dataesr/scientific-paragraphs-categorization huggingface dataset:
While this issue has been recognized, it has not been addressed or resolved in various studies that focus on global ocean color applications. For example, the current global ocean color algorithms for estimating chlorophyll-a concentration (Chla) are based on relatively large amount of in situ data whose probability distribution is shifted significantly to higher Chla compared with the global Chla distribution (O'Reilly and  Werdell, 2019). Similarly, in the development of previous synthetic optical databases with RT simulations (e. g. ,  IOCCG Report, 2006) , no special attempt was made to ensure consistency between the probability distributions of input IOP data and the distributions expected for global ocean. In the recent development of refined global ocean color algorithms for estimating the concentration of particulate organic carbon (POC), the in situ dataset was assembled with a goal to achieve reasonable consistency with a global POC distribution  (Stramski et al.
, 2022). This goal was, however, achieved at the expense of significant reduction in the amount of accepted in situ data compared to the size of overall pool of available in situ data. In this study our goal was to create a relatively large synthetic IOP dataset based on the initial dataset of several thousand measurements of spectral aph(λ), so that the probability distributions of IOPs in the final synthetic dataset are reasonably consistent with the expected distributions representative of the global ocean. As described above, the initial field dataset in support of this process consisted of 4382 spectra of aph(λ) and this number was further reduced to 2204 spectra that were accepted as a result of analysis and some criteria applied to the initial dataset. This reduced dataset of accepted aph(λ) spectra was then further modified to ensure that the final probability distribution of aph(440) resembles the global distribution of aph(440).
, 2007; Claustre et al. , 2008; Stramski et al. , 2008)  and the Mediterranean Sea  (Loisel et al. , 2011). This 17 th OWC is described in  Jorge et al. (2021). For illustrative purposes Fig. 4a  depicts the spatial distribution of 17 optical water classes (OWCs) over the global ocean obtained from satellite OLCI data following the methodology of  Mélin and Vantrepotte (2015). For further illustrative purposes, these 17 OWCs were grouped into 3 optical water groups (OWGs). Group 1 consists of OWC1 and OWC2 which are characterized by high water turbidity such as in coastal areas affected by discharge from large rivers. Although the focus of this study is to create the synthetic datasets representative primarily of open ocean and moderately turbid coastal waters, an explicit identification of Group 1 data that represent very turbid waters is of interest for comparisons with the database developed specifically for coastal waters by  Nechad et al.
(2015). The second OWG, Group 2, includes 6 OWCs from OWC3 through OWC8. This group represents mainly productive waters in both coastal and open ocean environments, such as those encountered in the North Atlantic during a period of phytoplankton bloom  (Levy et al. , 2005). Finally, Group 3 included the remaining 9 OWCs from OWC9 through OWC17. These water types are observed mainly in mesotrophic and oligotrophic regions of the global ocean. Based on this classification, 79. 6% of OLCI water pixels in Fig. 4a  belong to Group 3, 10. 8% to Group 2, and 9. 6% to Group 1. The histograms of OLCI-derived Rrs(443) associated with these three groups of data are shown in Fig. 4b. For comparative purposes we also assembled a dataset of in situ measurements of Rrs(λ), which were collected at various locations within the global ocean (Fig.
4c ). The histograms of in situ Rrs(  443 ) associated with Groups 1, 2, and 3 are depicted in Fig. 4d , which show a similar pattern to that in Fig. 4b. For in situ dataset of Rrs(λ), 69. 2% of data belong to Group 3, 15. 7% to Group 2, and 15. 1% to Group 1. The probability density function (PDF) of global satellite-derived aph(440), ag(440), adg(440), and bbp(550) are depicted in Fig. 5. We note that we refer here to satellite-derived absorption coefficients at 440 nm although they were derived from OLCI reflectances at 443 nm, which is a minor difference that is inconsequential for the purpose of this study. The comparison of Fig. 2a  and Fig. 5a  indicates that the distribution of measured aph(440) from our initial field dataset (Fig. 2a ) is shifted towards higher values compared to the global distribution of satellite-derived aph(440) (Fig.
5a ). The probability distribution of reduced dataset of measured aph(440) (N = 2204) that was created from the initial field dataset of aph(λ) show similar deviations from the global distribution (not shown). Thus, to create the final dataset of aph(λ) that has the probability distribution of aph(  440 ) consistent with the global satellitederived distribution, we adjusted the number of aph(440) measurements in each bin of the histogram of the reduced dataset either by removing the measurements from any given bin or adding the measurements to this bin. The removal or addition of aph(440) measurements associated with any given bin was done by subjecting all aph(440) measurements originally contained within a given bin to random selection. Specifically, in the case of addition the randomly selected aph(440) was added as a replicate of aph(440) to a given bin.
In the case of removal, the randomly selected aph(440) was simply removed from a given bin. As a result of this process we obtained a modified distribution of measured aph(440) which is fairly consistent with the satellite-derived distribution of aph(  440  The full synthetic IOP dataset created in this study consists of 3320 combinations of measured aph(λ) and synthetically-generated ad(λ), ag(λ), bb-ph(λ), and bb-d(λ). Below is a description of calculations of ag(λ), ad(λ), bbph(λ), and bb-d(λ). We note that all IOP coefficients are expressed in units of [m -1 ] and the light wavelength is in units of  [nm]. The four IOP coefficients were calculated using a similar methodology to that applied in previous studies aiming at generation of synthetic ocean optical databases  (IOCCG Report, 2006; Craig et al.
Symbols Mathematical expression Equation 𝑃𝑃 1 0. 79 log 10 �𝑎𝑎 𝑝𝑝ℎ (440)� -0. 37 (1)   Overall, the above-described synthetic IOP dataset includes 3320 scenarios of non-water IOPs, i. e. , IOPs associated with variable contributions of phytoplankton, non-algal particles, and CDOM to optical properties of seawater. In addition to the non-water absorption coefficients, aph(λ), ad(λ), and ag(λ), as well as the non-water scattering coefficients, bph(λ) and bd(λ), the radiative transfer simulations required input of scattering phase functions of particles, specifically for phytoplankton and non-algal particles. We assumed the particulate phase functions proposed by  Fournier-Forand (1994)  with the backscattering ratio 𝑏𝑏 𝑝𝑝ℎ � = 0. 01 for phytoplankton and 𝑏𝑏 𝑑𝑑 � = 0. 018 for non-algal particles.
Note that while the backscattering ratios are assumed to be spectrally constant, the phase functions vary with light wavelength because of spectral variations of bph(λ) and bd(λ). All IOP data in the final synthetic IOP dataset cover the spectral range from 300 to 750 nm with a 5 nm interval. This wavelength interval is consistent with the intended output of our radiative transfer simulations. is consistent with the mean sea surface temperature (SST) calculated from the monthly global NOAAv2 SST database at 1° spatial resolution from December 1991 through November 2021 (Jérôme Vialard, personal communication, https://www. psl. noaa. gov/data/gridded/data. noaa. oisst. v2. html). The salinity of 35‰ is also consistent with the global surface average  (Durack et al. , 2013). γ -0. 2 + 0. 3 𝑟𝑟𝑟𝑟𝑟𝑟(0,1) (.
Radiative transfer simulations The IOP dataset described in section 2, which includes 3320 combinations of non-water IOPs, provided the key input to radiative transfer (RT) simulations which were performed with the HydroLight v5. 0 radiative transfer code  (Mobley and Sundman, 2008). All RT simulations were run assuming vertically homogeneous IOPs within the water column and infinitely deep ocean, i. e. , no effect of seafloor on light field within the water column. For all simulations the computed radiometric and AOP variables were saved into the output data files at 10 cm depth intervals between the ocean surface and the 1 m depth, and at 1 m intervals between the 1 m and 50 m depth. Thus, the primary focus of our RT simulations is on the ocean surface layer that can potentially contribute to light leaving the ocean with significance to remote sensing with spaceborne or airborne optical instruments.
The second set of these simulations included Raman scattering by water molecules. layer. We note, however, that fluorescence by CDOM was not included in any simulations. The Raman scattering coefficient, phase function, and wavelength distribution function were set to their default values described in HydroLight technical documentation  (Mobley, 2012). The quantum efficiency of chlorophyll-a fluorescence was also set to its default value of 0. 02 in the HydroLight code. For each scenario of sun zenith angle and inelastic processes, we performed 3320 RT simulations, each for a different combination of seawater IOPs. Thus, given the three sun zenith angles, the three scenarios of inelastic processes, and 3320 combinations of IOPs, overall we performed 29880 simulations. The combination of the synthetic IOP dataset used as input to RT simulations (section 2) and the results for the radiance, other radiometric quantities, and AOPs obtained from these 29880 simulations (described in this section) constitute the synthetic ocean optical database developed in this study.
Comparisons of the synthetic database with in situ data In this section we compare the selected spectral IOP coefficients from the synthetic IOP dataset with in situ data of IOPs and the selected spectral AOPs from the synthetic database generated with the RT simulations with in situ data of AOPs. In these comparisons, we also include some empirical relationships between the IOPs or AOPs which were established in previous studies based on the analysis of in situ data. aph(440) (Fig. 7b ), and bbp(550) vs. aph(440) (Fig. 7c ). The scatter plots include two datasets, the in situ dataset and the synthetic dataset as described in section 2. We recall that in both types of datasets, aph(440) plotted on the xaxis is the same because the phytoplankton absorption data used in this study were obtained from field measurements with no modeling involved.
The scatter plots show a significant degree of overlap which indicates general consistency between the synthetic and in situ datasets. For illustrative purposes, the data from the synthetic IOP dataset are color coded to indicate the partitioning of data into the three OWGs, i. e. , Groups 1, 2, and 3 that were defined using the synthetic spectra of Rrs(λ) generated through RT simulations with input of the synthetic IOP data. As expected, the data with generally lowest values of IOPs belong to Group 3, the data with intermediate values of IOPs to Group 2, and the data with the highest IOPs (most turbid waters) to Group 1. We also note the reflect some degree of intrinsic difference in the dynamic range covered by the two datasets, it must also be recognized that some variability in the in situ dataset may be associated with the fact that these data were collected on numerous cruises by different groups of investigators using the methodology (instrumentation, data processing, data quality control, etc.
) that unavoidably was not the same across all different data sources. For additional comparative purposes, Fig. 7a, c    The color coding refers to the optical water groups as indicated. For comparing the AOPs from the synthetic database with in situ data, we have chosen two AOPs, the spectral remote-sensing reflectance, Rrs(λ), and the spectral diffuse attenuation coefficient of downwelling plane irradiance averaged within the water column from the sea surface to the first attenuation depth, <Kd(λ)>1. The scatter plot of our synthetic data of Rrs(555) vs. Rrs(443) are depicted in Fig. 8a. For comparison, the range of in situ data is illustrated by the dashed contour lines. These results are once again illustrated using color coding to represent different optical water types, specifically Groups 1, 2, and 3.
As seen, there is a relatively good agreement between the synthetic data and the range of variability of the in situ data for Groups 2 and 3 (Fig. 8a ). For Group 1 (very turbid waters), however, the synthetic data exhibit a smaller range of variability compared with in situ data. This result is not unexpected because our primary goal was to generate the synthetic database that is most representative of open ocean pelagic environments as well as coastal areas where water turbidity is low to moderate rather than very high. As described in section 2, turbid waters of Group 1 correspond to Optical Water Classes 1 and 2 as defined in  Mélin and Vantrepotte (2015). It is interesting to note that the synthetic optical database that was developed by  Nechad et al. (2015)  for coastal waters shows a relatively good consistency between the synthetic and in situ data for Group 1 (Fig.
8b ). However, in contrast to our synthetic database, the synthetic data of Nechad et al. (  2015 ) exhibit a limited range of variability compared with in situ data for Groups 2 and 3. Thus, the synthetic data of  Nechad et al. (2015)  for turbid waters in Group 1 can provide useful complementarity to our synthetic database whose main focus is on water types from Groups 2 and 3. The scatter plot of the synthetic data of <Kd(490)>1 as a function of blue-to-green band ratio of reflectance, Rrs(490)/Rrs(555), is shown in Fig. 9. These synthetic data are again color coded according to optical water classes defined in terms of Groups, 1, 2, and 3. For comparison, a few empirical relationships between these AOP variables established in previous analyses of field measurements are also displayed in Fig.
9  (Mueller, 2000; Werdell, 2005; Werdell, 2009). The relationship of  Mueller (2000)  was formulated during the early phase of SeaWiFS satellite mission to serve as an operational global algorithm for estimating Kd(490) from ocean color observations. Werdell (2005) provided an updated relationship with a primary goal to improve the estimation of Kd(  490 ) at low values of Kd(  490 ) that correspond to high values of reflectance band ratio. Figure  9  shows that these two relationships are generally consistent with our synthetic data across the entire range of variability encompassing data from Groups, 1, 2, and 3. This is reassuring given that the main purpose of our synthetic database and these two empirical the range where our synthetic data are classified as Group 1, so these are the optical water types associated with high water turbidity.
Summary We have generated a new synthetic database that consists of seawater IOPs as well as corresponding radiometric quantities and AOPs within the ocean surface layer down to a depth of 50 m and at the sea surface. The radiometric quantities and AOPs were obtained from radiative transfer (RT) simulations performed with Hydrolight code using the IOPs as input to the calculations. The list of variables included in the database is provided in Table  2. This database is organized following an easy to read netcdf structure and divided into two subsets of data for which the file name identifies the sun zenith angle and the RT simulation scenario related to the presence or absence of inelastic radiative processes within the water column. The first subset of data includes the seawater spectral absorption and backscattering coefficients as well as sea-surface radiometric quantities relevant to ocean color radiometry, Rrs(λ), Lw(λ), Ed(z=0 + , λ), and Lu(z=0 + , λ) where z=0 + is just above the surface.
The surface and depth-profile values of several spectral radiometric quantities and AOPs, as well as PAR are included in the second subset of data. The spectra of zeu and z1 are also provided in the second file. More details on the organization and content of the database are included in readme file that is also provided in the database. In closing, we present an example illustration of one of the radiometric variables included in the output data files generated by RT simulations. We recall that the primary result of HydroLight simulations is the spectral radiance that provides a comprehensive information about the angular distribution of light field, from which different irradiances and AOPs are calculated. However, it is the spectral downwelling plane irradiance, Ed(z, λ), which has been the most commonly measured radiometric quantity in ocean optics, so in Fig.
https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. The 3SAA does not yield the separate contributions of CDOM, ag(λ), and non-algal particles, ad(λ), to the overall non-phytoplankton absorption coefficient, adg(λ). Therefore, we also used another semi-analytical model (CDOM-KD2) described in Bonelli et al. (2021) to estimate ag(443) from OLCI-derived Rrs(λ). Having adg(λ) from the 3SAA and ag(λ) from the CDOM-KD2, the non-algal particulate absorption, ad(λ), was obtained as a difference adg(λ) -ag(λ). As a result of this analysis, we obtained a dataset of satellite-derived constituent absorption coefficients, aph(443), ag(443), ad(443), and adg(443), as well as the particulate backscattering coefficient, bbp(550),where we focused on the spectral band near 440 nm for absorption and 550 nm for backscattering.
). Both the modified histogram and the corresponding modified PDF of measured aph(440) are depicted in Fig. 5a for comparison with the global satellite-derived distribution. In total, this modified distribution consists of 3320 https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. measurements of aph(440) and, obviously, each of these measurements at 440 nm has an associated full spectrum of aph(λ) values between 300 and 750 nm. These 3320 spectra of aph(λ) represent one IOP component of the final synthetic IOP dataset.
variability of measured aph(440) illustrated in Fig. 5a along with the dynamic range of parameters P1, P2, P3, P4, the spectral slopes Sg, Sd, Sc-ph, and Sb-d, and the degree of randomness in the selection of these parameters https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. for any given value of aph(440) that initiates the process of calculating ag(λ), ad(λ), bb-ph(λ), and bb-d(λ), resulted in the generation of synthetic dataset of these IOP coefficients that cover a wide dynamic range consistent with in situ and satellite observations over the global ocean. Figure5b,c,d compares the probability distributions of satellite-derived ag(440), adg(440), and bbp(550) with the distribution of these coefficients from the final synthetic IOP dataset.
https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. in situ dataset exhibits somewhat wider dynamic range of variability than the synthetic dataset, especially when the IOP ratios, ag(440)/aph(440), adg(440)/aph(440), and bbp(550)/aph(440), are relatively high. While this result can.
includes a few empirical relationships between the IOPs in question, which were established in previous studies based on considerable amount of field measurements collected mostly in open ocean environments. As seen, the relationships between ag(440) and aph(440) based on the studies of Morel (2009)  and Bricaud et al. (2010)  agree quite well with the central tendency of variation within our synthetic dataset. We note that Morel (2009)  and Bricaud et al. (2010)  reported on the relationships between ag(440) and Chla which were very similar in these studies. For the purpose of illustration in our Fig. 7a, we replaced Chla with aph(440) using the formula Chla = aph(440)/0. 05582. Similarly, the studies ofHuot et al. (2008)  and Antoine et al. (2011)  reported on empirical relationships between bbp(λ) and Chla.
https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. Table 2: Symbols, variables, and units for the various quantities included in the final synthetic optical database. Total absorption, scattering, and backscattering coefficients of seawater m -1 anw Absorption coefficient of all non-water constituents m -1 aph, ad, ag Absorption coefficients of phytoplankton, non-algal particles, and CDOM m -1 bnw Backscattering coefficient of all non-water constituents m -1 bb-ph, bb-d Backscattering coefficients of phytoplankton and non-algal particles m -1 bnw Scattering coefficient of all non-water constituents m -1 bph, bd Scattering coefficients of phytoplankton and non-algal particles m -1 Eo, Eod, Eou Total, downwelling, and upwelling scalar irradiances W m -2 nm -1 Ed, Eu Downwelling and upwelling plane irradiances W m -2 nm -1 Lw, Lu Water-leaving and upwelling radiances W m -2 sr -1 nm -1 PAR Photosynthetically Available Radiation defined as the total quantum scalar irradiance within the spectral range 400-700 nm µmol photons s -1 m -2 Rrs Remote-sensing reflectance sr -1 Kx Diffuse attenuation coefficients for upwelling and downwelling plane irradiances or upwelling radiance (the radiometric quantity is indicated by subscript x) m -1 µd, µu Average cosines of downwelling and upwelling light fields dimensionless zeu Euphotic depth at which PAR is reduced to 1% of its surface value m z1 First optical attenuation depth at which spectral Ed or PAR is reduced to 36.
Notably, delta and theta EEG signals have consistently exhibited differences in SCD compared to non-SCD individuals, irrespective of the type of analysis performed, be it spectral power or functional connectivity  (26, 27). (  28 ) and (  29 ) have highlighted the scarcity of studies investigating EEG analyses in individuals with SCD compared to non-SCD individuals. Furthermore, discrepant findings have been reported within the existing literature. Some studies have observed an increase in delta and theta power among SCD patients compared to healthy elderly individuals (26), while others have demonstrated a decrease in delta power specifically in the frontocentral regions  (27). In the present study, we aimed to evaluate the topographical organization of brain networks in SCD individuals compared to non-SCD individuals using EEG data collected during a resting state, focusing on the delta and theta frequency bands.
Partial least squares structural equation modeling analysis The measurement model was elaborated using data from all 95 participants. Initially, a model was tested with all variables included, but variables that did not significantly contribute were eliminated, resulting in a more restrictive model. Specifically, six of height indicators in the 'Neuropsychological tests' construct were removed, leaving only scores from the free recall of the RL/RI-16 Test and the Letter and Category Verbal Fluency test. Agreeableness, Extraversion, and Openness to Experience were also eliminated from the 'Personality factors' construct. Table  2  presents the results of the reliability and validity assessment of the second model, which includes five reflective constructs: memory complaints, neuropsychological tests, emotional distress (depression and anxiety), metacognitive beliefs, and personality factors.
adni-info. org). These cut-offs were based on the first 12 items of the CCI-S that relate to memory concerns, and individuals scoring 20 or higher on these items were considered to have memory concerns. Demographic measures, neuropsychological tests, and questionnaires were analyzed using XLSTAT software. Two-tailed p-values were reported, and t-tests were used for normally distributed variables, while the Mann-Whitney U test was used for non-normally distributed variables.