dataesr/scientific-paragraphs-categorization huggingface dataset:
The forest fire took place also in Zhytomyr region at northwest of Kyiv city and in ChEZ till the end of April 2020. The local AirVisual network in situ observations, established by authors in Kyiv city, and other measurement techniques used are described in Section 2. The PM concentration measured by Kyiv AirVisual network, the AERONET AOD data comparison with in situ PM2. 5 concentration measurements, as well as PM2. 5 concentration intercomparison with HORIBA sensors data, discussed in Section 3. Aerosol columnar microphysical and optical properties considered in Section 4 followed by discussion in Section 5 and conclusions in Section 6.
Materials and Methods In this work, we use the set of data for the aerosol contamination event study in March-April 2020 from five sensors of the AirVisual network, the PM10 and PM2. 5 APDA-371 HORIBA standardized analyzer. The ground-based measurements of the aerosol optical depth (AOD) and other aerosol parameters with the AERONET Kyiv site sun-photometer was used for analysis as the powerful tool for investigations  [30, 31]. The HYSPLIT back trajectories algorithm has been applied to derive the transport of aerosol particles. Zoom Earth maps on-line instrument  [32]  and the satellite remote sensing measurements by MODIS and VIIRS instruments provided fire maps with large coverage area was used as well  [33].
AirVisual data The five stations of the AirVisual network, used in the study, have been established by authors at the beginning of 2020. The stations are distributed in Kyiv city in the way to cover the most part of city regions. The AirVisual network stations are distributed all over Kyiv with purpose to cover as much of the city territory as possible. These AirVisual stations are Artema Street, Golosiiv, Nauky Avenue, Trostyanetska, Chornobylska Street and APDA-371 HORIBA Popudrenka station. The Golosiiv site co-located with Kyiv sun photometer AERONET/PHOTONS site. The main sensor of the network is AirVisual Pro smart air quality monitor with the advanced laser technology that provides highly accurate readings of fine particles PM2. 5 with 2. 5 μm aerodynamic diameter down to 0. 3 μm  [34].
For the purposes of the paper, we use the data of Kyiv AERONET site, which continuously operates since 2008 at south-west part of Kyiv city  [25, 39]. The Kyiv AERONET site is located on the roof of the building where the Golosiiv Air Visual sensor installed. The site operates in Golosiiv forest (50. 3636N, 30. 4967E), in 10 km southward from the city center. For aerosol parameters analysis in this paper, we use the aerosol Version 3 direct sun algorithm (DSA) level 1. 5 parameters from the AERONET database: daily weighted averages of AOD at 500 nm, and the Angstrom exponent values computed using the 440-870 nm spectral channels. In addition, the particles columnar size distribution (SD), single-scattering albedo (SSA), and complex refractive index (RI) are analyzed in the paper.
Satellite data on fire locations Moderate Resolution Imaging Spectroradiometer (MODIS) is an instrument aboard the Terra (known as EOS AM-1) and Aqua (known as EOS PM-1) satellites. Terra and Aqua MODIS instruments are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths. The infrared channels provide observations of thermal anomalies/active fire. The observations are available in near real-time and for almost global coverage. The fire detection by MODIS based on absolute detection of a fire when the fire strength is enough to detect, and on detection relative to its background taking into account the variability of surface temperature and reflection of sunlight. The Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instrument collects global observations in the visible and infrared wavelengths across land, ocean, and atmosphere.
This model was developed by the National Oceanic and Atmospheric Administration's (NOAA) Air Resources Laboratory (ARL)  [36, 37]. HYSPLIT uses a large variety of meteorological data, which are results of observations and calculations by numerical meteorological models. The following meteorological parameters are employed for computing the trajectory: horizontal components of wind speed, temperature, pressure on the altitude of the trajectory and surface pressure. Most of the meteorological data include field of vertical motion. Due to the results of detailed analysis  [38]  the uncertainty of calculated trajectories is ~20% in the horizontal direction for the time periods of above 24 h in the free troposphere, and after 120 hours the uncertainty is approxi-mately~400 km in the horizontal and about 1300 m in vertical planes.
AirVisual in situ measurements Using four AirVisual sites observations, the PM2. 5 data in the atmosphere over Kyiv city have been collected and analyzed during the March -April 2020 period. In March four maxima of PM2. 5 were observed. The PM2/5 concentration increased up to 80 μg m-3 approximately, which is three times higher than the threshold of PM2. 5 pollution according to European standards of 25 μg m -3 (Figure  2a ). The largest pollution was observed at the Nauky Avenue site in the first half of March, and at Artema Street and Trostyanetska sites in the second half of March 2020 (Figure  2a ). On April, a significant increase of PM2. 5 aerosol contamination to 40-70 μg m -3 was registered in the first half of April with maximum detected at the Trostyanetska site. The extreme contamination with the PM2.
Discussion Changes of aerosol columnar properties during air pollution over Kyiv and surrounding areas were estimated using data of the AERONET Kyiv site. Variations in the main parameter of aerosol contamination AOD(500nm), which is proportional to the aerosol content in the atmosphere column, were discussed in Section 3. 2. Here we consider other characteristics that allow estimating the size of aerosol particles and making assumptions about their nature: Angström Exponent (AE), dependence on AOD and particles volume size distribution averaged on the atmosphere column.
March 29 April 17 (a) (b) Observations with sun-photometer at Kyiv site during the events of the forest fires in March and April of 2020 qualified to determine the aerosol size distribution were sparse because clouds. There were 20 successful blue-sky sun-photometer observations with appropriate quality data to determine the aerosol size distribution in the period March 28 -April 19, 2020. The data showed that particles size distribution was very variable (Figure  6 ). During 29 March, the coarse mode particles predominated over the fine mode only. In most of the other cases, the fine mode was dominant.
These data allowed us to estimate variations in the size of aerosol particles and to make assumptions about their nature according to the Angstrom AE parameter and its dependence on AOD at different wavelengths. In the nearest future, we plan to expand the network of AirVisual stations to obtain detailed information in other areas of Kyiv and to inform Kyiv residents about the state of air pollution. It also is planned to assess the risks of aerosol pollution on the health of Kyiv residents based on measurements in the AirVisual network and other existing tools. The discussed case of enormous PM2. 5 aerosol contamination in Kyiv city emerges the needs to accept strong measures for forest fire control and prevention in Kyiv region especially at north-west area where radioactive contamination from Chornobyl disaster is still significant and impacted on ecosystem  [56].
Data collection In 2020, three sampling visits were conducted from August to  December. During the first visit from 24th to 29th August, we visited 9 municipalities; the second survey from 07th to 12th September was carried out in 9 municipalities while we visited 7 municipalities during the last survey from 14th to 21st December. A total of 25 municipalities were visited, where questionnaires were administered to 1250 households (839 and 411 responded by male and women respectively) as showed in Fig. 1. Farmers were selected according to their experience in tomato, African eggplant and pepper production, and their farms were georeferenced with a GPS. To maximize chances of finding predatory mites, only plots that were already harvested were sampled  [5, 7]. A total of 10 plants were sampled per crop species (tomato, African eggplant and pepper) per plot.
Data analysis The geostatistical software ArcMap version 10. 1 was use to illustrate the distribution of pests and predatory mites. The frequency and abundance of pests and their associated predators were calculated using data from mite counts. Analysis of variance followed by Bonferroni test was used to discriminate the crops according to the losses in production estimated by the farmers. Analysis of variance was done using the software R version 3. 4. 2 (R Core Team 2016) to compare means at 5% level.
Acknowledgements The authors are grateful to  Institut National des Recherches Agricoles du Bénin (INRAB)  through the project " Programme National de la Recherche Agricole " (PNRA) and the  International Institute of Tropical Agriculture of Benin (IITA-Benin)  that provided financial and technical support for this research. They are also grateful to  Drs OSSENI Abdel-Aziz  and  Elysée HOUEDJOFONON ,  AKPLA Evrard ,  Richard HOUNDA-FOCHE  for their technical assistance.
Acknowledgements: This work was initiated during the thematic trimester program Systems out of equilibrium at the  Institut Henri Poincaré. We are grateful to the  IHP  for hospitality and to  Sorbonne University  for support. D. B. acknowledges  Michel Bauer  and  Jean-Bernard Zuber  for regular discussions. M. M. thanks  Tony Jin  for initial collaboration on the project and, in particular, for his contributions to analysing the spectrum in the two-particle sector. This work was in part supported by  CNRS , by the  ENS , by the  ANR  project " ESQuisses ", contract number  ANR-20-CE47-0014-01  and by the  EPSRC  under grant  EP/S020527/1.
Data collection On admission and during hospitalization, treating physicians filled out the CoCo-Neurosciences standardized electronic data collection including demographic, baseline data, comorbidities, COVID-19 symptoms, new neurological and psychiatric manifestations, treatments, outcome, and paraclinical findings. Then a group of doctors including neurologists, a geriatrician, and a radiologist reviewed medical records, completed data as well as possible and obtained follow-up information about survivors after hospital discharge.
Statistical analysis Continuous variables are presented as median [interquartile range (IQR)], as they were non-normally distributed using the Shapiro-Wilk test. Categorical variables are presented as numbers (percentages). Missing data were not imputed and are described in Supporting Information S2. The Kaplan-Meier estimator was used to compute the survival curve. Cox proportional-hazards models were used to assess the potential factors of instantaneous risk of mortality (RoM) from COVID-19 infection. This analysis consisted of three steps: (  1 ) univariate analysis on all variables; (2) forward stepwise analysis including variables from univariate analysis with p <. 1 and less than 15% missing data in nonsurvivors. Among two paraclinical variables of the same category (white blood cell (WBC) count and WBC groups, neutrophil count and neutrophil groups, platelets and thrombocytopenia, serum creatinine, and glomerular filtration rate), we included the one with smaller p.
Limitations Our population does not represent a general geriatric population, but our findings will be useful for older patients seen in neurological, psychiatric or specific geriatric settings. Geriatricians and neurologists often encounter in real-life conditions older patients with both new symptoms and significant medical and neurological histories. Our findings therefore can be applied to a real-world setting and aid neurologists treating older patients and geriatricians who manage patients with neurological disease. The limited sample size and missing data did not allow for inclusion of all possible variables into a multivariate analysis. Data were retrospectively collected which can lead to bias.
We excluded patients under 70 years of age, admitted outside of the AP-HP hospitals, outpatients, and patients with missing age or gender data (Figure 1). All patients (or their relatives in case of incapacity) received written information about the research and consented to the use of their data. The study received approval from the Sorbonne University Ethics Committee (No. 2020 CER-202028) and is registered on the clinicaltrials. gov website (NCT04362930).
ACKNOWLEDGMENTS The authors thank the  CoCo-Neurosciences study group  for their participation in the data collection (the list of study group is given in supplementary information Acknowledgment). This work was supported by the  Fédération Internationale de l'Automobile (FIA) Foundation and donors of Paris Brain Institute -ICM  for the Cohort COVID-19  Neurosciences (CoCo-Neurosciences ), a grant from  Fondation de France  (no. • 00113315 ). The research leading to these results received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06.
This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries' cycling data are characterized by various single-or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity-peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries.
This work aims to extend the applicability of the main peak of the HCIC curve as an indicator of the discharge capacity to usage scenarios including multistep fast charging both in regression and in prediction. Even if linear models have been shown to be enough for representing the relationship between IC main peak features and the capacity, the use of high currents during fast charging will lead to more complex relationships between the capacity and potential indicators. This work focuses on proposing models capable of representing the IC curve main peak area and capacity relationship even when considering the non-linearity introduced by the regular use of high currents during charge. This is achieved by conducting a regression analysis over 89 batteries from a publicly available dataset shared by the Toyota Research Institute  [24].
The proposed models are characterized by their simplicity, high generalization capabilities and low errors when considering capacity prediction. The paper is organized as follows. In Section 2, we introduce the Toyota Research Institute fast charging dataset by describing the characteristics of the batteries and aging experiments. In Section 3, we present the procedure used to extract the high current IC curves and to extract the peak features. The models and initial inference analysis on individual batteries are presented in Section 4. Then, in Section 5, we evaluate the fitting results of the models on groups of batteries aged considering similar fast charging policies. In Section 6, we show the performance of the models in a prediction scenario using a crossvalidation process based on the split of batteries.
Toyota Fast Charging Dataset Description The dataset includes data for 135 lithium iron phosphate (LFP)/graphite battery cells cycled using profiles including fast-charging conditions  [24, 25]. The cells have a nominal capacity of 1100 mA h and a nominal voltage of 3. 3 V. Their upper and lower cutoff voltages are 3. 6 V and 2. 0 V, respectively. The batteries were cycled while placed in a forced convection temperature chamber set to 30 • C. An example of a typical cycle is shown in Figure  1  (the example refers to battery #36). The cycle includes the following phases: 1. Fast charge including one or more current steps (red and green steps in Figure  1 ); 2. Rest phase, lasting between 5 s and 5 min, depending on the cell; 3. 1C CC charging up to 3. 6 V, followed by a CV stage, ending when the current reaches the low current threshold of C/50 or C/20 depending on the battery; 4.
Time (s) Current (A) Policy Battery 1 Cycle 100 The dataset is divided into three "batches" of 46, 48, 46 sets of data each. In this work, we use the first two batches only, for a total of 89 batteries, because they include tests for which it is possible to obtain the main peak of the IC curves. The analyzed 89 batteries were cycled under 63 different fast-charging policies, with first-step currents from 1C to 8C and second-step currents from 3C to 6C. The batteries have a widely varying cycle life ranging from 148 to 2238 cycles. Current Step 1 CC-3. 6C 1C CC Charge CV Charge 4C Discharge Time (s) Voltage (V) Voltage Step 1 CC-3. 6C 1C CC Charge CV Charge 4C Discharge In order to analyze the aging trends in the dataset, it is worthwhile to group batteries characterized by similar cycling conditions.
Incremental Capacity Main Peak Area Extraction We evaluate the high-current IC for the 89 batteries belonging to the first two batches of the dataset according to the procedure described in detail by Ospina Agudelo et al. in  [13]. In particular, we extract the main peak features, namely its position, height and area, from the 1C CC charging stage. For the available dataset, such a stage always starts when the battery reaches 80% SoC. In practice, the initial SoC value may be selected according to the application, depending on the battery technology and usage patterns. As discussed in  [13] , by definition, the main peak area, PA, can be interpreted as a partial capacity related to the full capacity, meaning that it can be used as capacity indicator regardless of the initial SoC value as long as the employed value is kept constant between PA computations.
Similarly, for the filtering of the IC 0 data, the GWMA gave better performance than simpler alternatives, such as the moving average or SG filters. With a suitable tuning of the filter window, the GWMA filter reaches desirable smoothing levels with very low distortion of the IC curve features. The higher performance of this filter compared to other approaches is confirmed by Li et al. for low-current IC  [27]. The filters parameters have been adjusted for the dataset. The window of the SG filter is set equal to five samples, the window for the GWMA filter is 20 mV, and ∆V = 25 mV. The value of ∆V was selected empirically by aiming to maximize the correlation between PA and SoH while avoiding that the voltage range used for the area computation falls outside of the available voltage data points.
We compute the fitting coefficients for each battery in Matlab by using the built-in function fit. The function gives as output the fitting coefficients of the models, the square of the correlation coefficient R 2 , and the RMSE. In order to summarize the obtained results, we computed the mean and the standard deviation over all the batteries of the models coefficients, R 2 and RMSE. The aggregated results are presented in Table  2. The average R 2 is satisfactorily high for all the models, with the polynomial and power law models having the highest averages over all the batteries. The power law model is also characterized by the lowest average RMSE (less than 2. 5 mA h). Unfortunately, its coefficients deeply vary among batteries, as shown by the highest values of standard deviation for the fitting coefficients (overtaking 45 %).
Conclusions The Incremental Capacity (IC) analysis applied to the batteries in the Toyota fast charging dataset shows that the peak area PA of the IC curves is a viable indicator to estimate the 4C discharge capacity Q and state-of-health for batteries cycled with fast charging profiles. During a fitting analysis performed over individual batteries subject to multistep fast charging profiles during first life, we identified that batteries with maximum and average fast charging currents under 5 A showed a linear Q -PA, which is in agreement with previous results considering usage patterns without fast charging. When employing higher currents, both maximum and on average, the Q -PA relationship may exhibit non-linear tendencies, which can accurately be represented by a logarithmic model. Logarithmic representations were favored over other non-linear alternatives, as the fitting results obtained with the logarithmic models lead to lower standard deviations in the adjusted model coefficients.
Funding: The work was supported in part by funds of the projects " Holistic approach to EneRgyefficient smart nanOGRIDS-HEROGRIDS " ( PRIN 2017 2017WA5ZT3 ) within the  Italian MUR 2017 PRIN program ,  FARB funds of the University of Salerno , and by funds of a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-16-IDEX-0008 ).
While the charging risk encountered on low Earth orbit (LEO) high inclination orbits, which can span over a large range of altitudes, is reasonably well recognized by spacecraft designers, its assessment still relies on a fairly limited description of the environment, e. g. , in terms of statistical variability. The applicability of current engineering standards is solely relying on DMSP-based spectral characteristics. Therefore, considering this new dataset brings complementary information to current auroral environment descriptions. We show here that the spacecraft charging time profile closely follows the time profile of the electron average energy and the electron energy flux, thus opening the possibility to predict charging events from a model of discrete auroral precipitation. The ultimate aim of this kind of study would be, in the future, to be able to predict spacecraft charging from magnetic indices deduced from solar wind parameters obtained in the solar wind at the L1 location, index-dependent auroral precipitation characteristics, and the satellite orbital parameters.
The AMBER spectrometers provide particle spectra with a maximum of 128 energy steps. However, 32 or 16 energy steps and a time resolution of either 0. 5 or 2 s are the most common instrument modes. The 32 energy channels are shown in Table  I. The 16 channel resolution combines two energy channels at a time (channels 1 and 2, 3 and 4, 5 and 6, and so on). The raw data are given in counts [#], which is directly proportional to the energy flux, except during the charging event.
IV. STATISTICAL STUDY The detected events are used for statistical analyses of the event location (magnetic local time (MLT) and invariant latitude), duration (for ions and electrons), spacecraft charging level, and event detection level. First, we selected all time intervals when good quality measurements have been obtained. Note that telemetry restrictions lead to some missing parts of Jason-3 orbits. This means that more events are to be expected in flight but are not recorded due to lack of data and differential charging not observed by AMBRE. These data are further referred to as the general dataset. The characteristic of the spacecraft charging events dataset is then compared to the characteristics of the general dataset, in order to avoid a bias generated by time intervals where no data could be recorded.