dataesr/scientific-paragraphs-categorization huggingface dataset:
• The Simulink model applies the DVDM. The motor drive is designed such that the phases are electrical, magnetical and thermal independent from one another. For this reason, in order to evaluate the performance of the drive during the steady state conditions, only one phase has to be simulated. The nonlinear dynamic modeling of each phase of the motor is taken into account by the DVDM and implemented in Matlab/Simulink as shown in Figure  5. In the control strategy, the rotor magnet position is measured by a position sensor and used to compute sinusoidal current references function of the position. These references, six in the case of the six phase motor, are modulated in amplitude by the speed control loop according to the torque requirements. A current control loop generates voltage references to obtain a phase current as close as possible to the related current reference.
To evaluate the efficiency and the performance of the power converter, the Matlab-Simulink/LTspice ® co-simulation model is used. The converter has been modeled in LTspice ® whereas the motor and its controller have been modeled in Matlab/Simulink. The models presented allow the consideration of nonlinearities for both the electrical machine and the SiC switching devices of the inverter. These models aid in the evaluation of the interaction between the PM brushless motor and the power electronic converter in a realistic case study for aerospace applications. The efficiencies are evaluated at 97% for normal operating conditions (100 A, 12. 5 kHz) and 94% for overload (140 A, 12. 5 kHz) conditions. The results showed that the module is a valuable candidate for aircraft applications aiming for high efficiency at similar power and switching frequency levels.
Acknowledgments: This work has been done in the frame of the European project  JTI Clean Sky Systems for Green Operations   WP2. 3. 4. 2   ETRD "Electrical Tail Rotor Drive " and has been associated with SoW S632_AH_Hybrid Propulsion_2015. The authors thank to CREE ® for providing the LTspice model of the power module and advice for suitable use of the devices.
This research assessed the potential co-benefits associated with selected black carbon (BC) emission 10 reduction measures on mitigation of air pollution and climate forcing in Southeast Asia (SEA). This paper presents Part 1 of the research with details on the emission inventory (EI) results and the WRF/CHIMERE model performance evaluation. The SEA regional emissions for 2007 were updated with our EI results for Indonesia, Thailand and Cambodia and used for the model input. WRF/CHIMERE simulated PM10, PM2. 5 and BC over the SEA domain (0. 25º x 0. 25º) of the year 2007 and the results were evaluated against the available monitoring data in the domain. WRF 15 hourly simulation results were evaluated using the observed data at 8 international airport stations in 5 SEA countries and showed a satisfactory performance.
The modeled PM2. 5/PM10 ratios estimated for four AIRPET sites ranged between 0. 47 -0. 59, lower than observed values 25 of 0. 6 -0. 83. Better agreement was found for BC/PM2. 5 ratios with the modeled values of 0. 05-0. 33 as compared to the observation values of 0. 05 -0. 28. AODEM (extended aerosol optical depth module) was used to calculate the total columnar aerosol optical depth (AOD) and BC AOD using the internal mixing assumption. The model AOD results were evaluated against the observed AOD by both AERONET and MODIS satellite in 10 countries in the domain. Our model results showed that the BC AOD contributed 7. 5 -12% of the total AOD, which was in the same 30 ranges reported by other studies for places with intensive emissions. The Part 1 results (this study) is used in Part 2 (Permadi et al.
25 There is not yet detail modeling studies conducted for the SEA domain, especially the maritime SEA which includes Indonesia with its large biomass open burning (OB) emissions. For such a modeling effort, first reasonably accurate regional EI database should be prepared to generate input data. Several global and regional EI databases are available which also cover the SEA domain. These datasets have been developed using the activity data taken from several international data sources  (Zhang et al. , 2009; EC-JRC/PBL, 2010)  or based on the results of large scale energy model 30  (Streets et al. , 2003). Efforts therefore should be put forward to update the SEA EI databases to generate the emission input data for the SEA regional modeling studies. Our research used integrated EI and modeling tool to provide the spatial and seasonal distributions of aerosol species (PM10, PM2.
5, and BC) in SEA for 2007 and the co-benefits (on air quality, health and climate forcing) of selected emission reduction measures for 2030. This paper (Part 1) presents the SEA emissions for the base year of 2007 and the WRF-CHIMERE performance evaluation. CHIMERE  (Menut et al. , 2013 and references therein)  was used to simulate three-dimensional (3D) aerosol concentrations in the domain using the meteorological fields generated by the Weather Research and Forecasting (WRF) model  (Michalakes et al. , 2004). The model results were evaluated using available ground-based measurements of PM10, PM2. 5 and BC in several SEA cities. The extended aerosol optical depth (AOFD) module (AODEM), detailed in  Landi and Curci (2011) , was applied to calculate the total columnar 5 AOD and BC AOD. The modeled total AOD was evaluated using the observed AOD from both ground based Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product.
Emission inventory and emission input data The emissions from major anthropogenic sources (except for biomass open burning) in Indonesia, Thailand and Cambodia were developed using the EI framework given in the "Atmospheric Brown Cloud Emission Inventory 15 Manual (ABC EIM)"  (Shrestha et al. , 2013)  as summarized in Table  1. Detail EI methodology Indonesia was presented in  Permadi et al. (2017b). For other countries in SEA, the emissions of SO2, NOx, CO, VOC, PM10, PM2. 5, BC and OC were taken from the available online gridded EI databases (grid size of 0. 5° ~ 50 km) compiled by the Center for Global and Regional Environmental Research (CGRER)  (Zhang et al. , 2009). The gridded CH4 and NH3 emissions that were not included in CGRER, were taken from the global Emission Database for Global Atmospheric Research The biomass open burning categories considered in this study included crop residue open burning (CROB) and forest fires (above-ground forest fires and peatland fires).
The CROB emission (aerosol and trace gases) for Thailand for 2007 was taken from  Kanabkaew and Kim Oanh (2011) , and both CROB and above-ground forest fire emissions for Indonesia were from  Permadi and Kim Oanh (2013)  also for 2007. For other countries in the domain, the emissions 25 from the above-ground forest fires were from  Song et al. (2009)  while those from CROB were from the Global Fire Emission Database v3 (GFED3)  (Van der Werf et al. , 2010). The emissions from peatland fires of all countries in the SEA domain were also taken from GFED3  (Van der Werf et al. , 2010). The GFED3 database was developed using a combination of MODIS burned area and active fires which is believed to better detect the peatland fires than those used the MODIS burn scars area product of MCD45A1 for forest fires detection as quoted by  Shi et al.
30 Biogenic emissions were calculated on-line in the CHIMERE model using the methodology described in  Simpson et al. (1999)  that considers seasons and vegetation cover types taken from the Global Land Cover Facility (GLCF) (http://glcf. umi aces. umd. edu) with a resolution of 1 x 1 km 2. CHIMERE incorporates the Model of Emissions of Gases and Aerosol from Nature (MEGAN) module  (Guenther et al. , 1995)  for estimation of VOC and NOx emissions  Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017 -315, 2017  Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 2 May 2017 c Author(s) 2017. CC-BY 3. 0 License. from natural vegetation. Emissions from all considered sources were compiled for the base year of 2007 and were gridded to 0. 25° x 0. 25° (~ 30 x 30 km 2 ) for the modeling input.
WRF and CHIMERE model configuration WRF version 3. 3 was used with lateral boundaries and initial meteorological conditions taken from the National Centers for Environmental Prediction (NCEP) final (FNL) global analyses that are available at 1º x 1º grid resolution for every six hours (http://rda. ucar. edu/datasets/ds083. 2/). WRF Pre-processing System (WPS) of geographical input 20 data (i. e. land use, vegetation index, soil type, and albedo) was also obtained from the NCEP database. Totally, 28 vertical levels were simulated with the lowest level having the physical height of about 38 m. Analysis nudging was performed in the Planetary Boundary Layer (PBL) and other layers for wind components (u and v), temperature (T) and relative humidity (RH). Nudging coefficients were set for all parameters at 0.
Chem. Phys. Discuss. , doi:10. 5194/acp-2017 -315, 2017  Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 2 May 2017 c Author(s) 2017. CC-BY 3. 0 License. and shallow convection option for cumulus parameterization. These schemes were selected as they are suitable for mesoscale grid size and have been used in the previous studies in the world  (Jankov et al. , 2005; Osuri et al. , 2012). This study used CHIMERE version 2008c with the MELCHIOR 2 chemical mechanism that was adapted from the original European Monitoring and Evaluation Programme (EMEP) and consisted of around 120 reactions and 40 chemical species. The vertical profiles of updated reaction rates in MELCHIOR 2 have been developed using tabulated 5 clear-sky photolysis rates taken from the Tropospheric Ultraviolet and Visible (TUV) model  (EC4MACS, 2012).
This version of CHIMERE has an aerosol module which consists of the total primary PM emission (BC, OC and other primary particles) and secondary inorganic PM species, such as nitrate, sulfate and ammonium, and secondary organic aerosol (SOA)  (Bessagnet et al. , 2004). CHIMERE applies the sectional approach to discretize particle size distribution into a finite number of bins. The considered particles size range was from 40 nm to 10 μm that are 10 distributed into 8 bins  (0. 039, 0. 078, 0. 156, 0. 312, 0. 625, 1. 25, 2. 5, 5, 10 μm)   (Pere et al. , 2011). Most of aerosolrelated dynamic processes, such as condensation, coagulation, wet and dry deposition, adsorption and scavenging, are incorporated in the model (http://www. lmd. polytechnique. fr/chimere/). This version of CHIMERE only allows tropospheric simulations below 200 hPa (~12 km).
The monthly AERONET data for 2007 was downloaded from the National Aeronautics and Space Administration (NASA) website (http://aeronet. gsfc. nasa. gov/) for the evaluation of the modeled AOD The AERONET data was of level 2 quality controlled, recorded at 10 AERONET stations (using Sun Photometer) listed in Table  S2 , SI. This 25 provided AERONET dataset that has already been pre-and post-field calibrated with cloud screening and quality assurance. The selected 10 AERONET stations had more complete datasets in 2007 and they represent all sub-climate zones in the domain. Sun Photometer measures AERONET AOD at six different wavelengths  (1020, 870, 675, 500, 440 and 380 nm). Therefore, to compare with the modeled AOD at 550 nm, the AERONET AOD at 500 nm were converted to that 550 nm using a logarithmic interpolation  (Chung et al.
Base year emissions 5 The obtained total national emission estimates of Indonesia, Thailand and Cambodia for 2007 were compared with the existing regional EI databases of EDGAR for 2007 and CGRER for 2006. Table  2  shows a reasonable agreement in the ranges of the estimates between the emission databases for three countries, Cambodia, Indonesia and Thailand. Detail EI results for Indonesia was presented in  Permadi et al. (2017b). There are certain discrepancies between the databases that may be explained by several factors, including the inter-annual variations in forest fires, uncertainty in 10 activity data levels and EFs used, as well as the different coverage of the emission sources by the EI works. Specifically, for the emission sources of N2O, our EI for the three (3) countries did not cover the direct emissions from cultivated soil (fertilized land) and the indirect N2O emissions from agriculture-related activities (microbial nitrification and denitrification) hence resulting in lower N2O emission estimates.
15 For Indonesia, our emission estimates were in between those of CGRER and EDGAR for a number of species. The estimates for PM10, PM2. 5 and BC actually agreed well between these databases while OC of CGRER appeared to be higher. However, the SO2 estimates differed a lot between the databases and our value was lower than others (mainly for on-road transportation and industry) which may be attributed to a more bottom up approach used in our EI that relied on actual S content used in the country and implementation of air pollution control devices  (Permadi et al. , 20 2017b). For example, our SO2 estimate for the power plants of Indonesia was 300 Gg y -1 which was more comparable with the CGRER estimate (409 Gg y -1 ) but much lower than the EDGAR estimate (1,000 Gg y -1 ). The most striking difference was for the CO2 emission that showed a much higher value by EDGAR.
25 the CO2 estimates of all 3 databases are similar for Indonesia. The emission estimates for Thailand and Cambodia also showed reasonable agreements between the available datasets. However, EDGAR appeared to provide higher BC emissions for these two countries while CGRER provided higher OC emission for Thailand, i. e. similar to Indonesia case above. The emission shares by source category for the three countries are presented in Figure  S2 , SI. The emissions of aerosol 30 species (PM10, PM2. 5, BC and OC) were mainly from the residential and commercial combustion in Indonesia (43-80%) and Cambodia (55-78%) while for Thailand the biomass OB (forest fire and crop residue) emissions were dominant, i. e. 31-74%. For SO2, the emission in Indonesia was mainly contributed by the transport sector (36%) and thermal power plants (33%) but the industry was the main contributor in both Thailand (66%) and Cambodia (33%).
Black carbon For the model evaluation purpose we used available measurements in the previous projects for SEA. The 24h BC measured by the optical method available at several SEA sites under the AIRPET project (Kim  Oanh et al. , 2014). The hourly-based EC (elemental carbon by a Sunset analyzer) measurements, available from the APN project  (Kondo et al. , 2009)  for the AIT site (sub-urban) which were used to calculate 24h BC levels. The model performance 5 evaluation was done using 24h BC data of both APN and AIRPET projects. The APN hourly EC dataset for the AIT site was available for both dry and wet seasons, from March to December 2007. The hourly EC (Sunset) and hourly BC (optical method, using the continuous soot monitoring system or COSMOS) measured simultaneously by the APN project at AIT were found to have a strong linear correlation (Kim  Oanh et al.
, 2014)  were compared with the 24h modeled BC extracted for the sites and dates of 2007. Figure  5  shows that the modeled 24h BC were lower 25 than the observed at all the sites. The ranges of observed values and the modeled values were in somewhat better agreement for the AIT site and MO Manila site than the other 2 sites. At AIT, the observed BC values was 1. 3 -3. 4 µg m -3 (January, February and May) were higher but quite comparable to the modeled range of 0. 5 -1. 8 µg m -3. At MO, the observed 24h BC was 7 -13 µg m -3 (January and February) was quite close to the modeled 24h BC of 4. 2 -13 µg m -3. More discrepancies were found for the Bandung site with the observed 24h BC values ranged from 4. 2 to data available prevented from a more comprehensive model performance evaluation. Note that due to the limited measurement data points, a statistical performance evaluation was not conducted for the BC simulation.
5/PM10 ratios could provide some information of the dominance of local sources 10 of PM2. 5. We compare the PM2. 5/PM10 ratios based on the modeled 24h PM2. 5 and 24h PM10 (PM10 = PM2. 5 + PM10-2. 5) and those computed the observed PM data available at the four (4) AIRPET monitoring sites discussed above. Overall, the modeled PM2. 5/PM10 ratios ranged from 0. 47 to 0. 59 while the observed values were higher, 0. 6 -0. 83. More pronounced difference was for TD of Hanoi, i. e. 0. 74 observed vs. 0. 47 modeled, and for TG of Bandung, 0. 83 observed vs. 0. 55 modeled. Better agreements were obtained for MO of Manila, 0. 61 observed vs. 0. 47 modeled, and 15 the AIT site, 0. 6 observed vs. 0. 59 modeled. The urban mixed sites of TD in Hanoi and TG in Bandung located in the traffic areas hence higher contributions of the primary PM2.
20 BC is emitted directly from the combustion sources with higher fractions in PM emitted from the diesel exhaust (Kim  Oanh et al. , 2010)  and lower fractions from biomass open burning (Kim  Oanh et al. , 2011). Hence the ratio of BC/PM2. 5, for example, can infer the contribution of the primary particles from these combustion activities. BC/PM2. 5 and BC/PM10 ratios were calculated using the observed 24h data at four AIRPET sites. Modeled BC/PM2. 5 ratios 25 ranged from 0. 05 to 0. 33 as compared to the observed ratios of 0. 05 -0. 28. For BC/PM10, the modeled values ranged from 0. 03 to 0. 16 while the observed values ranged from 0. 034 to 0. 17. Observed BC/PM2. 5 ratios were higher than the modeled values at TG of Bandung (0. 16 vs. 0. 1) and AIT (0. 055 vs. 0. 05) sites. In TD of Hanoi and MO of Manila, the observed ratios (0.
The results for August and November both showed some significant underestimation of AOD as compared to the MODIS observed values. There 30 are several reasons for these discrepancies, including the temporal and spatial inconsistency in the observed and modeled values used for comparison. For example, the Terra MODIS satellite daily passed a region for a particular time (i. e. 13:30) hence giving only a snapshot of the value while the model provided the hourly average for 13:00 -14:00 hence there is certain inconsistency in the monthly averages derived from these two datasets. Different spatial resolutions of modeled AOD (30 x 30 km 2 ) and MODIS AOD (10 x 10 km 2 ) can be another reason. In addition, the natural sources of aerosol, such as wind-blown dust, were not included in our emission input data hence the model would produce lower AOD (as well as PM10) values.
Overall, this qualitative analysis of the modeled vs. MODIS AOD provided some insight into the regional distributions although more efforts are still required for the model evaluation. Simulated monthly AOD values were also compared with the observed data retrieved from 10 AERONET stations 5 located in the domain, i. e. in Vietnam, Singapore, Hong Kong, Taipei, Thailand and Indonesia, which also showed lower simulated AOD values than the AERONET observed (Figure  8 ). The model appeared to capture better seasonal variability in most of the stations in Thailand and Vietnam than the stations in other countries. The strong seasonal variation of aerosol in SEA, largely caused by the biomass open burning, creates a huge challenge for models to reproduce. At Puspitek Serpong (Indonesia) where emissions of urban activities from the capital city of Jakarta would 10 dominate, the high AOD in October was reasonably captured by the model.
Summary and conclusions This study developed and evaluated the EI databases for Indonesia, Thailand and Cambodia for 2007. The results were compiled with the existing CGRER and EDGAR emission datasets to generate the emission input data of the entire SEA domain for regional WRF/CHIMERE modeling. Our EI results for the three countries were comparable to other existing databases and the differences are explained mainly by the differences in the sources covered by different EI 5 works. The BC emissions were mainly from residential and commercial combustion in Indonesia (71%) and Cambodia (70%) but was dominated by biomass OB emissions in Thailand (31%). The model performance for 2007 was evaluated using the hourly and daily observed data in the SEA domain. The WRF model outputs were in good agreement with the observed data at 8 international airport stations in Indonesia, Thailand, Vietnam, Cambodia and Philippines.
The spatial distributions of the columnar total AOD estimated for the WRF/CHIMERE output PM concentrations using AODEM were comparable with the observed (MODIS and AERONET) in 2007. In particular, exclusion of the 5 The EI data and WRF/CHIMERE performance for 2007 were satisfactory in terms of reproduction of the key aerosol species in the domain. In the accompanying paper  (Permadi et al. , 2017a)  we present the WRF/CHIMERE simulation results for PM and BC for the SEA domain in the business as usual emission scenario (BAU2030) and in the emission reduction scenario (RED2030) to quantify potential co-benefits on the air quality improvement, reducing number of premature deaths, and radiative forcing mitigation in Southeast Asia. 10      Boylan and Russel (2006). MFB: PM ≤ ±60%, and MFE: PM ≤+75%, bold showed the parameters that satisfy the criteria.
Acknowledgements This research was financially supported by the  French government  under the  Asian Institute of Technology (AIT)/Sustainable Development in the Context of Climate Change (SDCC) -France Network cooperation  and by the  United States Agency for International Development (USAID)  under the  PEER-SEA (Partnerships for Enhanced 15 Engagement in Research for Southeast Asia) Research project. We thank  APN  and  AIRPET  projects for making BC and PM data available at four sites for the model evaluation. AERONET principal investigators are highly acknowledged for making data available on-line. Prof. Talib Latif  from  University Kebangsaan Malaysia (UKM)  is highly acknowledged for sharing the information on PM10 monitoring data in Kuala Lumpur, Malaysia;  Dr Tony C Landi  ( Instituto di Scienze dell'Atmosfera e del Clima (ISAC), Italy)  and  Prof Gabrielle Curci  ( University of L'Aquila, 20 Italy ) are thanked for making the source code and users guide of AODEM available in the website.
5 1,644 BC 226 OC 674 CO2 508,022 N2O 180 Note: ne -not estimated (Streets et al. , 2003)) year of 2007 using the ABC EIM framework(Shrestha et al. , 2013). Detail methodology and results were presented inPermadi et al. (2017). bEDGARforbaseyear of 2007(Oliver et al. , 2001). Retrieved from http://edgar. jrc. ec. europa. eu/datasets_list. php?v=42. The CO2 emission, excluding forest fire post burn decay and decay of drained peatland, are given in brackets for comparison with our estimates c CGRER for base year of 2006(Zhang et al. , 2009). Retrieved from http://www. cgrer. uiowa. edu/EMISSION_DATA_new/index_16. html. For NH3, CH4, and CO2, emissions were taken from CGRER inventory in 2000(Streets et al. , 2003). Peatland fire for SEA for 2007 was taken from GFED v3. Retrieved from https://daac.
The TomExpress platform was developed to provide the tomato research community with a browser and integrated web tools for public RNA-Seq data visualization and data mining. To avoid major biases that can result from the use of different mapping and statistical processing methods, RNA-Seq raw sequence data available in public databases were mapped de novo on a unique tomato reference genome sequence and post-processed using the same pipeline with accurate parameters. Following the calculation of the number of counts per gene in each RNA-Seq sample, a communal global normalization method was applied to all expression values. This unifies the whole set of expression data and makes them comparable. A database was designed where each expression value is associated with corresponding experimental annotations.