dataesr/scientific-paragraphs-categorization huggingface dataset:
39 (10)  keV  [35] , and relatively high natural isotopic abundance, δ = 1. 245(  22 )%  [36]. In addition the 106 Cd is also favored for the possible resonant 0ν2ε transitions to excited levels of 106 Pd  [37, 38]. The 106 Cd is, in fact, one of the most studied nuclei  [37]. A new experiment to search for double beta decay in 106 Cd is running in the DAMA/CRYS set-up at LNGS using a 106 CdWO 4 crystal scintillator (215 g), enriched in 106 Cd to 66%. It is the third stage of the DAMA activity with this crystal scintillator. In the first stage the 106 CdWO 4 crystal was fixed inside a cavity filled with high purity silicon oil and viewed by two low radioactive photomultiplier tubes (PMT) throughout ∼ 20 cm long light-guides. After 6590 h of data taking in the low background DAMA/R&D set-up, a sensitivity at the level of T 1/2 ∼ 10 20 -10 21 yr was reached for different channels of double beta decay of 106 Cd  [37].
In the second stage, the 106 CdWO 4 crystal was viewed through a lead tungstate (PbWO 4 ) crystal light-guide by a low radioactive PMT and installed in the central well of the ultra-low background GeMulti set-up in the STELLA facility at LNGS. This set-up is equipped with four HPGe detectors (225 cm 3 each) and was used to search for coincidence events induced, e. g. , by the 511 keV gamma(s) produced by the positron annihilations in the εβ + or 2β + processes. After 13085 h of data taking, limits on 2ε, εβ + and 2β + processes in 106 Cd were few times improved  [39]  in comparison with the first stage  [37]. The new experiment, now running, was realized in order to increase the detection efficiencies of the coincidence events. For this reason the 106 CdWO 4 has been installed in coincidence with two large volume low background CdWO 4 crystal scintillators in close geometry.
This set-up consists of a passive shield made of high purity copper  An event-by-event data acquisition system records the amplitude, the arrival time, and the pulse shape of the events by means of a 100 MSamples/s, 14 bit Transient Digitizer DT5724 by CAEN, over a time window of 60 µs. The low energy part of the 106 CdWO 4 spectrum is dominated by the β decay of 113 Cd and 113m Cd, not of interest in this measurement. For this reason, the scintillation events of the 106 CdWO 4 with energy below about 500 keV are recorded by the DAQ only if there is a coincidence signal in at least one of the two CdWO 4 crystal scintillators. In this way the stored data are considerably reduced. For scintillation events in 106 CdWO 4 with energy above 500 keV the signals of the three detectors are always recorded whether or not a coincidence event is present.
U", "int. Th", " 40 K") and contributions from external γ quanta ("ext. γ" or "ext. Th. "). The peak of the 0ν2β decay of 116 Cd excluded at 90% C. L. is also shown. The energy spectrum of γ(β) events selected from the data accumulated over 25037 h with the 116 CdWO 4 detectors is shown in Fig. 3. The spectrum was fitted in the energy interval 660 -3300 keV by the model built from the 2ν2β of 116 Cd, the internal contamination by 40 K, 232 Th and 238 U, and the contribution from external γ quanta. The model functions were Monte Carlo simulated with the EGS4 package  [51] , the initial kinematics of the particles emitted in the decays was given by an event generator DECAY0  [52]. The fit gives the half-life of 116 Cd relatively to the 2ν2β decay to the ground state of 116 Sn as T 1/2 = [2. 69 ± 0.
Numerical solution The developed model was solved using Matlab R2014b software provided of the Statistics Toolbox (The MathWorks, Inc. , Natick, MA). The analysis of the effect of the heat and mass transfer variability on the critical process parameters during freeze-drying was performed using the Monte Carlo method. This method consisted in simulation of batches of 100 representative vials with random normal distributions of the considered model parameters (  ,  ,   0 ,   1 ,   1 and   2 ). Due to computer limitations, the number of simulated vials (100) was significantly smaller than the actual number of vials in the freeze-dryer (2310).
This leads to temporal, spatial and depth variations in the spectral shapes of a* ph (λ) that are encountered in the ocean. During pixel-by-pixel processing a* ph (λ) is either treated as a spectral constant or a bio-optical model is used in an attempt to capture its variability. Commonly used approaches for parameterizing a* ph (λ) are summarized in Table  1. A fixed shape for a* dg (λ) is typically modeled with the exponential function: a dg * (λ) = exp -S dg λ ), (14) where the exponential decay coefficient, S dg , which typically varies between 0. 01 and 0. 02 nm -1  (Roesler et al. , 1989) , is assigned as a constant value  (Hoge & Lyon, 1996; Maritorena et al. , 2002; Roesler & Perry, 1995). The choice of S dg is sometimes based on regionspecific in situ measurements  (Brando et al. , 2012) , global in situ datasets  (Lee et al.
, 2009) , or by optimally tuning the inverse algorithm's bio-optical parameters with a training dataset  (Maritorena et al. , 2002). Alternatively, the approach of  Lee et al. (2009)  estimates S dg dynamically from an empirical dependence on the blue to green reflectance ratio: S dg = 0. 015 + 0. 002 0. 6 + r rs (443)/r rs (55x )' where the reference wavelength, 55x, is centered on the greenest wavelength of the satellite instrument (e. g. 555 nm for SeaWiFS and 547 nm for MODIS). This method does not permit S dg to fall below 0. 015 nm -1 and predicts a decrease in S dg as water becomes clearer (through an increasing 443-55x ratio), which has not been ubiquitously observed, particularly in the presence of photobleaching. Generally speaking, the magnitudes of S dg (or S g ) and a dg (440) (or a g (440) share an inverse relationship  (Bricaud et al.
Look-up-table approaches-Look-up table (LUT) solution methods first use a forward model to generate a database of R rs (λ) and/or L t (λ). Each R rs (λ) or L t (λ) spectrum in the LUT corresponds to a unique combination of water column properties (IOPs and, if relevant, depth and seafloor reflectance) and atmospheric properties (if relevant, cloud cover, surface conditions, and sun geometry). The LUT is constructed by running a forward model (QSSA or RT) for each combination of parameters to be considered  (Hedley et al. , 2009; Liu & Miller, 2008; Mobley et al. , 2005). The IOPs used in the forward modeling arise from either in situ datasets or are generated with bio-optical models using constituent-specific parameterizations, such as mass-specific properties at reference wavelengths and associated spectral shape functions (see Sections 2.
1. 3 and 2. 1. 4). To derive IOPs, the sensor-observed R rs (λ) or L t (λ) for a given image pixel is compared with the spectra stored in the LUT. The LUT is searched until an entry or suite of entries is found that best match the sensor-observed spectrum. A best match is determined once the cost function (distance measure) achieves a pre-defined threshold, in a manner similar to spectral optimization. The returned IOP solution is defined as that which corresponds to the closest-matching forwardmodeled R rs (λ) or L t (λ) in the LUT. If the search returns a solution set -all spectra within a prescribed uncertainty tolerance, ε, of the measured spectrum -the solution might be computed as the average of all associated parameters consistent with the retrieved spectra. An advantage to this approach is that ε can be defined as a function of known sources of uncertainty, such as those associated with the remote sensor and data/parameterizations used to generate the database, and the divergence of the various consistent solutions can provide a measure of the uncertainty in retrieved IOPs.
The cost function can be spectrally weighted to account for band-specific instrument noise and sensitivity to constituent variability. Mobley et al. (2005)  and  Liu and Miller (2008)  constructed RT-based LUTs using the commercial software HydroLight  (Mobley & Sundman, 2008). Mobley et al. (2005)  generated a LUT containing 41,591 spectra for various combinations of water constituent IOPs, bottom type reflectance, water depth, sun angle, wind speed, and sky conditions from their study region. The approach was developed for inverting airborne-based hyperspectral R rs (λ) imagery to derive IOPs, seafloor type, and water-column depth without the need for in situ ancillary data. Liu and Miller (2008)   The LUT methodology offers several useful features. It does not require uniqueness of solution and can return a suite of solutions with which an estimate of uncertainty can be derived for each retrieved parameter.
Searching for solutions can also be computationally expensive. Even with limited sets of IOPs (e. g. , ten a*(λ) or b*(λ), the number of spectra in the LUT database will be 100 m , where m is the number of different constituents. This burden can be eased somewhat using suitable LUT search algorithms and with improved computational power. Hedley et al. (2009) , for example, dynamically constructed a LUT by adaptively constraining model runs to reduce the LUT size. Each sensor-observed R rs (λ) is projected onto principle component space and passed through a binary decision tree until it is optimally matched with similar spectra. Hedley et al. (2009)  found this approach to be computationally comparable to spectrum matching approaches (c. f. SAA class (i) methods), such as Levenberg-Marquardt nonlinear optimization.
Empirical approaches-Empirical algorithms derive IOPs from some predictor variable(s), typically sensor-observed R rs (λ) or derived parameters (e. g. C a ), using a large data set derived from in situ observations or AOP/IOP datasets synthesized using RT modeling. Most approaches follow statistical methods including, but not limited to, univariate polynomial regression  (Lee et al. , 1998b) , multiple linear regression (MLR)  (Mannino et al. , 2014) , principal component regression (PCR)  (Craig et al. , 2012) , and machine learning algorithms  (Chang, 2015; Doerffer & Schiller, 2007; Ioannou et al. , 2011; Jamet et al. , 2012). While these approaches diverge from the RT and QSSA methods and spectral shape parameterizations presented to this point, their inclusion in this review remains relevant as they provide viable methods for deriving IOPs from satellite ocean color radiometry.
Mannino et al. (2014)  used a stepwise backward approach and reduced the number of input R rs (λ) from five to two. Other approaches, such as principal component analysis (PCA; an orthogonal linear transform, also known as empirical orthogonal function (EOF) analysis  (Preisendorfer & Mobley, 1988) , can also be used to reduce dimensionality of the predictor variable set without an appreciable loss of information  (Barnes et al. , 2014; Craig et al. , 2012; Soja-Woźniak et al. , 2017). Using   (Craig et al. , 2012; Fichot et al. , 2008; Soja-Woźniak et al. , 2017). Barnes et al. (2014)  noted that PCs that cumulatively explain the bulk of the variance in the predictor dataset do not always serve as the best predictor variables but they can be used to identify important physical or biological driving forces a posteriori.
, 2014b; Chen et al. , 2015; Ioannou et al. , 2011; Ioannou et al. , 2013). Krasnopolsky and Schiller (2003)  recommended training two ANNs that act as forward and inverse models, respectively. By employing the forward and inverse ANNs in concert, an ANN-based algorithm can then dynamically assess its retrieval skill and flag poor quality values  (Doerffer & Schiller, 2007). Ioannou et al. (2011)  demonstrated an ANN-based approach using independent validation data and benchmarking with the SAAs of  Lee et al. (2002)  and  Maritorena et al. (2002). They also demonstrated ANN handling of noise (10-20%) in the input R rs (λ) dataset without compromised performance. Like all empirical approaches, however, machine learning methods require a large R rs (λ)/IOP training dataset (order of 1,000 records per  Ioannou et al.
(2011)  and order of 100,000 records per  Doerffer and Schiller (2007)  that spans a wide range of optical conditions. Accordingly, ANN approaches are often tuned and applied regionally  (D'Alimonte et al. , 2012; Doerffer & Schiller, 2007) ; however, through the appropriate use of RT modeling and/or in situ datasets, the development of globallyapplicable ANN-based algorithms has been demonstrated  (Chen et al. , 2014b; Ioannou et al. , 2011). 3 Sources and derivation of uncertainties.
Means and medians should also be computed -medians are less sensitive to data distribution, such that it is likely that the data are not normally distributed when the median differs substantially from the mean. Comparison between frequency distributions of derived products and those measured in situ (e. g. , in databases) are critical to assess the applicability of the specific dataset to derive inversion algorithms on global scales, as a non-representative dataset is likely to bias the global algorithm  (Werdell et al. , 2009; Werdell et al. , 2013a). Care should be applied to subsample the dataset to be spatially (and if possible temporally) representative of the variability of the full domain of interest.
However, like high resolution LEO sensors, GEO sensors do not provide near-daily global coverage and are multispectral with band sets similar to heritage ocean color missions such as SeaWiFS. We note, however, potential synergies between high radiometric quality LEO sensor data and high temporal frequency GEO sensor data have recently been considered, specifically approaches for combining the two data types for the purpose of producing improved temporally-rich ocean color data suitable for observing coastal waters  (Peschoud et al. , 2017; Vanhellemont et al. , 2014). Moderate spatial resolution sensors have been, and continue to be, used for deriving neardaily global IOP data products. They are well-calibrated sensors with dynamic ranges and signal-tonoise characteristics necessary for observing ocean properties  (Hu et al.
5. 3. 2 Optically shallow waters-Interest has steadily increased in utilizing ocean color data to monitor ecosystem health in optically shallow shelf waters  (Devlin et al. , 2015; Hedley et al. , 2016). However, most inverse IOP methods used to process global datasets were designed for optically deep oceanic waters with some exceptions  (Lee et al. , 2001; Lee et al. , 1999). These algorithms are easily confounded by light reflected off the seafloor in clear, shallow waters resulting in biased IOP retrievals  (Barnes et al. , 2013; McKinna et al. , 2015). A number of SAAs have been developed for remote sensing shallow waters using either multi-or hyperspectral airborne spectroradiometric data  (Dekker et al. , 2011) , however, historical approaches have focused more on bathymetry and seafloor mapping rather than deriving water-column properties  (Brando et al.
, 2009; Werdell & Roesler, 2003). Dedicated optically shallow ocean color algorithms have started to become available for deriving IOPs  (Barnes et al. , 2017; Barnes et al. , 2013; McKinna et al. , 2015). It is challenging, however, to incorporate these approaches into global data processing, as optically shallow waters make up only a small fraction of overall pixels and no robust mechanism currently exists to adequately flag their location during routine processing. In the approach of  McKinna et al. (2015) , for example, ancillary bathymetry and benthic albedo datasets are needed to help to constrain the dimensionality of the solution space to three unknowns (A ph , A dg , B p ). The need for large region-specific ancillary benthic albedo and bathymetry datasets can be alleviated, however, through the recently-developed algorithm of  (Barnes et al.
, 2017) ; an approach that would be well-suited to spectrally-rich ocean color data collected by missions such as PACE. As described in Section 3. 1. 4, coastal waters are often optically heterogeneous, making validation of algorithms difficult. This is compounded in optically shallow regions where withinpixel seafloor and bathymetric variability and stray light from adjacent features (e. g. sand cays and breaking waves) further complicate algorithm validation efforts. Not only are appropriate sampling protocols and methodologies required, but also a publicly accessible archive of in situ AOP/IOP data for algorithm development and validation. To that end, it is likely that the recently initiated NASA-funded COral Reef Airborne Laboratory (CORAL; https://coral. jpl. nasa. gov/) project will contribute greatly to the afore-mentioned knowledge gaps.
• • Additional research into the depolarization ratio for pure water scattering; • Assembly of additional publicly-available in situ and simulated hyperspectral datasets for algorithm development and testing, with attention to the temporal, spatial, and biogeochemical representativeness of these datasets; and, • Continued collection, assembly, and public dissemination of key biogeochemical variables that can be directly inferred from IOPs including, but not limited to: particle size distributions, phytoplankton pigment concentrations, phytoplankton classes and metrics of community composition, and particulate and dissolved organic carbon.
Acknowledgements The  NASA Ocean Biology and Biogeochemistry Program  supported all authors through membership on the  NASA PACE Science Team. We thank  Paula Bontempi ,  Jacek Chowdhary ,  Betsy Edwards ,  Georges Fournier ,  Bryan Franz ,  Mati Kahru ,  Timothy Moore ,  Michael Ondrusek , and  Lorraine Remer  for their comments, advice, and participation in science team meetings and workshops related to this review paper. We also thank  Brian Barnes  and one anonymous reviewer for their valuable comments and feedback regarding this work.
principal component regression (PCR; a combination of PCA and MLR), Craig et al. (2012)  developed an algorithm for deriving C a and a ph (λ) from R rs (λ) in optically complex waters. The first four PCs were used in an MLR model as they cumulatively explained over 99% of variance in the R rs (λ) predictor dataset. Similarly, Fichot et al. (2008)  and Cao et al. (2014)  used a PCR model to retrieve K d (λ) in the range 320-490 nm from multi-spectral R rs (λ) for a variety of water bodies ranging from oligotrophic to turbid estuarine systems. Barnes et al. (2014)   used PCR to derive K d (λ) in the UV from satellite-observed R rs (λ) in optically shallow reef waters. Thus far, the limitation of PCA-based approaches appears to be reliance on training with region-specific data in order to perform optimally.
Multi The modelling approach involved in Multi-Hydro model relies on solving physical equations that describe the catchment behaviour. Seven processes are generally simulated; (1) precipitation, (2) interception and storage, (3) infiltration, (4) overland flow, (5) sewer flow, (6) infiltration into the subsurface zone and (7) sewer overflow. The four modules that make up the core of Multi-Hydro are presented in Fig. 1:  -The surface component (MHSC) is based on the existing TREX model (Two-dimensional Runoff, Erosion, and Export model) developed by Colorado State University and used in Multi-Hydro only for rainfall-runoff modelling  (Velleux et al. , 2008). The surface module computes interception, storage and infiltration occurring at each pixel according to the properties of its land cover class.
(2012). -The Drainage module (MHDC) is based on the 1-D SWMM  (James et al. , 2010)   -The infiltration module relies on the VS2DT model developed by the US Geological Survey. It is used to simulate the infiltration into the unsaturated subsurface zone  (Healy, 1990; Lappala et al. , 1987). This module uses the infiltration depth calculated by the surface module as input, and simulates a 2D infiltration (vertical and 1-D horizontal) into the subsurface. This module was not used here because the analysis of the subsurface infiltration was not one of the objectives of this work. The four modules are connected via the Multi-Hydro core, which groups together a set of codes allowing interaction, retro-action (feedback) and data exchange between these modules. In this case study, these interactions are performed after each time loop of 5 min.
Precise information about all the components of the sewer network are also necessary for the drainage module. Such information is usually available for urban areas and can be obtained from the local authority in charge of the water management. Details about all pipes (geometry, length, diameter as well as inlet and outlet nodes) should be carefully validated, as well as all the system nodes (coordinates and elevation). The subsurface structure should be described as well if there is a need to simulate the infiltration through the unsaturated zone. The rasterization of the urban domain is the first step of Multi-Hydro implementation. During this process a unique class of land use is attributed to each individual pixel. This attribution can be done following at least two methodologies, illustrated in Fig.
Distributed data Spatially distributed data are used in this study to set up the Multi-Hydro model. The data were made available by various public institutions in the framework of research collaborations with Ecole des Ponts ParisTech. -Topography: the Digital Elevation Model (DEM) of the catchment was obtained from the IGN (French National Institute of Forest and Geographic Information). The spatial resolution of the data is 25 m with a 1 m resolution in height, which is far from meeting the needs of the studies carried out in this work. Linear interpolation was implemented to obtain data at a better resolution (between 5 and 10 m). -Land cover: Fig. 4  shows distributed data (available in GIS format) describing the land cover. The data were obtained from the DSEA 94 of Val-de-Marne County (Direction des Services de l'Environnement et de l'Assainissement).
Its quality is high, with a precision of up to 50 cm, but we had to deal with one land use class named "Other" in the original data. This class corresponds to unknown information and introduces missing data. A comparison with satellite images was done and the majority of missing data was filled with urban grass. -Sucy-en-Brie subsurface structure: the subsurface structure was elaborated using data obtained from the BRGM database (Office of Geological and Mining Research) from soil investigations done before construction works and archived in the BRGM database. The data indicate that a layer of clay mixed in some places with sand dominates the majority of the catchment subsurface. Down-  stream, near the river, there is a layer of sandy soil which is much more permeable. Physical parameters characterizing soil, which are needed for modelling, were obtained from the literature  (Lappala et al.
Rainfall and flow measurements data Rainfall data are provided by the General Council of Valde-Marne County. The data come from a 0. 2 mm resolution tipping bucket rain gauge located at the centre of the catchment. The data were processed and validated by the DSEA 94 and provided with a 5 min resolution. Eight rainfall events (Fig. 6 ) that occurred between 2010 and 2014 were selected. Their main characteristics are summarized in Table  1. The corresponding flow measurement data were available, also at 5 min resolution -coming from a flow sensor located at the outlet of the catchment. The choice was made in this work to use uniform rainfall information in order to focus on the sensitivity of the Multi-Hydro model to land use variability and to avoid the effect of rainfall spatial variability.
Acknowledgements. The authors acknowledge both the European project  INTERREG RainGain  (http://www. raingain. eu)and the  ANRT association  (http://www. anrt. asso. fr)for their financial support of this work. The first author thanks the  DSEA94 (Direction des Services de l'Environnement et de l'Assainissement)  for providing data sets used in this work. A partial financial support of the Chair "Hydrology for resilient cities" endowed by  Veolia  is gratefully acknowledged. Edited by:  Nadia Ursino Reviewed  by:  Jamal Alikhani  and one anonymous referee.