dataesr/scientific-paragraphs-categorization huggingface dataset:
The significance of fixed effects was assessed by the type II tests using the Anova function in the car package  [60]. Whenever Anova revealed a significant effect of a given fixed factor, pairwise comparisons were conducted using the emmeans package with Tukey's adjusted p-values  [61]. The variance explained by the fixed factors (marginal R 2 ) and those explained by both the fixed and random factors (conditional R 2 ) were calculated with the r. squaredGLMM function in the MuMIn package  [62].
Beta diversity Prior to beta diversity analysis, the OTU sequence counts of bacteria and those of archaea were normalized using the Trimmed Means of M-values (TMM) with the edgeR package  [63]  and the normalized counts were expressed as relative abundance counts per million (CPM). The major variance components of beta diversity in bacteria and archaea were determined by performing unconstrained principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarities using the phyloseq package  [58]. The effects of long-term inorganic fertilization and developmental stage on community dissimilarity were then tracked by permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in the vegan package  [64]. The PERMANOVA model was run with 9999 permutations constrained within block and sampling time-point nested within developmental stage.
The Mantel test based on Spearman's correlations was performed to explore the relationship between microbial communities and soil properties using the vegan package  [36]. Dataset of each kingdom (bacteria and archaea) were then separated into three subsets according to developmental stage (tillering, panicle initiation and booting) for in-depth analyses. For each data subset, we only kept OTUs that had at least three sequences and were found in at least three samples. The OTU sequence counts were subsequently TMM-normalized and the normalized counts were expressed as relative abundance CPM. We then performed canonical analysis of principal coordinates (CAP) to determine the effects of long-term inorganic fertilization and sampling time-point on each microbial community at each developmental stage using the phyloseq package  [58].
Long-term inorganic fertilization sensitive taxa For each kingdom (bacteria and archaea), we used complementary approaches to identify the OTUs responsible for the observed effects of long-term inorganic fertilization at each developmental stage  [66]. Correlation-based indicator species analysis was performed using the multipatt function in the indicspecies package  [67]  to calculate the point-biserial correlation coefficient (r) of an OTU's positive association to one or a combination of long-term inorganic fertilizations. Likelihood ratio test (LRT) in the edgeR package  [63]  was then used to test for differential OTU abundance between one or more of the long-term inorganic fertilizations. Thus, OTUs whose abundances differed between one or more of the long-term inorganic fertilizations at a false discovery rate (FDR) corrected value of p < 0.
05 were considered to be long-term inorganic fertilization responsive. Thereafter, the OTUs confirmed by both indicator species analysis and LRT were defined as long-term inorganic fertilization sensitive (lifs) OTUs. For each kingdom, an LME model was used to test for differences in relative abundance of lifs OTUs between developmental stages. The enrichment/depletion patterns of bacterial and archaeal lifs OTUs at each developmental stage were visualized by drawing ternary plots using the ggtern package  [68]. The same approach was conducted to identify lifs taxa at the higher taxonomic levels (phylum for bacteria, class for archaea because they contain a small number of phyla, and family for both kingdoms). The Proteobacteria phylum was also divided into its respective classes because it accounted for a large proportion of the bacterial community in our data set.
More specifically, for each subject, a different fractional anisotropy matrix of the HCP dataset was used  ( Van Essen et al. , 2013 ) , and the weights corresponding to an RSN-connection were modified and set to a value of ( 1 ± 20% ). A corresponding scaling of the matrices followed in accordance with COALIA's requisites and the type of each input matrix (inhibitory/excitatory). A total of 50 "virtual subjects ", 4 epochs per subject (i. e. , 200 data segments) were simulated; with a duration of 40 s each and sampling rate of 2048. An example of simulated cortical signals is shown in Supplementary Materials, Fig. S1.
Forward problem Scalp EEG signals were estimated from simulated cortical activity by solving the forward problem as follows: 𝑋 ( 𝑡 ) = ( 𝑥 1 ( 𝑡 ). 𝑥 𝑀 ( 𝑡 )) 𝑇 = 𝐺. ( 𝑠 1 ( 𝑡 ). 𝑠 𝑃 ( 𝑡 ) ) 𝑇 = 𝐺. 𝑆 ( 𝑡 ) (1) where (  ) represents scalp EEG signals, (  ) simulated cortical time series, and  the (  ×  ) Gain (lead field) matrix. More specifically,  quantifies the contribution of each cortical source to the generation of scalp signals by taking into account the geometrical and electrical characteristics of the head. Here, the gain matrix was computed using a realistic head model (Colin27 MRI template) using the boundary element method (BEM), implemented within the OpenMEEG package  ( Gramfort et al. , 2010 )  in the Fieldtrip toolbox  ( Oostenveld et al. , 2011 ). The sensor space was defined based on the GSN HydroCel electrodes configuration (EGI, Electrical geodesic Inc) with 256, 128, 64, and 32 channels, as well as the international 10-20 system with 19 channels.
Acknowledgments The authors would like to acknowledge the  Lebanese National Council for Scientific Research (CNRS-L) , the  Agence Universitaire de la Francophonie (AUF) , and the  Lebanese University  for granting Ms. Allouch a doctoral scholarship. This work was also supported by the  Labex Cominlabs  project  PKSTIM  and by the  Institute of Clinical Neuroscience of Rennes  ( Projects EEGCog  and  EEGNET3 ). The authors would also like to thank  Campus France ,  Programme Hubert Curien CEDRE  ( PROJET N°4 2257YA ), and the  Lebanese Association for Scientific Research (LASER)  for their support.
Data and codes availability Data simulated for this study are available at 10. 5281/zenodo. 6597385. Codes supporting the results of this study are available at https://github. com/sahar-allouch/var-RSNs. We used Matlab (2018) , Brainstorm toolbox  ( Tadel et al. , 2011 ) , Fieldtrip toolbox  ( Oostenveld et al. , 2011  ; http://fieldtriptoolbox. org ), Open-MEEG  ( Gramfort et al. , 2010 )  implemented in Fieldtrip, R ( R Core Team, 2022 ) for statistical analysis, and Seaborn  ( Waskom, 2021 )  and Matplotlib  ( Hunter, 2007 )  for visualization.
6 g). Beijing's largest dose occurs in spring (2. 9 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP though large variations in dose magnitude are found, with CAMS producing lower doses by a mean factor of 2. 4±0. 5, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44% or 41% respectively.
CAMS Reanalysis Dust Concentrations We utilize the global Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset  (Inness et al. , 2019)  to estimate climatological, vertically resolved dust mass concentrations at each airport. Aerosol and meteorological modelling and data assimilation are carried out by the ECMWF Integrated Forecast System within the CAMS framework. Total aerosol optical depth (AOD, covering all aerosol species) from satellite retrievals are assimilated into CAMS, and concentrations of individual aerosol species are adjusted according to the modelled proportions and vertical distributions of each aerosol type, of which mineral dust constitutes one of five  (Morcrette et al. , 2009). CALIOP data are not included in the assimilation. Thus, the CAMS dataset benefits from an observational constraint of AOD, affecting total aerosol column load, though how this is proportioned amongst different aerosol types and their vertical distribution is dependent on the model-simulated values.
Dust emission is driven by modelled surface wind speed, soil moisture, surface albedo and bare soil fraction conditions  (Bozzo et al. , 2020; Morcrette et al. , 2008; Remy et al. , 2019). Dust is represented by three size bins with upper/lower diameters of 0. 06, 1. 1, 1. 8 and 40 µm, and transport following emission is controlled by model winds and associated meteorology. We analyse data from January 2003 to December 2019, though in comparisons to spaceborne lidar we restrict this to 2007-2019 to be consistent with that data. The nearest CAMS grid box (size of 80 km) to the location of each airport is selected. Dust mass mixing ratios are available at 3 hourly time intervals with 60 vertical levels of which we use 23, corresponding to the surface to approximately 6 km altitude. Total dust concentrations are calculated as the sum of dust over the 3 size bins, and by converting mass mixing ratios (kg/kg) to dust mass concentration (kg/m 3 ) as a function of altitude using standard meteorological conversions.
CALIOP Spaceborne Lidar As an observational comparison to the CAMS dust profiles, we use spaceborne lidar data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite  (Winker et al. , 2010). CALIOP is an elastic backscatter lidar, providing information on the spatial and vertical distribution of aerosols at wavelengths of 532 nm and 1064 nm. Attenuated backscatter profiles are measured, from which aerosol extinction profiles are derived, where extinction is the absorption and scattering coefficient at each height, broadly indicating the amount of aerosol in the atmosphere. Additionally, CALIPSO measures the linear depolarization ratio at 532 nm, enabling identification of dust aerosols, which are strongly depolarizing due to their nonspherical shape, in contrast to other aerosol types  (Kim et al.
, 2018). CALIOP therefore provides an observationally driven estimate of the vertical distribution of dust, although limitations exist and certain assumptions are required, as described below. The CALIPSO orbit track repeats every 16 days, (until 2018 when the orbit was adjusted), though global coverage is never achieved due to the small footprint size. Here we analyze two different 532 nm CALIOP datasets: the standard Level 3 dataset, produced and distributed by NASA's CALIPSO project, (henceforth referred to as 'CALIOP L3') and the LIdar climatology of Vertical Aerosol Structure (LIVAS) dataset, created from the CALIPSO measurements  (Amiridis et al. , 2015; Amiridis et al. , 2013). For purposes of comparison to CAMS, the CALIOP datasets are both reduced to the same vertical resolution of CAMS.
We exclude the 'polluted dust' and 'dusty marine' aerosol types. In very dusty cases, in-layer attenuation may mean that the CALIPSO feature detection algorithm does not detect the entire vertical extent of a dust layer, and the layer base will be assigned an altitude 90 m above Earth's surface. Data beneath this altitude (or layer) do not contribute to the summed extinction coefficient profile or sample count of atmospheric layers classified as 'dust' in terms of aerosol-subtype  (Tackett et al. , 2018). Each CALIOP L3 airport gridbox is selected to encompass the equivalent CAMS gridbox. Although the CALIOP gridbox is larger than that from CAMS, the results showed negligible sensitivity to including multiple CAMS gridboxes for the comparison. We use data covering 2007 to 2019.
LIVAS We use the 'pure dust product' from the LIdar climatology of Vertical Aerosol Structure (LIVAS,  Amiridis et al. (2015) ). The LIVAS pure-dust dataset  (Amiridis et al. , 2013)   23/08/2022). LIVAS was developed to provide pure-dust and non-dust components of atmospheric scenes under the assumption of external aerosol mixtures  (Tesche et al. , 2009). The LIVAS 'pure-dust' product applies CALIOP L3 quality assurance parameters to height-resolved particulate depolarization ratio and backscatter coefficient profiles at 532 nm  (Konsta et al. , 2018)  to decouple the contributions from non-spherical (dust) aerosol and other (non-dust) spherical aerosol components of the backscatter profile  (Amiridis et al. , 2013; Kim et al. , 2018). LIVAS analyses are limited to those CALIOP L2 profiles already classified by CALIOP as either 'pure dust,' 'dusty marine,' or 'polluted dust'  (Kim et al.
Dose Calculations We calculate the dose of dust ingested by the engine core, imposed by the dusty environment in each location per flight, either for arrival (descent) or departure (ascent). Dust dose is defined as the total mass (g) of dust ingested over a time period (different to the dosage which is the rate of dust ingestion). Dust dose is calculated from  (Clarkson, 2020)  according to: 𝑑𝑜𝑠𝑒 = ∫ 𝑘 𝑓 𝑤 𝑐𝑜𝑟𝑒 𝐶 𝑑𝑢𝑠𝑡 𝜌 𝑎𝑖𝑟 𝑑𝑡 Equation 2 where kf is a dust concentration/dilution factor linked with the engine regime, wcore is the mass flow of air entering the engine core (kg s -1 ), Cdust is the ambient dust concentration (kg m -3 , taken from either CAMS or lidar datasets), ρair is the density of air (at a particular location, altitude and time taken from CAMS) and dt is the time integral, accounting for time spent in different engine regimes and flight phases (such as taxi, take-off) and at different altitudes.
However, both lidar datasets are significantly larger in magnitude than CAMS throughout the year, with CALIOP L3 exceeding a factor of 5 greater than CAMS at the lowest altitudes. LIVAS displays lower dust concentrations than CALIOP L3, but is still larger than CAMS by up to a factor of 2. 5 in the lowest 1 km. Between 1 km and 5 km both lidar datasets are within a factor of 2. 5 of CAMS. Similar comparisons were performed for the other airports with significant dust loadings (see supplement). Similar to Dubai, at Niamey, Marrakesh and Delhi the shape of the vertical profile and seasonal cycles agree, though the lidar magnitudes are a factor of 2-3 greater than CAMS for Niamey and Marrakesh, but are comparable for Delhi in JJA, the peak dust season. At Beijing, although the seasonal cycles were similar and dust concentrations on the same order of magnitude for CAMS and the lidar datasets, the vertical structure is very different with CAMS showing an elevated peak at around 2km while the observations showed an increase towards the surface.
CAMS vs spaceborne lidar calculated dose Next we compare the dust dose calculated from CAMS to those calculated from CALIOP L3 and LIVAS data: firstly in terms of magnitude, then in terms of seasonal cycle. Figure  8  shows the magnitude of arrival dust dose calculated from CAMS to those calculated from CALIOP L3 and LIVAS data. For certain airports such as the Canary Islands, Beijing, and to a certain extent Marrakesh, the agreement between CAMS and the lidar datasets is good -for these airports the CAMS median resides within the interquartile range of both lidar datasets. CAMS, compared to a factor of 2. 6 for LIVAS. In Niamey the lidar datasets give a dose 1. 7 to 3. 4 times that from CAMS, though agreement between CALIOP L3 and LIVAS is much better, predominantly because dust concentrations happen to be similar at holding altitude (1 km) and at the surface, even though CALIOP L3 concentrations are around 50% larger than LIVAS in between these altitudes.
In Delhi the LIVAS dust dose is higher than both CAMS (by 6-7 times) and CALIOP L3 in DJF and SON. This is due to the dust concentration profile being larger for LIVAS than CALIOP L3 at nearly all altitudes in these seasons (see supplement). In MAM CAMS shows a much lower dose than LIVAS and CALIOP L3, while in JJA median CAMS dust doses are similar to LIVAS and CALIOP L3, though CALIOP L3 shows a larger spread. Beijing is a second airport where the dose from LIVAS is the largest of the three datasets, a factor of 1. 6 larger than CAMS. The dust concentration profiles show LIVAS to be greater than CALIOP L3 across most altitudes in all seasons, and while LIVAS shows similar magnitudes to CAMS, LIVAS profiles show a stronger surface concentration with declining concentrations in altitude, while CAMS shows a smoother profile with more elevated peaks.
However, although the vertical profiles are very different, the dose calculations are fairly similar since concentrations around the holding altitude are similar, and to some extent the differences in vertical distribution compensate. CALIOP L3 shows lower doses for Beijing across all seasons, by a factor of 0. 3, as a result of the smaller dust concentration profiles. Overall there are large variations in magnitude of dose at some airports, particularly those featuring large surface concentrations and those where concentrations at holding altitude vary between the different datasets. Possible reasons behind the differences are explored in Section. For departure (see supplement), overall the similarities and differences between the datasets are the same as for arrival, with lower doses for departure by 10 to 23%.
However, in a few cases differences between datasets compared to arrival are sensitive to the overall vertical profile shape and magnitude, particularly if ground concentrations are very large. If concentrations are high near the ground, the extra engine power utilized during take-off, as well as the additional time spent on the ground in the taxi phase, can result in higher departure dose compared to arrival dose. For example, for Dubai in JJA, the total dose from CALIOP L3 is 18% higher for departure than arrival. This is because the departure dose is particularly Other notable features are that the variability is much larger for the lidar datasets compared to CAMS, with CALIOP L3 showing slightly more variability than LIVAS. This is partly a feature of the larger magnitudes seen in the lidar data compared to CAMS, but also due to the lower sampling rate for CALIPSO compared to the regular 3 hourly model output from CAMS.
Additionally, there may be differences between CALIOP L3 and LIVAS in filtering the largest, but less frequent, dust events. Section 5. 2 explores this further. Figure  9  shows the normalized seasonal cycles of dust dose in order to evaluate the seasonal timing of dust dose variability. CAMS replicates the seasonal cycles seen in the lidar data very well at Dubai, Canary Islands and Marrakech, but less well at Niamey, Beijing and Delhi. At Niamey CAMS underestimates the relative summer dust dose and overestimates the winter dose. At Beijing there is a suggestion that CAMS represents the spring peak dust dose too broadly, though the lidar datasets display disparities too. At Delhi, CAMS offsets the peak dust dose towards the late summer compared to the lidar datasets, leading to substantial differences in May and July, and also misses the secondary dust dose peak in autumn.
Conversely, in DJF dust concentration drops off much more rapidly due to reduced boundary layer depth, providing large relative reductions from hold at 2 km. A different pattern is shown for certain airports where an elevated dust plume is present in certain seasons, notably for Niamey in JJA where the hold dose actually increases by around 30% when holding altitude is raised to 2 km. This is also the case for the Canary Islands, Marrakesh and Beijing in JJA, though total changes in dose are much smaller (around 0. 02-0. 04 g), though the relative increase in hold dose can still be up to 30%. However, the vertical distribution of dust supplement). This will have implications for selecting a hold altitude with the least dust dose, and a choice based on the CAMS reanalysis dataset may be inaccurate.
CAMS vs Lidar datasets 1. The lidar profiles are filtered for clear sky (i. e. , cloud-free) and have much larger grid squares over which they are averaged (2 by 5 degrees, in order to improve sampling statistics) compared to CAMS (80 km). Therefore the differences in temporal and spatial sampling introduce unquantified differences between the datasets and contribute to the differences in smoothness between CAMS and the lidar datasets. 2. Likely uncertainty in CAMS originates from the data assimilation (DA) of satellite-derived aerosol optical depth (AOD) into incorrect aerosol speciations and/or vertical distribution. For example, if the first model guess contained too little dust compared to other aerosol species, and AOD was then incremented as part of the DA process, the dust optical depth (DOD) (and therefore dust mass concentrations) would remain too low despite the assimilation of satellite aerosol retrievals.
The sulfate aerosol species receives the most increments on a global scale from DA due to its ubiquity and relatively long lifetime. This may explain the reason why CAMS often shows too little dust compared to the lidar datasets, particularly closest to the surface, since at these altitudes total aerosol is more likely to exist as a mixture of different aerosol types. In locations more likely to contain pure dust without mixing from other aerosol types (e. g. , Marrakesh and the Canary Islands), agreement between CAMS and the lidar datasets is much better.
3. Although CAMS has been shown to represent annual, spatial and seasonal variability very well for total aerosol, with good agreement with observations  (Errera et al. , 2021) , CAMS reanalysis DOD is known to be somewhat lower than found in other datasets, such as MERRA-2 (e. g. ,  Zhao et al. (2022) ). The low bias of DOD over dusty regions is much more pronounced than that of total AOD, because of the issue of wrong speciation of data assimilation increments mentioned under point 2 above. Therefore we would expect comparisons against extinction or mass from lidars to be similarly low. CAMS reanalysis DOD underestimates have been shown to be on the order of around 40% compared to AERonet RObotic NETwork (AERONET) observations  (Errera et al. , 2021) , which would roughly explain the factor of 2 or greater differences seen in this study.
Given a fixed composition, MEC is larger for smaller dust particles  (Ryder et al. , 2013; Ryder et al. , 2019). Therefore in order to bring CAMS and CALIOP into better agreement, larger MEC values (representative of a greater proportion of smaller particles) would be needed to be applied to the lidar datasets. However, it has been shown that in Saharan dust plumes, CAMS may overpredict fine particles while underpredicting the coarse ones  (O'sullivan et al. , 2020) , a common feature in dust models  (Adebiyi and Kok, 2020; Adebiyi et al. , 2023). The largest CAMS size bin covers a wide range of diameters (1. 8 to 40 µm) with a single value of MEC. If the lowest dusty layers of the atmosphere are dominated by particles in this size range, but are more biased towards the lower end of the size range, this would justify a larger MEC at these altitudes, which would narrow the gap between the datasets.