dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability The ORCHIDEE-Hillslope model (r6515) code used in this study is open-source and distributed under the CeCILL (CEA CNRS INRIA Logiciel Libre) license. It is available at https://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/CodeAvalaibilityPublication/ ORCHIDEE-Hillslope-r6515, with guidance to install and run the model at https:// forge. ipsl. jussieu. fr/orchidee/wiki/Documentation/UserGuide. All ancillary data to run the model can be accessed upon reasonable request to the corresponding author, with the response received in 1 week. The code to simulate the wetland area by TOPMODEL (Version v1. 0) is publicly available on GitHub (https://github. com/yixixy/ Wetland_simulation_by_TOPMODEL)  77. The processing MATLAB codes are available at https://github. com/yixixy/Treeplanting_Wetlands_China.
Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data are available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3000 years of observation.
Results indicate that STM-E yields values for the 500year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single-location analysis.
, 2020)  or Cyclone Idai in Mozambique in 2019 (https://data. jrc. ec. europa. eu/dataset/ 4f8c752b-3440-4e61-a48d-4d1d9311abfa, last access: 1 February 2022). Waves are one of the major hazards associated with tropical cyclones, of critical importance regarding marine flooding, especially for volcanic islands like those in the Lesser Antilles, North Atlantic Ocean basin  (Krien et al. , 2015) ; Hawaii, northeast Pacific Ocean basin  (Kennedy et al. , 2012) ; or Réunion, southwest Indian Ocean basin  (Lecacheux et al. ,  Published by Copernicus Publications on behalf of the European Geosciences Union.
Objective and layout In the present work, we aim to tackle the problem of realistic return value estimation for small samples of tropical cyclones using a recently developed procedure named STM-E, which has already been successfully applied in regions exposed to tropical cyclones near Japan  (Wada et al. , 2018)  and in the Gulf of Mexico  (Wada et al. , 2020). STM-E exploits all cyclone data drawn from a specific geographical region of interest, provided that certain modelling conditions are not violated by the data. This means in principle that STM-E provides less uncertain estimates of return values than statistical analysis of cyclone data at a single location. To date however, the STM-E methodology has not been directly validated: the objective of the present work is therefore to provide direct validation of return values (in terms of bias and variance characteristics, for return periods T of hundreds of years) from STM-E analysis using sample data for modelling corresponding to a much shorter period T 0 (< T ) of observation, drawn from a full synthetic cyclone database corresponding to a very long period T L (T L > T ) of observation.
More recent destructive events include Hugo  (in 1989; Koussoula-Bonneton, 1994)  and Maria (in 2017, which severely impacted Guadeloupe's banana plantations). The tracks of both Hugo and Maria are illustrated in Fig. 1. Analysis of the HURDAT database  (Landsea and Franklin, 2013)  reveals that approximately 0. 6 cyclones per year passed within 400 km of the study area on average for the period 1970-2019. Almost all events emanated from the southeast. More than 80 % of the events passed close to the northern and eastern coasts of Guadeloupe's main island pair. To assess the cyclone-induced storm surge hazard,  Krien et al. (2015)  set up a modelling chain similar to that described in the introduction: they randomly generate cyclonic events using the approach of  Emanuel et al. (2006)  and compute SWH and total water levels for each event over a wide computational domain (9.
5-18. 3 • N, 45-65 • W) using the ADCIRC-SWAN wave-current coupled numerical model. Here, the wind drag formula from  Wu (1982)  was selected, but with a prescribed maximum value of Cd = 0. 0035. The interested reader can refer to  Krien et al. (2015)  for more implementation and validation detail. The resulting SWH data are the basis of the current study to assess the performance of STM-E in estimating the T -year return value, from data corresponding to T 0 years of observation, for the cases T 0 = 200, T = 500 and T 0 = 50, T = 100. In the present work, we use a total of 1971 synthetic cyclones passing nearby Guadeloupe (representative of 3200 years, i. e. about 0. 6 cyclone per annum) and the corresponding numerically calculated SWH. These results are used to empirically derive the 100-and 500-year SWH around the coast of Guadeloupe's main island pair for a smaller area of interest  (15.
8-16. 6  • N, 60. 8-62. 0 • W; see Fig. 2 ). These results are useful to assess flood risk at local scale, since they provide inputs of high-resolution hydrodynamic simulations (see for example the use of wave overtopping simulations at Réunion by  Lecacheux et al. , 2021). In the following, we analyse extreme SWH at 19 coastal locations around Guadeloupe's main island pair (on the 100 m iso-depth contour; see blue stars in Fig. 2 ) and at 12 locations along a line transect emanating to the northeast of the island, corresponding to increasing water depth (see red triangles in Fig. 2 ). To illustrate the SWH data, Fig. 3  depicts the spatial distributions of maximum SWH per location for the four cyclones with the largest single values of SWH in the whole synthetic cyclone database. All cyclones propagate from the southeast to the northwest with intense storm severity near the cyclone track, which reduces quickly away from the track.
Benchmarking against the full cyclone database and single-location analysis One obvious feature of the current synthetic cyclone database is that it corresponds to a long time period relative of T F = 3200 years, much longer than the return period of T = 500 years being estimated in the current analysis. Thus, we are able to estimate the 500-year return value at any location using the full synthetic cyclone data, by simply interpolating the sixth and seventh largest values, corresponding to the non-exceedance probability in 500 years. This provides a direct empirical estimate. From previous work, a key advantage found using the STM-E approach is that it provides less uncertain estimates at a location compared with conventional single-location analysis. We wish to demonstrate in the current work that this is also the case.
Discussion This work considers the estimation of T -year return values for SWH over a geographic region, from small sets of T 0 years of synthetic tropical cyclone data, using the STM-E (space-time maxima and exposure) methodology. We assess the methodology by comparing estimates of the T -year re-turn value (T > T 0 ) for locations in the region from STM-E with those estimated directly from a large database corresponding to T L (> T ) years of synthetic cyclones. We find that STM-E provides estimates of the T = 500-year return value from T 0 = 200 years of data in the region of the Guadeloupe archipelago with low bias. We also compare STM-E estimates of T -year return values for locations in the region with those obtained by extreme value analysis of data (for T 0 years) at individual locations.
We find that the uncertainty of STM-E estimates is lower than that of single-location estimates. Comparison of the performance of inferences for the T = 100-year return value from T 0 = 50 also suggests STM-E outperforms single-location analysis. For reasonable application of the STM-E approach, it is important that characteristics of tropical cyclones over the region under consideration satisfy a number of conditions. These conditions are shown not to be violated for a region Table  1. Performance of STM-E and single-location analysis in estimation of the bias and uncertainty of the 500-year return value, relative to empirical estimates from the full synthetic cyclone data, for analysis sample sizes of 20, 30, 40, 50, and 60 all extracted from the 200-year data set. Bias B is assessed as the average difference (over the iso-depth contour and line transect analyses) between the mean STM-E (or single-location) estimate and the return value estimate from the full cyclone database.
, 31} on the iso-depth contour and line transect corresponding to the relevant sample size, using the expressions Here, h( ; n, Mth) and h0 (n) correspond to the mean 500year return value estimated using sample size n from the inference method Mth (either maximum likelihood or probability-weighted moments) and directly from the full synthetic cyclone database; r( ; n, Mth) and r 0 (n) are the corresponding 50 % uncertainty bands. The table summarizes the findings presented pictorially in Figs. 6-8. In terms of bias, STM-E and single-location estimates underestimate the return value on average. STM-E is less biased than singlelocation estimates except for sample sizes of 20 and 30 using probability-weighted moments. STM-E also provides estimates of the 500-year return value with higher precision than the single-location analysis.
Acknowledgements. Numerical simulations were conducted using the computational resources of the  C3I  ( Centre Commun de Calcul Intensif ) in Guadeloupe. Jeremy Rohmer  acknowledges the funding of the Carib-Coast  Interreg  project (https://www. interreg-caraibes. fr/carib-coast, last access:  1 February 2022 ). MATLAB code for the analysis is provided on GitHub at Wada et al. (2021). Sample wave data for the analysis are available on Zenodo at Krien et al. (2021). Financial support. This research has been supported by  Interreg  (grant no. 2014TC16RFTN008 ). Jeremy Rohmer  received funding from the  Carib-Coast Interreg  project (https://www. interreg-caraibes. fr/carib-coast, last access:  1 February 2022 , grant no. 2014TC16RFTN008 ). Review statement. This paper was edited by  Joanna Staneva  and reviewed by two anonymous referees.
Code availability. MATLAB code for the analysis is provided on GitHub at https://github. com/ygraigarw/STM-E (Wada et al. , 2021). Data availability. Sample wave data for the analysis are available on Zenodo at https://doi. org/10. 5281/zenodo. 4627903 (Krien et al. , 2021). Author contributions. RW, JR, and PJ originally conceived the study. YK and JR conducted cyclone and wave modelling. RW and PJ worked on statistical methodology and modelling. All authors contributed to writing the paper.
(  8 ) and S for solid (unit of mmol m -3 solid) Eq. (10). 𝑤 (cm d -1 ) and 𝐷 𝑠𝑒𝑑 (cm 2 d -1 ) represent the burial/advection and molecular diffusion coefficient in the sediment Diffusive fluxes of solutes across the sediment-water interface are driven by the concentration gradients between the overlying seawater and the sediment column. Fick's first law is used to describe the solute flux due to molecular diffusion: 𝐽 𝑑 = -𝜙𝐷 𝑠𝑒𝑑 ∂𝐶 ∂𝑧 ( 11 ) where the 𝐷 𝑠𝑒𝑑 (cm d -1 ) is the effective diffusion coefficient corrected for tortuosity and given as 𝐷 𝑠𝑒𝑑 = 𝐷 𝑠𝑤 𝜃 2 , with 𝐷 𝑠𝑤 the molecular diffusion coefficient of the solute in free solution of sea-water and 𝜃 is the tortuosity derived from the formation factor (𝐹) and porosity (𝜙) of a sediment matrix  (Berner, 1980; Boudreau, 1997). This molecular diffusion coefficient is calculated as function of temperature and salinity using compiled relation of  (Boudreau, 1997) , implemented in the R package Marelac  (Soetaert and Petzoldt, 2020).
In our case, we used only the deposit mode. The event algorithm is used at programming language (R Core Team, 2021) via the "method-on-lines" approach  (Boudreau, 1996). In addition, the model made use of the event-handling capabilities specific numerical solvers written in the R deSolve package  (Soetaert et al. , 2010b). Preprocessing routine for model grid generation  (Soetaert and Meysman, 2012) , porewater chemistry parameter  (Soetaert et al. , 2010a) , steady state calculation  (Soetaert, 2014) , and time integration  (Soetaert et al. , 2010b)  were utilizes the R programming language. Further information about the model usage can be found in the model user vignette found in R-forge page (https://r-forge. r-project. org/R/?group_id=2422).
Code availability. As a whole, the model is bundled as a R package for easy accessibility and can be downloaded from R-forge (https://r-forge. rproject. org/R/?group_id=2422). The most recent version of the model, as well as its evolution, can be found on the project development page (https://r-forge. r-project. org/projects/diagenesis/) with subsequent expected release in CRAN. Full R vignette illustrating the capabilities of the model can be found on the model doc folder. The version used to produce the results used in this paper is archived on Zenodo (https://doi. org/10. 5281/zenodo. 6369288), along with the input data and scripts to recreate the simulation presented in this paper. FESDIA users should cite both this publication and the relevant Zenodo reference.
The DUNE ND  [314]  will consist of a collection of detectors located 574 m downstream of the neutrino source designed to measure with high precision the spectrum and flavor composition of the initial neutrino beam. The current design of the DUNE ND has three components: a liquid argon TPC (LArTPC) named ND-LAr, a gaseous argon TPC (ND-GAr) and the System for on-Axis Neutrino Detection (SAND). The ND-LAr has an active volume of 3 × 4 × 7 m 3 , where it is 5 m long in the beam direction, 3 m tall and 7 m transverse to the beam, with an active mass of 147 t. It is composed of several argon cube modules where each module is 1 × 1 × 3. 5 m 3. The ND-GAr is based on a high pressure gaseous Argon TPC (HPgArTPC). The HPgArTPC is held at a 10 atm pressure and has an active mass of 1. 8 tons. It is surrounded by an electromagnetic calorimeter called the Barrel ECAL designed to detect photons and neutrons, and provide the initial timing for interactions inside the HPgArTPC.
A schematic view of the experimental setup is shown in figure  22. About 60 modules will be exposed to 400 GeV protons from the CERN-SPS. This will yield 200 million proton interactions, one half on tungsten and the other half on molybdenum nuclei, and 1000 D s → τ → X decays will be collected. The D s double differential production cross section will be obtained using this sample, and tau neutrino production will then be constrained at the precision of 10%. In addition to ν τ production, DsTau is expecting to collect a large sample of charmed particle pairs, at the level of 10 5. The analysis of such event would provide by-products, such as measurement of the forward charm production, the intrinsic charm content in proton, the interaction cross section of charm hadrons, etc. Two test runs were performed at the CERN SPS H2 and H4 beam lines in 2016 and 2017 in order to test the various steps of the experiment from emulsion production to data analysis and to improve the detector structure and exposure scheme.
The CES modules aim at measuring the electric charge of hadrons produced in tau lepton decays, thus providing the unique feature of disentangling ν τ and ν τ CC interactions also in their hadronic decay channels. It complements the use of the ECC in the momentum measurement for hadrons and soft muons which are emitted at large angles and which do not reach the downstream tracker. The basic structure of the CES is made of three emulsion films interleaved by two layers of low density material. The baseline technology for the SND tracker systems consists of a scintillating-fibre tracker (SciFi). The SND muon identification system is designed to identify with high efficiency the muons produced in neutrino interactions and τ decays occurring in the emulsion target. The system consists of eight hadron filters of iron interleaved with tracking planes instrumented with RPCs.
DeepCore is a dense infill inside the larger IceCube detector. The detector is a 3D array of digital optical modules containing PMTs distributed through the Antarctic ice. Due to the fact that the charged τ lepton from a O  (20)  GeV CC ν τ interaction will decay over distances of 1 m, observing the track is not possible in DeepCore. In addition, the fact that the τ decay includes one or two neutrinos means that there will be significant missing energy which also makes the events harder to detect due to the steep atmospheric spectrum. In a 3D detector essentially two event topologies can be identified: tracks which involve muons from ν μ CC events or ν τ CC events where the τ decays to a μ, and cascades from all other neutrino interaction categories. This makes it experimentally impossible to identify on an event-by-event basis, a ν τ interaction in the atmospheric energy range.
DUNE far detector. The LBNF beam in both the CP-optimized and tau-optimized configuration will produce large numbers of ν τ CC interactions in the DUNE FD, but due to the 1300 km baseline of the DUNE FD, the first atmospheric oscillation maximum occurs below the kinematic threshold for creating a τ -lepton. Because of this, the effect of Δm 2 31 and sin 2 θ 23 on the ν τ appearance probability is partially degenerate. To disambiguate the effect of the two atmospheric parameters, it would be necessary to operate at a longer baseline. However, since the DUNE FD will be located deep underground, it will also be able to collect a large sample of atmospheric neutrinos which probe a much larger range of L/E. In addition, two of four DUNE FD modules are expected to be installed three years before the LBNF beam is commissioned.
Therefore, atmospheric neutrinos will play a complementary role to the beam neutrino samples. We use the Honda flux prediction for Homestake mine  [367]  and the cross section prediction from GENIE 2. 12. 2  [329]  to determine that the DUNE FD should observe ∼1ν τ CC event per kton-year. This corresponds to ∼10ν τ CC events per module each year. Inspired by reference  [357] , we assume a 30% signal efficiency and a 0. 5% NC background efficiency for selecting ν τ CC events where the τ -lepton decays hadronically. We assume that the calorimetric energy resolution for ν τ CC events is ∼17% and the θ zen resolution is ∼5 •. Figure  32  shows the expected spectra for up-going ν τ candidates after smearing true energy and zenith angle. For the most up-going neutrinos, the first and second oscillation maxima are visible.
The primary interactions were simulated using two different event generators, Sibyll 2. 3d  [395, 396]  and DPMJET 3. 2017  [397, 398] , as implemented in the CRMC simulation package  [399]. Although we only present sum of the neutrino and anti-neutrino event rate for each flavor, we note that there is a similar number of neutrinos and anti-neutrinos in the LHC's neutrino beam. Comparing the different experiments, we can see that the neutrino event rate drops toward smaller pseudorapidities, or equivalently when moving away from the beam axis. This is caused by a decreasing neutrino beam intensity away from the beam's center, which is most pronounced for muon neutrinos but also visible for tau neutrinos. Comparing the predictions of Sibyll and DPMJET, we note that there are large differences between the two event generator predictions, especially for tau neutrinos.
The FASERν2 detector is envisioned to be composed of 3300 emulsion layers interleaved with 2 mm-thick tungsten plates. It will also include a veto detector and interface detectors to the FASER2 spectrometer, with one interface detector in the middle of the emulsion modules and the other detector downstream of the emulsion modules to make the global analysis and muon charge measurement possible. Both the emulsion modules and interface detectors will be put in a cooling system. The total volume of the tungsten target is 40 cm × 40 cm × 6. 6 m, and the mass is 20 tonnes. The detector length, including the emulsion films and interface detectors, will be about 8 m. The high muon background in the LHC tunnel might be an experimental limitation. The possibility of sweeping away such muons with a magnetic field placed upstream of the detector is currently being explored.
Considering the expected performance, emulsion films will be replaced every year during the winter stops. Analyses of the data collected in the emulsion modules will be based on readout of the full emulsion volume by the hyper track selector (HTS) system  [428]. The readout speed of the HTS system is 0. 45 m 2 hour -1 /layer. Currently, an upgraded system HTS2, which will be about 5 times faster, is under commissioning and a further upgraded system HTS3 with about 10 m 2 h -1 /layer is under development. The total emulsion film surface to be analyzed in FASERν2 is ∼530 m 2 yr -1 implying a readout time of ∼2400 h yr -1 with HTS or ∼420 h yr -1 with HTS2. It will be possible to finish analysing the data taken in each year within a year using either of the above systems. Reconstruction of the emulsion data will make possible the localization of neutrino interaction vertices, the identification of muons, the measurement of muon and hadron momenta by the multiple Coulomb scattering method, and the energy measurement of electromagnetic showers.
In-ice or water optical experiments. IceCube/IceCube-Gen2: the IceCube Neutrino Observatory  [541]  is located near the geographic South Pole. IceCube instruments one cubic kilometer of Antarctic glacial ice and is the largest instrumented volume neutrino detector currently operating. IceCube consists of 86 cables called 'strings' which are equipped with 60 digital optical modules (DOMs) deployed between 1450 m and 2450 m deep in the ice. The typical horizontal distance between strings is 125 m, except in the more densely instrumented 'DeepCore' region in the center of the detector  [359]. The DOM consists of a glass pressure vessel housing a single downward-facing 10 inch photomultiplier tube (PMT)  [615] , flasher LEDs for calibration  [616]  and digitizing electronics  [617]. The PMT signal is digitized with a sampling rate of 300 MSPS and the digitized signals are given a global time stamp accurate to 2 ns.
The Upgrade will use multi-PMT modules similar to KM3NET  [621, 622] , which will also serve as a testbed for a future next-generation neutrino detector. The envisioned expansion of IceCube is IceCube-Gen2  [111] , a broad-band neutrino observatory designed to resolve the sources of cosmic neutrinos. The detector includes a radio component (described below) and an expansion to the existing optical array, as well as a surface air shower array. The optical array will consist of 120 strings, drilled to a depth of 2. 6 km, spaced 240 m apart. The array will be sensitive to 5 times fainter neutrino sources than the current IceCube detector, and the array is expected to see one ν τ event per year, a factor of 5 increase over the current rate. IceCube-Gen2 is expected to set very strong constraints on cosmic neutrino source models and BSM physics which affect flavor ratio after 10 years of operation.