dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the MEX dataset shows a reversed situation, with dawn BS occurring at further altitudes for intermediate SZAs until the terminator where the situation is reversed with dusk shocks at further altitudes than dawn shocks. The distribution of the θ bn angle values encountered in the MAVEN dataset is not Gaussian, with significantly more perpendicular BS than parallel ones: 80% of the crossings occurred at θ bn values above 60°. It should be pointed out that this inhomogeneous sampling is not the result of a bias of detection of parallel BS being more difficult to identify from the data visual analysis: this θ bn distribution is very similar to the distribution of the θ bn values expected from the crossings by the MAVEN spacecraft with the modeled conic shape published by  Edberg et al.
(2008)  -and included in the 3DView webtool http://3dview. irap. omp. eu/-during the same period as our dataset. Note that updated crossing lists would not change the situation with more perpendicular than parallel BS encountered due to the spacecraft trajectory until 2021. Parallel BS are usually more difficult to identify than perpendicular BS where the transition is more abrupt. In the case of perpendicular BS, one usually has a 'clean' solar wind upstream with an absence of particles reflected. On the contrary, the transition at almost parallel BS is disturbed and wide, with a foreshock consisting of ions going up the solar wind  (Meziane et al. , 2017)  and with different transients whose signatures may resemble those of a BS, including hot flow anomalies found at Mars  (Collinson et al.
, 2015). The extension of the foreshock and the presence of transients could thus have resulted in an overestimation of the BS distance. However, our analysis shows that, on the contrary, perpendicular BS are located at higher altitudes than parallel ones. A last possible uncertainty on the influence of the θ bn angle on the BS location could be from the conic section assumption that is made in the derivation of the terminator distance R TD in this study. In principle, if the eccentricity chosen was not appropriate with our dataset, extrapolating from the BS crossing location to the terminator could lead to an overestimation or underestimation of the R TD value. However, this method is rather robust with respect to our conclusions. Let us choose only a subset of our dataset, that is, the list published by XF17 instead of a combination with the list by  Gruesbeck et al.
(2018) , to keep consistent with the choice made of the conic parameters given by XF17. In this case the correlation of the sine of θ bn with R TD is unchanged (the Pearson correlation coefficient is changed by only 0. 01 with negligible p-value). If we keep the same dataset but use other conic parameters by  Edberg et al. (2008)  -that is, (X 0 , L, e) = (0. 55, 2. 10, 1. 05) instead of (0. 42, 2. 30, 0. 87) for XF17 -again the correlation factor is changed by only 0. 01 with negligible p-value. The choice of the conic parameters thus does not affect our observation of the θ bn influence. Finally, alternative explanations were provided by several authors to explain the presence of BS expansions. Thomas and Winske (1990)  developed two-dimensional hybrid simulations of planetary BS to study the foreshock ion population.
They mentioned a similar asymmetry with the BS being closer on the quasi-parallel side than on the quasi-perpendicular side and considered this was a kinetic behavior since the asymmetry decreased during the simulations while the BS radius increased compared to the gyroradius. At Earth, magnetopause and BS expansions were also linked to radial (low cone angle) IMF configurations by several authors (e. g. ,  Suvorova et al. [2010]  or  Wang, Xie, et al. [2020] ), possibly due to the dependence of the magnetosonic wave velocity on the θ bn angle. This is however not observed in our Martian dataset where the cone angle has no significant influence, with even slightly smaller BS R TD values for radial IMF orientations. The analysis of the MAVEN dataset thus shows that quasi-perpendicular BS occurs at higher altitudes than quasi-parallel ones, independently from the influence of other possible drivers.
The Akaike Information Criterion The Akaike information criterion (AIC; see Appendix A) is a method based on information theory that is dedicated to performing model selection  (Akaike, 1974). AIC estimates, among several models fitting an observed dataset, the amount of information lost by each model to reproduce the data regularized by the dimension of the model. The model with a minimum AIC value is considered the best candidate (with no judgment on its absolute quality), and only the relative AIC difference value among the two models is meaningful to compare their relative likelihood. We performed AIC calculations with a dedicated R code, assuming either a linear dependence of the Martian BS terminator distance R TD with respect to several parameters (RTD = a + ∑ i X i ⋅ b i ), or a power law dependence 𝐴𝐴 ( 𝑅𝑅𝑅𝑅 𝑅𝑅 = 𝑎𝑎 ∏ 𝑖𝑖 𝑋𝑋 𝑏𝑏 𝑖𝑖 𝑖𝑖 ).
One can note that using an initial power law functional form (linearized with a logarithm) for the LASSO regression instead of an initial linear form leads to similar results:     Overall, the LASSO results are consistent with the previous results based on partial correlations and the AIC approach. The EUV and magnetosonic Mach are the major drivers of the BS location, before parallel versus perpendicular BS (θ bn angle), and then solar wind dynamic pressure or crustal fields (depending on the dataset). The IMF intensity and orientation angles (mostly the clock angle and MSE pole vs. equator) are predictors of nonnull importance (except probably θ bv and θ vn ) but are weak. We shall remind that the use of the LASSO approach does not aim at providing precise functional forms of the Martian BS location variability with respect to its drivers, which needs a theoretical understanding of the influence of each predictor but to compare the relative importance of the various possible drivers by assuming at first order a linear regression model (or a power law model).
Discussion Based on the previous analyses -direct analysis of the drivers, as well as partial correlations or model selection methods such as AIC or LASSO methods -, the statistical study of the Martian BS crossings datasets by MAVEN and MEX shows that the terminator distance of the BS increases: 1. when the magnetosonic Mach number decreases (first order importance): the BS is a fast magnetosonic standing wave induced by the supersonic flow encountering the ionized Mars obstacle, which propagates with a velocity determined by the magnetosonic Mach number; the magnetosonic Mach drives do not only the jump conditions at the shock through Rankine-Hugoniot relations but also the shape of the BS, in particular, the flaring of the BS with a shock cone inversely proportional to the Mach number according to MHD modeling, which modifies the cross section of the BS and its terminator distance 2.
g. , larger shock R TD values along the convection electric field), or significantly reduced after controlling for the first order importance drivers and for the θ bn influence with using partial correlations. We showed in the previous sections how to direct simple analysis can lead to an underestimation or an overestimation of the influence of minor drivers when these are cross correlated with major drivers. The use of specific methods such as partial correlations, AIC or LASSO, allows for an efficient disentangling of the various drivers at play. To check the efficiency of these methods, we performed a simple test: we kept for MAVEN the real driver's datasets (thus keeping the existing cross correlations), but artificially modified the real R TD dataset with the following procedure: we first perform a regression of the R TD dataset with respect to the various possible drivers and identify the slope value associated with a specific driver we want to test (e.
g. , the "clock angle" of the MSE pole vs. equator influence); then we modified the specific influence of this specific driver, by removing (little influence dataset) from the real R TD dataset, adding 10 times (strong influence dataset), or adding twice with also noise (noisy moderate influence dataset) the slope associated with this specific driver. When applying the partial correlations, AIC and LASSO methods to these three artificial R TD datasets (little influence, strong influence, noisy moderate influence), one gets results that are coherent with the expectations: all methods show an increased and coherent ranking of the specific driver for an increased influence of this driver (i. e. , for the strong influence dataset compared to the noisy moderate and little influence datasets), while the other drivers influence keeps unchanged.
This simple test thus confirms these methods are able to identify any significant driver that impacts the RTD dataset, that is, any driver that shows a recurrent pattern, whatever its nature and its reference coordinate system (in the geographical coordinate system for crustal fields, in the MSO system, in the IMF related coordinate system etc. ). These methods are more powerful than direct analysis regarding the detection of minor drivers whose influence may be hidden/underestimated/overestimated based on simple direct analysis due to cross correlations with major drivers. Parametrizing in detail the BS location as a function of its physical drivers is beyond the scope of this paper since this needs a detailed theoretical understanding of the response of the BS to each driver and to a combination of these drivers that may be inter-related as demonstrated above.
(1966)  used a   -1∕6  SW dynamic pressure dependence for the nose distance of the Earth's magnetopause and BS, later used at different planets including Mars  (Verigin et al. , 1993). Crider (2004)  suggested a SW dynamic pressure influence on the Martian MPB terminator distance with a softer slope than -1/6:   -0. 05. In comparison, the LASSO regularization gives power law index values of -0. 0596/-0. 0268 for MAVEN/MEX datasets when using a power law functional form 𝐴𝐴 ( 𝑅𝑅𝑅𝑅 𝑅𝑅 = 𝑎𝑎 ∏ 𝑖𝑖 𝑋𝑋 𝑏𝑏 𝑖𝑖 𝑖𝑖 ) , which is close to the Crider (  2004 ) value of -0. 05 based on MGS data, or to the MEX only values of -0. 02/-0. 03 by BH16. Recently,  Nemec et al. (2020)  compared the influence of several drivers on the MAVEN BS crossings (EUV, SW dynamic pressure and crustal field intensity). They suggested a non-negligible but small influence of crustal fields, but only local crustal fields were considered, and the magnetosonic mach number or IMF orientation parameters were not investigated, however the power law index suggested -0.
Wang, Xie, et al. (2020)  also used 3D MHD modeling simulations to investigate the dynamics of the Martian BS, and proposed a parametric model including seven parameters, suggesting that the Mms and SW dynamic pressure were the main drivers of the BS location, but they included neither EUV nor crustal fields. It would also be interesting to further compare with a consistent method the dynamics of the BS with the dynamics of the inner plasma boundaries (Magnetic Pile-Up Boundary or Induced Magnetosphere Boundary, Ion Composition Boundary, PhotoElectron Boundary…) since the dynamics of a boundary may impact another boundary, such as at Earth between the magnetopause and the BS  (Wang et al. , 2016)  or at Mars between the Induced Magnetosphere Boundary and the BS  (Ramstad et al. , 2017). In parallel to modeling approaches, we plan to use the future machine learning and artificial intelligence techniques to provide automatic catalogs of BS crossings for the MAVEN and MEX missions and thus increase our dataset to reduce the influence of cross-correlations, and eventually identify complex nonlinear relationships between the BS location (or other plasma boundaries) and external/internal drivers.
Conclusions The recent studies of the Martian environment, thanks in particular to the Mars Express and Mars Atmosphere Volatile EvolutioN missions, underline the complexity of the Martian interaction with the incident solar wind, which shapes the plasma boundaries. The bow shock of the planet is known to depend on extreme ultraviolet fluxes and magnetosonic Mach number, while the influence of other possible drivers is less constrained or unknown such as crustal magnetic fields or the Interplanetary Magnetic Field intensity and orientation. In this study, we analyzed the influence of a number of possible internal and external drivers of the shock location, based the first time on two missions  (MAVEN and MEX). We used consistent methods that allow us to compare both mission's datasets, as well as to take into account the cross correlations between the drivers that can modify the interpretation of the data.
We first analyzed the direct influence of the possible drivers of the shock based on linear correlation coefficients or absolute variations of the shock location. Then, we showed that many of the possible drivers are correlated to each other due to their nature and/or the trajectory of spacecraft. We used a partial correlation approach to investigate in detail the impact of these cross-correlations on the interpretation of the MAVEN and MEX shock crossings datasets. This approach appears crucial to investigate the influence of second order drivers on the shock that may be linked to each other or to stronger drivers. Finally, we used two known different model selection methods, called Akaike Information Criterion (AIC, see Appendix A) and Least Absolute Shrinkage Selection Operator (LASSO, see Appendix B) to compare the relative importance of the shock drivers.
The results for the magnetosonic Mach number and for the IMF-related parameters are based only on the MAVEN dataset, due to the absence of a magnetic field for MEX. The analysis of the MAVEN data also shows perpendicular shocks are located at significantly higher distances than parallel shocks, but no conclusive explanation was reached despite a number of possible reasons studied, including an anisotropy fast magnetosonic wave velocity depending on the θ bn angle that is mentioned in the Earth's and Venusian bow shock literature  (Chai et al. ,2015; Peredo et al. , 1995; Russell et al. , 1988). Other variables possibly influence the shock location, such as the IMF intensity, clock angle, or other IMF orientation angles (cone angle θ vb , θ vn ) that could generate asymmetries of the shock surface, for example, a possible close to equator versus pole asymmetry in the Mars Sun Electric field frame as identified at Venus and probably related to mass loading effects  (Alexander et al.
, 1986; Jarvinen et al. , 2013). However, the correlations between these last possible drivers and the Martian shock location are significantly reduced after controlling for cross correlations with the main drivers of the shock including the θ bn angle influence. Moreover, no further shocks were observed along the direction of the convective electric field, contrary to suggestions by previous authors based on smaller datasets or proxy methods. Providing an analytical proxy of the shock location that accounts precisely for the combined influence of its drivers appears unrealistic given the complex interplay of the internal and external physical processes at work, each When increasing λ from zero (i. e. , for LASSO switched off or for the classical solution) to larger values, the regularization term sets more coefficients to zero, thus removing them from the selected model.
Acknowledgments This work was supported by the  French space agency CNES. The authors acknowledge the support of the MAVEN and  Mars Express  instrument and science teams, as well as are the CDPP/AMDA team and  E. Penou. B. S. -C acknowledges support through  UK-STFC Ernest Rutherford Fellowship   ST/V004115/1  and  UK-STFC  consolidated grant  ST/ S000429/1  and  ST/W00089X/1. X. F acknowledges support through  NASA  Grant  80NSSC19K0562.
Data Availability Statement The MGS, MEX and MAVEN datasets of shock crossings are available in the https://doi. org/10. 5281/ zenodo. 6240624 repository. All MGS, MEX and MAVEN instruments calibrated data are available on the AMDA web interface (http://amda. cdpp. eu, in the directory/Parameters/Resources/AMDA Database) as well as on the NASA Planetary Data System (https://pds-atmospheres. nmsu. edu/data_and_services/atmospheres_data/ MAVEN/maven_main. html and https://pds-geosciences. wustl. edu/missions/mgs/index. htm) for MAVEN and MGS, and in the Planetary Science Archive (https://www. cosmos. esa. int/web/psa/mars-express) for MEX, with in particular the MARSIS data available at https://archives. esac. esa. int/psa/ftp/MARS-EXPRESS/MARSIS/.
Numerical Simulation of the Temperature Distribution To support our experimental measurements and for a better understanding of the transient temperature distribution at the microscale, we implemented a numerical simulation using the 3D finite-element (FE) software package COMSOL Multiphysics. In this simulation, we considered two computational domains, the electrically heated tip, and the glass-ceramic sample plate. The 3D geometry corresponding to the real heated tip and the general assembly were generated, as shown in Figure  2a. Due to the relatively simple geometry and the desired greater accuracy of the simulation, the contact layer between the heated tip and the sample was deliberately discretized into smaller elements. The contact area between the two conforming surfaces was approximately 230 µm in diameter.
Image Processing The original raw images were acquired using a 16-bit grayscale format with Andor Solis acquisition software. A 40 × 0. 65NA microscope configuration enabled an analyzed field of view of 190 µm × 190 µm. Individual fluorescence images with a pixel size of 512 × 512 were saved in TIF format and processed using a custom-developed MATLAB script. Afterwards, the mean gray value (MGV) was derived from the fluorescence image and correlated with the temperature-calibration curve. In the image analysis, the offset originating in the dark current and the thermal noise were first subtracted from the raw EMCCD data, after which the data was normalized with the fluorescence-intensity profile at the initial room temperature measured at different focal planes along the z-axis of the sample.
Observational Data Sets The realism of the simulation is assessed by comparing the model outputs with the following observation data sets. We used satellite sea surface temperature (SST) and sea ice concentration data sets from the OSTIA project spanning January 1985 to December 2016 (downloaded from marine. copernicus. eu portal;  Donlon et al. , 2012). This data set is optimally interpolated from multiple satellite sensors together with in situ observations onto a 0. 05° grid (1. 5 × 5. 6 km for Barents Sea) at a daily frequency. The feature resolution is 10 km and the accuracy of the daily SST data is ∼0. 57 K  (Donlon et al. , 2012). The sea ice edge is defined as the 15% contour. We also used observations of AW inflow transport and associated heat transport from the Fugløya-Bear Island section along 20.
0°E (which corresponds to the Barents Sea Opening; black dashed line Figure  1 ). This data set is available through the Norwegian Marine Data Centre portal (www. nmdc. no) and is compiled as monthly means from long-term current meter moorings for the period 1997-2016. The time series is built by averaging the part of the section below 50 m between 71. 5°N and 73. 5°N where there is an inflow, and only considering the part of the water columnwith T > 3°C  (Ingvaldsen et al. , 2004a (Ingvaldsen et al. , , 2021)). We computed a comparable section using the same criteria based on our simulation. We analyzed temperature and salinity fields from the data set named EN4 (EN4. 2. 0, www. metoffice. gov. uk/ hadobs/en4). EN4 comprises in situ ship CTD profile data and Argo float data optimally interpolated onto a 1°, monthly z-grid with 42 levels  (Gouretski & Reseghetti, 2010).
For the transport calculations, we used the CDFTOOLS package designed to work on NEMO model grids including ORCA12 (see https://github. com/meom-group/CDFTOOLS). The surface heat flux was calculated as Q surf = Q sw -Q lw -Q lat -Q sen net shortwave (i. e. , solar) (Q sw ) and net longwave (i. e. , infrared) (Q lw ) radiative fluxes, and surface turbulent latent Q lat and sensible Q sen heat fluxes. These parameters were integrated over the surface area of the same box, which has a total area 6. 1 × 10 11 m 2 (Figure  1 ). The integrated fluxes follow the convention where positive values represent flux into the ocean. We calculated freshwater transport as F adv = ∫∫((S ref -S)/S ref )V dx dz, where S is salinity in PSU (practical salinity units) and S ref is a reference salinity taken as 35 PSU  (Smedsrud et al.
Acknowledgments This project was funded through the  joint UK-France PhD program  by  DGA/ Dstl , and overseen by  Carole Nahum  and  Timothy Clarke. Additional support for this research was provided through the  NERC-BMBF Changing Arctic Ocean Grant   NE/R01275X/1 , the  NERC   ArctiCONNECT grant   NE/V005855/1  and the  NERC CANARI  Grant  NE/ W004984/1. The pan-Arctic simulation was performed using HPC resources from the french  GENCI-CINES  center (Grant  2018-A0050107420 ).
Data Availability Statement Version 1. 1 of the CREG12. L75-REF08 Canadian Regional model based on NEMO used for producing the model output is preserved at https://doi. org/10. 5281/zenodo. 5789520, available via Open access without registration. This documentation includes links to boundary forcing, atmospheric forcing and initialization files. Version 3. 0. 2 of the CDFTOOLS software used for CREG12 transport calculations is preserved at https://github. com/meomgroup/CDFTOOLS, available via under the CeCILL license (http://www. cecill. info/licences/Licence_CeCILL_ V2-en. html) and developed openly at GitHub. The OSTIA satellite SST and sea ice data used for comparison with model output in the study are available at Copernicus Marine Services via https://doi. org/10. 48670/moi-00168 with Open access and registration.
The AW transport observation data used for validation of the model output in the study are available at the Norwegian Marine Data Centre via http://metadata. nmdc. no/metadata-api/landingpage/ce3d486edd053c2eff2508f3ef392bb4 with Creative Commons Attribution 4. 0 International License without registration. The EN. 4. 2. 2 data used for observational watermass property analysis in the study are available at Met Office Hadley Centre observations data sets via https://www. metoffice. gov. uk/hadobs/en4/ with Crown Copyright, Met Office provided under a Non-Commercial Government License (http://www. nationalarchives. gov. uk/doc/non-commercial-government-licence/version/2/).
As the basis of oceanic food webs and a key component of the biological carbon pump, planktonic organisms play major roles in the oceans. Their study benefited from the development of in situ imaging instruments, which provide higher spatio-temporal resolution than previous tools. But these instruments collect huge quantities of images, the vast majority of which are of marine snow particles or imaging artifacts. Among them, the In Situ Ichthyoplankton Imaging System (ISIIS) samples the largest water volumes (> 100 L s -1 ) and thus produces particularly large datasets. To extract manageable amounts of ecological information from in situ images, we propose to focus on planktonic organisms early in the data processing pipeline: at the segmentation stage. We compared three segmentation methods, particularly for smaller targets, in which plankton represents less than 1% of the objects: (i) a traditional thresholding over the background, (ii) an object detector based on maximally stable extremal regions (MSER), and (iii) a content-aware object detector, based on a Convolutional Neural Network (CNN).