dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this work, the electro-thermomechanical modeling of the high electron mobility transistor was performed. This modeling allowed to describe the thermal and mechanical behaviour as a function of the operating conditions. The finite element model is developed under the Comsol multiphysics software. After applying the loads to the model, the maximum operating temperature of the HEMT increases in a non-linear evolution as a function of the dissipated power. Also, degradation phenomena like displacements and deformations in the transistor structure have been observed. Therefore, we developed an optimization approach to reduce the effect of these degradations. In this case, we took only three geometric parameters, this method is based on the integration of the CMA-ES method with the kriging metamodel.
Data Analysis We studied the total number of caterpillars collected, as well as the abundance of the guilds casebearers, semiconcealed, and free-living caterpillars, the three most common species, and the most common free-living caterpillar family. The analyses at the species level were necessarily limited to caterpillars that were reared to adulthood. For each group, the density of caterpillars was calculated as the number of caterpillars divided by the total dry mass of the leaves sampled. To obtain normally distributed residuals, caterpillar density was square root transformed for statistical analyses. We first fitted mixed models for total caterpillar density at the branch level with tree identity as a random effect using the R package 'lme4'  [110, 111]  and including branch height as a factor.
This revealed that one tree had exceptionally high caterpillar densities on a low sun-exposed branch. Such branches were not sampled from other trees. When this outlier was removed from the data, branch position did not predict caterpillar abundance (results not shown). We then excluded this branch and pooled data from multiple branches per tree for the final analyses. We calculated the Simpson diversity (1-D) based on both the moths reared from each tree and identified cases of casebearers using the R package 'vegan'  [111, 112]. We calculated the parasitism rate for each tree as the proportion of parasitoids. To test the predictions in Figure  1  regarding abundance, diversity, functional traits, and parasitism, we used a combination of OLS linear models (for linear predictors) and Mantel tests  [110, 113].
To perform model selection, we took the "dredge" approach using the R Package 'MuMIn'  [114]. Thus, for each dependent variable, this function fitted all possible OLS models with the predictors and their interactions with phylogenetic isolation, and then sorted the results by the value of the Akaike Information Criterium corrected for small sample size (AICc). We subsequently took the ten best-fitting models (Supplementary File S2), and determined for each predictor and interaction how often it was included in these ten models (Table  1 ). We also noted the direction of the effect if it was consistent among models and was not part of an interaction (Table  1 ). To gauge how these ten best-fitting models performed differently from each other, we report the difference in AICc value between the top model and the tenth best-fitting model (Table  1 ).
We examined the results of the top model and performed outlier exclusion when warranted (Supplementary File S2), and then indicated the level of significance of the selected predictors in the top model (Table  1 ). To test how the similarity among trees in caterpillar abundance is predicted by the genetic divergence among trees, we performed Mantel tests  [113]. We used Provesti distances calculated from microsatellite data (Supplementary File S1) relative to similarity in the square root of caterpillar density and similarity in upper model residuals, using the R package 'ade4'  [115]. To estimate which variables predict community composition, we performed a Permutational Multivariate Analysis of Variance (PERMANOVA) using the R package 'vegan'  [111, 112, 116] , which partitions the variation in community composition to potential predictors  [117].
Acknowledgments: Fieldwork was carried out with kind permission of the  Forestry Experimental Station in Murowana Goślina (Poznan University of Life Sciences ). Microsatellite fragment analysis by capillary electrophoresis was performed by the  Molecular Biology Techniques Laboratory (Faculty of Biology, Adam Mickiewicz University in Pozna ń). We are grateful to  Joanna Ziomek  and  Urszula Eichert-Nowak  for their help with searching for caterpillars. We are grateful for the insightful comments and suggestions of the reviewers.
All these different methodologies, although of differing sophistication, still rely on the availability of in situ data. The wealth of information provided by the JGOFS expeditions has been invaluable for understanding the AS, but there has been little subsequent in situ sampling in the region, as has been previously remarked  (Hood et al. , 2016). For example, in the Global Ocean Data Analysis Project v2 (GLODAP;  Olsen et al. , 2019)  database, there are no reported observations in the AS of two important carbon variables, dissolved inorganic carbon (DIC) and total alkalinity (TA), more recent than 1998, with a similar > 98 % of data predating 2000 for pCO 2. Thus, the global products of TK09 and L15 are based upon conditions in the AS from 20 years ago. Since quantities like surface pCO 2 concurrently trend with rising atmospheric CO 2 concentration  (Tjiputra et al.
, 2014) , the dearth of recent sampling means that uncertainty in the AS's carbon system will only grow with time. The gap in data collection also means that the AS is proportionally underrepresented in global datasets: whereas the AS is 2 % of the ocean surface, DIC and TA measurements in the AS are < 1 % of the GLODAP ensemble, which is also the case with pCO 2 reported in the Surface Ocean Carbon Atlas (SO-CAT;  Bakker et al. , 2016; Pfeil et al. , 2013). Where data are sparse in the AS, numerical circulation models have been used to complement the lack of spatiotemporal coverage. These models fill the domain with their own estimates of carbon variables, such as pCO 2 , while also providing detailed information on the factors affecting them (for example, DIC, temperature, biological productivity, etc.
, 2020)  and intraseasonal  (Valsala and Murtugudde, 2015)  variability exists. The study begins with a description of pCO 2 datasets used, along with the model configuration and methods of analysis in Sect. 2. Following this in Sect. 3 is a description of the model validation and results, with discussion in Sect. 4. We conclude in Sect. 5 with perspectives and recommendations regarding future studies of pCO 2 and air-sea CO 2 flux in the AS.
The pCO 2 data In this study, sea surface pCO 2 is used as the primary in situ data for model validation. Whereas models favor DIC and TA  (Wolf-Gladrow et al. , 2007) , shipboard pCO 2 can be measured underway, and hence there are more observations available. Additionally, since model pCO 2 is calculated from DIC and TA (see Sect. 2. 2), pCO 2 measurements act as an independent dataset. Here, pCO 2 validation stems from in situ ungridded data merged from SOCAT v. 2019 (downloaded from https://www. socat. info/index. php/version-2019/, last access: 3 September 2019) and the Lamont-Doherty Earth Observatory (LDEO) surface pCO 2 database  (Takahashi et al. , 2019). Both databases aggregate all available in situ surface pCO 2 data, including JGOFS. SOCAT and LDEO contain > 180 000 and ∼ 90 000 data points on the AS, respectively.
SOCAT has more data because it includes multiple methodologies. As a result, SOCAT data are preferred, and LDEO observations are included for the years 1980-81 when SOCAT data are unreported. SOCAT fugacity (fCO 2 ) values are converted to pCO 2 and mole fraction (xCO 2 ) using reported SST and SSS data included in the products using routines from the CO2SYS software package  (Van Heuven et al. , 2011). The anthropogenic effect of increasing surface pCO 2 is removed by calculating a fit linear trend of 2 µatm yr -1 , slightly higher than ≈ 1. 5 seen in  Tjiputra et al. (2014). The pCO 2 values are calibrated to the year 2005, the representative year used for the model's atmospheric xCO 2. The year 2005 is chosen for the model's xCO 2 concentration because it is the end of the historical period for the Intergovernmental Panel of Climate Change (IPCC) models used in its fifth report published 2014.
The earliest SOCAT data comes from 1962, and different databases used in this study stem from similarly different time spans. As a result, we assume there is a baseline seasonal cycle of pCO 2 and air-sea CO 2 flux which has held stable over the past decades. Alternative pCO 2 products are used for comparison purposes. A complete list of these datasets and their characteristics is provided in Table  1. For all the comparison datasets, air-sea CO 2 flux is calculated from monthly values. The pCO 2 values are calculated using Keeling curve data (downloaded from https://gml. noaa. gov/ccgg/trends/gl_data. html, downloaded 1 February 2022) of atmospheric xCO 2 for the respective calibrated year of each dataset. The same climatological winds as used in the model (Sect. 2. 2) are applied to the pCO 2 products.
The gridded product TK09 is chosen because previous modeling studies in the AS use it as validation (see "Introduction"). The L15 climatology, while based upon the same in situ data mentioned above, represents different processing methodologies and, as a high-resolution, global pCO 2 dataset, also serves to provide independent context to the model validation. pCO 2 is also calculated from DIC and TA provided by the statistical fits to JGOFS data by  Sarma (2003)  and to the gridded GLODAP climatological product. The statistical fits of  Sarma (2003)  are used twice, first using model SST, SSS, and chl a, and second with World Ocean Atlas (WOA) 2009 SST and SSS with SeaW-iFS chl a. GLODAP-derived pCO 2 also uses WOA 2009 SST and SSS applied to the annual DIC and TA values. Calculations of pCO 2 are performed using the CO2SYS software package  (Van Heuven et al.
5 md -1 w SD , small detritus sinking velocity 1. 0 md -1 w LD , large detritus sinking velocity 10. 0 md -1 w CaCO 3 , vertical sinking speed of calcium carbonate 20 md -1 0. 07 moles of CaCO 3 are produced for each mole of organic carbon. The dissolution rate is a constant 0. 0057 d -1 in the water column and 0. 002 d -1 in the sediments. Surface fluxes of DIC and TA due to evaporation, precipitation, and river input are included as virtual fluxes proportional to SSS forcing. Inside the module, surface carbon chemistry is calculated using routines from the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP) carbonate chemistry routines (http: //ocmip5. ipsl. jussieu. fr/OCMIP/phase3/simulations/, last access: 1 February 2022). Carbon chemistry coefficients used here include K 1 and K 2 CO 2 dissociation from  Millero (1995)  and original data from  Mehrbach et al.
(1973)  which was refit by  Dickson and Millero (1987). A summary of the biological parameters used in the biogeochemical model is provided in Table  2. The model is run with 360 d years and interpolated, climatologically averaged monthly forcing. The different climatological products derive from datasets spanning slightly different periods, and so here we assume that the dynamics represented within them have not changed in the time since. Heat flux, evaporation and precipitation, and restoring SSS are provided by the Comprehensive Ocean-Atmosphere Data Set (COADS; da  Silva et al. , 1994). SST forcing is provided by a monthly climatology of Pathfinder data from 1985 to 1997  (Casey and Cornillon, 1999). Wind stress is produced using the QuikSCAT/SCOW monthly climatology from 1999 to 2009  (Risien and Chelton, 2008).
Tracer values for the initial conditions and the boundaries are given by WOA 2009 for temperature, salinity, NO 3 , and oxygen. Horizontal velocities u,v for initial and boundary conditions derive from the Simple Ocean Data Assimilation (SODA) analysis  (Carton and Giese, 2008). Initial and boundary conditions for DIC and TA come from GLODAP from 300 m down to the bottom. Surface TA was calculated using the relations from  Lee et al. (2006) , and the corresponding DIC was calculated using WOA phosphate, silicate, T , and S values along with L15 pCO 2. DIC and TA values between the surface and 300 m are calculated using density weighting. The model is spun up for 30 years, with 5 additional years for analysis. Atmospheric xCO 2 values are set to 380 ppm, equivalent to 2005 levels, with an annual sinusoidal perturbation of 2.
The deviations seen between in situ data and model output are greatest at depths less than 500 m. Deviations in nearsurface NO 3 (Fig. S3 ) can be large for intermediate values (5-20 µM) but overall do not show a systematic bias. DIC (Fig. S5 ) also has large deviations (∼ 50 µM) in the top 500 m and with a slight positive bias. It is in TA (Fig. S6 ) that deviations, while similarly ∼ 50 µM eq, show a consistent nearsurface underestimation. The surface currents in the model also demonstrate the monsoonal shifts and reversals seen in the AS (Fig. S7  in the Supplement). Regarding pCO 2 , in situ data from the merged SOCAT-LDEO database show that ∼ 90 % of pCO 2 values in the AS are positive (Fig. 2a , inset), indicating a positive flux to the atmosphere that is applicable geographically (Fig.
However, in the spring (Fig. 3e ) open-ocean pCO 2 finds its peak with a domain average of 439 µatm, which is not reflected in the in situ dataset (Fig. 3d  and e ). Maximum model pCO 2 is found in the summer monsoon near upwelling regions (Fig. 3h ), with values attaining > 800 µatm in Oman. Fall model pCO 2 (Fig. 3k ) still has elevated values averaging 427 µatm but less than the summer period. Certain regions in the model show persistent maxima in pCO 2 , such as the Gulf of Oman and the Strait of Hormuz, which are not reflected in the few data collected there. Model pCO 2 values in the Gulf of Aden increase during spring and then peak during the summer, a pattern which is unclear from the data. Annual and seasonal pCO 2 means, with standard deviations in parentheses, are displayed in Table  3  for both the data and model.
For the entire dataset, as well as for the spring and summer seasons, the model's correlation with data is ∼ 0. 5. Winter and fall have lower values at 0. 2 and 0. 06, respectively. Variability expressed as normalized standard deviation shows that overall, and during spring and summer periods, the model underestimates data variability (∼ 0. 5 µatm). During the winter and fall, however, the model overestimates variability (1. 1 and 1. 6, respectively). For all periods apart from summer, model pCO 2 has a positive bias  (9. 1, 24. 6, 48. 4, and 33. 7  µatm for the annual, winter, spring, and fall, respectively). During the summer, the model has a negative bias of -3. 1 µatm. The source of bias in pCO 2 is linked to the four state variables SST, SSS, DIC, and TA. Comparisons with the model are made with SST and SSS from the merged SOCAT-LDEO database, while DIC and TA come from the ungridded GLO-DAP product (Fig.
3  and 4 ). In  Sreeush et al. (2019a) , ROMS resulted in a systematic posi-tive pCO 2 bias, whereas the offline Ocean Transport Tracer Model (OTTM) produced negative bias in pCO 2 in comparison to TK09. The search for the model bias source is hindered by the lack of in situ data in the region. As already noted, GLO-DAP has 334 locations with DIC and TA in the top 50 m. The few available in situ data that do exist in the AS have a number of deficiencies for the purpose of validating model output. First, the data available are both old and concentrated around the years 1995 and 1997. While the JGOFS studies were quintessential in diagnosing the seasonal cycle of pCO 2 , they preclude being able to decipher the secular trend in surface pCO 2 due to increasing atmospheric CO 2 concentrations.
Spatial distribution of air-sea CO 2 flux and pCO 2 The model results both affirm the conclusions of previous studies in terms of CO 2 flux direction and seasonality and yet find difference in magnitudes. As previously stated, the AS is an atmospheric CO 2 source, with most flux occurring (56 %) during the summer monsoon (Fig. 6 ). In our results, however, there is no region during any of the seasons where CO 2 uptake takes place. While somewhat expected, this is still in disagreement with some of the other pCO 2 datasets previously considered, such as in  Sarma (2003) , in which negative pCO 2 values appear, such as during the winter monsoon near the south coast of India. The model's positive pCO 2 bias may be to blame for this, making it so that no negative pCO 2 appears. Despite the positive pCO 2 bias, a few other patterns are clear in comparison to other CO 2 flux estimates.
Indeed,  Sabine et al. (2000)  noted that pCO 2 in the spring was much lower than would be expected given the SST but attributed this to drawdown due to biological production. The model, however, indicates that this is precisely the season when biological production is at its lowest. The presence of these springtime maxima can be seen in other models, as is visible in the results of  Valsala and Maksyutov (2010)  and a synthesis by  Sarma et al. (2013). Since the model indicates temperature is producing the maxima, it reduces the concern that erroneous DIC or TA values in the model are driving this signal. The model SST matches well with the in situ data (Figs. S8  and S9 ), and the forcing datasets for SST and heat flux correspond to data that predate or include the pCO 2 sampling period (i.
Additionally, we find that spatial gradients of DIC and temperature do not overlap as they do elsewhere in the ocean. Instead, temperature follows a southwest-northeast monsoon wind pattern, whereas DIC is enhanced nearest to the coasts. The resulting apparent deficit of normalized DIC in the central, oligotrophic AS has not been emphasized previously. Finally, we find that despite the intense biological activity in the AS, primary production by phytoplankton is insufficient to counter the increased carbon supply provided by vertical circulation during bloom periods. Models can be used to expand spatiotemporal coverage when data are scarce. However, models' limitations often manifest when there are no new data to test their fidelity. Limitations in the spatiotemporal coverage of existing datasets stem from biases in sampling during the summer monsoon, sampling close to the Omani upwelling region, and sampling limited in scope to the years of JGOFS expeditions of the 1990s.
Summary of pCO 2 datasets used in this study. Included is whether the product is gridded and, if so, its spatial and temporal resolution. Reference year (Ref. year) indicates the year from which Keeling atmospheric xCO 2 values are used to calculate CO 2 flux. Purpose designates use case within the article. pCO 2 calculated indicates whether product provides pCO 2 (no) or whether pCO 2 was calculated using DIC, TA, temperature, salinity, and possibly chl a (yes).
Acknowledgements. Support for this research comes from the  Center for Prototype Climate Modeling (CPCM) , the  New York University Abu Dhabi (NYUAD) Research Institute. Computations were conducted at the  High Performance Cluster (HPC) at NYUAD, Dalma. We deeply thank both  Benoit Merchand  and  Muataz Al Barwani  for their technical support. We are also grateful for the work of two anonymous reviewers and associate editor  Peter Landschützer , who greatly improved the manuscript. Financial support. This research has been supported by  Tamkeen  under the  NYU Abu Dhabi Research Institute  (grant nos. G110  and  CG009 ). Review statement. This paper was edited by  Peter Landschützer  and reviewed by two anonymous referees.
Code availability. ROMS-AGRIF is provided by https://www. croco-ocean. org (last access: 1 February 2022). Data availability. Model output used in this study can be accessed and cited from the following: https://doi. org/10. 5281/zenodo. 5937512 (de Verneil, 2022). The Surface Ocean CO 2 Atlas (SOCAT; Bakker et al. , 2016) can be downloaded from https://www. socat. info/index. php/version-2019/ (last access: 1 February 2022). The Lamont-Doherty Earth Observatory pCO 2 database can be downloaded from https://www. ncei. noaa. gov/access/ocean-carbon-data-system/ oceans/LDEO_Underway_Database/ (last access: 1 February 2022). The Simple Ocean Data Assimilation (SODA) reanalysis data can be downloaded from http://apdrc. soest. hawaii. edu/datadoc/soda_2. 2. 4. php (last access: 1 February 2022).
6. 0 (Arnaud 1996). The metallicity parameter fit to the central bin, where the signal to noise is highest, is treated as independent. Outside the central bin, the metallicity parameters are determined using pairs of consecutive annuli. For all spectral fits, the Galactic foreground extinction is fixed at N H = 7. 4 × 10 20 cm -2 , the value reported in  Dickey & Lockman (1990)  for this region of the sky. The Galactic foreground column density as well as the  Grevesse & Sauval (1998)  relative solar abundances are fixed parameters for the photoelectric absorption (PHABS) model used for Galactic extinction. We fit the unbinned spectral data with the Cash statistics  (Cash 1979)  implementation in XSPEC (modified c-stat). We fit the projected temperature and metallicity of the annuli and the resultant temperature, deprojected electron density profile, and entropy parameter profile using the joint analysis of cluster observations (JACO) code  (Mahdavi et al.
ALMA Band 3 observations We use millimetre-wave observations performed by ALMA on December 24, 2018, to probe the location and dynamics of the molecular gas traced by the lowest transition of the dominant A48, page 5 of 20   Notes. The regions used for the X-ray spectral extractions are displayed in the left-hand panel of Fig. 3. form of carbon monoxide, CO(1-0), which has a rest frequency of 115. 271 GHz. Through the ALMA Cycle 7 project 2018. 1. 00940. S, the central region of RXC J2014. 8-2430 was observed in Band 3 with the 12-m 'main' array of ALMA. The data were reduced by the calibrated measurement set (CalMS) service of the European ALMA Regional Centre using the Common Astronomy Software Applications (CASA) version 5. 4. 0, with the methodology closely following the ALMA pipeline.
GMRT radio observations As part of a larger observing programme (project code 14JHC01), RXC J2014. 8-2430 was observed at 610 MHz by the GMRT for 3. 1 h in 6 equal blocks during the night of June 21, 2008. The data were recorded every 16. 8 s in 256 channels covering 32 MHz in one polarisation (RR). The flux calibrator 3C48, also used for bandpass calibration, was observed for 16 min directly following the last block. We processed the archival radio observations of RXC J2014. 8-2430 using the Source Peeling and Atmospheric Modelling (SPAM) package  (Intema et al. 2009)  for the data reduction, following a standardised GMRT data reduction recipe (e. g. see  Bonafede et al. 2014). About 40% of the data were lost due to various non-active and malfunctioning telescopes, and another 30% due to RFI and peculiarities in the data.
8-2430 was detected in six separate VLITE observations at distances between 0 •. 5 and 1 •. 4 from the phase centre between December 10, 2017, and November 22, 2020. All observations were calibrated, imaged, and catalogued following standard VLITE processing as described in  Ogrean et al. (2015). The central radio source is unresolved in all VLITE observations and the catalogued fluxes obtained from the VLITE database pipeline  (Polisensky et al. 2019)  were averaged together. We conservatively adopt a 20% flux density error that includes the local image noise as well as uncertainties related to source fitting and the flux scale. The new VLITE flux measurement of S 338 MHz = 838. 8 ± 167. 8 mJy is shown in red in Fig. 6.