dataesr/scientific-paragraphs-categorization huggingface dataset:
The large differences are mostly due to different estimates of the emissions from transportation between the global inventories, more particularly CEDS and EDGAR. It should be noted that the two regional inventories, MEIC and REASv3 show CO emissions closer to the EDGAR emissions in the 2000s and closer than the CEDS emissions in the mid-2010s. NOx emissions show similar patterns among the global and regional datasets, except for MACCity, which was developed before the Chinese plans for emissions reductions. In 2018, the NOx emissions from all the inventories are within 15% of each other. NMVOCs emissions agree rather well in the 2010s, except for the old MACCity dataset and the ECLIPSEv6 inventory, which is also based on future scenarios. For SO2, the agreement is rather good at the beginning of the 2000s, but the trends in the emission differ after 2013-2014: the EDGAR v5 and v6 emissions show smaller trends than the other datasets.
The emissions provided by CAMS-GLOB-ANT_v5. 3 first follow the EDGAR emissions: as the recent trends are based on CEDS, the trend in the CAMS emissions is closer to the trend shown by the regional emissions. As a result, the values of the CAMS-GLOB-ANT SO2 emission in 2015-2019 are between the EDGAR and the emissions provided by the other datasets. Table  6  shows the changes in the emissions of several species for the same years as considered in the previous tables. This table shows the large increases in the emissions of all species in this country from 2000 to 2012. After that date, some measures have started to be been taken to start reducing intense pollution events, as part of the Indian National Clean Air Programme  (Ganguly et al. , 2020). Most emissions have continued to increase but at a slower rate than before 2012.
SO2 emissions are driven by the emissions related to power generation and industries, which have more than doubled between 2000 and 2014, followed by rather constant emissions. For BC, emissions from industrial activities and the residential sector represent 40% and 36% of the emissions, respectively. The emissions from the industrial sector decreased by about 20% from 2014 to 2023, which explains the slight decrease of the BC emissions during the 2012-2023 period. Figure  13  shows a comparison of the emissions of CO, NOx, NMVOCs and SO2 with the global inventories mentioned in the previous sections and with the REASv3 regional inventory for Asia. The MACCity emissions, based on an old dataset  (Granier et al. , 2011)  and the RCP8. 5 scenario  (Riahi et al. , 2011)  provides very different emissions than the other datasets.
The other inventories are generally in a good agreement, thought the recent trends in the CAMS-GLOB-ANT emissions can differ significantly: for example, the NOx emissions are rather constant after 2015, while they increase in the ECLIPSE dataset (based on a future scenario). For NMVOCs, the CAMS-GLOB-ANT emissions are rather stable after 2015, which is consistent with the HTAPv3 and EDGARv6: however, in 2015, CAMS-GLOB-ANT emissions are about 15% lower than the HTAPv3 emissions and 20% higher than the EDGARv6 emissions. For SO2, the CAMS-GLOB-ANT emissions are in good agreement with the HTAPv3, EDGARv5 and EDGARv6 emissions, and significantly higher than the CEDS emissions. For all species, the CAMS-GLOB-ANT emissions are globally in agreement with the REAS regional inventory.
Availability of the data The gridded maps (CAMS-GLOB-ANT_v5. 3) as monthly means data described in this manuscript can be accessed as NetCDF (Network Common Data Format) files at https://doi. org/10. 24380/eets-qd81  (Soulie et al. , 2023). They can also be accessed through the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system at https://permalink. aeris-data. fr/CAMS-GLOB-ANT and with a login account (https://eccad. aeris-data. fr/, last access: July 2023). For review purposes, ECCAD has set up an anonymous repository where subsets of CAMS-GLOB-ANT_v5. 3 data can be accessed directly  (Soulie et al. , 2023 ; https://eccad. aeris-data. fr/essd-surf-emis-cams-ant/, last access: July 2023). The CAMS-GLOB-ANT dataset is licensed under the Creative Commons Attribution 4.
Conclusions The paper describes version 5 of CAMS-GLOB-ANT, a new inventory publicly available for global air quality and climate forecasts and reanalyses. The emission dataset is based on different datasets, i. e. the global inventories EDGAR and CEDS, as well as on temporal profiles from the CAMS-GLOB-TEMPO, ship emissions from CAMS-GLOB-SHIP datasets. A dataset called CAMS-GLOB-AIR provides also 3- CAMS-GLOB-ANT includes the emissions of 36 species, including the greenhouse gases CO2 (excluding short cycle and short-cycle), CH4 and N2O, atmospheric pollutants (CO, NOx, NMVOCs, SO2, BC, OC, NH3 plus 25 speciated hydrocarbons). The spatial resolution of the gridded emissions is 0. 1x0. 1 degree in latitude and longitude, and the temporal resolution is monthly. The paper discussed the changes in the emissions for the main greenhouse gases and air pollutants, with a focus on CO, NOx, NMVOCs and SO2.
Comparisons are made with the other global inventories publicly available and with regional emissions datasets available for Europe, the USA, China and India. It should be noted that updated versions of the CAMS-GLOB-ANT dataset will be developed during the coming years. The current limitations of the inventory will be considered, such as the constant NMVOCs speciation after 2012 (following the EDGAR VOCs speciation), the lack of data for the extrapolation of the agriculture waste factor, and possibly the inclusion of the CONFORM adjustment factors for the Covid-19 lockdowns directly into the emission dataset.
https://doi. org/10. 5194/essd-2023-306 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. CEDS, the Community Emissions Data System (https://www. pnnl. gov/projects/ceds, last access: July 2023) is developed at the Joint Global Research Institute in Maryland, USA. CEDS emissions are developed within an open-source framework that produces annual emission estimates for research and analysis. The methodology used to derive the global emissions is detailed in Hoesly et al. (2018) and in McDuffie et al. (2017). We have used the version of CEDS from O'Rourke et al. (2021a) which provides global emissions at a 0. 5x0. 5 degree on a monthly basis for the following species: SO2, NOx, BC, OC, NH3, NMVOCs, CO, CO2, CH4, N2O. This dataset is available at: https://doi. org/10.
25584/PNNLDataHub/1779095 (last access, July 2023) We have used the country totals provided by the version of CEDS released in April 2021. The country emissions are given for 57 sectors from 1750 to 2019 (O'Rourke et al. , 2021b) are available at: https://doi. org/10. 5281/zenodo. 4737769 (last access, July 2023). The sectors included in the CEDS emissions by country are given in Table.
https://doi. org/10. 5194/essd-2023-306 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. Aircraft emissions are based on the CEDS aircraft emission data for 2000-2014, as described in Hoesly et al. (2018). For the years up to 2014, the emissions are taken from CEDS, which provides aircraft emissions for CO2, CH4, N2O, CO, NMVOCs, BC, OC, NH3 and SO2. After 2014, a linear extrapolation using the trends calculated for the period 2012-2014 is applied to obtain the emissions for the most recent years. These dates were chosen because the trends are stable after 2011, except for the year 2020, as indicated in Section 4. The resulting dataset is called CAMS-GLOB-AIR: the current version of CAMS-GLOB-AIR is version 2. 1, and it provides the emissions from aircraft from 2000 to 2023, for 25 levels.
fight the Covid-19 pandemic. Another dataset, called CONFORM (Covid-19 adjustmeNt Factors fOR eMissions) described in Doumbia et al. (2021) provides adjustment factors at the same spatial resolution as the CAMS-GLOB-ANT emissions. The CONFORM adjustment factors are available for several sectors compatible with the CAMS-GLOB-ANT emissions. The CONFORM factors have not been implemented in the CAMS-GLOB-ANT dataset, in order to allow modelers to perform sensitivity studies, such as studies of the impact of the Covid-19 lockdowns on the global atmospheric composition.
available and the following global inventories: EDGAR version 5, EDGAR version 6 (published after the development of CAMS-GLOB-ANT version 5. 3) and CEDS described in Section 2 and HTAP version 3 (Crippa et al. , 2023 and https://edgar. jrc. ec. europa. eu/dataset_htap_v3, last access: July 2023). EDGAR and CEDS calculate the emissions on the basis of activity data and emission factors, while the HTAP dataset is based on a mosaic https://doi. org/10. 5194/essd-2023-306 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. approach, and uses emissions reported by countries and regions when available. ECLIPSE version 6 (https://previous. iiasa. ac. at/web/home/research/researchPrograms/air/ECLIPSEv6b. html, last access: July 2023) is based on future scenarios after the year 2000.
Comparisons were performed with the global datasets, together with the officially reported emissions by the United State Environmental Protection Agency (EPA). The air pollution emissions trends data from https://doi. org/10. 5194/essd-2023-306 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. 1970 to 2021 provided by EPA at https://www. epa. gov/air-emissions-inventories/air-pollutant-emissionstrends-data (last access: July 2023) were used. A rather good agreement is seen for the CO and NOx emissions in the USA, except for the EDGARv6 emissions of CO, which are significantly lower: this is due to the use of smaller emission factors for road transportation and lower small scale combustion of biofuels in the EDGAR6 dataset.
https://doi. org/10. 5194/essd-2023-306 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. Comparisons were performed with the global datasets, as well as with two regional inventories for China: the REAS (Regional Emission inventory in Asia) version 3 published byKurokawa et al. (2020)  were used, downloaded from the REAS website (https://www. nies. go. jp/REAS/, last access July 2023), as well as the MEIC (Multi-resolution Emission Inventory model for Climate and air pollution research: meicmodel. org. cn, last access July, 2023). REAS and MEIC provide emissions for a large number of species (except for CH4) for 1950-2015 (REASv3) and 2008-2017 (version 1. 3) for MEIC, respectively.
Data analysis We preprocessed the raw data generated by the CI-Orbitrap using a new software called Orbitool (Orbitool version 1. 40) developed specifically for the data-analysis of time series data from Orbitrap  (Cai et al. , 2021). We denoised, mass calibrated, and time-averaged the data into 30-minute steps using Orbitool. We then identified peaks corresponding to HOM found in the atmosphere  (Ehn et al. , 2012)  and exported the time series of these signals for further analysis. In this work, the term "HOM" includes all compounds with compositions C 9-10 H 14-16 O ≥7 and C 5 H 6 O 7 for HOM monomers, and C 19-20 H 28-32 O ≥11 for HOM dimers. Noteworthy, other compositions can be and are classified as HOM  (Bianchi et al. , 2019) , but in this study we aimed to keep the data set more compact for ease of analysis by only inspecting the most prominent and atmospherically relevant signals.
We preprocessed the PTR-ToF and Vocus-PTR raw data using tofTools, a MATLAB based software package  (Junninen et al. , 2010). Additionally, the PTR instruments were calibrated for sensitivity, and we used that information to get the precursor concentrations from the ion signal. Because the Orbitrap's sensitivity as a function of signal intensity is nonlinear, we determined our instrument's sensitivity behaviour by analysing the α-pinene oxidation products and their natural isotopes following the method of  Riva et al. (2020). We excluded all isotopes that contained deuterium, because using selectively deuterated precursors distorts these signals. We created a correction function based on our instrument's sensitivity behaviour, and used it to correct the time series data for the nonlinearity.
Additionally,  Riva et al. (2020)  found that the relative ion transmission of the Orbitrap mass spectrometer is dependent on the mass of the ion. As we did not separately determine the ion transmission of our instrument during these measurements, we corrected our data assuming the same transmission curve as  Riva et al. (2020)  determined for theirs. This assumption brings some uncertainty, however, as we are mainly focused on comparing adjacent ions in the mass spectrum, the transmission is unlikely to make a large difference for the purpose of this study. In contrast, in order to assess the impact of deuteration on the total HOM formation, we did also estimate total HOM yields which could have considerable errors if an erroneous correction is https://doi. org/10. 5194/egusphere-2022-1131 Preprint.
Introduction: Cardiotocography, which consists in monitoring the fetal heart rate as well as uterine activity, is widely used in clinical practice to assess fetal wellbeing during labor and delivery in order to detect fetal hypoxia and intervene before permanent damage to the fetus. We present DeepCTG ® 1. 0, a model able to predict fetal acidosis from the cardiotocography signals. Materials and methods: DeepCTG ® 1. 0 is based on a logistic regression model fed with four features extracted from the last available 30 min segment of cardiotocography signals: the minimum and maximum values of the fetal heart rate baseline, and the area covered by accelerations and decelerations. Those four features have been selected among a larger set of 25 features. The model has been trained and evaluated on three datasets: the open CTU-UHB dataset, the SPaM dataset and a dataset built in hospital Beaujon (Clichy, France).
Results: The AUC of the model is 0. 74 on the CTU-UHB and Beaujon datasets, and between 0. 77 and 0. 87 on the SPaM dataset. It achieves a much lower false positive rate (12% vs. 25%) than the most frequent annotation among the nine obstetricians for the same sensitivity (45%). The performance of the model is slightly lower on the cesarean cases only (AUC = 0. 74 vs. 0. 76) and feeding the model with shorter CTG segments leads to a significant decrease in its performance (AUC = 0. 68 with 10 min segments). Discussion: Although being relatively simple, DeepCTG ® 1. 0 reaches a good performance: it compares very favorably to clinical practice and performs slightly better than other published models based on similar approaches. It has the important characteristic of being interpretable, as the four features it is based on are known and understood by practitioners.
The SisPorto system, developed by Ayres-de-Campos et al. , consists of a quantitative adaptation of FIGO's guidelines. The last iteration of the system, SisPorto 4. 0, has been released in 2015 (14). Georgieva et al. published in 2017 the OxSys system  (15) , based on the decelerative capacity, an average measure of the downward movements in the FHR signal. More recently, the construction of large clinical databases of CTG signals with corresponding clinical information and fetal outcomes  (16, 17)   (22, 23). In the present study, we introduce DeepCTG ® 1. 0, a model able to predict fetal acidosis during labor and delivery using CTG signals and evaluated on several clinical datasets.
Signals preprocessing and filtering The CTG signals fed to the model correspond to the latest available data before delivery. The signals are available on a 4 Hz frequency. There are a significant proportion of missing points in the signals, equal to 0 or -1 in the raw signals (between 5% and 20% depending on the datasets), and they were preprocessed using the following approach: -Missing segments of data lasting less than 10 min are interpolated using linear interpolation. Missing segments lasting more than 10 min are not filled. -For every case, the latest 30 min segment without any missing data (after interpolation) is selected. If such a segment does not exist, or if it starts more than 90 min before delivery, the case is discarded. This led to the discarding of 10 cases over the three datasets (including 1 pathological case), representing less than 0.
7% of the total number of cases. Some existing studies perform missing data imputation using linear interpolation  (21, 26) , and some other use more advanced interpolation methods like spline interpolation  (22, 27)  or autoregressive models  (28). We have implemented and tested a more advanced gap imputation technique based on cubic Hermite spline interpolation and used in another paper  (22) , using the python function scipy. interpolate. CubicHermiteSpline (https://docs. scipy. org/doc/scipy/reference/generated/scipy. interpolate. CubicHermiteSpline. html), but it did not improve the performance of the model. We have not found other studies that limit the maximum duration to fill the data gaps: choosing 10 min helped to limit the size of the interpolated intervals while discarding a limited number of cases.
Features based on FHR baseline Three features are computed from the FHR baseline: the minimum, median and maximum values computed on the 30 min segment fed to the model. The FHR baseline is defined as the mean of the signal after accelerations and decelerations have been excluded. This creates a circular definition as accelerations and decelerations are defined as periods when the signal is consistently above or below the baseline. Because of this circular definition and of the high variability of some recordings which makes hard to distinguish between a change in the baseline and an acceleration or a deceleration, designing a methodology to automatically compute the FHR baseline from the CTG signal is a complex and illdefined problem. To overcome this issue, we calibrate our methodology for baseline estimation on the baselines, accelerations and decelerations annotated by a consensus of obstetricians available in the FHRMA dataset.
Houzé de l'Aulnoit et al. have implemented and compared 11 published baseline estimation methods with baselines annotated by a consensus of four obstetricians in the FHRMA dataset  (29). The same team has developed a methodology based on a weighted median filter and has shown that it outperforms other existing approaches on the FHRMA dataset  (30). We compute the FHR baseline with a similar weighted median filter relying on the following methodology: -The FHR signal is supposed at any time to be in one of two states: a non-stable state, corresponding to accelerations and decelerations, and a stable state corresponding to the remaining time periods. -The probability p stab (t) for the signal to be in a stable state at time t is modeled using a prediction model trained on the annotations available in the FHRMA dataset.
Estimation of the probability of stability We note FHR aÀb the bandpass filtered FHR signal with cut-off frequencies a and b expressed in beats per minute (bpm), which corresponds to the FHR signal where only frequencies within a certain range are kept. Computing FHR aÀb with different ranges of frequencies enables to isolate specific features observed at different frequencies in the FHR signal. We note d(X) the first derivative of a signal X, estimated by a simple finite differences algorithm, and envelope(X) the analytical envelope of signal X, which captures the slowly varying features of a signal X. In practice, FHR aÀb is computed with a Butterworth sixth-order filter using the python function scipy. signal. butter (https://docs. scipy. org/doc/scipy/reference/generated/scipy. signal.
butter. html), and envelope(X) is computed using the python function scipy. signal. hilbert (https://docs. scipy. org/doc/scipy/reference/ generated/scipy. signal. hilbert. html). The probability of stability p stab (t) is estimated using a logistic regression fed with the eight following variables: jd(FHR 0 bpmÀ0:1 bpm )j, jd(FHR 0:1 bpmÀ1 bpm )j, jd(FHR 1 bpmÀ3 bpm )j, jd(FHR 3 bpmÀ7 bpm )j jenvelope(d(FHR 0 bpmÀ0:1 bpm ))j, jenvelope(d(FHR 0:1 bpmÀ1 bpm ))j, jenvelope(d(FHR 1 bpmÀ3 bpm ))j, jenvelope(d(FHR 3 bpmÀ7 bpm ))j Smoothing the signal with several intervals of frequencies (0-0. 1 bpm, 0. 1-1 bpm, 1-3 bpm, 3-7 bpm) enables to model the signal variability at a large range of frequencies and to adapt to the diversity of accelerations and decelerations observed in CTG signals. Those ranges of frequencies capture the observed variability of accelerations and decelerations, and we use four ranges of frequencies only to limit the number of variables fed to the logistic regression model estimating p stab (t).
The absolute value of the first derivative of FHR aÀb is used because points with a high derivative are likely to correspond to the slope of an acceleration or deceleration. However, using the first derivative only is not enough because it does not enable to distinguish between the baseline and the trough of an acceleration or deceleration. That is why we use the envelope of the derivative as well, that has a high value throughout the acceleration or deceleration. More details on the motivations for using those variables to model the stability of the signal are given in Boudet et al. (30). The 66 recordings in the FHRMA dataset have been randomly separated into two datasets: a training dataset with 40 recordings used to train the logistic regression, and a test dataset with 26 recordings to evaluate its accuracy.
Training and evaluation of the model The model is trained to predict a binary outcome corresponding to fetal hypoxia. The default outcome we use is pH < 7. 05. The model is fed with a subset of the 25 features extracted from the CTG signals: several subsets of features are considered. It is trained using the python package scikit-learn  (33). The cases are weighted to give the same total weight to cases with normal and pathological outcomes. This is required because the CTU-UHB and SPaM datasets are highly imbalanced and contain much more normal cases than pathological ones. The main evaluation metric used is the area under the receiver operating characteristic curve (AUC), which is used in almost all papers evaluating fetal hypoxia prediction models. For every dataset, we estimate the performance of the model when it is trained on the other datasets, to ensure that the evaluation is representative of the performance that could be reached when using the model in a new center.
On the CTU-UHB dataset, the performance of the model is compared with the nine expert obstetricians' annotations and with other published models, although this comparison is hard because other models are often evaluated on a different set of cases or with other outcomes. Also, we evaluate the impact of two key parameters on the performance of the model: the inclusion of cesareans in the dataset and the length of the CTG segment used to compute the features fed to the model. Those evaluations are performed on all datasets using cross-validation with five folds.
Missing data imputation method We have evaluated the performance of the model with two different methodologies for missing data imputation: linear interpolation and cubic Hermite spline interpolation. This evaluation is performed on all datasets using a k-fold crossvalidation strategy with five folds. Both models achieve very similar results (AUC = 0. 760 with linear interpolation and AUC = 0. 756 with cubic Hermite spline interpolation). This result is consistent with Asfaw et al. (34) , which evaluates several data imputation techniques and concludes that using more advanced techniques than linear interpolation does not lead to a statistically significant improvement in the accuracy of the model. We have also evaluated how decreasing the maximum duration to fill the data gaps from 10 min to 1 min impacted the performance of the model.