dataesr/scientific-paragraphs-categorization huggingface dataset:
4. Then we convolute the average spectra to the population of primary particles at the position x and y at the time t with the energy E at 100 km, generating the convoluted spectra of the particle i created by the primary particle with the energy E at z altitude. 5. Finally, we compile all the convoluted spectra of the particle i at the altitude z, generating the spectra of particle i at the position x, y at the altitude z at the time t. To have an acceptable database size, we chose to focus on specific altitudes, which are z = 0 km, 3 km, 5 km, 7 km, 12 km, 18 km, and 40 km. This choice makes sense for ground level, avionic and stratospheric balloon applications. In Figure  4 , we present an example of the methodology to generate the spectra of neutrons at the position LAT, LON, at the time T and at the altitude Z.
Extracting Secondary Particle Spectrum from EAS Simulation Compiling Simulation from the Same Energy To be able to create a database of EAS transfer in the Earth atmosphere, we started by compiling all the results of our simulations by altitude and by particle, for a given energy of a primary proton. Then, we created an average response for each energy based We did not make simulation below 1 GeV because our preliminary simulations showed that these particles could not create EAS big enough to reach below 30 km of altitude.
Extracting Secondary Particle Spectrum from EAS Simulation Compiling Simulation from the Same Energy To be able to create a database of EAS transfer in the Earth atmosphere, we started by compiling all the results of our simulations by altitude and by particle, for a given energy of a primary proton. Then, we created an average response for each energy based on a Monte Carlo simulation, the ∆N i ∆E i z , with N i being the quantity of particles in the energy bin E i , the kind of secondary particle i at the given altitude z. As an example, we plotted in Figure  10  the spectrum for neutrons at 18 km of altitude. We can observe that the energy response is similar for each energy but that a more energetic primary creates more secondary neutrons. Moreover, higher energetic primaries create more secondaries, allowing better statistics to be obtained.
The denser the atmosphere, the more energy the secondaries lose and the faster they stop. Finally, there is a very small contribution, less than 1%, of secondaries generated by the 1 GeV primary protons at 7 km. Finally, by compiling all the energy responses, we get the spectrum plotted in Figure  12  for the secondary neutrons at 18 km and at 7 km based on the primary spectrum from Figure  8  (see Equation (  1 )). In conclusion, by simulating monoenergetic proton entry at the top of the atmosphere, we developed a database of secondary particle spectra generated by EAS, designing RAMSEES to be adaptable to any primary particle spectra and making it compatible with every condition of space weather or cosmic ray model. In parallel, we simulated energies higher by two orders of magnitude than any other model, because we observed that for ground applications it is necessary to simulate the highest energies (>1 TeV) of primary protons to correctly evaluate the spectrum of secondary particles involved in SEE.
Model Results and Analysis In this part, we present RAMSEES results compared to experimental data and to the results given by the existing tools EXPACS and MAIRE. In Figure  13 , we present RAM-SEES results for neutrons at five altitudes: 0, 7, 12, 18 and 40 km. As expected, the neutron Finally, by compiling all the energy responses, we get the spectrum plotted in Figure  12  for the secondary neutrons at 18 km and at 7 km based on the primary spectrum from Figure  8  (see Equation (  1 )). Finally, by compiling all the energy responses, we get the spectrum plotted in 12 for the secondary neutrons at 18 km and at 7 km based on the primary spectrum Figure  8  (see Equation (  1 )). In conclusion, by simulating monoenergetic proton entry at the top of the phere, we developed a database of secondary particle spectra generated by EAS, d ing RAMSEES to be adaptable to any primary particle spectra and making it com with every condition of space weather or cosmic ray model.
Model Results and Analysis In this part, we present RAMSEES results compared to experimental data and results given by the existing tools EXPACS and MAIRE. In Figure  13 , we present SEES results for neutrons at five altitudes: 0, 7, 12, 18 and 40 km. As expected, the n In conclusion, by simulating monoenergetic proton entry at the top of the atmosphere, we developed a database of secondary particle spectra generated by EAS, designing RAM-SEES to be adaptable to any primary particle spectra and making it compatible with every condition of space weather or cosmic ray model. In parallel, we simulated energies higher by two orders of magnitude than any other model, because we observed that for ground applications it is necessary to simulate the highest energies (>1 TeV) of primary protons to correctly evaluate the spectrum of secondary particles involved in SEE.
Model Results and Analysis In this part, we present RAMSEES results compared to experimental data and to the results given by the existing tools EXPACS and MAIRE. In Figure  13 , we present RAMSEES results for neutrons at five altitudes: 0, 7, 12, 18 and 40 km. As expected, the neutron spectrum decreases rapidly with energy, in opposition to its evolution with the altitude where it increases from 40 km to 18 km and then decreases from 18 km to 0 km. There are two counterbalanced phenomena at play to explain this: (1) when the altitude decreases, the atmosphere density increases and the interaction is then more likely (and so the number of secondaries increases), and (2) because there are more and more interactions, the number of remaining primary particles decreases with decreasing altitude (and so the number of secondaries decreases).
Comparison with Experimental Data To validate our results, we compared it to experimental data measured by teams around the world. In Figure  14 , we compare RAMSEES results with the experimental data fro different instruments. The first set of data was collected at Pic du Midi, France (2. 9 altitude) from May 2011 to 2018 with a Bonner Sphere System (BSS) neutron spectr and the second one at Plateau de Rosa, Italy (3. 5 km of altitude) in January 1998 Bubble Detector Spectrometer, a passive system, by Hubert et al. [41]. The Pic d experiment collected data for 8 years. When comparing our predictions at 3 km a in-situ data at Pic du Midi, we need to consider that there is a difference of 100 m tude between them. Despite the 100 m of altitude difference, RAMSEES shows agreement with the data from Pic du Midi with less than a factor 1.
Comparison with Experimental Data To validate our results, we compared it to experimental data measured by several teams around the world. In Figure  14 , we compare RAMSEES results with the experimental data from two different instruments. The first set of data was collected at Pic du Midi, France (2. 9 km of altitude) from May 2011 to 2018 with a Bonner Sphere System (BSS) neutron spectrometer and the second one at Plateau de Rosa, Italy (3. 5 km of altitude) in January 1998 with a Bubble Detector Spectrometer, a passive system, by Hubert et al. [41]. The Pic du Midi experiment collected data for 8 years. When comparing our predictions at 3 km and the in-situ data at Pic du Midi, we need to consider that there is a difference of 100 m of altitude between them. Despite the 100 m of altitude difference, RAMSEES shows a good agreement with the data from Pic du Midi with less than a factor 1.
55 of difference. In the case of Plateau Rosa, we observe a factor 2. 38 of difference between RAMSEES model and the experimental data below 1 GeV. However, there are two main factors to consider here. First, the data were acquired during one month at a solar minimum, implying a maximum of atmospheric radiation, and secondly, the 500 m of altitude of difference influences the spectrum, making it higher in the case of Plateau the Rosa than at 3 km. At higher energies than 1 GeV, we observe a divergence between our prediction and the in-situ measurements. We think the divergence is caused by the solar minimum allowing more cosmic rays to enter the Solar System and the Earth's atmosphere. All together, we can say that the RAMSEES model fits with the experimental data at 2. 9 km but shows discrepancies at 3.
Conclusions and Discussions In this paper, we presented our model RAMSEES (Radiation Atmospheric Model for SEE Simulation), which is a database, built by Monte-Carlo simulation, composed of the response of the atmosphere to cosmic rays. We modeled the atmosphere with the results given by the NRLMSISE-00 model and used the CRDB and the Swordy plot as reference for the primary protons coming from space. Then, we simulated the Extensive Air Shower (EAS) phenomenon by making Monte Carlo simulations of the interactions of monoenergetic proton via the Geant4 toolkit with the QGSP_BERT physics list. We stopped our simulation at 100 TeV per proton because there is only one proton per geometry per second and because of the very high computational time required at high energy. Finally, we compiled the results of the monoenergetic primary simulation with the spectrum of protons coming from space, creating for each altitude a spectrum of neutrons and of protons.
Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types.
The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle.
Introduction Investigating the propagation of natural light in the ocean can be addressed experimentally through in situ measurements and theoretically through numerical radiative transfer (RT) simulations. Understanding the relationships between the radiometric quantities (i. e. , radiance and irradiances) that characterize the light fields within and leaving the ocean and the inherent optical properties (IOPs) of the water column, as well as boundary conditions at the sea surface (i. e. , surface illumination conditions and sea state) and at the ocean bottom (i. e. , bottom depth and albedo), requires comprehensive datasets of multiple variables acquired over a broad range of environmental conditions. For example, of particular interest are the relationships between the spectral remotesensing reflectance of the ocean (in sr -1 ), R rs (λ), which is an apparent optical property (AOP) derivable from radiometric quantities, and the seawater IOPs that are directly linked to various seawater constituents because these relationships form the cornerstone of various applications of optical (ocean color) remote sensing.
Recent technological developments and broader accessibility of optical in situ instrumentation have led to a significant increase in optical datasets collected across diverse oceanic environments, and efforts have been undertaken to merge data from various sources within publicly available databases (e. g. ,  Werdell and Bailey, 2005; Valente et al. , 2019; Casey et al. , 2020). Although the importance of field data collection across diverse environments cannot be overstated, existing database compilations are subject to certain limitations. In addition to typical measurement errors, it is difficult to ensure consistent data quality and characterization of uncertainties across all merged data because individual datasets are often obtained with different instruments as well as measurement and data processing methods.
Also, even large databases such as NASA's SeaWiFS Bio-optical Archive and Storage System (SeaBASS, https: //seabass. gsfc. nasa. gov/, last access: 1 March 2023) cannot ensure the balanced representativeness of collected field data in terms of a broad range of optical conditions across diverse ocean environments. In this context, radiative transfer (RT) simulations, which are free of measurement errors, provide a useful tool to generate comprehensive synthetic databases and complement the existing datasets of field measurements in support of studies in ocean optics and optical remote sensing. Over the past decades, various radiative transfer models that employ different numerical solution techniques have been developed and used to address a wide range of problems related to the optics of natural water bodies (e.
g. ,  Mobley et al. , 1993; Mobley, 1994; Stamnes et al. , 2017). Since the 1990s, the HydroLight code based on the invariant imbedding technique  (Mobley, 1989; Mobley et al. , 1993; Mobley, 1994)  has been among the most commonly used radia-tive transfer models in oceanographic optics. The Hydro-Light code solves the scalar (i. e. , polarization of light is not included) time-independent radiative transfer equation for a horizontally homogeneous water body in which the IOPs can vary with depth and under given boundary conditions at the surface and at the bottom of the water body. Inelastic radiative processes within the water column that include Raman scattering by water molecules, fluorescence of chlorophyll a pigment, and fluorescence of colored dissolved organic matter (CDOM) can be included in HydroLight simulations.
Radiative transfer simulations with HydroLight code have proven useful for generating synthetic databases of light field characteristics (i. e. , radiance and irradiances) within and leaving the ocean and the AOPs derived from the simulated radiometric quantities for various scenarios of seawater IOPs that provide input to the simulations. In particular, as a result of efforts dedicated to inverse bio-optical algorithms and coordinated under the auspices of the International Ocean Colour Coordinating Group  (IOCCG Report, 2006) , a widely used publicly available synthetic database was generated within the spectral range 400 to 800 nm with a 10 nm resolution for clear sky conditions with three different sun zenith angles (0, 30, and 60 • ), a sea-surface state corresponding to a wind speed of 5 m s -1 , and 500 different IOP combinations driven by chlorophyll a concentration, Chla (in units of mg m -3 ), within the surface ocean layer.
The input IOP data included the spectral absorption coefficients of phytoplankton, a ph (λ), non-algal particles (also referred to as depigmented or detrital particles, which can include various types of particles such as organic detritus, mineral particles, heterotrophic bacteria, and depigmented phytoplankton cells), a d (λ), colored dissolved organic matter (CDOM), a g (λ), and the spectral backscattering coefficients of phytoplankton, b b-ph (λ), and non-algal particles, b b-d (λ) (λ represents the wavelength of light in a vacuum expressed in units of nm and the IOP coefficients are typically expressed in units of m -1 ). The output parameters provided by simulations available in the public database included the following AOPs: spectral remote-sensing reflectance, R rs (λ), remotesensing reflectance just below the sea surface, r rs (λ), irradiance reflectance just below the sea surface, R(z = 0 -, λ), and the diffuse attenuation coefficient for downwelling plane irradiance, K d (λ, z), at the depths z = 0 -, 5, and 10 m (where 0 -indicates a depth of just beneath the sea surface).
Another synthetic database that is publicly available was developed as part of the CoastColour Round Robin project  (Nechad et al. , 2015). This project was focused on coastal waters and IOPs were described by 5000 combinations of Chla, a g (λ), and the mass concentration of mineral particles. The HydroLight simulations were run from 350 to 900 nm at 5 nm intervals for a cloudless sky, three sun zenith angles (0, 40, and 60 • ), and a wind speed of 5 m s -1. The output parameters included in the publicly available database Earth Syst. Sci. Data, 15, 3711-3731, 2023  https://doi. org/10. 5194/essd-15-3711-2023 are the water-leaving reflectance, RL w (λ) = π R rs (λ), K d (λ), photosynthetically available radiation, PAR, and the euphotic depth, z eu. Most recently, a synthetic database was also developed by the first NASA PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) Science Team, where the ocean contribution to top-of-atmosphere radiances were simulated by Hy-droLight  (Craig et al.
, 2020). These simulations were performed from 350 to 800 nm with a 5 nm step for a cloudless sky, three sun zenith angles (10, 30, and 60 • ), a wind speed of 5 m s -1 , and a set of 720 IOP combinations driven by a ph (λ). The publicly available output of these HydroLight simulations is R rs (λ). While these existing synthetic databases have provided valuable information to the ocean color radiometry (OCR) community, especially for the purpose of algorithm development where the ocean AOPs are linked to IOPs, there are several reasons that have motivated the present study, which is aimed at generating a new optical synthetic database. First, inelastic Raman scattering and fluorescence processes were ignored in the previous RT simulations. These inelastic radiative processes are known to be important for simulating realistic characteristics of light fields within and leaving the ocean, including R rs (λ), which is a primary optical quantity used in ocean color remote sensing.
For example, Raman scattering by water molecules may have an important influence on light within and leaving the ocean and AOPs, especially in the green and red parts of the spectrum (e. g. ,  Marshall and Smith, 1990; Stavn, 1993; Sugihara et al. , 1984; Westberry et al. , 2013). Second, the three synthetic databases described above are based on the use of the spectral absorption, a w (λ), and scattering, b w (λ), coefficients of pure seawater in the visible part of the spectrum as defined by  Pope and Fry (1997)  and  Morel (1974) , respectively. However, more recent measurements and theoretical considerations provide new recommendations for spectral values of a w (λ) and b w (λ)  (IOCCG Protocol Series, 2018; Zhang and Hu, 2009; Zhang et al. , 2009). Third, the probability distributions of different IOPs that were used as input to previous RT simulations do not appear to match well with the IOP distributions observed in extensive field datasets or satellite-derived datasets representing the global ocean.
This issue may have a biasing effect when the synthetic databases are used to develop optical algorithms based on AOP vs. IOP relationships, especially when the underlying goal is to represent a broad range of IOPs encountered within the global ocean, even if the primary interest is in open-ocean pelagic environments. Finally, the previous synthetic databases were developed specifically for OCR-oriented studies and publicly accessible data generally include only the surface reflectances, R rs (λ), R(λ), r rs (λ), and K d (λ), at selected depths. These databases do not include many of the various output variables obtained from RT simulations, such as various underwater AOPs, which can be useful in supporting a broader range of studies in ocean optics beyond ocean color remote sensing. In this article, we present a new synthetic optical database generated using RT simulations that addresses some of the limitations of similar databases developed in the past.
First, we describe the development of the synthetic IOP dataset that is required to run RT simulations. The key roles in this development are played by the measured data of the phytoplankton absorption coefficient and the desired consistency between the probability distributions of synthetic IOPs and the global distributions based on satellite observations. Following this, we describe different configurations of RT simulations that were performed with the HydroLight code. The next section is dedicated to consistency between the new optical synthetic database and in situ data, including some previously reported empirical relationships. We provide example illustrations of consistency for both the IOP and AOP data. The closing section summarizes the structure of synthetic database files and provides an example illustration of one output radiometric variable, the spectral downwelling plane irradiance, calculated with RT simulations.
General overview of methodology The scope of the synthetic database generated with RT simulations and the degree of its representativeness of diverse marine optical environments within the global ocean depend most critically on the dataset of seawater IOPs used as input to RT simulations. In the present study, our approach to generate the IOP dataset was driven largely by the underlying goal to obtain the probability distributions of IOPs that are generally consistent with the distributions observed in the global ocean dominated by open-ocean pelagic environments. The key IOPs involved in the creation of our IOP dataset include the spectral absorption and backscattering coefficients associated with the main categories of seawater constituents representing suspended particulate matter and CDOM.
Specifically, the absorption coefficients of the different constituents are the spectral absorption coefficients of phytoplankton, a ph (λ), non-algal particles, a d (λ), and CDOM, a g (λ). Note that the sum a ph (λ) + a d (λ) = a p (λ) represents the particulate absorption coefficient with combined contributions of phytoplankton and non-algal particles, and the sum a measurements of phytoplankton absorption made on nearsurface samples collected across diverse oceanic environments. Thus, the a ph (λ) data are not "synthetic" in the sense that these data were not obtained from a modeling approach, although some spectral interpolation or extrapolation was applied to measured data as described in more detail below. In contrast, the remaining four constituent IOPs in the IOP dataset, i. e. , a d (λ), a g (λ), b b-ph (λ), and b b-d (λ), are "synthetic" in the sense that they are entirely based on calculations using a modeling approach with some assumptions about the magnitude and spectral behavior of the modeled IOPs.