dataesr/scientific-paragraphs-categorization huggingface dataset:
Unadjusted mortality rates We analysed counts of deaths in the routine data using a multivariable Poisson mixed effects regression model (log link function). A child's time at risk started at the later of programme enrolment and ART initiation, and either ended at death (if recorded before age  15)  or was censored at the earliest of turning 15 years old, being transferred to another programme, and last being seen alive, based on recorded clinic, laboratory and pharmacy visits. We excluded data beyond programme's centre-specific "database closure dates", from which we consider records incomplete. Covariates included were consistent with how CHIV are distinguished in Spectrum  [4]. These were time-fixed sex and CD4 at ART initiation, and time-varying time since ART initiation (<6 months, ≥6 months and <1 year, ≥1 year) and current age (<1, 1 to 2, 3 to 4, 5 to 9, 10 to 14 years).
To generate adjusted mortality estimates, we applied an approach similar to that used for adult on-ART mortality rate inputs for Spectrum  [20]  (see Web Appendix B in Appendix S1 for a graphic of our methods). To begin, for CHIV in the routine data who were considered LTFU by the treatment programmes, we simulated mortality outcomes over the sixmonth period after becoming LTFU, based on a model determined from the tracing study data (the "simulation model" below). Thereafter, the analysis proceeded similarly to the unadjusted analysis above. Consistent with the tracing study LTFU definitions, a child was considered LTFU (at the date last seen alive) if neither death nor transfer to another treatment programme were recorded, and the child did not access care within the six months preceding the centre database closure date.
Other researchers have cautioned against generalizing programmespecific results  [30, 33] ; and future updates of these mortality estimates would benefit from a larger dataset on outcomes in those LTFU, spanning additional years and regions. The large number of missing outcomes in the tracing study may have also introduced bias and highlights a remaining knowledge gap about those not successfully traced. Further studies within IeDEA are planned to determine outcomes either by tracing or linkage to death registries  [33] ; for example, the IeDEA central African region is initiating the first of its planned studies in Rwanda in 2021. We did not attempt to adjust mortality estimates for regions outside of Africa due to the absence of tracing study data for these regions. While associations between covariates and mortality were comparable in African and other regions, we are limited in our ability to directly compare regions' mortality rates.
Although the positions of the cross sections may be modified manually to improve the accuracy of local studies, this possibility was not considered here. A post-treatment of the simulated water longitudinal profiles enables the retrieval of the water surface elevations and the water heights on the grid of the DTM. This method has already been evaluated for flash flood forecasting purposes, showing an interesting capacity to represent the observed inundations  (Le Bihan et al. , 2017). The caRtino 1D version used here corresponds to an evolution (reprogramming in R) of the initial software. Since this approach enables a full resolution of SWE equations, in steady state condition in the presented applications, it accounts for backwater effects and longitudinal channel geometry variations within river reaches.
For this reason, well-documented case studies have been selected here, both in terms of peak discharges of the flood and observed inundation patterns. In particular, extensive sets of peak discharge estimates on ungauged river sections, gathered within the HyMeX program  (Ducrocq et al. , 2019) , are available for each selected event. These largely complement flood discharge data that are available for a generally limited number of stream gauging stations. Additionally, a comprehensive knowledge of the inundation characteristics is available for the selected flood events, thanks to a high number of high water marks (HWMs) and to field observations of the limits of the inundated areas. The HWM data were extracted from the French national HWM database (https://www. reperesdecrues. developpement-durable.
gouv. fr, last access: 28 May 2021). These data are systematically checked before incorporation in the database and, therefore, should not include large errors. However, errors up to 50 cm should be considered as common, considering the accuracy of topographic surveys (HMW location and elevation) and/or possible inappropriate choices of HWM locations (increase in water surface elevation in front of obstacles, capillary rise of moisture in walls, etc. ). Some larger errors may also remain for a very limited number of HWMs and may result locally in large estimated simulation errors. But all these error sources are common to the three methods and should not affect the comparison results. The detailed mapping of inundation ex-tents, available for the Argens 2010 and Aude 2018 events, was achieved by local authorities based on field surveys in the weeks following the floods.
Common input data and simulation workflow The main steps of the simulation workflow are presented in Fig. 1. The mapping approaches are all implemented on segments of the computation domains (river reaches), with the segmentation being based on the structure of the hydrographic networks (confluences). A 5 km 2 upstream catchment surface has been selected as a lower limit to define the river network (1 km 2 for the Alpes-Maritimes case study). An independent computation is conducted on each river reach. This principle of segmentation of the computation domains facilitates the computation over large areas (several hundreds kilometers of rivers in the case studies presented) and has the advantage of easily enabling parallel computing if necessary (not implemented here). For SWE hydraulic approaches, the computation is extended 1 km downstream each river reach to limit the influence of the downstream boundary condition on the results (normal flow depth).
, 2017) , which may help in representing discharge uncertainties by selecting multiple scenarios  (Leedal et al. , 2010). The definition of the best real-time computation strategy is even more complex in the case of flash floods because of their very fast evolution dynamics. The delay necessary to run and provide forecasts may indeed highly limit the capacity of emergency services to analyze forecasts and adapt their response strategies by referring to inundation scenarios they are prepared for. Finally, an optimal compromise probably has to be found in the case of flash floods between the accuracy of inundation forecasts and the rapidity of forecast delivery. Data availability. All simulation and evaluation results are integrated in the HyMeX database (https://doi. org/10. 6096/mistralshymex.
Acknowledgements. Peak discharge data were obtained as part of the  HyMeX research program  (http://hymex. org, last access:  28 May 2021 ), with financial support from the  MISTRALS program  of the  CNRS  and the  Ministry of Ecological and Solidarity Transition (DGPR/SCHAPI). Rainfall data were provided by  Météo France , and DTM data were obtained from IGN. Financial support. This research is part of the  PICS  research project (https://pics. ifsttar. fr,last access:  28 May 2021 ) and has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-17-CE03-0011 ). Review statement. This paper was edited by  Giuseppe Tito Aronica  and reviewed by  Paul Bates  and  Francesco Dottori.
Study design, enrollment, and patient selection Diversion-p64 ( ClinicalTrials. gov identification code: NCT02600364) was a single-arm, international, multicenter, prospective study to assess the safety and efficacy of the p64 FMD. Between December 2015 and January 2019 patients were enrolled at 26 centers from 10 different countries, including eight European countries, Argentina and Russia (online supplemental figure  3 ). Recruiting sites had to be familiar with the p64 FMD. All participating sites were required to perform five 'lead-in cases' with p64 FMD under the supervision of an experienced proctor prior to study participation. The eligibility criteria are listed in the online supplemental material. The per-protocol population contains all enrolled patients who met all eligibility criteria and had at least one p64 FMD implanted at the end of the intervention.
Statistical Analyses Data of in vivo study were statistically analyzed using the Student's t-test (one-way ANOVA) by Statistica software for Windows 7. 0 to determine significant differences between groups; p values less than 0. 05 were considered significant. The values were expressed as means ± SD. For in vitro study, statistical analyses were performed by one-way analysis of variance (ANOVA) combined with Tukey's post-test to compare all treatments to the negative controls, using the GraphPad Prism Software (version 5 for Windows, GraphPad Software Inc. , San Diego, CA, USA). p < 0. 05 was considered significant.
Such technique also facilitates the checking for nonlinearity between variables, excluding any kind of classical time-domain approach (i. e. , dataset splitting, polynomial test, use of dummy variables, threshold tests, etc. ). By following historical US economic events, the method also allows to 'extract' deep information at the level of sub-periods of time. It indicates whether the origins of unemployment come from demand side (i. e. , unemployment is the result of declining aggregate demand reflected by output) or supply side (i. e. , unemployment is driven by the level of wages affecting the final output). Not at least, the turbulences are historically localized either through their cost implications (e. g. , oil crises) or via different shocks coming from inside the economy (e. g. , economic crises, terrorist attacks, civil wars, political system changes).
Second, unlike  Aguiar-Conraria et al. (2020) , in our paper, productivity is called upon to explain how Okun's effect evolves over both time and frequency under periods with different economic volatility. The approach is inspired from  Benigno, Ricci, and Surico (2015) ), who claim that the economic volatility influences the probability of adverse shocks which modify the level of wages affecting unemployment. The economic volatility is observed via the wavelet tool, both unemployment and output historically exhibiting periods with high and low volatility. Third, in comparison to  Aguiar-Conraria et al. (2020)  who cover the period 1949Q1-2018Q2, our analysis considers an extended updated sample (i. e. , 1948Q1-2020Q4) as well as a dataset with monthly frequency  (i. e. , 1948M1-2020M12). This allows us to see how the co-movement between unemployment and output evolves inside the month, checking also for robustness.
More precisely, the coefficient reduced from -1. 87 to -1. 37 after the year 1984, perfectly coinciding with the period of Great Moderation defined by  Dornbusch, Fischer, and Startz (2013)  as being the period from 1984 to 2007, characterized by low economic volatility. Finally, it is noteworthy to mention the contribution of  Grant (2018) , who dedicates his work to estimating the time-varying parameter of Okun's law based on the potential output and natural rate of unemployment. Grant (2018)  finds that the widely accepted Okun's coefficient of -2 has low probability to be validated during the Great Recession. The second strand of literature offers comparisons between countries regarding the magnitude of Okun's coefficient, the main results revealing a huge heterogeneity. For example,  Ball, Leigh, and Loungani (2017)  focus on the case of the US since 1948 in parallel with a dataset, and include 20 other advanced economies since 1980.
e. , inputs) affect the overall output  (Sickles & Zelenyuk, 2019). The variable links the GDP with productivity, this nexus having complex implications during the periods with different economic volatilities  (Benigno et al. , 2015). For unemployment rate, GDP and participation rate, the source of data is Federal Reserve Economic (FRED) Data, Federal Reserve Bank of St. Louis, online database (2021). The productivity index is taken from US Bureau of Labor Statistics (BLS), online database (2021). Following  Okun's (1962)  approach, the HP filter  (Hodrick & Prescott, 1997 ) is used to extract the cyclical components of the series, ground of estimations. The variables are treated as percentages in the case of unemployment rate, productivity index and participation rate, while natural GDP appears in its natural logarithm form.
Conversely, the series are in antiphase for phase difference or -. Herein, for x,y ∈ -, -2 , x leads y, while y leads x when x,y ∈ 2 , , respectively. All methods are computed in Matlab software, based on the code offered by  Grinsted et al. (2004) , with corrections of  Ng and Chan (2012). In this case, the power range goes from blue color (low power) to yellow color (high power), while the phase difference between the two series are indicated through arrows. More precisely, the variables are in phase when the arrows are oriented to the right (positively related), having a cyclical effect. The variables are out of phase when the arrows are pointed to the left (negatively related), registering an anti-cyclical effect.
Robustness check The robustness check follows: (1) an alternative dataset constructed by capturing the cyclical components of both time-series as' year-on-year' quarterly growth rate; (2) an alternative timefrequency tool proposed by  Rua (2010) , namely the wavelet (1) The thick black contour shows the 5% significance level, while the COI is depicted as a lighted shadow. The power ranges goes from blue color (low power) to yellow color (high power); (2) The unemployment is leading when the arrows are oriented to the right and up, while GDP is leading when the arrows are oriented to the right and down. The GDP leading when the arrows are oriented to the left and up, while the unemployment is leading when the arrows are pointed to the left and down. (3) The variables are in phase when the arrows are oriented to the right, being out of phase when the arrows are pointed to the left (negatively related).
cohesion (WC); and (3) a scenario with monthly frequency by routinizing all considered cross-wavelet tools. 1) The alternative dataset is constructed in order to remove the trend component of both time-series by considering their' yearon-year' quarterly growth rate. Related WTC plot is shown in Fig. 4. The plot shows that the WTC constructed based on' year-onyear' quarterly growth rates reveals almost the same results as in the case of WTC with the HP dataset. Up to 4 quarters band of scale, all four episodes, are clearly revealed at high frequency: 1958-1963, with both expansion and recession sequences; oil shocks from 1970; recession of 2000 s; and pandemic disease from 2019 to 2020, with limitation given by the COI's 'edge effects'. Further, at 5-39 quarters band of scale, the arrows are oriented to the left and up, validating the idea of cyclical unemployment as the output drives unemployment.
The WC spectra generated based on both the HP filter and' yearon-year' quarterly growth rate series are plotted in Figs. 5  and 6. Both plots are almost identical, offering similar findings when compared with the WTC ones. Okun's effect is clearly evidenced especially over medium and high frequency, reinforcing the results of WTC's plots. Therein, the color is blue, indicating that the unemployment and output exhibits an anti-cyclical co-movement. Unfortunately, no information about direction of co-movement is offered by the WC tool. At high frequency, up to 4 quarters band of scale, the blue color is also present but alternates in intensity, validating the idiosyncratic characteristic of co-movement in the short-term. (3) The monthly dataset includes the monthly unemployment rate communicated by FRED (2021), while the GDP is estimated by linearly converting to monthly frequency the official quarterly GDP offered by FRED (2021).
7-10. The plots indicate that the results remain robust to dataset frequency switch, the Okun's effect with monthly dataset being  clearly reinforced especially over both medium-and long-terms. As in the previous cases, the findings exhibit an idiosyncratic comovement in the short-term. According to  Hamilton (2018) , the HP filter can induce spurious dynamic differences between the filtered values in the middle with respect to those at the end of sample. In order to check if this issue alters the monthly result, a set of WTC and WC plots is employed by dropping the year 2020, as Figs. A5-A6 (Appendix A) show. As the plots show, no influence on co-movement is observed by removing the 2020 year, the estimations remaining robust for the rest of period.
(1) The thick black contour shows the 5% significance level, while the COI is depicted as a lighted shadow. The power ranges goes from blue color (low power) to yellow color (high power); (2) The unemployment is leading when the arrows are oriented to the right and up, while GDP is leading when the arrows are oriented to the right and down. The GDP leading when the arrows are oriented to the left and up, while the unemployment is leading when the arrows are pointed to the left and down. (3) The variables are in phase when the arrows are oriented to the right, being out of phase when the arrows are pointed to the left (negatively related). II. WC: The color code reveals the intensity of correlations, going from blue color (positive correlation) to yellow color (negative correlation).
An example of the thrust correction factor, αγ, is shown in Extended Data Fig. 3b. The propulsion system electronics continually perform these indirect thrust estimates during operation. Direct thrust measurements are obtained using a thrust balance (Methods). Figure  3b  shows the measured thrust range achievable, and a comparison between direct and indirect measurements. By carefully designing the grids (Methods, Extended Data Fig. 3a, d ), ions are well focused with a low divergence between 10° and 15°, as shown in Fig. 3c. The beam divergence has been measured with an automated array of electrostatic probes (Methods). The iodine divergence is slightly lower than that of xenon because of the improved ionization efficiency, which reduces unionized neutrals in the plume and lowers the ion-neutral collision frequency.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-021-04015-y. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Ion optics One of the key elements of a gridded ion thruster are the ion optics, which in our case consists of a two-grid assembly. Particle-in-cell (PIC) simulations (using the open-source code XOOPIC  44  ) have been used to model ion extraction and acceleration by the grids. Extended Data Fig. 3d  shows a PIC simulation of ion acceleration through a single set of grid holes. Ions are well focused through the holes with no direct impingement, and only low-energy ions generated by ion-neutral charge-exchange collisions and possible downstream electron-neutral ionization with unionized neutral gas in the plume, strike the second grid (called the accel grid), as is typical in gridded ion thrusters  5. For given grid dimensions, if the total accelerating voltage is too low, the space charge of the ions between the grids can lead to under-focusing and direct impingement on the upstream surface of the second grid.
Indirect thrust measurements. The integrated electronics in the propulsion system includes current and voltage measurement sensors that continually measure the applied accelerating voltage, and the current to both grids. For gridded ion thrusters, the ion-beam current that is extracted from the plasma source is balanced by an electron current to the first grid to maintain charge balance (Extended Data Fig. 2b ). This current, after subtracting off the small current from the accel grid, then matches the net electron current emitted by the thermionic cathode neutralizer. During ground testing, and operation in space, the grid current and voltage measurements allow estimates of the extracted beam current and thrust to be made in real time. Direct thrust measurements. Direct thrust measurements were performed with the propulsion system attached to a thrust balance placed inside the vacuum chamber.
Diagnostics for in-flight testing The propulsion system includes eight thin-film platinum temperature sensors for measurement of the temperature (with an accuracy of 0. 1 °C) at key locations, including all electronic subsystems, the propellant tank, and the interface flange between the tank and plasma source tube. The input current and voltage from the satellite, as well as output currents and voltages from different subsystems, such as the cathode neutralizer, grids and RF antenna, are continuously measured. The data acquisition frequency is set by the satellite onboard computer and is equal to 1 Hz.
b, The propulsion system installed in the Beihangkongshi-1 satellite before launch. Photograph reproduced and adapted by the authors with permission from Spacety. © 2020 Spacety Co. , Ltd. (Changsha). Extended Data Fig. 2  | Electrical system architecture and thruster electrical schematic. a, The main control unit interfaces with the satellite onboard computer and implements global control and safety algorithms. The RF generator supplies power to the RF antenna via a matching network to match the impedance of the plasma and generator for efficient power transfer. The cathode supply controls and monitors the electron-emitting cathode filament, the flow control unit manages the propellant tank and flow path heaters, the grid control unit manages the applied voltage to the acceleration grids, and the ignition unit controls the igniter needed for initial gas breakdown in the source tube to produce a plasma.
Acknowledgements We thank the  European Space Agency (ESA)  for supporting the in-orbit demonstration through the  TURBO  project,  ARTES C&G  programme, contract number  4000131883/20/NL/MM/can , the  French Government  ( Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation ) for supporting this work through the i-Lab 2017 grant (le Grand Prix I-LAB 2017 -19th  Concours Nationale d'Aide à la Création d'Entreprises des Technologies Innovantes) , and the  French National Space Agency  ( Centre National d'Etudes Spatiales ) for support of the project through the  INODIN grant ,  R&T action R-S19/PF-0002-108-92. We thank Spacety, and particularly  J. Zheng , for mission support during the in-orbit demonstration. We also thank the  Laboratoire de Physique des Plasmas at Ecole Polytechnique  for funding open access of our paper.