dataesr/scientific-paragraphs-categorization huggingface dataset:
In Martinique, such a survey has been available since 2013  [32, 33]. The main goal was to collect new information about the rocks (geology, lithology) and reveal the subsoil (regolith, hydrogeology) up to 200 m depth using the Time Domain Electromagnetic Method (TDEM). Recently, as requested by the Hazard and Risk Department of the Environmental and Ecological Ministry, some technical studies  [34] [35] [36] [37]  on shallow and deep landslides based on TDEM-derived data and spatial modeling have demonstrated the full potential of a coupled approach to assess landslide-prone areas. Thus, when the municipality of 'La Trinité', located on the east coast of Martinique, wished to develop an area untouched by any development but located in a moderate susceptibility/hazard class, it was necessary to understand what conditions the potential instabilities might provoke.
Study Site The study site is located in the municipality of La Trinité, Martinique (Figure  1 ). La Trinité is affected by natural hazards, particularly slope instabilities, with 17 landslides and nine mudflows inventoried and included in the French landslide database (BDMVT). The site belongs to the unit of the Vauclin-Pitault submarine volcanic chain (Miocene,  [38] ; Figure  2 ). This unit is represented by the Vert-Pré porphyric basalt lava flows (14. 8 to 14 My; Figure  2 ) and the more recent dark porphyric andesite lava flows (2. 8 to 2. 2 My; Figure  2 ). The area, subject to future development by end-users, is located to the north of the municipality (Figure  1B ). This area, with a surface of about 1 km 2 (Figures  1  and 2 ), is limited in the south by crests carved in recent andesite lava flow formations and dominated by the Morne Congo (266 m a.
Inversion Processes Data were inverted using the Spatially Constrained Inversion algorithm (SCI)  [53]. Each item of TDEM data was interpreted as a 1D earth model (sounding) divided into n layers, each being defined by thickness and resistivity. During the inversion, constraints were applied vertically and spatially on nearby soundings (independent of the flight line and ground clearance). Results were obtained with smooth inversion (25 layers from 0 to 300 m depth). This inversion method is effective to image complex geological structures with the lowest dependency on the starting model. In addition, the altitude of the transmitter is inverted for and the Depth of Investigation (DOI) was evaluated as a final step of the inversion  [54].
3D Geological Model Based on TDEM inversion and with the help of the regolith map, a 3D geological model was achieved with GeoModeller ® software developed conjointly by the French Geological Survey (BRGM) and Intrepid Geophysics Company. The goal was to obtain the internal structure of the area depicting the different material interfaces that may be prone to landslides. GeoModeller ® was developed to build implicit 3D geological models constrained by geological data  [55]. By using the positions of geological limits (the contacts between two geological objects) and structural data (S0, S1, fold axes, etc. ), the tool defines the different unit boundaries and builds all the associated geological surfaces. The detailed description and rules can be found in  [55]. GeoModeller ® was used successfully to produce 3D geological models of complex tectonic structures [55,56].
In this way, it was possible to assess the influence of future planning on slope stability; 2. The second step consisted of spatializing the FoS to define the most unstable areas according to a spatialized physically based model (i. e. , ALICE ® ) developed by the French Geological Survey  [65, 66]. ALICE ® is able to support different landslides' geometries, the spatial and inherent heterogeneity of the surficial deposits and geology and their geotechnical parameters, different triggering factors (i. e. , GWL and seismicity-PGA), and land use change. The geometry of the studied area is entered as a dataset in raster format: topography, geometry of geological, and/or surficial deposits layers represented by a DTM (i. e. , basal surface of the layer). Geomechanical characteristics are associated with each geologic and surficial deposit layer.
More details about the tool and its possibility can be found in  [65, 66] ; 3. The third step focused on the run-out calculation based on the ALICE ® results with a cellular automaton (BORA ® ) developed by the French Geological Survey for the French Indies and Polynesia  [67]. For the steps 1 and 2 the Morgenstern and Price  [68, 69]  equations were used; four parameters are requested: 1. Surface topography and internal structure geometry; 2. Effective cohesion c' (kPa); 3. Effective angle of friction ϕ' ( • ); 4. Specific bulk unit weight yd (kN•m -3 ). The topography is obtained from the DEM Litto3D ®  [70]  obtained by LiDAR technology at a cell size of 1 m. This is a unique and continuous land-sea datum database giving a three-dimensional representation of the shape and position of the soil on the littoral fringe of the French territory (including the French West Indies).
However, for the studied area, no events were reported in the French official database before the tropical storm in 2009  [39]. In consequence, the established rules of the landslide hazard map are based on feedback from other landslide observations on the island  [70]. Thus, it would be better to use the term Landslide Expert Susceptibility Map (LEM) to avoid any confusion. Five degrees of landslide susceptibility were defined (S1 → S5, Table  1 ).
Analysis of Slope Stability and Run-Out Spatialization The goal of the FoS and run-out spatialization was to provide an additional answer compared to the 2D cross section analysis. FoS are computed with the same type of information used in the previous step. New information must allow the enhancement of the LEM of the area represented in moderate class. This type of class has large uncertainties in the framework of town planning. It must be emphasized that knowing the thicknesses of the regolith and materials from TDEM data is a real asset for this study. If the geotechnical values and different interfaces are the same as in the previous step, it is necessary to note that the excavations and overload scenarios were not taken into account in this step; ALICE ® does not take into account this type of hypothesis.
TDEM and Its Application The use of TDEM is still rare to analyze landslides and assess landslide-prone areas. It is clear that the French West Indies enjoy full coverage, which is currently exploited. However, this use requires specific and challenging processing for these environments. Indeed, firstly, the ground clearance of the loop was obtained by degrading the available 1 m Digital Elevation Model to a 25 m grid (consistently with the AEM footprint) and subtracting it from the DGPS elevation; we did not use the data from the laser, which proved to be biased in such a topographic environment. Tilt measurements were processed, taking into account the local topography in order to consider an effective tilt at each TDEM data location  [75]. As part of an environmental study in an anthropized area, particular attention was paid to properly removing noise from the TDEM data.
Conclusions The study stresses the way to improve locally, in a complex volcanic environment, landslide susceptibility maps for areas subject to discussion about future development. Indeed, landslide susceptibility/hazard map produced by the official French method (Plans de Prévention des Risques) offer great flexibility to practitioners for the classes identified as moderate. The consequences are that often one must answer multiple questions to know if it is possible to develop the identified area without hazard and thus protect the population and property. In response to a request from the municipality of 'La Trinité', a scenario approach was retained. It is based on transdisciplinary skills that have made it possible to use new data, such as data from the TDEM, a 3D model, or susceptibility spatial modeling for different types of landslides with different depth.
Monitoring hydrodynamic cavitation of liquids through "labs on a chip" (i. e. microchannels with a shrinkage, such as microdiaphragms or microventuris) is an improvement in experimental approaches devoted to study the mechanisms involved in these multiphase flows. The small sizes of the reactors do not require big substructures. Flow rates of around 1 L/h make possible the characterisation of rare, toxic or expensive pure fluids or mixtures. Moreover, because of that microfluidic approach, an unique inception of the cavitation from a laminar flow regime is also possible, that provides precious databases for simulation or modelisation. Lastly, "labs on a chip" are an extremely versatile solution to perform novel experiments, as they are embeddable in tools basically designed to proceed with small samples (confocal microscopy, spectroscopy).
r/r cav < 1 means that the global static pressure in the constriction has fallen below P vap. Re > 3000 is characterising the establishment of a turbulent flow regime in the constriction. Empty symbols correspond to single liquid phase flows. Filled symbols correspond to two-phase flows. Data recorded with 2-butanol and isopropanol were the first ever published experimental evidences of a transition from a laminar liquid single phase flow towards a cavitating one  [17]. The onset of cavitation is perfectly monitored by the drop of the pressure, as a consequence of the Bernoulli law. Alternatively, it is obvious that the cavitation of DEE is due to a turbulent flow regime in the constriction. The special feature of water resides in the fact that the two-phase flow transition occurs nearby r/r cav < 1 and Re > 2000.
If we consider that exfoliation is likely to occur only during that period, the 25 passes correspond to a total processing time of 1 ms. The same calculation was performed with the data of the batch B (Tab. 2). At the end of the process, the solutions were centrifuged for 45 min at 4000 rpm (A) and 1500 rpm (B) respectively. The supernatant from each solution was collected for a TEM analysis (Philips CM300). Figure  11  exhibits sheets issue from the batch A (Fig. 11a ), batch B after 380 passes (Fig. 11b ) and batch B after 800 passes (Fig. 11c ). Some small flakes (Fig. 11a ) look like few layers graphene. Their size is too small to allow a Si PR Si  diffraction analysis and to validate that they are graphene layers. As a matter of fact, the conjunction of initial small microparticles, low cavitation number and high centrifugation rate was not a conducive environment for the production of bigger flakes; shear rates and centrifugation rates control the flake size  [25] : the highest, the lowest average size.
ACKNOWLEDGMENTS Part of this work was made possible through the  French ANR LUNAPROBE  Contract No. ANR 09-NANO-P057-36. This work has also been partially supported by the  LabEx Tec 21  ( Investissements d'Avenir -Grant  Agreement No. ANR-11-LABX-0030 ). The authors acknowledge helpful assistance of the technical staff of the Nanofab facilities from the  Neel Institute (CNRS) of Grenoble.
9 cm -1 shallower than the ab initio value and reproduces the experimental data very well. The rovibrationally inelastic cross-sections were calculated at the close-coupling level using the Newmat package  [41] , which we recently used in the study of the inelastic collisions of He with vibrating NO +  [42]. This code solves the close-coupling equations in the space-fixed frame using a log derivative propagator. The vibration of the diatomic molecule is treated by first solving the exact diatomic equations using the diatomic potential and a finite basis representation of imaginary exponential as described by Colbert and Miller  [43]. The diatomic rovibrational wave functions are then evaluated along a Gauss-Hermite grid and used to calculate the vibrational part of the intermolecular potential matrix elements.
NGRIP ice core data We employ a proxy record of δ 18 O ratios from NGRIP. The δ 18 O ratios obtained from ice cores are commonly interpreted as proxies for surface air temperature variability  (Johnsen et al. , 1992 (Johnsen et al. , , 2001;; Dansgaard et al. , 1993; Andersen et al. , 2004). The layer-counted chronology of this record is the Greenland Ice Core Chronology 2005 (GICC05)  (Svensson et al. , 2008) , which starts at 60 kyr before AD 2000, abbreviated as b2k herein. We use the published version of this record, with measurement values of δ 18 O ratios reported at a temporal resolution of 20 years. Dating uncertainties in terms of maximum counting errors (MCE) are given for each of these time steps in the published dataset  (Andersen et al. , 2006; Rasmussen et al. , 2006). As a result of the layercounted dating, the MCE increase monotonically toward the past; see  Svensson et al.
Suigetsu lake sediment data Sediment cores obtained from Lake Suigetsu, Japan, allow for a floating, varve-counted chronology of the approximate interval 10. 2-40. 0 kyr BP, where BP refers to the year 1950  (Marshall et al. , 2012; Schlolaut et al. , 2012; Staff et al. , 2013). Counting errors are reported in terms of 1σ , from which we obtain the corresponding maximum counting error as MCE = 2σ  (Andersen et al. , 2006). Using this chronology and combining it with speleothem data for its more recent past, a comprehensive 14 C record was obtained that provides a unique comparison curve for atmospheric radiocarbon age measurements over the time interval 10. 2-52. 8 kyr BP  (Bronk Ramsey et al. , 2012). We used the 14 C data, as well as the 14 C age measurements, together with the corresponding varve-counted time stamps from Bronk Ramsey et al.
Since the proxy measurement uncertainties are not reported in the published version of this dataset, we assume for p(x|z) a normal distribution, centered at the reported values and with very small σ = 0. 01 ‰. The results we present in the following would not change if the proxy measurement errors were completely neglected by setting p(x|z i ) = δ(xx(z i )), the Dirac delta distribution located at the measured value x(z i ). We employ three different functional forms for the age uncertainty distribution p(t|z) in order to illustrate how this choice impacts the final representation. First, the rather unrealistic choice of a normal distribution with σ = 2 • MCE 1/2 /2, which corresponds to independent dating errors, is used in Fig. 3b. In this case, the variability of the δ 18 O record -as seen in the blue line of Fig.
Acknowledgements. We thank  Christine Hatté  and  Denis-Didier Rousseau  for helpful discussions and comments. Niklas Boers acknowledges funding by the  Alexander von Humboldt Foundation  and the  German Federal Ministry for Education and Research. Michael Ghil  was partially supported by  ONR's Multidisciplinary Research Initiative (MURI)  grant  N00014-16-1-2073  and by  NSF  grant  OCE-1243175.
Data availability. The ice core data (Andersen et al. , 2004; Svensson et al. , 2008; Rasmussen et al. , 2014)  used in this study are publicly available at http://www. iceandclimate. nbi. ku. dk/data/. The Suigetsu sediment data  (Bronk Ramsey et al. , 2012)  can be downloaded from http://www. suigetsu. org/publications. html. Both datasets were last accessed at these websites on 15 May 2017.
Acknowledgements This manuscript benefitted from the reviews of two anonymous reviewers that are thanked for their constructive comments. We would like to thank  Michel Fialin  for his help with EPMA measurements, and  Brandon Mahan  for his aid in preparing the starting materials. We acknowledge the financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). This work was supported by  IPGP  multidisciplinary program PARI, and by  Paris-IdF SESAME  Grant no. 12015908. This work has been written while IB was in BGI, supported by  DFG  Projekt  RU 1323/10-1. JB acknowledges funding from the  PNP research program  at  INSU/CNRS , and the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n°  207467  (DECore).
The two datasets are in agreement, allowing the recognition of the transition zone between the Pliensbachian and the Toarcian. The major palaeoclimatic changes associated with large carbon-cycle perturbations at the Pliensbachian -Toarcian transition have, to date, been primarily studied in marine settings. This study presents one of the best continental sequences in the world for documenting this transition. Identification of the transition also facilitates correlation with the worldwide Toarcian Oceanic Anoxic Event and negative (organic) carbon isotope excursion, as well as identifying a warming trend. Fu ¨rsich et al. (2015) reconstruct the Early Jurassic-Early Callovian palaeoenvironmental and depositional history of the NW Afghan -Tajik Basin in southern Uzbekistan. The oldest sediments date from the Early Jurassic through the end of the Early Bajocian, during which.
Acknowledgements Errors are likely to be significant because of error-prone small misorientations, although  Wheeler et al. (2009)  show how they may be mitigated by filtering out the shortest WBVs. Adjacent measurement points with misorientations above a threshold value are omitted from gradient calculations, so as to exclude high angle boundaries which lack organised dislocation substructures. The magnitude and direction of the WBV can be displayed on maps in a variety of ways. Given that the shortest WBVs are the most error prone, the display may be chosen to show only those above a particular magnitude (cf. Fig  3a). 2. In the integral form, contour integration round the edge of a region on an EBSD map gives the net dislocation content of that region, though the spatial distribution of dislocations (domains of high or low density) within the region are not constrained.
support by the French Agence Nationale de la Recherche is acknowledged (project DREAM, ANR-13-BS09-0001-01). This work benefited from support from institutes INSIS and INSU of CNRS. It has been supported by a grant from Labex OSUG@2020 (ANR10 LABEX56) and from INP-Grenoble and UJF in the frame of proposal called Grenoble Innovation Recherche AGIR (AGI13SMI15). Visiting exchanges for SP and MM were financed by ESF RPN MicroDICE (08RNP003) and by CCFS visiting research funds. MM benefited from a invited researcher fellowship from WSL, at SLF-Davos (2016-2017).
Financial support by the French Agence Nationale de la Recherche is acknowledged (project DREAM, ANR-13-BS09-0001-01). This work benefited from support from institutes INSIS and INSU of CNRS. It has been supported by a grant from Labex OSUG@2020 (ANR10 LABEX56) and from INP-Grenoble and UJF in the frame of proposal called Grenoble Innovation Recherche AGIR (AGI13SMI15). Visiting exchanges for SP and MM were financed by ESF RPN MicroDICE (08RNP003) and by CCFS visiting research funds. MM benefited from a invited researcher fellowship from WSL, at SLF-Davos (2016-2017).
More accurate retrievals of chlorophyll-a (r 2 > 0. 80) based on field datasets were found using NIR-Red three-band models. In addition, enhanced chlorophyll-a retrievals were found using the two-band algorithm based on bands of recently launched satellites such as Sentinel-2/MSI and Sentinel-3/OLCI, indicating a promising application of these sensors to remotely estimate chlorophyll-a for coming decades in turbid inland waters. Our findings suggest that empirical models based on optical properties involving water constituents have strong potential to estimate chlorophyll-a using multispectral data from satellite, airborne or handheld sensors in productive tropical estuaries.
Therefore, for a geographic and/or seasonal region, it is essential to conduct a thorough investigation of the local relationships between in situ-measured chlorophyll-a and spectral bands of airborne or handheld sensors before using satellite datasets  [14]. In general, these empirical algorithms are often based on a relationship between chlorophyll-a and reflectance, R rs (λ), which is derived from the bio-optical theory of inherent optical properties (IOPs), such as total absorption (a) and backscattering (b b ) coefficients  [15, 16]. The underlying principle is that changes in the concentrations and distribution of organic and inorganic particulates and dissolved substances in the water affect the observed reflectance, R rs (λ), in different wavelengths, λ, according to  Gons [17]  and Preisendorfer  [18] : R rs (λ) = f (λ) Q(λ) b b (λ) a(λ) + b b (λ) (1) a = a Chl-a + a N AP + a CDOM + a water (2) b b = b b,water + b b,particles (3) where f (λ) describes the sensitivity of the reflectance to variations in the solar zenith angle [19], and Q(λ) expresses the bidirectional properties of reflectance  [20] ; a Chla , a N AP , a CDOM and a water are the chlorophyll-a, the non-algal particles (NAP), the colored dissolved organic matter (CDOM) and the pure water absorption coefficients, respectively; and b b,water and b b,particles are backscattering due to water and organic/inorganic particles in suspension, respectively.
e. , Case 2 waters  (IOCCG 2000) , blue/green ratio models do not adequately represent the chlorophyll-a variability since CDOM and NAP may originate from additional sources, such as runoff of sediments, nutrients and organic matter and the resuspension of sediments from shallow bottoms  [28, 29]. Thus, major efforts have been made in the last decade to test and evaluate different algorithms to estimate chlorophyll-a in inland and coastal waters using multispectral data from datasets with different sources, such as handheld, airborne or spaceborne sensors  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43]. In turbid and productive estuaries, the best empirical approaches for estimating chlorophyll-a have been obtained with the use of NIR-Red models  [44, 45]. However, the understanding of these relationships has mainly focused on temperate and subtropical estuarine systems using modern satellite sensors, such as MODIS, MERIS and SeaWiFS  [14, 30, [44] [45] [46] [47] [48].
Field Measurements Shipboard data were collected during six major field campaigns conducted between May and September 2015. During each field campaign, a set of 12 sampling stations, well-distributed across MMELS, was established (Figure  1 ). At each sampling station, above-water optical measurements and water sample collection for laboratory analysis were carried out simultaneously. Surface water samples (each sample = 2. 0 L volume) were collected at a depth of 0. 2 m below the water surface. The samples were stored in a cooler with ice under dark conditions and were transported to the laboratory to determine the chlorophyll-a (Chl-a) and total suspended solids (TSS) concentrations. Above-water hyperspectral reflectance measurements were performed using TriOs RAMSES radiometers, with a spectral resolution of approximately 3.
Algorithm, Model Evaluation and Validation An algorithm was developed in MATLAB ® to identify the optimal wavelength positions in the NIR-Red models based on reflectance measurements. This algorithm consists of testing all possible sets of linear best-fit functions between observed and estimated chlorophyll-a within the spectral ranges recommended in previous studies for each optimal wavelength. Thus, a linear best-fit function was chosen for each model considering the following, in sequential order: the maximum and minimum values of the coefficient of determination (r 2 ) and the root-mean square error (RMSE) of each chlorophyll-a model. The models were evaluated considering different subsets, such as MMELS, Mundaú lagoon, Manguaba lagoon and optical classes defined in the reflectance spectra classification.