dataesr/scientific-paragraphs-categorization huggingface dataset:
X-TRACK regional products are freely distributed, providing Level 3 altimetry products from different altimetry missions for coastal and regional applications. The main objective of this article is to demonstrate how the reanalysis of satellite altimetry observations provides new ways of understanding the coastal ocean, focusing on the X-TRACK project. The article is organized as follows: Section 2 covers the most recent evolutions of the X-TRACK processing software and products and evaluates the quality of the corresponding data sets, using tide gauges as reference. Section 3 illustrates through some examples how improvements in altimetry products bring valuable information on the coastal ocean dynamics. Finally, Section 4 highlights main results and discusses some perspectives.
An improved strategy to compute sea level anomaly time series The X-TRACK software reprocesses in delayed time the Geophysical Data Records provided by space agencies (GDRs products). Its inputs are the measurements and parameters provided in the GDRs for a number of altimeter missions plus additional corrections and auxiliary data specifically distributed by the CTOH. The selected corrections for each X-TRACK-processed altimetry mission are given in Table  1. The historical processing (before 2016) is described in  Vignudelli et al. (2005)  and  Roblou et al. (2011)  and only a brief summary is presented here. It starts with the selection of the altimetry measurements located in the region of interest, defined as a regional polygon based on a precise land mask. A two-step editing strategy is then used to recover the maximum of meaningful sea level observations.
X-TRACK recent updates The editing strategy of the X-TRACK software has been entirely revisited in the 2016 version. The general principle remains the same but each corrective term is now edited in a different way in order to take into account its individual characteristics (i. e. the corresponding geophysical variations along the track). The processing of the ionospheric, wet tropospheric and sea state bias corrections are presented below. In the other altimeter corrections, usually derived from models, very few values are discarded by the editing process. Flagged corrections are simply replaced by their nearest valid neighbors.
soest. hawaii. edu/gshhg/dist_to_GSHHG_v2. 3. 4_1m. nc derived from version 2. 3. 4 of the GSHHS database). In a conservative approach, all the wet tropospheric correction values for which p > 0 are rejected. Finally, farther from the coast, the other possible outliers are discarded by an algorithm that detects large differences between the correction values at two consecutive points along the track for each cycle. The resulting data gaps are either interpolated (away from the coast) or filled with the closest values qualified as valid (in the land/sea transition areas). Using a technique based on the discrete wavelet transform (see  Donoho (1995)  for more details), a cleaned and noise-free wet tropospheric correction is then computed. Fig. 2  illustrates the correction editing and reconstruction for the example of the cycle 8 of Jason-1 along the ground track 222 in the Mediterranean Sea.
Sea level data quality: gain inferred from X-TRACK updates In order to assess the impact of the X-TRACK updates described in Section 2. 2 on the data availability, we first compare the quantity of valid altimetry SLA (i. e. , not flagged) provided by the two versions of the software (X-TRACK 2011 versus X-TRACK 2016). This comparison is performed in the Mediterranean Sea. All Topex/Poseidon and Jason-1&2 1-Hz measurements are considered from March 1993 to May 2013 (a total of 747 cycles). The 1-Hz along-track regional delayed-time SLA product distributed by the AVISO operational service is also used for information. Fig. 3a  shows the number of valid SLA measurements over the 20-year period for all the tracks crossing the Mediterranean Sea for AVISO (in green), X-TRACK 2011 (in red) and X-TRACK 2016 (in black); the results are represented as a function of their respective distance to the coast.
The thick curves indicate the mean behavior of the three SLA datasets. The differences between the two X-TRACK and AVISO products are observed not only near the coasts but also offshore, with $20 cycles less on average for AVISO. For both versions of the X-TRACK products, the data availability is the same at distances larger than 20 km from the coast. It is larger for X-TRACK 2011 at distances shorter than 10 km (7% more cycles on average), whereas the availability of the X-TRACK 2016 data is slightly higher between 10 and 20 km. The standard deviations of the corresponding SLA time series have also been computed. They are also represented as a function of the distance to the coast (Fig. 3b ). AVISO shows lower standard deviation values than X-TRACK everywhere ($0. 5 cm), probably due to a different spatial filter applied on the alongtrack SLA data and to a long wavelength error correction used to remove correlated noise due to orbit errors or uncertainties in geophysical corrections (not applied in X-TRACK processing).
On Fig. 3b , we can also observe that in X-TRACK 2016, compared to X-TRACK 2011, the signal variance is significantly reduced and much closer to AVISO at distances shorter than $40 km from the coast. In the near-shore area, significant differences are obtained with the two versions of the X-TRACK processing, both in terms of data availability and SLA standard deviation. In order to assess the respective quality of the altimetry datasets computed with X-TRACK 2011 and X-TRACK 2016, they were compared to in-situ ''ground truth" sea level measurements. A number of tide gauge stations distributed at very different coastal sites have been used for this purpose. Here we show only the results for the 6 stations listed in Table 2. To make in situ sea level as consistent as possible with the altimeter SLA data, the tidal signals were removed from the hourly tide gauge observations (using harmonic analysis) and the same dynamic atmospheric correction (DAC) was applied.
Whatever the distance of the corresponding satellite observations to the tide gauge, the near-shore altimeter SLA values processed with the X-TRACK 2016 software are in better agreement with the equivalent ground truth, which confirms that the recent updates made to the X-TRACK editing strategy lead to the computation of more accurate coastal altimetry SLA data. In this section we have shown only results for the Topex/Poseidon and Jason-1,2 missions as their combination leads to the longest time series of SLA data. Similar results (not shown) have been obtained for all the altimeter missions available in the X-TRACK products (Table  3 ).
Added-value coastal altimetry products: tidal constants The improvement of the coastal altimetry SLA data has naturally led the CTOH to develop derived regional addedvalue altimetry products based on the X-TRACK processing software. The T/P-Jason SLA time series are long enough (more than 23 years at a near 10-day sampling) to derive the empirical harmonic constants (amplitude and phase lags) for a number of tidal constituents (Table  4 ) by using harmonic analysis. In practice, the latter is applied on the SLA record fully corrected except for ocean tides, at each point along the ground-tracks (every 6-7 km). The set of constituents analyzed directly depends on the length and the temporal sampling of the SLA record, as these parameters drive the separation between the tidal constituents. In case of limited time series (such as for the T/P-Jason interleaved track observations), admittance methods are used for astronomical tides to relax the formal   Table  3  List of X-TRACK SLA products available and the first and last dates of the time series.
This is done using monthly climatologies of geostrophic current anomalies 2Q1 Sig1 Q1 Ro1 O1 MP1 M1 Ki1 Pi1 P1 K1 Psi1 Phi1 Tta1 J1 SO1 OO1 KQ1 OQ2 MNS2 E2 2MK2 2N2 Mu2 N2 Nu2 MSK2 M(SK)2 M2 M(KS)2 MKS2 La2 L2 T2 S2 R2 K2 MSN2 KJ2 2SM2 2MK3 M3 SO3 MK3 S3 SK3 N4 3MS4 MN4 M4 SN4 MS4 MK4 S4 SK4 2MN6 M6 MSN6 2MS6 2MK6 2SM6 MSK6 3MS8 (GCA) calculated from 20-years of altimetry data obtained with X-TRACK  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013). Cross-track GCA are first calculated from the low-pass filtered X-TRACK SLA (Loess filter with a 40 km cut-off frequency in order to reduce measurement noise). A monthly climatology of GCA is then computed by averaging the corresponding monthly values of the long time series.
The comparison with local in-situ observations (Section 2. 3, Santander tide gauge) confirms that the improvement in coastal sea level data accuracy obtained in X-TRACK 2016 results in a better coastal current observation. A more detailed analysis of the space-time variability of the regional circulation is beyond the scope of the present study and will be included in a forthcoming publication. However, this example illustrates the potential of satellite altimetry to depict and study the variability of coastal current systems (see also  Bouffard et al. , 2008; Durand et al. , 2009; Birol et al. , 2010; Melet et al. , 2010; Jebri et al. , in press). This is especially true in regions where the ocean circulation is poorly sampled by in situ measurements, such as in the southern hemisphere.
Coastal sea level variability Satellite altimetry data are largely used to study the mean sea level dynamics (see for example:  Llovel et al. , 2009; Meyssignac and Cazenave, 2012; Carson et al. , 2016). Because of the larger data uncertainty when approaching land, their ability to capture coastal sea-level variations (SLV) deserves specific attention. Here, we analyze the changes in SLV from the open to the coastal ocean provided by two different data processings: X-TRACK 2016 and AVISO. Along-track datasets from the T/P, Jason1 and Jason2 missions over the 1993-2012 time period are used. In this example, we focus on the sections of the altimetry tracks that are located less than 200 km off the coast of Western Africa (Fig. 8a  and c ). Along each coastal section, we computed the SLV changes (in terms of RMS) relative to the ''offshore'' SLV (defined here as the SLV averaged over the 160-200 km offshore portion of the satellite track, chosen because of the width of the regional continental shelf).
8  shows that, as in Fig. 3 , more data are available in the X-TRACK product, compared to AVISO (red points). As a result, the SLV evolution from the open to  the coastal ocean is observed more than 10 km from the coastline in AVISO (5 km for X-TRACK, i. e. closer to land). The data availability remains also more constant in X-TRACK up to 15-20 km from land. The sea level statistics obtained are then more robust and coherent in space for X-TRACK than for AVISO. In both products, on average over the region, the SLV steadily increases toward the coast in the 200 km coastal band (blue lines): it is $25% larger between 5 and 10 km offshore than between 160 and 200 km offshore. This increase appears to evolve linearly with respect to the distance to the coast in X-TRACK, due to the larger and more stable data availability.
g. Ruiz  Etcheverry et al. , 2015). In this sense, tidal constants retrieved from near-shore altimetry SLA measurements offer information more representative of the tidal dynamics solved by the models. Because it is derived from improved near-shore satellite altimetry data, the X-TRACK tidal constant product provides very accurate tidal information over all the continental shelves in Earth, as discussed on Section 2. 4. Like tide gauge data, the X-TRACK altimetry-derived tidal constant database is consequently a very useful tool as an independent source of information for the validation of tidal models in the coastal zones. The X-TRACK tidal constants were used for the validation of the FES2012 and FES2014 global tidal atlases (the most recent atlases of a series of global finite element tidal solutions initiated by  Le Provost et al.
, 2010). The X-TRACK tidal constant product makes possible to easily create a synoptic view of the tidal model performance along the altimeter tracks. Finally, if the X-TRACK tidal constant dataset can be used to validate tidal models, it can also be used for data assimilation in regional tidal models as a complement to tide gauge observations. Some experiments have already been done with very good results, for example in the North East Atlantic Ocean (see for example the regional tidal models distributed through the tidal prediction webservice TIPS, http://tips. noveltis. com/).
Conclusion and perspectives In this paper we presented the upgrades done in X-TRACK 2016. We estimated their impact on statistics (spatial and temporal coverage of the computed data sets, sea level variations) and in terms of comparisons with independent in-situ data. Results clearly show the benefit of the changes performed in the X-TRACK processing software: a more efficient detection of outliers in altimetric Fig. 9. Vector differences (in cm) between the most recent global tidal models and the X-TRACK tidal product, for the M2 main tidal constituent, for each region where the X-TRACK data are available. corrections, a better reconstruction of the missing or rejected correction values, and a substantial increase in the correlation between near-shore altimetry SLA and tide gauge records.
Acknowledging user needs, a tidal constant product has also been developed in addition to Level-3 SLA data sets. Three examples of applications based on the X-TRACK product are shown. They illustrate how a better altimetry data post-processing technique not only allows a significant increase in the altimetry data accuracy over a large part of the coastal ocean, but also in information on near-shore sea level variations and related ocean dynamics. The seasonal variations of the boundary circulation derived from altimetry in the Bay of Biscay are better resolved. Along Western Africa, the estimation of coastal sea-level variations from space is improved. Along-track empirical tidal constants derived from X-TRACK sea level time series complements the tidal information provided by tide gauge data and tidal models.
For the next few years, the X-TRACK software will continue to evolve. It will include the new generation of Ka-band and SAR altimeter measurements (SARAL/ AltiKa, Sentinel-3a&b) and benefit from the improvements in retracking algorithms  (Gommenginger et al. , 2011; Passaro et al. , 2014)  and in altimeter corrections  (Fernandes et al. , 2015; Ray et al. , 2011). The X-TRACK products will also be systematically extended to highsampling rate SLA data (ranging from 10-Hz to 40-Hz, depending on the altimeter mission) in order to improve the resolution of the sea level observations near the coasts  (Birol and Delebecque, 2014). The transfer of the product distribution to the operational AVISO+ service has already started (http://aviso. altimetry. fr). All X-TRACK products should be easily accessible from a user-friendly web interface in the near future.
Acknowledgements This study was funded by the  French Institut National des Sciences de l'Univers (INSU/CNRS) , in the framework of its observational programs, and by the  CNES  through its  scientific TOSCA/OSTST program. Support from these institutions is gratefully acknowledged. The X-TRACK altimetry data are developed and validated by the  CTOH/LEGOS  (http://ctoh. legos. obs-mip. fr/)and distributed by the  CTOH/LEGOS  and by AVISO+ (http:// www. aviso. altimetry. fr). The standard altimeter products were produced and distributed by AVISO+, as part of the SSALTO ground processing segment. We acknowledge  Laurent Roblou  for his support in the early developments of the X-TRACK software. We also wish to acknowledge the contribution of the French services that make the tide gauge data available: SONEL (http://www.
Image processing steps As previously mentioned, the 18 high-resolution 3D images of the initial sample were acquired and then processed with the ImageJ software  [21]. ImageJ is a public domain, opensource Java-based software for image processing and analysis and is extensible via plugins and macros. Image processing steps described below were performed on reconstructed volumes. First, the quality of each volume was improved by the adjustment of contrast and A C C E P T E D M A N U S C R I P T 8 brightness and by the application of median filtering over neighbours of two voxels. Fig. 2a  shows a slice of a high-resolution 3D image after treatment. Next, two thresholding operations separated the three different phases: voids, aluminum matrix and intermetallic inclusions (see Fig. 2b ). At this stage, it could have been possible to stitch the 18 volumes together to obtain a high-resolution 3D image of the whole sample.
Image analysis The final 3D image of the initial sample (Fig. 2d ) was used to quantify the sample's architecture using Java programs implemented in ImageJ software. Three parameters were calculated to characterize the scale of the architecture: the relative density and its variation in the sample, the pore size distribution and the strut size distribution. For highly porous materials, 3D data are needed to obtain a complete characterization. The principle of these quantifications is described in  [22]. We give here below a brief summary of this procedure.
Building of the FE model First, the solid phase (i. e. , intermetallics and aluminum) was meshed using Avizo 8 software with 1 228 000 tetrahedral elements having an average edge size of 76 µm. Because the goal of this study was to take into account the inclusions on the fracture properties, the calculation A C C E P T E D M A N U S C R I P T 11 was performed with the finest mesh which could be used in relation with the computer capacity (Dell, Intel Xeon, RAM 100 Go). That is why no specific convergence study was performed in this work, dedicated to the comparison of calculations including or not the presence of inclusions. To mesh the volume, the first step consisted of the generation of a surface meshing. A triangulated closed surface separating the volume from the voids was obtained thanks to a marching cube algorithm  [23]  implemented in Avizo  [24].
After different steps of optimization of the triangulated surface, the three-dimensional mesh was created by the advancing front method implemented in Avizo  [25]. The 3D mesh generated by Avizo was made of tetrahedra with four nodes (at each corner). Then, the Avizo generated mesh had to be transformed into an input file readable by Abaqus. The presence of the intermetallics in the solid phase had to be taken into account in the model. For this purpose, a special Java program was developed to create an Abaqus input file. This procedure is similar to what was done in  [26]  to model a fatigue test of a stainless steel hollow sphere structure by associating each shell element of a mesh to the local thickness of the hollow spheres. The Java program first calculated the barycentre coordinates of each tetrahedral element of the mesh.
f is generally considered as lower than 0. 1 in Abaqus  [30]. The values generally reported in the literature for metallic alloys for q 1 , q 2 and q 3 are 1. 5, 1 and 2. 25 respectively (q 3 = q 1 2 )  [31]. The model implemented in Abaqus assumes that nucleation rate increases with plastic strain rate following equation (2) proposed by Chu et al. [32] : 𝑓 ̇= 𝐴𝜀̇p l eq ( 2 ) ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T 14 where ḟ is the nucleation rate, ̇p l eq is the plastic strain rate, and A is a proportionality coefficient which follows the normal distribution given by the equation (  3 ): 𝐴 = 𝑓 𝑁 √2𝜋 𝑠 𝑁 exp [- 1 2 ( 𝜀 𝑝𝑙 -𝑠 𝑁 𝑠 𝑁 )] (3) where ε N is the mean value of the normal distribution, s N is the standard deviation of the normal distribution, and f N is the volume fraction of nucleated voids.
The following values are generally reported in the literature for metallic alloys: ε N = 0. 3, s N = 0. 1 and f N = 0. 04  [30, 33]. No data were found in the literature to describe the mechanical behavior of the elements containing inclusions. Nanoindentation was therefore performed in several inclusions to estimate the Young's modulus of the inclusions, using a G200 apparatus (Agilent Technologies, USA) equipped with a Berkovich tip. A series of 10 indents was made with a speed of 10 nm. s -1 , a dwell time of 10 s and a maximum penetration depth of 300 nm. The Young's modulus was determined thanks to the Oliver-Phar method  [34]. Before the measurements, the precise location of the tip was calibrated by indenting a fused silica reference sample. This depth was chosen to characterize only the inclusions (having a size between 20 and 120 μm).
INTRODUCTION The International Union of Basic and Clinical Pharmacology/British Pharmacological Society IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www. guidetopharmacology. org) is an expert-curated resource of ligand-activity-target relationships, selected from high-quality pharmacological and medicinal chemistry literature. It has its origins in IUPHAR-DB, first compiled in 2003 and focused on receptors and channels  (1) (2) (3). From 2012 to 2015, the scope expanded to define the data-supported druggable genome and the resource was re-named GtoPdb  (4, 5). This phase added many new target families and consolidated ligand-to-target relationships D1092 Nucleic Acids  Research, 2018, Vol. 46 , Database issue for approved drugs and clinical candidates. Over the last 2 years, under the guidance of the IUPHAR Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR) and its 96 expanded subcommittees (comprising 512 international scientists), we have added quantitative ligand-target relationships showing promise for future drug discovery.
In particular, pharmacological information is difficult for immunologists to access without chemoinformatic expertise not typically present in immunology labs. Immunology is supported by excellent databases--e. g. ImmPort (www. immport. org), ImmGen (www. immgen. org), Innat-eDB (www. innatedb. ca/), Reactome (www. reactome. org) and IMGT (www. imgt. org)--but these do not provide easy links to pharmacological data. The development of the 'IUPHAR Guide to IMMUNOPHARMACOLOGY' (or GtoImmuPdb for short), with an immunologist-friendly portal, aims to address this gap in information exchange. All data added for the immunopharmacology project are also available from the original GtoPdb site and vice versa.
GUIDE TO PHARMACOLOGY CURATION AND EX-PANSION The value of the database arises from our approach to document selection, data curation and annotation. Our curation 'rules' are better seen as flexible guidelines because expert judgement is exercised at all stages of the process, with the overriding pragmatic principle being to expand content value while maintaining stringency. This qualitycentric, 'small data' approach contrasts with automated data-mining, large-scale curation operations and 'big data' amalgamation efforts. Curation includes checking and fixing author errors (e. g. M versus nM), adding PubChem cross-pointers between unresolved stereoisomers to the appropriate stable R and S forms, and splitting salts and parents for cheminformatic consistency while also crosspointing to reported salt forms (e.