dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The Mask R-CNN model is a versatile instance segmentation method that has proven its performance in many areas, and is particularly suitable for clast size measurement applications for several reasons. First, convolutional neural networks are useful for the classification of non-trivial concepts, such as non-overlapping pebbles. This capability allows the minimization of sampling bias by disqualifying partially visible objects. In addition, the processing speed associated with the tool's ability to operate without human supervision after image scaling makes it a remarkably efficient asset. The methodology developed for this study is validated with an uncertainty of ±8 pixels (±4 mm for a resolution of 0. 5 mm/pixel) against a manually measured dataset. Part of this uncertainty can be attributed to the loss of a dimension, a photograph being the projection of a 3D scene on a 2D plane, as well as to the simplification of the morphology of the pebbles into ellipses.
[12]. The prompt charmonium production processes activated in Pythia are those from the leading-order colour-singlet and colour-octet mechanisms. Their implementation and the parameters used are described in detail in Ref. [13]. The EvtGen package  [14]  is used to generate hadron decays and the Geant4 package  [15]  for the detector simulation. The QED radiative corrections to the decays are generated using the Photos package  [16].
(  1 ), since B(ψ(2S) → µ + µ -) is less precisely known. L is the integrated luminosity, which is calibrated using both Van der Meer scans  [19, 20]  and a beam-profile method  [21]. A detailed description of the two methods is given in Ref. [22]. The knowledge of the absolute luminosity scale is used to calibrate the number of tracks in the vertex detector, which is found to be stable throughout the data taking period and can therefore be used to monitor the instantaneous luminosity of the entire data sample. The integrated luminosity of the data sample used in this analysis is determined to be 35. 9 pb -1. The total efficiency, tot (p T ), is a product of three contributions: the geometrical acceptance, the combined detection, reconstruction and selection efficiency, and the trigger efficiency.
Conclusions We have measured the differential cross-section for the process pp → ψ(2S)X at the centre-of-mass energy of 7 TeV, as a function of the transverse momentum in the range p T (ψ(2S)) ≤ 16 GeV/c and 2 < y(ψ(2S)) ≤ 4. 5, via the decay modes ψ(2S) → µ + µ -and ψ(2S) → J/ψ π + π -. The data sample corresponds to about 36 pb -1 collected by the LHCb experiment at the LHC. Results from the two decay modes agree. The ψ(2S) prompt cross-section has been separated from the cross-section of ψ(2S) from b-hadrons through the study of the pseudo-decay-time and the two measurements have been averaged. In the above kinematic range we measure σ prompt (ψ(2S)) = 1. 44 ± 0. 01 (stat) ± 0. 12 (syst) +0. 20  -0. 40 (pol) µb, σ b (ψ(2S)) = 0. 25 ± 0. 01 (stat) ± 0. 02 (syst) µb. The measured ψ(2S) production cross-sections are in good agreement with the results of several recent NRQCD calculations.
Acknowledgments We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at  CERN  and at the LHCb institutes, and acknowledge support from the  National Agencies: CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  CERN ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS (Romania) ;  MinES of Russia and Rosatom (Russia) ; MICINN, XUNGAL and  GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We also acknowledge the support received from the  ERC  under  FP7  and the  Region Auvergne. We thank  B. Kniehl ,  M.
Argo, the international array of profiling floats, is a major component of the global ocean and climate observing system. In 2010, the NAOS (Novel Argo Observing System) project was selected as part of the French "Investissements d'Avenir" Equipex program. The objectives of NAOS were to consolidate the French contribution to Argo's core mission (global temperature and salinity measurements down to 2000 m), and also to develop the future generation of French Argo profiling floats and prepare the next phase of the Argo program with an extension to the deep ocean (Deep Argo), biogeochemistry (BGC-Argo) and polar seas. This paper summarizes how NAOS has met its objectives. The project significantly boosted France's contribution to Argo's core mission by deploying more than 100 NAOS standard Argo profiling floats.
INTRODUCTION Argo is an international array of about 4000 profiling floats that measure temperature and salinity throughout the deep global oceans, down to 2000 m  (Riser et al. , 2016; Roemmich et al. , 2019). Argo is the first-ever global, in situ ocean observing network in the history of oceanography, providing an essential complement to satellite systems, for deeper understanding of the role of the ocean in the Earth's climate and for predictions on evolutions of the ocean, weather and climate. France has been playing an important role in the international Argo program since its inception in the late 90's. France accounts for slightly less than 10% of the international effort by deploying about 65 floats per year, and is a key contributor to the Argo datamanagement system. Indeed, through Ifremer, France operates one of the two Global Data Assembly Centers (GDACs) as well as the French DAC that processes float data deployed by France and from other European countries.
Argo-France is part of the French Ministry of Research's national roadmap on large research infrastructures (TGIR). At a regional scale, Argo-France is supported by the IUEM and Villefranche sur Mer Observatories (IUEM and OOV OSU). In 2010, the 10-year NAOS (Novel Argo Observing System) project was selected as part the French "Investissements d'Avenir" Equipex program. The objectives of NAOS were to consolidate France's contribution to the Argo core mission (global temperature and salinity measurements down to 2000 m), and also to develop the future generation of French Argo profiling floats and prepare the next phase of the Argo program with an extension to the deep ocean (Deep Argo), biogeochemistry (BGC-Argo) and polar seas (e. g. ,  Roemmich et al. , 2019; Claustre et al. , 2020). NAOS was established through a partnership between Ifremer, UPMC (now Sorbonne Université), CNRS, IUEM, Shom, nke instrumentation and CLS.
CONTRIBUTION TO THE CORE ARGO PROGRAM At the outset of the Argo program, France opted to make a nominal contribution of 65 floats per year: about 50 floats from Ifremer and 15 floats from Shom. Through the Euro-Argo ERIC agreements, France proposed to augment its long-term contribution from 65 to 80 floats per year to reach about a quarter of the European effort and 10% of the global effort. The floats purchased as part of NAOS accordingly increased France's contribution to Argo by 15 floats per year from 2012 to 2017. Altogether, 101 floats were purchased: 30 in  2012-2013, 37 in 2014-2015 and 34 in 2016-2017. The deployments started in late 2012 and the 101st float was deployed in September 2019. Their data were processed by the Coriolis data centre and provided to Argo International, to the Copernicus Marine Service  (Le Traon et al.
Furthermore, BGC-Argo observations were used to initialize and validate physical-biogeochemical coupled models in the North Western Mediterranean region. Models were shown to soundly reproduce the biogeochemical cycles  (Kessouri et al. , 2017 (Kessouri et al. , , 2018)). In a pan-Mediterranean context, NAOS BGC-Argo float datasets have been analyzed to identify large-scale biogeochemical characteristics of the basin, which were generally poorly assessed because of a critical lack of pertinent data. De Fommervault et al. (2015a) thus investigated the large-scale distribution of nitrate in the Mediterranean, by comparing time-series of BGC-Argo floats along an East-West gradient. They also characterized the seasonal evolution of nitracline in the Mediterranean's four main sub-basins, thanks to the unprecedented temporal resolution of the database of nitrate profiles collected by the first wave of NAOS BGC-Argo floats.
The Step Forward: Interannual Variability After 7 years of operations, the NAOS Mediterranean array has provided an exceptional dataset on the physical and biogeochemical dynamics of the basin. The collected data have already been intensively exploited and the NAOS Mediterranean pilot array has been internationally acknowledged as a successful example of the potential of a global BGC-Argo array (Biogeochemical-Argo Planning Group, 2016). The network is still operational and its data offer further avenues of research yet to be explored. For example, the interannual variability of Mediterranean biogeochemical cycles -clearly one of the potential applications of the existing BGC-Argo networkcalls for investigation. A first attempt to analyze interannual variability of NAOS BGC-Argo data in the Mediterranean is presented below.
We focus specifically on the North Western Mediterranean region, on which several papers have been published using NAOS BGC-Argo data (see above), and for which the density of collected profiles is particularly high. In the North Western Mediterranean region, additional BGC-Argo floats on top of the NAOS fleet have been deployed under the responsibility of the Argo-France (GMMC) and Argo-Italy programs. These floats have the same sensor configuration as NAOS BGC floats (although most of them have no NO 3 sensor). Corresponding datasets were processed using the same methods as for the NAOS floats. When NAOS data are combined with the other float data, the number of collected profiles over the period 2013-2019 comes to 1472, with 545 NO 3 profiles (see map in Figure  3 ). The merging of all the data from the North Western Mediterranean region (the so-called bloom bioregion defined by  Mayot et al.
Conclusions on the Use of BGC-Argo Floats in the Arctic Ocean The Arctic Ocean is an important component of the Earth system, especially in the current context of global warming. Yet Arctic amplification and all its related impacts, notably on biogeochemical cycles and ecosystems, remain poorly understood, mostly because they are poorly documented. Collection of data in the ice-covered Arctic Ocean still remains very limited because of difficult access to this extreme environment  (Smith et al. , 2019). Extending the BGC-Argo fleet to this region could significantly help fill this gap. The work achieved in Baffin Bay by NAOS has contributed to this goal. The three main approaches for collecting long high-frequency time series in the Arctic Ocean are moorings, ice-tethered platforms (ITPs) and free-drifting profiling floats.
Mooring provide Eulerian observations at a known position in delayed mode. ITPs are mostly dedicated to physical oceanography, and provide observations from given drifting ice floes (neither Eulerian nor Lagrangian) that can be located and uploaded readily in real time. As for Argo floats, up to now, only a few have been deployed in icy polar oceans, and even fewer BGC-Argo floats. These offer a complement to moorings and ITPs by providing, very often, Lagrangian observations at depth in real time (ice-free) or delayed (ice-covered) mode. The expanding suite of sensors carried by BGC-Argo floats allows the study of transient processes and seasonal cycles in biogeochemical fluxes and marine ecosystems, as illustrated by the data obtained in Baffin Bay. This approach offers considerable potential, as shown by our scientific results, but some major technological challenges remain to be tackled before BGC-Argo floats can be deployed in larger numbers in ice-covered sectors of the Arctic and Antarctic Oceans.
, 2017)  (see section "Biogeochemical floats in the Mediterranean Sea"). The objectives of the NAOS Deep Argo pilot project were to demonstrate the feasibility of measuring high-quality temperature, salinity and dissolved-oxygen concentration data from Argo floats at depths greater than 2000 m, to demonstrate the scientific value of such datasets, and to contribute to defining and initiating the extension of the Argo network toward the deep ocean. To address these objectives, it was decided to implement a Deep Argo pilot array in the subpolar gyre (SPG) of the North Atlantic Ocean, which plays a predominant role in connecting surface and deep ocean, through the formation and spread of newly ventilated intermediate and deep-water masses. In this context, several key oceanic processes can be studied on the short-term, including deep convection, deep water mass pathways and mixing.
The implementation of the pilot array was made possible by the OVIDE  (Lherminier et al. , 2007; Mercier et al. , 2015)  and RREX  (Petit et al. , 2018)  projects. These complementary observing programs provided not only deployment opportunities but also the large-scale and interannual context for data interpretation (e. g. ,  Maze et al. , 2012; Daniault et al. , 2016) , as well as independent datasets for sensor evaluation and data qualification.
Data Management and Qualification We first investigated the behavior of the SBE41CP conductivity sensors that equip the Deep-Arvor floats. Twenty-one Deep-Arvor floats purchased as part of NAOS and complementary projects were considered. For each, a calibrated ship-based reference profile was acquired at float deployment. Comparisons made to the reference profiles revealed that most floats exhibited a fresh bias at depth (see Figure  13 , Le  Reste et al. , 2016; Racapé et al. , 2019). While there is certainly a pressure effect on the sensor, the question of whether this pressure effect is well corrected is also raised. We thus investigated the pressure dependency of any salinity bias by computing the salinity difference between the float and the reference profile at matching density (sigma-theta) coordinates.
(2019). Note that for four floats, the slope estimates were not significantly different from zero, in agreement with the findings of Le  Reste et al. (2016)  for float #6901631 and 6901632. The origin of this pressure response and the way to correct it is still under discussion in the Argo community and might be due to an overestimation of the conductivity pressure effect correction coefficient (CPcor) provided by Seabird  (Zilberman et al. , 2019). Two floats experienced permanent or temporary jumps toward larger fresh bias (#6901631 and 6901757) and two floats experienced a linear salty drift (#6901758 and 6902818). These issues are a current concern among the Argo community and are not specific to the Deep-Arvor. One major contribution of the NAOS project is its implementation of the procedure for managing oxygen data in the Argo data stream.
8 kg/m 3. Data from NAOS floats in the North Atlantic Ocean were used to investigate circulation of the deep-water masses belonging to the lower limb of the MOC  (Daniault et al. , 2016)  as well as deep convection processes and oxygen supply to the deep layers. Five of the NAOS Deep-Arvor floats were deployed in the Charlie-Gibbs Fracture Zone (CGFZ), a gap in the Mid-Atlantic Ridge known to channel ISOW from the Iceland Basin to the Irminger Sea. The floats drifted in the core of ISOW at 2750 m. Based on the mean circulation scheme of  Daniault et al. (2016) , we expected that all the floats would veer northward downstream of the CGFZ and flow cyclonically in the Irminger Sea. However,  Racapé et al. (2019)  showed that none of the floats reached the Irminger Sea and one float even revealed a new direct route from ISOW toward the subtropical gyre without looping in the Irminger Sea.
Oxygen data acquired by the floats showed that the ISOW layer was mainly composed of highly oxygenated ISOW and less oxygenated North East Atlantic Deep Water (NEADW), a complex water mass from the East Atlantic. Racapé et al. (2019)  also showed that mixing between ISOW and NEADW was favored by northward intrusions of the North Atlantic Current. The ISOW-NEADW signal flowing westward from the CGFZ toward the Deep Western Boundary Current (DWBC) was progressively diluted by Labrador Sea Water and Denmark Strait Overflow Water (DSOW). These results highlight how Deep Argo data can improve our understanding of deep circulation and in particular, how oxygen data are essential for a better grasp and characterization of the mixing and spreading of deep water masses. Owing to the core Argo floats deployed in the SPG of the North Atlantic Ocean, including NAOS floats (see section "Contribution to the Core Argo Program"), we investigated deep convection processes in the Irminger Sea and Labrador Sea.
C. et al. , 2015)  and cycling every 10 days in order to also contribute to the core Argo program. The Deep Argo array will be a mixture of 4000 and 6000 m floats to monitor, in a cost-effective manner, both the deep layer (2000-4000 m) and the abyssal layer (>4000 m) that represent about 39 and 12% of the total ocean volume, respectively  (De Lavergne et al. , 2016; Le Reste et al. , 2016). The proportion of 4000 and 6000 m floats in each basin will depend on the bottom topography but also on the spatial pattern and temporal variability of the signal of interest (heat content and large-scale circulation), which remain to be determined. In expanding deep-ocean data coverage, the Deep Argo array will improve our ability to monitor regional to global variability in ocean heat and freshwater contents and associated sea-level rise, our scientific understanding of deepocean dynamics and global overturning circulation, as well as short-and long-term climate forecasting.
The quality control procedure of Deep Argo data will also be addressed as part of the Euro-Argo-RISE project. The transition from regional pilot array to global array should conform to the following principles  (Roemmich et al. , 2019) : maintenance and expansion of the spatial domain of the existing regional pilot array; initiation of new regional arrays, starting with areas where the deep layers are believed to undergo substantial decadal changes, seasonal cycles, or other variability, and that are close to deep-water and bottom-water formation sites. The European strategy is in line with these principles and specifies that: We also plan to expand the North Atlantic Deep Argo array southward (in the subtropical gyre and tropical area of the Atlantic Ocean) in the coming years, and to contribute to the Deep Argo array in the Southern Ocean whose deep and abyssal layers are warming at the highest rates  (Bindoff et al.
, 2019; Claustre et al. , 2020) , the Argo-France objective for the next decade is to sustain a contribution to the Argo core mission and the Deep Argo and BGC-Argo extensions. The plan is to deploy 70-80 floats/year, with 30 T&S floats, 15 deep floats, 15 floats with oxygen sensors and 15 BGC floats measuring the six core BGC-Argo variables. France will continue its leadership in Argo datamanagement activities with the operation of one of the two GDACs as well as the French/European DAC. In addition, France will play a leading role in BGC-Argo data management and scientific validation. Argo-France will continue to work in areas of strong interest for the French research community: North Atlantic, Mediterranean Sea, tropical regions (Atlantic in synergy with the PIRATA array and Pacific in synergy with the TPOS2020 array), Arctic Ocean and Southern Ocean.
These contributions will be aligned with European and international strategies to ensure that they build toward a truly global and optimized array both for the core Argo program and its Deep and BGC extensions. Integration with satellite observations, other in situ observing systems and models will remain a strong focus of Argo-France (e. g. ,  Gasparin et al. , 2019; Le Traon et al. , 2019). The integration of BGC-Argo observations with ocean-color satellite data in biogeochemical models (e. g. ,  Cossarini et al. , 2019)  opens up exciting prospects of outstanding advances in biogeochemistry research and operational applications, akin to what has been achieved for physics through core Argo and satellite altimetry data (e. g. ,  Le Traon, 2013). As far as float technology is concerned, two main developments are planned for the coming years: (1) a Deep-Arvor 6000 m float that will sample deeper layers and offer a carrying capacity for additional sensors (e.
On this basis, we first plan to maintain the Deep Argo array in the SPG of the North Atlantic Ocean. According to the Deep Argo sampling strategy, the SPG area (10 • W-60 • W, 45 • N-65 • N) would require a permanent fleet of about 40 deep floats. As a point of comparison, the core Argo array in the SPG currently comprises 150 floats. Based on topographic and key current features, the SPG domain can be grossly divided into five subzones for coordinating repeated deployments and favoring a homogenous coverage of the domain: the Labrador Sea, the Irminger Sea, the Iceland Basin, the Newfoundland Basin, and the Iberian Plain. The random distribution of 40 floats in water depth > 2000 m in the SPG is shown in Figure 15 (8 floats per zone). The current Deep Argo database in the SPG comprises about 1200 profiles (Figure 12), mainly fed by NAOS Deep-Arvor floats (14).
Investissements d'avenir" program. This support, managed by the National Research Agency (French ANR), is referred to as ANR-10-EQPX-40. Mediterranean activity was partially cofunded by the remOcean (European Research Council, grant 246777) and BioArgo (CNES-TOSCA) projects. The Canadian Foundation for Innovations (CFI) John R. Evans Leaders Fund #30124 covered the acquisition of 9 Pro-Ice floats. The Canadian Excellence Research Chair in Remote Sensing of Canada's New Arctic Frontier covered the wages of some research assistants. The European Commission Horizon 2020 grant #727890 to the Integrated Arctic Observation System INTAROS covered travel expenses related to the deployment of the Pro-Ice floats and coordination efforts. Regarding Pro-Ice deployments, support was given by NSERC for ship-time: • 2016: NSERC Ship-Time program (# RGPST-486341-2016).
• 2017: NSERC Ship-Time program (# RGPST-501172-2017). • 2018: Ship-time was supported by the Sentinel North program of Université Laval, made possible, in part, thanks to funding from the Canada First Research Excellence Fund. • 2019: NSERC Ship-Time program (# RGPST-531424-2019). • As well as help from the Green Edge project (http: //www. obsvlfr. fr/proof/php/GREENEDGE/greenedge_ autonomous. php). Takuvik's laser ice-detection project was financially supported by the French National Center for Scientific Research (CNRS) through their program for innovation and technology development Defi Instrumentation aux limites (2015). The French cruises involved in the NAOS Deep-Arvor float deployment were supported by the French Oceanographic Fleet (TGIR FOF). This work was also supported by the AtlantOS project funded by the European Union's Horizon 2020 research and innovation program under Agreement #633211.
ACKNOWLEDGMENTS V. Le Saout  ran the  NAOS  project office right from the start of the project. Her help has been invaluable to the project's success. NAOS float technology developments were led by  S. Le Reste  and  X. André  with extreme professionalism. We thank  N. Lebreton  from the  Coriolis deployment team  for her assistance during the acceptance tests and deployments of profiling floats. We thank colleagues from  LOPS/TOIS  and ship crews of the RV Thalassa, Atalante, Pourquoi Pas?,  Sarmineto del Gamboa  and investigators involved in the NAOS  Deep-Arvor  float deployment. We thank crews and captains of the  RV Tethys-II ,  Le Suroit  and  Pourquoi Pas ? for at sea activity in the Mediterranean related to BGC-floats. Many thanks also go to the projects MERMEX (Chantier Med INSU),  BOUSSOLE  ( CNES-TOSCA ),  Argo-Italy  and  MOOSE , which provided logistics, assistance and an exciting scientific environment for the Mediterranean NAOS activity.