dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Design considerations The detailed and extensive simulation model of an proposed Ultra-fast EV charger is designed on the Simscape physical modeling tool (MATLAB). The complete EV charger model contains many blocks and subsystems, like Power systems, SPICE power converters, driver circuits, control systems, physical components, battery model etc. The Backward Euler's method is used for the Simscape simulation modeling solver (MATLAB), which solves one or more ordinary differential equations (ODE) using the Euler method by setting a fixed step size of 1e-6 seconds (sampling rate). The simulation analysis is performed on a Li-Ion 100kWh capacity battery (nearly the same capacity is used in the latest 2022 EV e. g. , Mercedes EQS, Ford Mustang Mach-E, Tesla Model S, etc. ).
B. Simulation model and results Physical modeling of EV chargers is performed using Simscape (MATLAB) to verify the theoretical design and above calculations. The Block model of a high-power fast EV charger based on subsystem is shown in figure  5  and system parameters are summarized in Table  6. MATLAB Simscape enables you to rapidly create models of physical systems within the Simulink environment  [46]. Simscape helps build physical power converter models based on physical connections and configurable SPICE models which effectively provide the designed system's dynamic response  [47]. The dynamic behavior of Ga2O3 Power MOSFET is verified by comparing characteristic analysis of the MOSFET model with Salvico TCAD as shown previously in figure  2. Ga2O3 power MOSFET module used in Simscape physical power converters modeling with the characteristic curve is shown in figure  6.
VI. CONCLUSION The research provides the new Ga2O3 power devices based high power Ultra-fast charger using a bi-directional power converter. The proposed solution improves the charging infrastructure power capacity, efficiency and reduces charging time. Research proves that the efficiency of high-capacity power converters increases up to more than 98% by using UWBG power devices. High electric field density, switching ability, current density with low leakage current, and forward voltage drop provide a new market for Ga2O3 power devices. The proposed solution resolves the complexity of charging for currently available or future high-voltage EV batteries. A high breakdown voltage of nearly VB > 8kV makes it perfectly suitable for directly grid-tie applications. Paper successfully presented the Ga2O3 power device response using TCAD and SPICE MATLAB model.
Image analyses and population tracking After the introduction of individuals, high-definition pictures of the entire double-spiral arena were taken every minute throughout the experiment, i. e. for 8 h. In order to successfully visualise individual micro wasps (0. 5 mm long) at this large spatial scale, two high-definition (fullframe 36 Mpx sensors) digital cameras were used synchronously, each centred on one end of the spiral, and covering half of the latter. This definition of 72 Mpx per minute ensured that Trichogramma appeared as dark objects of about 8 pixels on the light background. Images were stored in uncompressed RAW format, representing about 220 GB of image data per replicate (2 × 480 images). Images were subsequently (off-line) analysed with a custom pipeline written in ImageJ  [35].
Details and source code are available in Cointe et al. [34]. Raw images were converted to Tiff RGB, imported to ImageJ, and transformed to grayscale using a linear combination of brightness and saturation channels that maximised insect detection. Analysis then consisted in computing for each image a background image (as a moving window of 20 min), subtracting the background, thresholding, and particle analysis, yielding an ensemble of particles as putative trichogram detections. This procedure ensures that any immobile (e. g. dead) individuals would not be detected. In parallel, the geometry of the double-spiral was resolved from the first image. In particular, the skeleton of the tunnel was reconstructed. Raw particle data and data on spiral geometry were then exported to R  [36]. Based on coordinates, size, shape and position relative to tunnel boundaries, a filter was applied to remove spurious detections.
Having a diversity of retrieval codes, with different peculiarities, gives the possibility to identify the differences between the models and to evaluate their impact on the retrievals. In addition, each retrieval scheme provides different tools that may be of help to discriminate spurious detections. In this work, we present a comparison of three different retrieval codes for the analysis of ozone observed with the NOMAD-UVIS instrument on board the ExoMars Trace Gas Orbiter (TGO). We analyzed the data set already introduced in Patel,  Mason, et al. (2021) ,  Patel, Sellers, et al. (2021) ,  Khayat et al. (2021) , acquired between April 2018 (MY 34, L s = 163°) and November 2020 (MY 35, L s = 320°), and we extended it to November 2021 (MY 36, L s = 132°). In total, this data set comprises more than 8,300 solar occultations.
We compare: (a) an onion peeling method (OP), (b) a full occultation Optimal Estimation Method (FOEM), and (c) a direct onion peeling method (DOP). The OP method is similar to that used for Mars and Venus stellar occultations  (Gröller et al. , 2018; Piccialli et al. , 2015; Quémerais et al. , 2006). The FOEM and DOP approaches are based on ASIMUT-ALVL, the BIRA-IASB radiative code  (Vandaele et al. , 2006). This is a modular program initially developed for Earth observations missions, and then implemented for planetary atmospheres, such as Venus  (Vandaele et al. , 2008)  and Mars  (Piccialli et al. , 2021; Vandaele et al. , 2018). We describe the NOMAD-UVIS data set in Section 2. We provide details for the three retrieval tools used in this work in Section 3 and then present the retrieval comparison in Section 4.
Calibration UVIS raw data requires several corrections before being useable for a proper transmittance calculation. A complete description of the calibration process can be found in  Willame et al. (2022)  and  Mason et al. (2022). First, the dark current (DC) has to be removed: the DC is estimated from a temperature dependent interpolation between the two dark measurements recorded at the beginning and end of each occultation. Second, a noise removal algorithm is applied in order to remove anomalous pixels and cosmic rays to obtain a clean CCD frame. Finally, the UVIS data suffer from straylight contamination. The straylight, coming from the UVIS range itself but also from the near infrared, has to be removed: the straylight in the illuminated region at the center of the CCD is estimated using an interpolation from the non-illuminated parts lying at the top and bottom of the CCD where only straylight is present  (Willame et al.
Full Occultation Optimal Estimation Method (FOEM) Ozone retrievals were performed using the ASIMUT-ALVL radiative code developed at IASB-BIRA  (Vandaele et al. , 2006). A detailed description of the retrieval method can be found in  Piccialli et al. (2021). In this method, ASIMUT analyzes simultaneously all transmittances within a solar occultation at the various tangent heights, eventually deriving a single O 3 profile to best fit the ensemble of spectra. We divided the atmosphere in layers whose boundaries were the different tangent altitudes of the observation, adding a single layer of 5 km width above the highest tangent height up to 120 km, which was considered the upper limit. Aerosols were not taken in account into the retrieval procedure. ASIMUT, in fact, enables the user to fit the baseline spectrum.
Comparison of Filtering Methods Between UVIS and SPICAM SPectroscopie pour l'Investigation des Caractéristiques Atmosphériques de Mars (SPICAM) is a UV and IR spectrometer that has been observing the Martian atmosphere since 2004 on the Mars Express satellite  (Bertaux et al. , 2006; Montmessin et al. , 2017). SPICAM measured ozone on Mars with its UV channel that operated from 2004 until 2014. This channel ranges from 118 to 320 nm and detects the ozone Hartley band centered 10. 1029/2022EA002429 14 of 18 around 255 nm. SPICAM data have been used to produce ozone column retrievals  (Lefèvre et al. , 2021; Perrier et al. , 2006; Willame et al. , 2017)  and vertical profiles from stellar occultations  (Lebonnois et al. , 2006; Montmessin & Lefèvre, 2013). Recently, the SPICAM team published a new ozone vertical distribution data set combining SPICAM stellar and solar occultations in the UV  (Määttänen et al.
Moreover, the two instruments did not observe during the same Mars Years. As a reminder, SPICAM observed in MYs 27-30, while NOMAD during MYs 34-36. Nevertheless, both filtering techniques led to very similar results, most of the ozone detections during the second half of the year and above approximately 50 km throughout the year being discarded. However, at perihelion some ozone detections are still present in the NOMAD data set, while these are completely filtered out in the SPICAM data set. There may be various explanations for this, such as different filtering criteria, different spectral ranges or a different choice of the null-test sample. Additional comparisons between SPICAM and UVIS at different latitudes can be found Supporting Information S1. These results reveal that retrieving ozone from occultation measurements is not a straightforward task: it is very sensitive to the data quality and the fitting methods, and our example shows that it is a problem shared by two different instruments.
Data Availability Statement Public access to all ExoMars TGO data are available through the ESA Planetary Science Archive (archives. esac. esa. int/psa/) with additional access to NOMAD data through the PI institute (nomad. aeronomie. be). The results retrieved from the NOMAD measurements used in this article are available on the BIRA-IASB data repository: https://repository. aeronomie. be/?doi=10. 18758/71021079 (Piccialli & Vandaele, 2022).
Observational data collection We collected 387 paired field measurements of SOC concentrations (g kg -1 ) in croplands with or without biochar addition from 58 locations (see the site map in Fig. S1 ) from published literatures. Soil properties (clay content (Clay), bulk density (BD), soil moisture (SM)), climatic conditions (mean annual temperature (MAT), mean annual precipitation (MAP)), biological variable (net primary productivity (NPP)) and biochar-related characteristics: application rate (Rate_BC), the interval between biochar 110 application and soil sampling (Age_BC), feedstock type (Feedstock_BC), pyrolysis temperature (Temp_BC) were also collected when available. Auxiliary information (e. g. , location, and managements, crop cover types) and more detailed information can be found in  Han et al.
(2021). Because some sites have multiple biochar addition experiments (e. g. , pyrolysis temperature × aging time of biochar), the 115 control SOC concentrations at the same site were averaged, and the SOC concentrations with biochar addition for a given rate (Rate_BC) were also averaged, omitting other characteristics of the BC (like pyrolysis temperature). In total, 134 paired SOC data were used for model calibration (Fig. S1 ). The depth of soil sampled varies among sites, but is less than 30 cm in general. The biochar application rate has a wide range of 0. 9~120 t ha -1 with a median value of 20 t ha -1 (Fig. S2a ). Most biochar addition experiments are short-term with the median Age_BC of 1. 2 year (Fig. S2b ). The main types of cultivated crop are 120 maize, rice and wheat. We also used three published global SOC datasets for croplands without biochar addition  (Sun et al.
125 Soil properties that were not reported in the literature were extracted from gridded datasets using the coordinates of the sites: clay content from Global Soil Dataset for use in Earth System Models (GSDE,  Shangguan et al. , 2014)  and SM from the satellite observations of Soil Moisture Active Passive (SMAP,  Entekhabi et al. , 2010). Missing soil BD in control treatments were filled according to the relationship between SOC and bulk density based on 4765 cultivated soil data from the 2nd national soil survey  (Song et al. , 2005) , and a decrease of 7. 6%  (Omondi et al. , 2016)  from the control soil BD was assumed to https://doi. org/10. 5194/gmd-2023-114 Preprint. Discussion started: 10 July 2023 c Author(s) 2023. CC BY 4. 0 License. fill the missing BD values in the biochar addition experiments.
The climate variable MAT is extracted from WorldClim  (Fick & Hijmans, 2017) , and the mean annual aridity index (AI, i. e. , precipitation/potential evapotranspiration) used in the soil moisture equation (Eq. 10) was obtained from the Global Aridity Index and Potential Evapotranspiration Database  (Zomer et al. , 2022). The biological variable (i. e. , NPP) is from the MODIS NPP dataset  (Zhao & Running, 2010).
Parameter optimization and model evaluation All field SOC observations in control plots were assumed at a steady state. SOC pools in MIMICS reach an equilibrium state 260 after about 200 years of model run (Fig. S5 ). To accelerate this process, we used the analytical spinup method  (Xia et al. , 2012)  to obtain the steady SOC state with the site-level inputs of annual mean crop NPP, MAT, Clay, SM and BD in the parameter optimization. The Shuffled Complex Evolution Algorithm (SCE-UA) has been proven to be a robust method for parameter optimization  (Duan et al. , 1994; Muttil & Jayawardena, 2008) , and the SCE-UA method from the spotpy package in python  (Houska et al. , 2015 ; https://pypi. org/project/spotpy/) was applied here. Parameters are optimal when the root mean square 265 error (RMSE, Eq.
17) between simulated SOC and observed SOC concentrations is minimized. The Akaike information criterion (AIC, Eq. 18,  Akaike, 1974) , which considers both model error and the number the model parameters, was also calculated to evaluate different MIMICS versions. Where SOC obs,i and SOC sim,i are the observed and simulated SOC at each i site. n is the number of observations, and p is the number of free model parameters. The parameters optimized in different MIMICS versions using the entire SOC dataset (i. e. , 58 sites) are shown in Table  S4. Note that the parameters of soil moisture functions (Eq. 10-12) are directly derived from the original literature  (Parton et al. , 275 2000; Camino-Serrano et al. , 2018; Yan et al. , 2018)  and not optimized in MIMICS-TM. We validated the models against our datasets including SOC and auxiliary information (Fig.
2) (Fig. 2e , Fig. S7 ). As compared with MIMICS-TS, for the MIMICS-TSM versions that accounts for the effects of soil moisture, R 2 (0. 50~0. 52) does not show significantly improvement with RMSE and AIC of 4. 41~4. 53 g kg -1 and 186. 0~189. 2, respectively (Fig. 2 ; Fig. S7 ). When using 20% data for the independent model evaluation, MIMICS-TS predicted SOC better with the highest accuracy (R 2 =0. 38) and the lowest RMSE (4. 96 g kg -1 ), but a 335 higher AIC (52. 14) among all different model versions (Table  S5 ). We also evaluated the performances of different MIMICS versions using cropland SOC data not used in the calibration  (Sun et al. , 2020; Geisseler et al. , 2017; Zhou et al. , 2017b). MIMICS performs comparably well on these SOC datasets. R 2 of different MIMICS versions that were evaluated using the three SOC datasets from Sun et al.
cross-validation was also used to evaluate each model version (i. e. , MIMICS-def, MIMICS-T, MIMICS-TS, MIMICS-TSM b ) by randomly selecting 80% data for parameter optimization, and the remaining 20% data for model evaluation. The random selection is repeated for 10 times, and the mean R 2 , RMSE and AIC were calculated by comparing simulated SOC with the observed SOC in test datasets. For the version of MIMICS with biochar addition, we run parallel simulations with control (without biochar addition) and experimental simulation (with biochar addition) for Age_BC year at hourly time steps, restarted from the previous SOC equilibrium. Not that these simulations for biochar addition are transient runs and thus SOC is not in a steady state. The two 290 model runs are forced by site-level 6-hour temperature data from Climatic Research Unit and Japanese reanalysis data (CRU-JRA, Kobayashi et al.
3954. 1 1 Sensitivity tests of MIMICS for simulating cropland SOCMIMICS versions with adsorption and soil moisture effects perform well in comparison with site-level SOC concentrations on croplands collected in this study and other datasets (Fig. 2; Fig. S8), although the soil moisture effects are not notable. We also tried a test by assuming that soil moisture affects the microbial growth rate through mediating microbial growth (V max ) and https://doi. org/10. 5194/gmd-2023-114 Preprint. Discussion started: 10 July 2023 c Author(s) 2023. CC BY 4. 0 License.
Moreover, a network results in increased errors for data with different resolutions, if trained with only one-resolution data  ( Jung et al. , 2022  ). An unsupervised resolutionagnostic method (uraQSM)  ( Oh et al. , 2022 )  was proposed to measure the QSM with different resolutions, but uraQSM needs data of various resolutions to train the network. In this study, we propose a morphology-based self-supervised QSM reconstruction method, namely msQSM, which is appropriate for arbitrary resolution data and only needs one-resolution data for training. Through extensive experiments on different resolution and species datasets from multiple centers, we verify that the proposed method achieves state-of-the-art results among the unsupervised learning methods for QSM and comparable performance with respect to the best available conventional method.
, 2014 ). Here, we set   = 0. 2. The QSM tuner parameters were optimized using the Adam optimizer with  1 = 0. 9,  2 = 0. 999 , and a learning rate of 10 -4 , which dropped to 90% every five epochs. The training data were cropped patches of size 64 3 with an overlap of 50% between adjacent patches, whereas there was no need to crop the testing data. The QSM tuner network was trained for 50 epochs on an NVIDIA Tesla A100 GPU and implemented in Python 3. 7 and PyTorch 1. 8. 1.
MR data acquisition and processing The proposed msQSM was trained on an in-house dataset acquired from 39 healthy volunteers (training set) using a 3T GE scanner with resolution = 1 × 1 × 1 mm 3 , FOV = 256 × 256 × 136 mm 3 , TR = 41. 81 ms, TE 1 ∕ΔTE = 3. 27/2. 35 ms, and 16 echoes. Informed consent was obtained from all the participants. To evaluate the performance of msQSM, the data provided for the 2016 QSM challenge  ( Langkammer et al. , 2018 )  were selected since these data can be reconstructed into the gold standard-COSMOS. The data were acquired from a healthy volunteer using a 3T Siemens Scanner with 12 sampling orientations, and the acquisition parameters were as follows: resolution = 1. 06 × 1. 06 × 1. 06 mm 3 , FOV = 160 × 160 × 160 mm 3 , and TR/TE = 25/35 ms. For verifying the ability to reconstruct arbitrary resolution QSM, multiple resolutions and different species data were utilized.
Quantitative evaluation Table  1  shows the quantitative results of the proposed msQSM and other methods in terms of the quantitative metrics of structural simi-larity index (SSIM), peak signal-to-noise ratio (PSNR), normalized root mean square error (NRMSE), and high-frequency error norm (HFEN). For all criteria, iLSQR attains the lowest HFEN (32. 49) and STAR-QSM has the highest SSIM (0. 9857). QSMnet has a comparatively higher PSNR (42. 53) and relatively low  HFEN (32. 65). msQSM achieves the highest PSNR (42. 57 dB) and the lowest NRMSE (0. 3480), and has only 0. 11% lower SSIM and 3. 63% higher HFEN than the best metrics. Compared to the best previous unsupervised method, namely uraQSM, msQSM achieves a 7. 27% lower NRMSE and 6. 39% lower HFEN. From the quantitative evaluation, we corroborate that our method outperforms the other deep learning methods and has better generalization performance based on the test on the dataset not used in training.
For uraQSM, the authors proposed an AdaIN code generator to encode the resolution feature, which needs to be trained with various resolution data, and then, uraQSM can be used for any resolution  ( Oh et al. , 2022 ). uraQSM performs well on reconstruction tasks at the three resolutions used in the training, but uraQSM s has inferior performance when the resolution of the test data is not used in training since the network cannot be used for that by simply adjusting the AdaIN code. The proposed msQSM can measure arbitrary resolution QSM although trained using only one resolution data. As shown in Fig. 5  , msQSM, as well as iLSQR, shows a stronger ability than uraQSM s to reconstruct multiple-resolution data with almost no artifacts. Mathematically, in the whole dipole inversion process, the resolution information is only used when calculating the dipole kernel to transfer a field map to a QSM as Eq.
Acknowledgements This work was supported by the  Guizhou Provincial Science and Technology project  ( QKHZC[2019]2810 ), the Guizhou senior innovative talent project ( QKHPTRC-GCC[2022]041-1 ), the  National Natural Science Foundation of China  ( Grant No. 62161004  ),  Guizhou Provincial Science and Technology Projects  ( QianKeHe ZK [2021] Key 002 ), and  Guizhou Provincial People's Hospital Talent Fund  ( Peng Yunsong) under Grant Hospital Talent Project  ( [2022]-5 ).
Introduction The conceptual graph model  (Sowa 1984; 1992)  provides a way to represent knowledge using labelled graphs  (Chein and Mugnier 2009 ) called conceptual graphs. The querying method of this model is based on the main operation of conceptual graphs, a graph homomorphism called projection: this operation determines if knowledge in a conceptual graph called query graph can be deduced from the knowledge base which is a conceptual graph called factual graph. Semantic Web and conceptual graphs have similarities. Both aim at representing knowledge. In the Semantic Web, the query language (SPARQL) is based on the language for representing factual knowledge (RDF), whereas in conceptual graphs, graphs are used to represent queries and factual knowledge. The availability of a simple but powerful query language, and its implementation in various tools is a part of the success of Semantic Web approaches.
Query language CGQL is a query language for conceptual graphs with four kind of queries, inspired by the ones of SPARQL: ask, select, describe and construct. The select query of Figure  1  extracts the first name and the nickname, if it exists, of humans named Smith that have a first name and may have a nickname. The result of this query will be an association of named nodes (?first and ?nick) of the query graph with the nodes of a factual graph via the projection of the query graph in the factual graph. 1  Figure  2  shows that the translation between CGQL and SPARQL can be automatized: a block in CGQL is translated into a section in SPARQL, a type of a concept node can be specified in SPARQL with the rdf:type and rdfs:subClassOf relations, and a relation in CGQL is a relation in SPARQL.
Experimentation We developed an implementation of CGQL to demonstrate the viability of the approach. The implementation is based on classes and algorithms of Cogitant, an existing conceptual graph engine. Cogitant is a free (GPL license) C++ library  (Genest 2018). It provides a complete implementation of the conceptual graph model, including the projection operation and some extensions such as datatypes. Since we got inspired by SPARQL, we compare ourselves with three SPARQL engines: Apache Fuseki 1  ; Openlink Virtuoso 2  and Corese 3   (Corby and Faron-Zucker 2007). We chose the SPARQL benchmark SP 2 Bench  (Schmidt et al. 2010)  ; it is a well-known benchmark that has two interesting features: First, it comes with a free (BSD license) data generator which can produce set of RDF triples of an arbitrary size under the Turtle 4  syntax.