dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset is composed of X-band polarimetric radar PPIs and disdrometer data for 15 organized convective systems observed in 2006. A priori information on DSD parameters (benchmark method) is derived from the polarimetric radar observables by applying power law relationships. The proposed retrieval method of DSD parameters leads to the following results as compared to the benchmark: (i) we found a better spatial consistency of the retrieved parameters, (ii) the reconstructed polarimetric radar observables are closer to the observations, (iii) The validation with disdrometer data confirms an improved estimation of the DSD parameters.
It is commonly accepted that rainfall DSD can be represented by a Gamma law governed by three parameters characterizing: the number of drops, the characteristic diameter, and the distribution shape  [14] ; other laws such as lognormal or four-parameters extended gamma have also been proposed, but are less commonly used. Many studies have been devoted to analyzing how the DSD parameters vary with rain bulk variables (rain rate, radar reflectivity factor, liquid water content, median-volume diameter) and to finding expressions for the DSD function and parameters that reduce the variability  [14, 15]. A Section 2 introduces the radar and disdrometer dataset gathered in West Africa, and the previous findings (benchmark). Section 3 provides a detailed description of the forward problem relating polarimetric radar variables at attenuating frequency to the DSDs along the radar beam.
X-Band Polarimetric Radar Data The Xport X-band polarimetric radar was developed by the Institut de Recherche pour le Developpement (IRD) as a transportable unit to study rainfall associated with tropical convection  [44]. The radar operates at 9. 4 GHz with simultaneous transmission and reception of horizontal (H) and vertical (V) signals thanks to an orthomode feed. The peak transmitted power in each polarization is 50 kW, and the 1. 4 m antenna provides a 1. 4 • radar beam. For the present dataset, the radar pulse length is set to 1 µs, and the radar pulse repetition frequency is 1 kHz. The raw data from 128 instant pulses are processed in order to provide the radar variables for every range gate (150 m length) along a given azimuth. The stored radar variables are: radar reflectivity in horizontal and vertical polarization (Z H and Z V ), differential phase shift (φ DP ), cross-polarization correlation coefficient (ρ HV ), and Doppler radial velocity calculated for each polarization (V H and V V ).
Optical Disdrometer Data Optical disdrometers  [43]  were installed in three locations in the AMMA super-site of Benin, West-Africa: the further away from the radar was in Copargo (1. 53 • E; 9. 83 • N) situated 18 km from the radar; the two others were closer, in Nangatchiori (about 10 km from the radar) and Djougou at the radar site. These disdrometers provide the diameter and fall velocity of the drops. For the present dataset, the rain drop spectra are acquired every minute with 22 diameter classes from 0. 06 mm to 6. 5 mm. The whole database (described in  [43]  is composed of 11. 640 spectra, belonging to 93 different storms and a total of 1220 mm rainfall acquired in 2005 (Nangatchori, Djougou), 2006 (Copargo, Djougou), and 2007 (Djougou) by one (or two) of the disdrometers. The quality of the dataset was verified by comparing the rain rates distributions and daily total collected by each disdrometer with the closest rain gauge  [43].
Previous Findings from This Dataset The authors of  [43, 47]  provide an extensive analysis of the AMMA DSD dataset. They investigated which form of DSD and normalization best fitted the observed spectra. Their main conclusion is that the double moment normalization of the DSD introduced by [14] provides a good framework to represent the shape, fit the parameters, and explain the variability of the observed DSDs. The DSD is therefore expressed: N G (D m , N * 0 , µ; D) = N * 0 F D D m , µ (1) where the three parameters of the normalized DSD distribution are the volume-weighted mean diameter D m , the scaling parameter of concentration N * 0 and the shape parameter µ. D m and N * 0 are defined by: D m = M 4 M 3 with M n = ∞ 0 N(D)D n dD (2) N * 0 = 4 4 πρ w LWC D 4 m with LWC = πρ w 6 M 3 (3) where M n is the moment of order n of the DSD distribution, ρ w is the density of water, and LWC is the total liquid water content.
The characterization of the shape parameter µ from disdrometer spectra is more delicate. The authors of  [43]  showed that very different values of µ are obtained if the moment method is used or if µ is fitted directly on the shape of the DSD. The combined radar/disdrometer dataset, together with the AMMA-CATCH rain gauges, have been used in  [8, 35]  to study the influence of DSD variability on the polarimetric variables and to test various attenuation correction, rain rate, and DSD estimation algorithms. The authors of  [8]  used reflectivity (Z corr H ), differential reflectivity (Z corr DR ), both corrected for attenuation and (K DP ) to test various expressions of DSD parameters N * 0 and D m. The best estimation of these two parameters was evaluated on four rain events by comparison with the Copargo disdrometer.
The comparison between radar and disdrometer derived DSDs exhibited a correlation coefficient above 0. 6 for N * 0 and 0. 5 for D m and low relative biases in both cases (less than 3% for N * 0 and 1% for D m , respectively). The proposed power-law expressions are: log 10 (N * 0 ) = a + b Z corr H + c log 10 (K DP ) + d log 10 (Z corr DR ) (5) D m = e Z corr DR f (6) With a = 2. 16, b = 0. 039, c = 0. 41, d = -2. 04, e = 1. 699, f = 0. 353. K DP in • km -1 , Z corr H and Z corr DR (in dB), D m in [mm] and N * 0 in m -3 mm -1. As detailed in Section 4, the fields of N * 0 and D m derived from the above expressions is used as an a priori solution for the new DSD retrieval scheme presented in the next sections. The above relations proposed for an a priori model were established on the dataset used in  [8] , over Benin.
A Priori Information: DSD Parameters and Associated Covariance Matrix A priori values (N * 0 , D m ) of the DSD parameters are obtained using the empirical power laws suited to the local climatology (Equations (  5 ) and (  6 )), as previously detailed. A priori value of µ is more difficult to set as the bulk radar variables do not carry any information about the shape of the DSD. The choice of a priori value of µ is based on  [43] , who showed by fitting gamma laws on observed DSD of the same dataset that convective precipitation tends to have higher values of µ than stratiform precipitation. They showed that the values of µ fall in the interval µ ∈ [0. 5, 12]. We choose to set a constant a priori value of µ = 2 for all the radar gates as the stratiform rainfall is more frequent in the radar PPIs.
A global overestimation of D m in a priori is corrected by the algorithm-this will be further confirmed by the results in Figure  4. The retrieved values for µ are globally higher than a priori value (constant µ = 2) and especially in the convective part. This is consistent with what was observed statistically with the disdrometer by  [43]. The radar variables have a relatively low sensitivity to µ (except for Z DR ) and µ is therefore less constrained by the observations than the two other DSD parameters. The retrieved values of µ appear weakly correlated with the retrieved values of D m without imposing such cofluctuation in the retrieval algorithm. According to prior studies using disdrometer data  [12, 20]  D m and µ are anticorrelated, which is in contradiction with our results. On the other hand,  [47]  did not find any evidence of D m -µ relation on the present dataset.
The improvement of the inverse method compared to the a priori two-step method is visible. The global statistics are improved in terms of correlation and bias, with a slight reduction in nRMSE for Dm). Note that the retrieval skill is almost unchanged when a calibration error of + or-2dBZ is applied to the reflectivities (not shown, see Table  A1 ). Figure  5  indicates an improvement in DSD retrieval. However, some differences remain between the disdrometer and radar retrieved DSDs. The distribution of median diameter D m is narrower on radar than on the disdrometer dataset. Some of these differences are unavoidable given the different nature of the two datasets. The radar samples a larger volume of the atmosphere and higher above ground than the disdrometer. We can also notice that the disdrometer has points for Dm > 2.
7 mm that the retrieval has not. This can be caused by excessive filtering of the polarimetric variables or a wrong representation of the oblateness low. Figure  6  illustrates the improvement in DSD retrieval compared to a priori solution over the entire disdrometer record. The figure shows the density plot of observed [N * 0 , D m ] pairs, superimposing the disdrometer (blue) and radar (red) derived datasets. We used radar retrieval of the 12 September 2006 event (7 PPI from 17:59 UTC to 18:48). The disdrometer statistics include all the events sampled for the 2006 and 2007 rainy seasons. The overall agreement is stronger for the inverse method (left plot) than for a priori solution based on  [8]. In particular, the range of retrieved N * 0 is better matched with the disdrometer for the inverse method, while a priori solution generates lower values.
In addition to (i) the observed radar variables and (ii) the radial profile of the 3 DSD parameters to be retrieved, many other parameters and a priori information need to be prescribed to the inverse model The results of the inversion were evaluated through direct comparisons with disdrometer data and also by analyzing the global consistency of the retrieved DSD maps compared to the '2 step' empirical method from  [8]. Fifteen rainfall events, for a total of 244 PPI, were used for direct comparison between the disdrometer and the radar DSD retrieved at the closest pixel. The correlation between the time series, of D m and for N * 0 is increased from r = 0. 49 for a priori estimate to r = 0. 58 for the final solution, and the normalized bias is significantly reduced for both N * 0 and D m.
For that purpose, various datasets representative of different climatological contexts are needed. In addition, it would be interesting to compare the present method with the method proposed by  [38]  in order to assess their respective advantages and drawbacks and to explicitly take into account the differential phase shift in the formulation of the inverse algorithm. If these methods were to be used with operational radars and in real-time, the optimization of the processing time compared to retrieval accuracy should be analyzed. Some improvements and new implementations could be added to the inverse model. In the current version, the calibration of reflectivities is assumed to be correct, but a calibration (slowly varying) constant could be added to the model and retrieved as a parameter.
occidentalis  (Barrionuevo et al. , 2004) , 87 g for males and 72 g for females of T. europaea  (Crowcroft & Godfrey, 1960). There are no published data for T. aquitania, thus we took the same values as for T. europaea. 3D models of the humeri and ulnae were made by segmentation of CT scans with Avizo (Thermo Fischer Scientific, Waltham, MA). As far as possible, the left bones have been used. However, when the left humeri or ulnae were not well preserved, the right bones were symmetrized to obtain standardized data. A summary of the known information for each individual is available as a Table  S1. 2. 2 | Study of morphological variations using 3D geometric morphometrics.
| Data acquisition and statistical analysis To analyse the shape variation of the humerus and ulna between the three Talpa species, we used geometric morphometrics. This method corresponds to the statistical analysis of landmark-based shape variation  (Zelditch et al. , 2012). A set of 3D landmarks were manually placed on each 3D model, using the software Landmark (Landmark Editor Version 3. 6: Institute for Data Analysis and Visualisation,  Wiley et al. , 2005). Two types of landmarks were chosen for analysis of the 52 humeri and ulnae of moles: anatomical landmarks which define fixed anatomical points on the bones and sliding semilandmarks which will enable the analysis of parts devoid of anatomical landmarks, such as the diaphyses and joint surfaces. Sliding semilandmarks were allowed to slide on 3D curves while minimising the bending energy between the template and the object to measure.
The use of such landmarks is interesting because it allows the shape of the whole bone to be described and analysed, including areas without anatomical landmarks. (Gunz & Mitteroecker, 2013). We, thus, placed on the humerus 30 anatomical landmarks and 537 sliding semilandmarks to define the 62 3D curves (Figure  3a ; Table  S2a ). Concerning the ulna, 19 anatomical landmarks and 360 sliding semilandmarks have been placed, to define the 39 3D curves (Figure  3b ; Table  S2b ). Each anatomical landmark corresponds to the location of known anatomical features in moles of the genus Talpa (Figures  2, 3 ). The raw material (meshes and landmarks) is available on request. Statistical analysis and 3D visualisation were performed using R software (version 4. 1. 1; R Development Core Team, 2021). A Generalized Procrustes analysis (GPA) by minimising Procrustes Shape Distances was performed on the data for both humeri and ulnae  (Rohlf & Slice, 1990) , using the "gpagen" function of the "geomorph" package  (Adams et al.
These deformations are always carried out between only two items: a reference and a target. The TPS algorithm minimises the "bending energy" between two homologous points datasets, which means the energy of local deformation requires skipping from one shape to another  (Gunz & Mitteroecker, 2013). Thus, the 3D humerus (n°18,086-18,089) and the 3D ulna (n°18,082) are deformed at first to match the consensus shape and a second time to match the theoretical shape associated with the maxima and minima of the axes. TPS, which is an interpolation technique, provides a one-to-one correspondence not only between the reference points of the starting shape and the target but also between each point of the 3D space in which the shapes associated with the minimum and maximum are embedded  (Klingenberg, 2013).
| Shape covariation between the humerus and ulna To quantify and visualise shape covariation and the morphological integration between humerus and ulna, we performed two block PLS  (Rohlf & Corti, 2000)  implemented in the "two. b. pls" function of the "geomorph" package  (Adams et al. , 2021)  for each dataset separately. This approach is relevant for understanding how correlation and independence patterns change within a species and evolve  (Goswami & Polly, 2010). PLS has been used previously to compare the morphological integration of the limb long bone between different populations or species (e. g. , in horses:  Hanot et al. , 2019 and in rhinoceroses: Mallet et al. , 2020). This method allows us to extract the eigenvectors and eigenvalues from two blocks of covarying data, each of these blocks representing the shape variation of one bone, the ulna or the humerus.
A covariance matrix is constructed by combining the two sets of variables, and PLS finds the principal components of covariation between these datasets. This method then generates the axes of variation common to both blocks, the first axis explaining most of the covariation, as in PCA  (Goswami & Polly, 2010). Thus, patterns of covariance between the two blocks (humerus-ulna) can be explored, and the PLS axes can be plotted. Finally, a PLS coefficient (rPLS) is calculated allowing us to estimate the degree of morphological integration. A significance test is obtained by 10,000 permutations of the landmarks in one block relative to those in the other. A significant P-value was obtained when the observed PLS was higher than those of a distribution of values obtained from randomly permuted blocks and implies that the blocks are significantly integrated.
Measurements and statistical analyses (Table 1) All measurements were made with a Nikon Measuroscope for the material from St-Maximin, Bouxwiller, Cuzal, Laprade and Lissieu, and the casts from Casa Ramon. All the teeth studied from Egerkingen were drawn at the NHMB, (magnification × 31, 5) and their measurements calculated from the drawings (protocol tested for the specimens of alsaticus, available from Bouxwiller or Cuzal and the casts of nievesae). The teeth are weakly and unilaterally hypsodont; the walls of the crowns are generally vertical, but sometimes curved, the corresponding cusps being bulged. Therefore, the dimensions slightly vary from the top to the bottom of the crown. As often as possible, we measured maximal mesiodistal length and buccolingual width. To avoid any bias, we decided to measure all teeth without taking into account data from the literature.
3 ) would come from Egerkingen α and Ek H004 (o. c. pl. I, Fig. 4 ), from Egerkingen γ. However, in the NHMB collection, these two specimens are labeled "Egerkingen", without precision on the fissure. No rodents are indicated as coming from Egerkingen β. Therefore, it is impossible to relate the observed morphological and size variations to stratigraphical levels or to timing of faunal changes. Hartenberger (1969)  identified all theridomyid specimens from Egerkingen as Protadelomys cartieri, except for three specimens: Ek 592 was determined as ?Protadelomys sp. , Ek H003 and Ek H004 as Pseudosciuridae gen. and sp. indet 1 and 2. , later as Paradelomys sp. and Treposciurus sp  (Escarguel, 1999). Plesiarctomys from Egerkingen has been described in  Wood (1970). Among the 50 observed specimens including jaws and isolated teeth, 44 upper teeth and 45 lower teeth were found in different fillings (Ef, Eg, Ek or He) (see Additional file 1: Data S1).
90 and 0. 94 for ?P. alsaticus from Bouxwiller and Cuzal, respectively; ?P. maximini shows the lowest ratio of 0. 81; the ratio in ?P. nievesae is 0. 86 like that of P. lugdunensis from Laprade. Consequently, the ratio between P4 and M1 lengths appears fairly similar between the supposed successive taxa ?P. maximini, ?P. alsaticus, P. cartieri, and P. lugdunensis. Our dataset does not attest a relative size increase of P4 in the supposed lineage, bearing in mind the low sample sizes. 2-Linguobuccal deepening of the lingual sinus and increase in size of the lingual cusps of upper molars. This sinus is reduced to absent in ?P. nievesae, ?P. maximini, and ?P. alsaticus. It is usually present in these taxa but weak, narrow, shallow, and never significantly deepened linguobuccally. On molars, size increase mainly involves the hypocone, which is clearly smaller than the protocone in ?P.
Acknowledgements This work has benefited from the collections and facilities of the  Naturhistorisches Museum, Basel  and from the warm welcome of his curator,  Loïc Costeur , for the study of Protadelomys from  Egerkingen. Emmanuel Robert  welcomed us at the  Université de Lyon  and allowed us to study the Protadelomys from Lissieu. Thanks to  Didier Berthet , from the  Lyon Musée des confluences , who provided us complementary material of this species. Overall, this work would not have been possible without the material collected by the team of paleontology of the  Institut des Sciences de l'Evolution (Université de Montpellier ), since the 1970s for Bouxwiller and  Saint-Maximin  and since the end of the 1980s for the Quercy localities. Notably, we benefited from  Gilles Escarguel 's work on these collections, following that of  Jean-Louis Hartenberger , and we would like to thank the managers of these ISEM collections,  Anne-Lise Charruault ,  Suzanne Jiquel  and  Bernard Marandat.
Two main tectonic faults are crossing the area. We focus on a 30 Â 30 km area SW of the White Bandama catchment presenting a uniform distribution of every distinct geological unit (Fig. 1 ). We have information from 30 existing boreholes, logged to get an integrated and detailed description of the aquifer. Fortunately, all boreholes drilled since 1994, due to Japanese Water Supply Project in Northern Ivory Coast, were registered in a national database. These boreholes were all made in the dry season to ensure the durability of the resource of water regardless of the season. Each borehole has a unique identity and position, technical specifications, information about the lithology and water chemistry. Their depth range from 41 m to 61 m. We accessed the detailed lithological logs (lithologs) that were prepared from drill cuttings by the French research office (BURGEAP), as well as the depth of water inflow.
When possible, we measured the water level on each of them on the day of geophysical data acquisition. This water level is always at least 20 m above the recorded water inflow depth showing the aquifer is under pressure. In addition to these deep boreholes, it exists in the study area some shallow traditional hand dug wells in villages pumping water from the upper unconfined aquifer from depth between 9 and 16 m. The deep wells present high productivity, around 10 m 3 /h, whereas the shallow ones deliver only 3 m 3 /h.
EMS data are inverted using the IX1D v3 software  (Interpex, 2006) , through an occam's inversion scheme. Then a resistivity-depth model structure can be defined by a number of layers, resistivities and thicknesses for all the EMS as shown in Figure  2. The obtained data were analyzed and interpreted after their inversion using the IX1D v3 software. For every EMS, the layer model was obtained through an iterative process (1D inversion) which, in simple terms, results from minimizing the prediction and observation differences through an assessment of how well the suggested models fit the processed data. The inversions are one dimensional and are performed for each measurement independently.
We separated our results in three groups depending on the main lithology of the acquisition site. In group 1, the EMS were carried out over granite substratum, in group 2, over schist substratum, and, in group 3, over post-birimian migmatites substratum. We present 3 interpreted EMS characteristic of group 1 in Figures  3 4 5 , one from group 2 in Figure  6  and one from group 3 in Figure  7. In each of these figures, we present on the left side the lithologs obtained in the associated borehole. On the right, the smooth layer model in green, and the 4-to 6-layer interpreted layer model in red are shown. The vertical black arrows give the final 3-layer model summing up all these results. According to the group 1, three typical layer models (6-layer model, 5-layer model and 4-layer model) were used to interpret the electromagnetic soundings in this study, as they fit the apparent resistivity data more reasonably.
Ancillary data In addition to aerosol measurements, we used the station's permanent ozone, radon, and meteorological measurements for temperature, global radiation, relative humidity, and wind. The meteorological data can be downloaded from https://cliflo. niwa. co. nz/ (last access: May 2021). Ozone data are from long-term measurements of the station conducted with a Thermo Scientific Model 49i ozone analyser. Radon data were used to assess land influence. The measurements were done with a high-sensitivity site background radon detector designed by ANSTO (Australia; https://www. ansto. gov. au/, last access: May 2021)  (Chambers et al. , 2014). As the station is coastal, tides and wave heights can also have an effect on the atmospheric composition. This is why we also used wave height data from the Greater Wellington Regional Council (http://graphs.