dataesr/scientific-paragraphs-categorization huggingface dataset:
For the TSO 2 profiles, a cleaning despiking filter was applied to remove the outliers. More specifically we used the Savitzky-Golay smoothing filter which keeps the profile shape while removing high-frequency spikes in the data. For the fluorescence data, we used three procedures to remove the bad profiles, the dark count, and the spikes. The bad profiles have been removed using a reference depth of 300 m. Then the fluorescence is averaged below 300 m and when the values are higher than the average profile (multiplied by a factor of 3), the profiles are identified as outliers. Secondly, the in situ dark count is calculated from the 95 th percentile between 300 and 400m and then removed from the fluorescence data. Finally, the data are despiked using an 11-point rolling minimum and maximum.
, 2020). To improve the accuracy of the predicted variables, an ensemble of ten NN has been proposed (one for each variable) and recently applied in the northwestern Mediterranean Sea  (Fourrier et al. , 2022)  to estimate the trends of nutrients and CO 2 variables (Total Alkalinity or TA, Dissolved Inorganic Carbon or DIC, pH) from surface to deep waters. In this study, the pCO 2sw was derived from DIC and pH at the total scale predicted by CANYON-MED and the CO2SYS v3. 0 Matlab toolbox  (Sharp et al. , 2020). The pCO 2atm was estimated from the dry mole fraction of CO 2 (xCO 2 ) measured at the OHP atmospheric station (Observatoire de Haute Provence, St Michel l'Observatoire, France) and the vapor pressure formula proposed by  Ambrose & Lawrenson (1972) , then corrected by  Millero and Leung (1976).
The Corsica station (ERSA) is closer to the study site but data was not available before March 2018. In addition, the comparison of pCO 2atm data between the OHP station and ERSA is quite close (less than 1 μatm on average), which suggests that the data from the OHP station are consistent with our study area. F CO2 (mmol m -2 d -1 ) is determined according to the equation: F CO2 = k Â a Â (pCO 2SW -pCO 2atm ) where k is the gas transfer velocity of CO 2 according to  Wanninkhof (2014)  in cm h -1 and ? is the solubility function of CO 2 (in mol L -1 atm -1 ) from  Weiss (1974). The k factor is calculated using the formula: k = 0:251 Â U 2 10 Â (Sc=660) 0:5 where U10 is the wind speed (m s -1 ) measured at the ODAS buoy at DYFAMED (Meteó France) and Sc the Schmidt number (no dimension) determined according to the equation of  Wanninkhof (2014).
Uncertainty in the air-sea CO 2 flux corresponds to the 20% uncertainty in k  (Wanninkhof, 2014)  and the uncertainty of predicted pCO 2sw is based on the root mean square error of predicted DIC (12. 35 μmol kg -1 ) and pH (0. 0156) of CANYON-MED for the western Mediterranean Sea  (Fourrier et al. , 2020). The error package of the CO2SYS v3. 0 toolbox  (Orr et al. , 2018)  provided a pCO 2sw uncertainty estimated to 15 μatm using constants K1 and K2 from  Mehrbach et al. (1973)  as refitted by  Dickson and Millero (1987)  and the dissociation constant for HSO 4 form  Dickson (1990). Finally, as the uncertainty of pCO 2 is dominated by the uncertainty in estimated pCO 2sw , the small contribution from atmospheric CO 2 is neglected (less than 1 matm;  Landschützer et al. , 2014).
The Western Corsican Current (WCC), one branch feeding the NC, is located in the north of Corsica and characterized by higher salinity with lower current velocity than the NC. In this study, the WCC was observed during all transects and was particularly intense in January (section A). The TSO 2 time series over the A to G sections are represented for downcast 0-600m profiles after data processing with Z mld values (Figure  3 ). From 11 to 23 January (section A), warmer waters were located near the coast in Nice and Calvi (above 14°C) and colder in the central zone of the Ligurian Sea (13-13. 5°C). The deepest Z mld observed was estimated at around 200 m. The salinity was lower near the coast and above the Z mld in the central zone  (38-38. 4 ) while high salinity was measured below in the intermediate water  (38.
Acknowledgments The authors would like to thank the members of  ALSEAMAR  for participating in the preparation, deployment and piloting of the gliders. We would also like to thank  Caroline Ulses  ( LEGOS ) and  Cathy Wimart-Rousseau  ( GEOMAR ) for their help in estimating CO2 fluxes and comparing them with model outputs. Finally, we are grateful to the LOV and IMEV infrastructures for allowing us to deploy and recover the gliders during these operations. Finally, we would like to thank  ERIC EMSO  and  Meteo France  for providing the data required for this study.
We show that there is no evidence to relate the origin of endothermy and the development of some turbinal bones by using a comprehensive dataset of µCT-derived maxilloturbinals spanning most mammalian orders. Indeed, we demonstrate that neither corrected basal metabolic rate nor body temperature significantly correlate with the relative surface area of the maxilloturbinal. Instead, we identify important variations in the relative surface area, morphoanatomy, and complexity of the maxilloturbinal across the mammalian phylogeny and species ecology. The ecological and evolutionary success of mammals was highly affected by their ability to maintain a relatively environmentallyindependent and stable body temperature, which allows dispersal to a wide range of habitats normally prohibited to ectotherms  1, 2.
However, extant mammalian species differ in their thermal and metabolic characteristics. Diminished temperature regulation, relatively low body temperature and basal metabolic rates have been documented among marsupials, monotremes, xenarthrans, subterranean rodents, and afrotherians  [20] [21] [22] [23]  , as well as some mammals that undergo different forms of heterothermy (e. g. , long-term hibernation or daily torpor  24, 25  ). Such thermal and physiological exceptions have been hypothesized to be linked to peculiar maxilloturbinals  6. Using a comparative threedimensional (3D) µCT dataset of 424 skulls and unstained ethanolpreserved heads for 310 species across all major mammalian orders, we explored the anatomical diversity of the maxilloturbinal based on relative surface area, morphology and complexity.
There is also no significant correlation between body temperatures (T b ) and Maxillo RSA (Fig. 2C , s = 1. 10 10 -1 , R 2 = -9. 87 10 -3 , p = 0. 71) and species with similar T b may have different Maxillo RSA. Conversely, species with comparable values of Maxillo RSA may have a different T b. Despite the use of three different datasets, the ventilation rate did not significantly correlate with Maxillo RSA (Supplementary Fig. 5 ). However, since the statistical power of these linear regressions are low (based on 3 to 6 species, Supplementary Fig. 5 ), such results may need to be interpreted further in light of additional data that overlaps with our sampled species. There is no consistent pattern between Maxillo RSA and the different forms of heterothermy, such as long-term hibernation, aestivation and short-term daily torpor.
Given our results, we may infer that the Maxillo RSA and the maxilloturbinal complexity may be influenced by other factors unrelated to metabolism, body temperature, or heterothermy. Alternatively, there may be a potential trade-off between maxilloturbinal and nasoturbinal or even with other structures such as the trachea  16. However, based on the dataset from Martinez et al. 18  we demonstrated in 132 species that there is no trade-off between the sizecorrected surface area of the maxillo-and the nasoturbinal (Fig. Supplementary Fig. 7 ). In addition, due to the known variation of the nasoturbinal epithelial cover among mammals  33, 35, [37] [38] [39]  and the scarcity of the data on trachea, it will be extremely challenging to undertake at the scale of mammals. Also, there is an important caveat because such work would be based on soft tissues that will not be comparable to extinct species except by ancestral state reconstructions.
Data acquisition 424 individuals with undamaged maxilloturbinal belonging to 310 mammal species were selected from museums (Supplementary Data 1: folder 1) and scanned using high-resolution X-ray microcomputed tomography (µCT). The use of museum specimens was carried out in accordance with the relevant permissions and ethical approvals of the different museums. Of the 424 individuals, 32 were downloaded from Morphosource 71 and 6 from DigiMorph (Supplementary Data 1: folder 1). The left maxilloturbinal was segmented following Martinez et al. 18, 58, 72  (Supplementary Fig. 8 ) with AvizoLite 2020. 1 (VSG Inc. ). When the left maxilloturbinal was damaged we used the right maxilloturbinal. Using the brush tool, the bony part of the maxilloturbinals were manually segmented (Supplementary Figs.
The normality of the data was tested on the residuals of the linear model with the Shapiro-Wilk's test and the function shapiro. test from the stats r package  74. The heteroscedasticity of the linear model was tested with the Breusch-Pagan test and the function bptest from the lmtest r package  79. When we could assume the normality and the homogeneity of the data, the potential correlation was tested with the function lm and summary from the stats r package  74. When the normality and/or the homogeneity was rejected, the potential correlation was tested with the nonparametric Spearman's rank correlation using the function cor. test from the stats r package 74 (Supplementary Fig. 5 ). For Maxillo RSA, to avoid negative values, we added the lowest residual value, 2. 64 and 2. 67 respectively (Supplementary Data 1: folders 1, 2, 3).
These two PGLS comprised 99 and 89 species respectively. We also performed PGLS and linear regressions between cBMR and the relative surface area of the maxilloturbinal based on body mass (Supplementary Fig. 3A ) as well as between T b and the relative surface area of the maxilloturbinal based on body mass (Supplementary Fig. 3  B ). The phylogenetic signal of the Maxillo RSA was calculated with Blomberg's K 80 and Pagel's lambda  81  with the phylosig function from the R package phytools  82  (Supplementary Data 1: folder 4). The phylogenetic figures were generated by the R package phytools  82. We used a maximum clade credibility (MCC) phylogeny obtained from 10,000 trees sampled in the posterior distribution of  83  and pruned to match the species in our dataset. The MCC consensus tree was inferred using TreeAnnotator v.
Ecology tested it to have a general idea on the potential relation between the ecology and the Maxillo RSA. Five ecological categories were defined as follows: terrestrial, arboreal, amphibious, subterranean and flying (Fig. 1 , Supplementary Data 1: folders 1, 2, 3, 5, 6, 7). The normality and the homogeneity of the data were tested as described above. When we could assume the normality and the homogeneity of the data, we performed an ANOVA on the PGLS model including the tested variables, as well as the ecology and their potential interactions. We also performed ANOVA between a single variable and the ecological data. This was performed with the function anova from the stats r package  74. When we could not assume the normality and/or the homogeneity of the data, we performed the non-parametric Kruskal-Wallis test to test the variables as well as the ecology and their potential interactions (Supplementary Table  1 ).
This was performed with the function kruskal. test from the stats r package  74. We similarly tested the impact of heterothermy on the Maxillo RSA with a first test including two categories (no heterothermy, heterothermy) and a test including four categories (no heterothermy, hibernation, aestivation, other; Supplementary Data 1: folders 1, 3). In order to test the significance of only using the maxilloturbinal surface area, we performed PGLS and linear regressions between maxilloturbinal and nasoturbinal surface area with data extracted from Martinez et al. 18  and based on 132 species (Supplementary Fig. 7 ).
Acknowledgements We thank  E. Amson ,  P. G. Cox ,  F. Delsuc ,  B. Dubourguier ,  S. Ferreira-Cardoso ,  L. Hautier ,  L. Kerber ,  R. Lebrun ,  M. Lovy ,  C. Molinier , and  H. G. Rodrigues  for different types of support, including interesting discussions. We thank  A. C. Fabre ,  V. Fernandez ,  A. Goswami , and  S. Renaud  for providing some scans that were not included in the final version of this study. Thanks to  B. Clark ,  V. Fernandez , and  R. P. Miguez  for access to the mammal collections and the computed tomography facilities at the  Natural History Museum London. We thank the 3D data repositories morphosource, DigiMorph and their contributors (see Supplementary Data 1: folder 1). Three-dimensional data acquisitions were performed using the micro-computed tomography (μCT) facilities of the MRI platform member of the national infrastructure France-BioImaging supported by the  French National Research Agency  (Grant  ANR-10-INBS-04 , " Investments for the future "), and those of the  Laboratoire d'Excellence Centre Méditerranéen de l'Environnement et de la Biodiversité  ( LabEx CeMEB ,  ANR10-LABX-0004 ).
Funding was provided by the  Synthesis of Systematic Resources (SYNTHESYS)  project, which is financed by  European Community Research Infrastructure Action  (FP7: Grants  GB-TAF-5737  (PHF),  GB-TAF-6945  (PHF), and  GB-TAF-1316  (QM) to the  National History Museum London) , by  Agence Nationale de la Recherche (Défi des autres savoirs , Grants  DS10 ,  ANR-17-CE02-0005 RHINOGRAD 2017 , PHF),  PEPS , adaptation, adaptabilite  SHREWNOSE  (Grant  ANR-10-LABX-0025-01 , PHF), the  European Research Council  ( ERC-2015-CoG-683257   ConvergeAnt  project),  Czech Science Foundation  project  GAČR [n. 20-10222 S ], and the  Alexander von Humboldt foundation  ( FRA -1222365 -HFST -P , QM). This is a contribution of  ISEM 2023-123 ,  Univ Montpellier ,  CNRS ,  EPHE ,  IRD, Montpellier, France.
A. Motivation of the Study Predicting solar activity is a daunting task due to the many factors that in uence it, including magnetic elds and solar ares. Moreover, solar activity is known to undergo cycles of varied amplitudes and lengths, making accurate prediction of the timing and intensity of the cycles di cult. Solar activity exhibits both linear and nonlinear trends. The 11 years solar cycle, for instance, which is the most observable variation in solar activity, is a comparatively linear pattern. While the solar activity within the cycle involves complicated nonlinear variations. Several models have been proposed in the literature to predict solar activity via sunspot numbers (SSN). The recently developed models are mainly based on machine learning (ML) and deep learning (DL). The ML and DL models are designed to learn intricate, nonlinear correlations in a dataset, and they are successful in capturing complex patterns within the data and have demonstrated promising performance in the prediction of solar activity using SSN.
Related Work The solar activity can be reliably predicted via SSN using different modelling techniques  [6]. In the literature, several models have been proposed to predict the future dynamics of solar activity using SSN. The vast majority of the models employ the daily and monthly average SSN time series data. However, the SSN time series data re ect seasonality because sunspots have an 11-year solar cycle, making the modelling process extremely complex and cumbersome  [7]. The statistical modelling process is a method that applies statistical analysis to datasets to discover correlations between variables and establish future projections of the target variable. If a statistical model predicts future values based on past values, it's classi ed as autoregressive. Autoregressive models work on the concept that past values have an in uence on current values, making the statistical technique popular for analyzing natural phenomena and other time-varying processes.
Data Pre-processing The incidence of solar activity can be identi ed through the international sunspot number (SSN). The monthly SSN is widely used to develop SSN forecasting models. In this study, the data consisting of the monthly average SSN over the period of January 1749 to December 2020 is used. The data is obtained from Sunspot Index and Long-term Solar Observation (SILSO)  [31]. The sample of the data is presented in Table  1. The collected data is preprocessed and formatted to make it suitable for modelling. In this context, the data is set up so that the SSN value from the previous month can be used to predict the SSN for the current month (one step ahead). This can be expressed mathematically in Eq. (  1 ). 1 Following that, the pre-processed data is split in a ratio of 70:30. The 70% is used for training and the remaining 30% is used for testing.
Support Vector Machine (SVM) The Support Vector Machine (SVM) technique is one of the popular machine-learning methods employed in myriad data-driven modelling applications, including classi cation, regression and pattern recognition. It has a simple structure and generalizes well in many implementations. The architecture of SVM was rst introduced by Vapnik, in 1995  [32]. SVM models are characterized by high learning capacity, reduced error rate, reduced complexity. SVM learning functions can learn the linear and non-linear relationships in the given dataset, depending on the application  [33]. In the case of SVM linear regression, nonlinear mapping function is used to map the input vector to an m-dimensional feature-space, and the linear model is then constructed in this space  [34]. x t = f (x t-1 ) The dataset for the training is presented as , in which denotes the input variables vector, represents the experimental parameters and m is the total data count.
Descriptive Statistics Table  II  shows the descriptive statistics of the dataset. A descriptive statistic is an organized and summarized characteristic of the data. It can be observed from Table  2  that the average value of the SSN is 81. 80, the standard deviation is less than the mean and therefore the SSN pattern is fairly stable. The skewness value of 0. 93, and kurtosis value of 0. 34, indicated that the data was somewhat normally distributed. RM SE = √ ∑ (x i -xi ) 2 n M AE = ∑ |x i -xi | 1 n i = 1,2, 3 … … … …. n x i xi - x i R R 2.
Results of the SVM, LSTM and SVM-LSTM Models This section focuses on description of the results obtained from the simulation of the stand-alone machine learning (SVM), deep learning (LSTM) and the hybrid (SVM-LSTM) techniques for the prediction of the solar activity in terms of SSN. The three models were simulated using MATLAB 2019b software on a PC with a COREi7 processor. In the course of developing predictive machine learning model, the choice of the network structure and the learning parameters has a great in uence on the prediction accuracy of the models. The training data is used to obtain the optimal models' parameters, and the testing data is used on the trained models to test the prediction accuracy  [44]. For the SVM, the key parameters include the epsilon , bias, mu and the number of iterations.
Authors' contributionRabiu Aliyu Abdulkadir provided concepts, data preprocessing, simulation and writing original draft. Mohammad Kamrul Hasan contributes concept, supervision, methodology, guiding data preprocessing and writing review and editing. Shayla works for simulation guidance, and result analysis. Thippa Reddy Gadekallu worked for literature research and guiding methodology. Bishwajeet Pandey is responsible for guiding simulation, gures and writing-original draft. Nurhizam Sa e worked for results discussion and article format. Mikael Syväjärvi is responsible for guiding conceptualization, identifying future scenarios and writing-review and editing. Funding This is supported by the Universiti Kebangsaan Malaysia under research grant, number: DIP 2022-021. Mikael Syväjärvi acknowledge nancing by Swedish Energy Agency and support from European Commission in European Union's Horizon 2020 research and innovation programme under grant agreement no.
Multiphysics Modeling of FFF Process In this work, heat transfers, phase transformations and adhesion physics are implemented in a coupled multiphysics model. The model does not represent the rheology of the deposited filament. Assuming an instantaneous flow, a non-deformable oblong section will be taken to build the model. The simulation is performed in a single computational step where the use of boolean conditions allows the model to evolve in order to represent the successive filaments deposition. This allows a strong coupling between heat transfers and phase transformations. The numerical resolution was performed on a 2D model simulated by FEM on COMSOL ® software. In agreement with the work of  [2] , we develop a dimensionless modelling with the simplifying hypothesis that the thermal exchanges occur mainly in the plane orthogonal to the deposition direction.
Based on our surveys and discussions with farmers, it was fixed at 1%. Thereafter, an average generation rate of PPW of gear 'm' produced annually per 200-m line was calculated for each stage of production, each use and within each sample according to the following formula: G Wml = 1/n ∑ n i=1 W mli (4) Where G Wml represents the average generation rate of PPW gear 'm' per 200-m line of the study site sample (kg/year) and n is the number of responses within the sample. Finally, for each study site and each stage of production, estimates at lagoon scale were made in order to determine the weight of PPW generated annually by a 200-m line (G Wl in kg/year) and the corresponding corrected standard deviation (sd) of our samples. Using the DRM database from 2018, extrapolations based on the number of collection lines and the surface area (ha) of maritime concessions authorized were then performed to obtain the total generation rate of PPW (GW in tons/year) for each study site.
Statistical analysis Model residuals being abnormal and heterogeneous (P < 0. 05), statistical analyses were performed with Exact Permutation Anova (Permanova) to compare the sample G Wl in function of the pearl farming sites, the rearing stage and the use of the gear (i. e. structure, farming and protection) per site. If necessary, post hoc exact permutation tests (PHPT) were also performed for non-parametric pairwise comparison with a 'false discovery rate' adjustment. All these analyses were executed with R and Rstudio software v4. 2. 2.
The occurrence of illegal practices, acknowledged in the aquaculture industry and evidenced by a drone survey at Takapoto (DRM, 2018), underscores the extent of their influence. Similar difficulties were encountered by  Bahers et al. (2019) , highlighting the significance of informal activity in SIDS and the lack of comprehensive statistical databases, hindering accurate estimation of waste generation. To bolster our analysis, future investigations should consider enhancing our approach with the addition of criteria such as the number of collected and sold pearl oysters per POP, and additional tools such as drone analysis. Despite these limits, collectively, our findings not only explain but also clearly underline the limitations of a situation that has prevailed for several decades. The absence of an efficient pearl farming waste management system, coupled with a lack of a sense of responsibility among farmers, constitute the root causes of practices that are not only environmentally 'unsustainable' and polluting, but above all, dangerous for the populations of these islands and the entirety of lagoon ecosystems in the long term.