exceeding 0. 11 µatm in its absolute value. This is due to error compensation by averaging; the reduction of the positive difference in the middle of biome 13 in OSSE 3 increases the impact of negative small differences in this region. Error compensation also contributes to positive biases computed for OSSEs 6-11 for biome 12 (  data from Argo floats correct the negative bias in the southern part of the biome close to the African coast (Fig. 5c ). Thus, the strong positive bias in the northern part becomes dominant and results in a total positive bias. A large improvement is obtained in biomes 16 and 17: from -8. 04 µatm for OSSE 1 to -1. 89 and -1. 91 µatm for OSSEs 3 and 10 in biome 16, respectively, and from -14. 9 µatm for OSSE 1 to -2. 05 and -1. 55 µatm for OSSEs 3 and 10, respectively, in biome 17 (Table  5 ).
Over the whole region (80 • S-80 • N, 70 • W-30 • E) , OSSE 1 has a mean difference of -6. 57 µatm, it is -1. 7 and -2. 34 µatm for OSSEs 3 and 10. The difference between OSSEs 3 and 10 results from the Labrador Sea and Baffin Bay: OSSE 10 has fewer data in this region compared to the OSSE 3. However, there is an improvement in OSSE 10 compared to OSSE 1 and 3 in the Greenland Sea (Fig. 5 ). It results from the addition of mooring data in the Greenland Sea region (Fig. 1c ). Figure  5d , e and f present the standard deviations (SD) of differences for all four outputs for each OSSE FFNN (Fig. 5d  -OSSE 1; Fig. 5e -OSSE 3 ; Fig. 5f  -OSSE 10) (Eq. 5). Over most of the Atlantic Ocean, SD varies between 0 and 10 µatm for OSSEs 3 and 10. In each case there is a strong SD along the coasts and in the Labrador Sea and Baffin Bay.
A higher correlation was also obtained for these two OSSEs compared to OSSE 1 over the region covering the Greenland Sea, the Norwegian Sea and Barents Sea (mostly biome 9). In the Southern Hemisphere the correlation with NEMO/PISCES pCO 2 is also larger when Argo data are included, especially in biomes 16 and 17: 0. 7 and 0. 57 for OSSE 1, 0. 83 and 0. 85 for OSSE 3, and 0. 78 and 0. 89 for OSSE 10 (Table  6 ). However, there is a low corre-lation along the African coasts, which is in agreement with our previous results for mean difference and SD (Fig. 5 ). It reflects the predominantly open-ocean data used for this exercise. A well-pronounced decrease in correlation is observed for biome 15 (subtropical seasonally stratified Southern Ocean). Such a decrease can result from the spatial distribution of data or from the predictor dataset.
Summary and conclusion The aim of this work was to identify an optimal observational network of pCO 2 over the Atlantic Ocean. The analysis was based on results obtained with a feed-forward neural network model trained on the SOCAT database. The SO-CAT database has sparse coverage in the Southern Hemisphere. The approach consisted of adding the position of mooring data and Argo trajectories in the Atlantic Ocean to find an optimal distribution and combination of data to reconstruct pCO 2 with a good accuracy. The advantage of the SOCAT database is the long time period covered by its  -1. 03/-0. 26 -0. 99/-0. 28 -2. 57/-0. 23 -1. 17/-0. 03 -0. 18/-0. 15 0. 42/-0. 10 0. 19/0. 20 -0. 68/0. 06 -1. 15/-0. 64 -1. 17/-0. 65 OSSE 3 -0. 80/-0. 04 -0. 74/-0. 04 -2. 36/-0. 02 -1. 16/-0. 02 -0. 07/-0.
The inclusion of errors from in situ measurements is one of the next steps of this work. The real measurements contain instrumental and representation errors. The inclusion of errors in pseudo-observations will help to estimate the impact of observations on the reliability of OSSEs presented in this work. It will include the errors for predictor values (SSS, SST, SSH, CHL, MLD, pCO 2,atm ) that are measured directly or derived from remote sensing (e. g. SST, chlorophyll, SSH), as well as the errors related to the computation of pCO 2 from pH and alkalinity. The new FFNN runs could provide important information on the effect of biases from observational datasets and identify predictors or targets that have large errors and thus must be corrected. The consistent introduction of error estimates for each predictor will provide this information.
the Royal Society (grant no. RP\R1\191063) at the UEA. Mathieu Vrac acknowledges support from the CoCliServ project, which is part of ERA4CS, an ERA-NET project initiated by JPI Climate and cofunded by the European Union. Financial support. This research has been supported by the European Commission, H2020 Research Infrastructures (AtlantOS, grant no. 633211), and GreenGrog (GMMC). Review statement. This paper was edited by Katsuro Katsumata and reviewed by Luke Gregor and one anonymous referee.
Acknowledgements. The authors would like to thank an anonymous referee and  Luke Gregor  for their helpful comments and questions and  Florent Gasparin  for providing the reference data of Argo distributions. At present  Anna Denvil-Sommer  is under funding from https://doi. org /10. 5194/os-17-1011-2021 Ocean Sci. , 17, 1011-1030, 2021.
Code availability. Code that provides an estimation of OSSE 3 for July and code used to create figures can be found at https://doi. org/10. 5281/zenodo. 5145897 (Denvil-Sommer, 2021). Data availability. Data used within this study are available upon request. Please contact the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-17-1011-2021-supplement.
For each sample, 3 slides were prepared and examined by the same observer (SC) under an optical microscope at 100× and 400× magnification by viewing 2-mm-wide and 60-mm-long traverses, with 3 mm between traverses. Up to 200 fragments per sample were identified to the genus level based on the shape of epidermal cells, trichomes and stomata surrounding cells. The counting data of each plant genus were converted to percentages. Epidermal fragments of floral parts, stems, petioles, and midribs were not registered due to the difficulties in species identification. Recorded plants were pooled into 5 functional groups (see  Espunyes et al. 2019a, b)  for a similar approach), namely leguminous woody plants (hereafter LW), non-leguminous woody plants (NLW), graminoid plants (Gr), leguminous forb plants (LF) and non-leguminous forb plants (NLF).
The results can be found in Supporting Information S1 and Table  S2. All statistical analyses were performed using R software version 3. 6. 3 (R Core Team 2020) and the accepted significance level was fixed at P < 0. 05. The R-packages "mgcv" (version 1. 8-23; Wood 2006) and "plsdepot" (version 0. 1. 17; Sanchez 2012) were used to create the variable FNc and the PLS multivariate regression between diet composition and quality, respectively. The R-package "MuMIn" (version 1. 43. 6; Barton 2018) was used for model averaging and the package "car" (version 3. 0-0; Fox & Weisberg 2011) was used to calculate the VIF of the variables. The package "betareg" (version 3. 1-3; Cribari-Neto & Zeileis 2010) was used to perform the beta regressions and the package "visreg" (version 2. 5-1; Breheny & Burchett 2017) was used to represent the effect of a predictor variable of interest on the response variable after correcting for the effect of the other model predictor variables (partial residual plots).
ACKNOWLEDGMENTS The authors are thankful to all the field assistants and volunteers for their amazing work in trapping Pyrenean chamois and collecting feces during such a long period. They are grateful to  Météo France  for providing the climate data. This study was funded by the  research partnership program  " Approche Intégrée de la Démographie des Populations d'Isard " ( N°2014/08/6171 ) between the  Office Français de la Biodiversité  (formerly  Office National de la Chasse et de la Faune Sauvage ) and the  Servei d'Ecopatologia de la Fauna Salvatge (SEFaS). Johan Espunyes  acknowledges a pre-doctoral grant from the  Government of Andorra  ( ATC015 AND 2015/2016 ,  2016/2017 , and  2017/2018 ). Emmanuel Serrano  was funded by the  Spanish Ministerio de Economía y Competitividad (Mineco)  through a  Ramon y Cajal agreement  ( RYC-2016-21120 ).
Data In this paper, we study the main PHANGS-ALMA sample of 74 nearby galaxies, made up mostly of star-forming spiral discs, as explained in Sect. 2. 1. For all those galaxies, we have gathered NIR Spitzer IRAC data, as described in Sect. 2. 2. Next, Sect. 2. 3 describes the ALMA observations, tracing molecular gas, while our strategy to measure star formation is presented in Sect. 2. 4.
Additional archival and new observations Ten galaxies in the main PHANGS-ALMA sample were not covered by S 4 G but had ancillary 3. 6 µm observations available from IRSA, either from the SINGS survey  (Kennicutt et al. 2003)  or from other individual observations. For those, we downloaded the SINGS products from IRSA, when available, or the Spitzer Enhanced Imaging Products otherwise. The latter are Super Mosaics where contiguous Astronomical Observation Requests (AORs) were grouped and reduced together using an automated pipeline on the Spitzer MOsaicker and Point source EXtractor (MOPEX) package  3. We refer the reader to the online documentation on the Spitzer Enhanced Imaging Products for more details 4. Four PHANGS galaxies did not have any archival Spitzer IRAC observations at 3. 6 µm, so we obtained their imaging in Cycle 14 via a dedicated programme (pid 14033, PI J.
6  (Leroy et al. 2021b). For most of this paper, we rely on measurements at matched kpc resolution from the PHANGS multi-wavelength database presented in  Sun et al. (2020b)  and J. Sun et al. (in prep. ). The PHANGS-ALMA CO field of view was divided into a regular tiling of hexagonal apertures, with a separation of 1 kpc in the plane of the sky between centres of adjacent hexagons. The measurements were performed at the centre of each hexagonal aperture by directly sampling a set of intensity maps that were convolved to a common physical resolution of 1. 5 kpc.
Summary and conclusions We present a catalogue of two-dimensional masks for PHANGS galaxies delimiting different morphological environments. The masks are based on Spitzer 3. 6 µm emission, and they identify stellar structures such as discs, bulges, bars, spiral arms, rings and lenses (Fig. 1  shows some examples). We mostly rely on archival Spitzer imaging (largely from the S 4 G survey), but we also carried out new Spitzer observations of four PHANGS galaxies that did not have archival IRAC data  (NGC 2283, NGC 2835, NGC 3059 and NGC 3137) , following an observing and data reduction strategy analogous to S 4 G. Bulges and discs are defined based on photometric decompositions of near-infrared images via GALFIT or similar tools  (Salo et al. 2015  for S 4 G galaxies;  Laurikainen et al. 2004  or new fits otherwise).
Contrary to some previous claims, and in agreement with other studies from the literature, we do not find evidence for systematically enhanced star formation efficiencies in spiral arms. There are individual galaxies where star formation is more efficient in arms, but also many others where star formation is less efficient. Therefore, spiral arms seem to act to pile up gas, and consequently star formation, but do not preferentially lead to shorter depletion times. In conclusion, our observations suggest that there can be substantial diversity in molecular gas and star formation within and among galaxies, linked to galactic structure. We plan to revisit some of these topics in detail in future publications, exploiting the rich multi-wavelength data sets produced by PHANGS. In this context, the environmental masks that we have presented here should help illuminate some of the physical processes by which stellar structures regulate star formation in the local Universe.
e. Σ mol > 0, Σ SFR > 0). A constant Galactic conversion factor (4. 35 M pc -2 (K km s -1 ) -1 ;  Bolatto et al. 2013) , which is a relatively widespread choice, results in slopes that are very similar to the ones that we find for our preferred PHANGS α CO , but they tend to be slightly smaller (i. e. minimally less linear). The largest discrepancy arises if we follow the prescription from B13, with a conversion factor that depends on the local cloudscale molecular gas surface density, metallicity, and the average disc surface densities at kpc scales, including the gas and stellar components. This results in considerably steeper powerlaws, but also reduces the dynamical range in molecular gas surface densities and the fits have large uncertainties. We note that the plots and fits for the B13 prescription only show a subset of 51 galaxies (out of 74), as H i data are not yet available for all PHANGS galaxies.
Acknowledgements. This work was carried out as part of the PHANGS collaboration. We would like to thank the anonymous referee for very detailed and valuable comments that helped us improve the manuscript. We would also like to thank  Seppo Laine  and the whole  Spitzer Helpdesk  for advice regarding IRAC data reduction. This work is based on observations and archival data obtained with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2012. 1. 00650. S, ADS/JAO. ALMA# 2013. 1. 00803. S, ADS/JAO. ALMA# 2013. 1. 01161. S, ADS/JAO. ALMA# 2015. 1. 00121. S, ADS/JAO. ALMA# 2015. 1. 00782. S, ADS/JAO. ALMA# 2015. 1. 00925. S, ADS/JAO. ALMA# 2015.
694343 ). The work of JS and AKL is partially supported by the  National Science Foundation (NSF)  under Grants No. 1615105 ,  1615109 , and  1653300 , as well as by the  National Aeronautics and Space Administration (NASA)  under ADAP grants  NNX16AF48G  and  NNX17AF39G. RSK and SCOG acknowledge financial support from the  German Research Foundation (DFG)  via the collaborative research centre ( SFB 881 ,  Project-ID 138713538 ) "The  Milky Way System " (subprojects  A1 ,  B1 ,  B2 , and  B8 ). They also acknowledge funding from the  Heidelberg Cluster of Excellence "STRUCTURES" in the framework of Germany's Excellence Strategy  (grant  EXC-2181/1 , Project -ID 390900948 ) and from the  European Research Council  via the  ERC Synergy Grant "ECOGAL" (grant   855130 ). HS and EL acknowledge financial support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No.
CMF is supported by the  National Science Foundation  under Award No. 1903946  and acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 694343 ). AGR acknowledges support from the  Spanish  funding grants  AYA2016-79006-P (MINECO/FEDER)  and  PID2019-108765GB-I00  (MICINN). AH was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and.
The main features of these memories are summarized in Table  I. Note that the RADSAGA 65-nm SRAM  [15]  has a tunable core voltage that can be used to vary the sensitivity of the memory chip, spanning in the 0. 3-1. 2 V range. For the scope of this article, the presented data and the main focus will be devoted to a core voltage of 0. 3 V. The data presented  for the commercial SRAMs all refer to their nominal core voltages of 1. 1 V. Note that the Cypress SRAM has an internal error-correction code (ECC), which has been disabled for the purposes of this study. The SRAMs have been tested with several beams  [16]  and most of the experimental details are reported in that article. All the SRAMs have been irradiated through the back-endof-line (BEOL). This was shown to have an impact for SOI SRAMs  [17]  with respect to irradiation from the substrate.
For the presented bulk SRAMs, however, such configuration could not be achieved. For the purpose of this work, data referring to LEP, HEP, and HI irradiations are reported. Concerning LEPs, the core of the experimental work was completed at the Centro Nacional de Aceleradores (CNA)  [18]. There, the SRAMs have been irradiated with mono-energetic proton beams in the 0. 5-5 MeV energy range. LEP data for the ISSI SRAM were collected at the Radiation Effects Facility (RADEF)  [19] ,  [20]  at the University of Jyväskylä. HEP testing was accomplished at the Kernfysisch Versneller Instituut (KVI)  [21]  for the ISSI and Cypress SRAMs and at the Paul Scherrer Institute (PSI)  [22]  for the RADSAGA 65-nm SRAM. HI testing was performed at KVI for all the SRAMs. Table  II  reports the HI characteristics in terms of species, energy, linear energy transfer (LET), and range.
It is assumed that, given their longer range, the ions will reach the SV while losing a negligible amount of energy in the BEOL. On the contrary, LEPs have a shorter range that may bring them to stop either inside the SV or in its vicinity. Thus, LEP data points have not been placed at the tabulated LET  [26]  for that primary energy before the BEOL. A more realistic LET has been estimated based on the interaction with the BEOL. While the latter cannot be known for the commercial memories, the experimental cross section helped deducing their SiO 2 equivalent BEOL thicknesses. On the other hand, for the RADSAGA 65-nm SRAM, it is known from manufacturing documentation that the BEOL would be equivalent to a layer of SiO 2 12-μm thick. Whether known or deduced from the data, this equivalent thickness was used to calculate the energy lost by the primary protons while passing through the BEOL by means of the stopping and range of ions in matter (SRIM) software  [27].
The nested RPP technique was used for the RADSAGA 65-nm and Cypress SRAMs. On the other hand, a single RPP was found to be suitable for the ISSI SRAM since it fairly reproduces both the LEP and HEP responses. The data of the RPP models for all the SRAMs are reported in Table  III. The BEOL are assumed to be made with SiO 2 for all the cases. For the nested RPPs, the external volume sides are obtained directly by a few high-LET HI (>5 MeV/(mg/cm 2 )) cross section data points. The collection efficiency (alpha) is obtained by making the ratio between the LET of the PDI peak data point and the LET of that HI. For the RADSAGA 65-nm SRAM, the reference proton LET is 0. 4 MeV/(mg/cm 2 ), whereas for the Cypress SRAM, this is 0. 3 MeV/(mg/cm 2 ). The thickness is kept constant  [33]  and equal to that of the innermost SV (targeted to reproduce the PDI enhancement).
FLUKA MC simulations were performed for all the models and for several mono-energetic proton cases to assess the consistency of the model with respect to the experimental data. The uncertainty on the calculated cross sections varies with each energy. On average, an uncertainty of ±35% can be taken for all the data points and models based on the energy deposition distributions. Other uncertainties may be present on the parameters chosen for the SV such as BEOL thickness, critical charge, SV size, and thickness. Figs. 1-3 present the comparison among the mono-energetic experimental and simulated cross sections for the RADSAGA 65-nm SRAM, ISSI SRAM, and Cypress SRAM, respectively. For the RADSAGA 65-nm SRAM, the consistency is verified at low (0-3 MeV), intermediate  (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) , and high energy (>20 MeV).
Note that the MC simulations are run with isotropic spectra as input, hence accounting for the angular response of the modeled component. A certain degree of uncertainty, which is not so easy to quantify, is present, anyhow. This is due to the lack of experimental data at different angles of incidence. That is, the angular dependence here considered is that emerging as a result of the modeling at normal incidence, but no verification with respect to experimental data was possible. For this first assessment, the data refer to a single orbit and a single shielding configuration. The environment under consideration is that of the ISS for solar minimum conditions. Both proton and HI fluxes are transported through 100 mils of aluminum with the CREME/universal heavy ion propagation code (UPROP) online tool.
It can also be seen as a safety margin to apply to the estimated UR when LEP data are not available. The analysis is made considering the three devices presented in this article, four different space radiation environments, and two different shielding configurations. All the environments are calculated through the CREME96 online tools. The ISS environment (I1 and I5) is calculated at 500-km altitude, 51. 6 • inclination, solar minimum, quiet conditions; the low-Earth orbit (LEO) environment (L1 and L5) at 1400-km altitude, 52 • , solar minimum, quiet conditions; the geostationary orbit (GEO) environment is calculated for both quiet (GQ1 and GQ5) and stormy solar conditions (GW1 and GW5, worst day). The shielding configurations are with 100 and 500 mils of aluminum. For the RADSAGA 65-nm SRAM, the contributions to the UR (both in absolute and percentage terms) are reported in Table VI for both the MC simulations and for the approximated Dodds' method.
ACKNOWLEDGMENT The authors acknowledge  Yolanda Morilla  and  Pedro Martìn-Holgado  from  CNA ;  Heikki Kettunen ,  Mikko Rossi , and  Jukka Jaatinen  from RADEF;  Marc-Jan van Goethem ,  Harry Kiewiet ,  Emil van der Graaff , and  Sytze Brandenburg  from KVI; and  Wojtek Hajdas ,  Laura Sinkunaite , and  Miroslaw Marszalek  from  PSI  for their support during data collection at the respective facilities. Special thanks go to  Helmut Puchner , from  Cypress Semiconductors , for providing the information required to disable the embedded ECC within the Cypress 65-nm chip.
This work was supported in part by the  European Union 's  Horizon 2020 Research and Innovation Program  through the  Marie Sklodowska Curie (MSC)  under Grant  721624  and in part by the  European Space Agency [ESA/European Space Research and Technology Center (ESTEC)] at the University of Jyväskylä  under Contract  4000124504/18/NL/KML/zk. Andrea Coronetti  is with CERN, 1211  Geneva, Switzerland ,.
APOLO and VIM1 are important thermomorphogenesis regulators in Arabidopsis thaliana Abnormal morphological phenotypes, including DNA methylation-dependent late flowering, were previously reported for the vim1/2/3 triple mutant in contrast to the vim1 single mutant exhibiting no evident phenotype  (Woo et al. , 2008). We thus wondered in what developmental context the APOLO-VIM1 interaction may occur and exert a regulatory role over target genes. By exploring the eFP Arabidopsis transcriptomic database  (Winter et al. , 2007) , we found that VIM1, VIM2 and VIM3 genes showed different transcriptional dynamics following heat stress (38°C followed by recovery at 25°C; Supplementary Figure  3A , blue box). Therefore, we evaluated the expression of these three genes and APOLO at 29°C, a warm temperature usually faced by Arabidopsis in the wild  (Parent and Tardieu, 2012).