dataesr/scientific-paragraphs-categorization huggingface dataset:
The Total Flagging Impact on Data Availability Figure  7a  demonstrates that flagging affects the data availability of both datasets the most at northern high latitudes above 55 • N (i. e. , Greenland, North America, Scandinavia, and Russia), with mean flagging intensities of 80% and 68% for SMOS SM and SMAP SM, respectively, and in mountainous areas such as the Himalayas and the Rocky mountains. The high flagging intensities for the northern high latitudes are attributed to their permanent (i. e. , 100% flagging intensity for Greenland) and seasonal snow cover, frozen conditions, and land ice. However, the overall flagging impact clearly differs between the two datasets (Figure  7a ). For SMOS SM, more values are flagged in general and with a larger spatial variability. In addition, some spots in South America, in particular the Andes, the Congo basin in Africa, India, and Oceania are also marked by a high flagging percentage in the SMOS SM dataset.
As expected, the least amount of data is available for the Northern Hemisphere (NH) during the December-January-February (DJF) and March-April-May (MAM) seasons for both datasets and vice versa for the Southern Hemisphere (SH). For SMOS and SMAP SM, a local maximum can be detected for the northern mid latitudes in June-July-August (JJA), MAM, and September-October-November (SON) around 36-37 • N (36-45% and 5-17% for SMOS and SMAP respectively), corresponding to latitudinal orientation of the Himalayas. A steep south-to-north gradient in flagging intensity is also observed for both SMOS and SMAP on the NH, starting from a local minimum at 47-48 • N in JJA, at 40 • N in MAM and SON, and without local minima in DJF, consistent with seasonal latitudinal shifts of the snow/ice boundary. The maximum difference in flagging impact (~37%) is found for JJA around 63 • N.
This might be explained by a difference in sensitivity towards wet snow and would imply that SMOS SM flags more pixels for wet snow conditions than SMAP SM. In MAM, the flagging intensity of the two datasets differs the most (~33%) around the latitude where they both have the local maximum (i. e. , 37 • N). In SON and DJF, the maximum difference in flagging intensity (~32%) is detected around the Equator (~1 • S), which corresponds to the location of the ITCZ/tropical region that is characterized by heavy precipitation and dense vegetation. This difference is explained by relatively high data unavailability for SMOS SM and high data availability for SMAP SM. This result is noteworthy as SMOS SM does not even have critical flags for precipitation or dense vegetation, while SMAP SM's documentation states that it has critical flags for precipitation and dense vegetation (Table  2 ).
The New AMSR2-Based Flagging Method in Analyzing the Flagging Intensity The data availability based on the AMSR2-based flag (AMSR2flag) is compared to the cumulative critical snow/frozen flags of SMOS SM and SMAP SM (see Figure  8 ), which consist of respectively seven and two flags. Similar spatial patterns in snow/frozen flagging intensity (SFFI) are visible for all three datasets and all three datasets have snow/frozen flagging systems that flag the northern high latitudes and the Himalaya mountain area (Figure  8a ). Nonetheless, there are some considerable differences between the three datasets in the regions depicted by specific spatial patterns and the magnitude of SFFI impact. The Andes and the Kaukasus ranges are clearly masked for SMOS SM and AMSR2flag, which is not the case for SMAP SM.
Within each dataset and between the three datasets, large variability is visible in the SFFI magnitude for this region. In MAM, JJA, and SON there are local maxima in the SFFI of the three datasets at 36 • N, where SMOS flags the most intense and SMAP the least. This is coherent to the earlier finding of local maxima in the total flagging intensity of SMOS and SMAP, of which we can now conclude that this difference is (partially) the result of differences in snow/frozen flagging. The maximum seasonal variability of SSFI is 76% at ~68 • N latitude for SMOS SM, 93% at 58 • N latitude for SMAP SM, and 93% at ~67 • N latitude for AMSR2flag. The latitudes of maximum seasonal variability in SSFI impact of SMOS SM and AMSR2flag are in line with the average latitude of the NH now/ice boundary, whereas it is situated ~10 degrees more southwards for SMAP SM.
The maximum difference of SFFI impact on data availability between any two out of the three datasets is 55% in JJA at ~75 • N, which is mainly related to the relatively low flagging intensity of AMSR2flag at high northern latitudes in summer. This could confirm the previous suggestion that AMSR2flag is more sensitive to contrast in frozen/unfrozen states, and therefore, does not mask pixels that are covered by wet snow and consist of a partial unfrozen top layer in JJA. This is not the case for SMOS SM and SMAP SM, as their individual snow/frozen flags do not depend on the emissivity but on the land-cover (including permanent snow/ice) or modeled temperature. An extension of the physical-based algorithm or a combination with land-cover data of permanent snow cover could extend the AMSR2flag to cover all snow and frozen conditions.
Performance Evaluation with Ground Observations In order to place the previous results in perspective, the performance of the new snow/frozen flagging method applied on AMSR2 must be evaluated in comparison to the SFI flags of SMOS SM and SMAP SM. An exact match is not expected because of the spatial representativeness error of comparing SFI flags derived from big satellite footprints with single site sensors. Nonetheless, this analysis provides a good overview of the quality characteristics of the different datasets. Figure  9  shows that the highest accuracy levels correspond to t1 for all SFI flag datasets, suggesting that the SFI flags represent also the light snow/frozen conditions. AMSR2flag is performing above average under both threshold conditions (median accuracy of respectively ~78% and ~74%).
The earlier findings on the maximum difference in SFFI impact (Figure  8 ) between two of the datasets was detected to be largest (93%) in MAM at ~78 • N latitude. Although there were no stations found for this study at such a high latitude, the most Northward one is situated within 10 degrees, namely at ~70 • N in Alaska. Here we expect that some underflagging occurs for the AMSR2flag, while overflagging is likely present in the SMOS SM SFI flag. This suggestion is in line with the significant differences found between the SFI flags of SMOS SM and SMAP SM. In order to truly investigate whether AMSR2flag is underperforming for higher latitudes relative to lower latitudes and to provide a thorough overview of the spatial pattern in performance, we compared the three performance metrics in space for the threshold of highest performance, t1 (Figure  10 ).
From these two findings it could be inferred that more observations are available in the SM time series of SMAP and AMSR2flag due to less overflagging, while the quality of more of these observations is disputable resulting from more underflagging, in comparison to the SMOS SM datasets. Please note that the differences in overflagging are bigger than the differences in underflagging, which is related to the high percentage of FP in time for the SMOS SM SFI flag dataset for the south Alaskan sites and almost all U. S. sites, especially those in the east of the U. S. Despite the earlier findings of relatively high FDR numbers for SMAP SM SFI flag and AMSR2flag for the same locations, the percentages of FP in time are less exceptional than for SMOS SM SFI flag, which is in line with the relatively higher accuracy of SMAP SM SFI flag and AMSR2flag compared to SMOS SM SFI flag as demonstrated in Figures  9  and 10.
In particular AMSR2flag performed better for these statistics, although the SMAP SM SFI flag outperformed AMSR2flag considering the percentages of FN in time for the stations in east U. S. Thus, this indicates less underflagging in the SMAP SM SFI flag dataset and less overflagging with the AMSR2flag. Considering Alaska, the AMSR2flag has fewer outliers of FP and FN in time than SMAP SM and SMOS SM SFI flags.
Conclusions This study reviewed and evaluated the flagging systems of three passive microwave SM datasets. Although the multi-sensor and single-sensor flagging systems could not be compared directly, the many flagging differences demonstrated that the flagging of multiple conditions was quite complex. It can be difficult for a data user to find the right information on how the flags are used within each processing chain. Moreover, the quantification of the exact flagging impact on the data availability could not be directly derived from the metadata of the sensor datasets as it currently stands. The flagging systems differ in the number of flags (from seven to 15 critical flags for ESA CCI SM and SMOS SM respectively) and underlying data source types. The flagging category with the most critical flags combined across all products (11) was the snow/frozen category.
Acknowledgments: The SMOS L3SM products were obtained from the  Centre Aval de Traitement des Données SMOS (CATDS) , operated for the " Centre National d'Etudes Spatiales" (CNES, France)  by  IFREMER (Brest, France). A partial contribution to this work was made at the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with the  National Aeronautics and Space Administration. The authors would also like to thank  JAXA  and  ISMN  for making Tb, TS, SWE data available online. The authors also wish to thank the guest editor,  Lionel Jarlan , and the three anonymous reviewers. Their comments and suggestions have been very helpful for improving the quality of this paper.
Names of Critical Flags Number of Data Sources of Critical Flags per Category Names of External Datasets External- External- Eternal- Internal- Model- Satellite- Mixed only only Datasets only Datasets Datasets Datasets Snow, Permanent Ice, NOAA IMS Frozen database, Snow/frozen 4 3 Ground (from 1 1 1 1 GMAO modeled GEOS-5, effective soil MODIS IGBP temperature) Precipitation 1 1 Precipitation 1 0 0 0 GMAO GEOS-5 Static Water, MODIS Open water 2 2 Radar-derived 0 1 0 0 MOD44W Water Fraction database RFI 1 0 na 0 0 0 0 na Urban areas 1 1 Urban area 0 0 1 0 CU GRUMP Vegetation 1 1 Dense Vegetation 0 1 0 0 MODIS NDVI (climatology) and IGBP Topography 1 1 Mountainous Terrain 0 0 1 0 GMTED-2010 Other 56 0 na 0 0 0 0 na Total 67 9 2 3 3 1.
Names of Critical Flags Number of Data Sources of Critical Flags per Category Names of External Datasets * For the flags applied on the ESA CCI SM products, internal-only data sources means that they are directly based on the Tb's of the individual input products via LPRM. External- External- Eternal- Internal- Model- Satellite- Mixed only only Datasets only Datasets Datasets Datasets * Snow Snow/frozen 1 1 Coverage or Temperature 2 0 0 1 na Below Zero Precipitation 0 0 na 0 0 0 0 na Open water 1 0 na 0 0 0 0 na RFI 1 1 na 0 0 0 1 na Urban areas 0 0 na 0 0 0 0 na Vegetation 1 1 Dense vegetation 0 0 0 1 na Topography 0 0 na 0 0 0 0 na others_no _convergence _in_the_model _thus_no _valid_sm _estimates, soil moisture value exceeds physical Other 4 4 boundary, 0 0 0 1 na weight of measurement below threshold/data set deemed unreliable, all datasets deemed unreliable Total 8 7 2 0 0 4.
This is achieved in two steps: by determining the dependence of the regular fundamental characters of the Lax matrix on the spectral parameter, and by subsequently computing the polynomial character relations in the representation ring of E 8 (viewed as a polynomial ring over the fundamental characters) corresponding to the exterior powers of the adjoint representation. The last step, which is of independent representation theoretic interest, is of significant computational complexity and is solved by a reduction to an equivalent large-size linear problem which is amenable to an efficient solution by distributed computation. This is beyond the scope of this paper and will find a detailed description in  [23] : I herein limit myself to announce and condense the ideas of  [23]  into the 2-page summary given in Appendix C, and accompany this paper with a Mathematica package 1 containing the solution thus achieved.
The Hamiltonian structure inherited from the embedding of the system into a Poisson-Lie-Bruhat cell translates into a canonical choice of symplectic form on the universal family of Prym-Tyurins, and it pins down (up to canonical transformation) a marked pair of Abelian third kind differentials on the curves. Altogether, the family of curves, the marked 1-forms, and the choice of preferred cycles lead to the assignment of a set of Dubrovin-Krichever data (Definition 3. 1) to the family of spectral curves. Armed with this, I turn to some of the uses of Toda spectral curves in the context of Fig. 1. Isomorphism 1: Toda spectral curves have long been proposed to encode the Seiberg-Witten solution of N = 2 pure gluodynamics in four dimensional Minkowski space  [60, 86] , as well as of its higher dimensional N = 1 parent theory on R 4 × S 1  [95]  in the relativistic case.
3. 3. Hamiltonian structure and the spectral curve differential. The fact that the isospectral flows (2. 16) turn into straight line motions on PT(Γ u,ℵ ) is the largest bit in the proof of the algebraic complete integrability of the E 8 relativistic Toda. We conclude it now by working out in detail a choice of Darboux co-ordinates {S i , ϑ i } 8 i=1 , with ϑ i ∈ S 1 , such that the Hamiltonians (2. 18) are functions of S i alone. In the process, this will complete the construction of the Dubrovin-Krichever data of Definition 3. 1. Composing the surjection (3. 17) with the Abel-Jacobi map gives an Abel-Prym-Tyurin map A PT : Γ u,ℵ → PT(Γ u,ℵ ) p → P g • A(p). (3. 31) Since PT(Γ u,ℵ ) is principally polarised, an analogue of the Jacobi theorem holds for A PT [68, Lemma 2. 1], and the Abel-Prym-Tyrin map (3.
(3. 55) Proof. The statement just follows from writing down the symplectic change-of-variables given by looking at S as a type II generating function of canonical transformation, first in u and then in α, µ(γ i ) = exp λ(γ i )∂ λ(γ i ) S , κ i = ∂S ∂u i = dκ i , ϑ i = ∂S ∂α i = dθ i , (3. 56) and use of Lemma 3. 5. Keeping in mind the discussion below Definition 3. 1, the constructions of this section bestow on S g a canonical choice of Dubrovin-Krichever data as follows F ←→ S g B ←→ B g D ←→ i Σ i (B g ) E 1 ←→ L ←→ dλ λ E 2 ←→ M ←→ dµ µ Λ ←→ Λ PT (3. 57) which is complete but for the choice of the Lagrangian sublattice Λ L. The latter is left unspecified by the Toda dynamics -and, in the applications of the next two Sections its choice will vary depending on the circumstances.
I further write B Toda (p, q) P * g E Ψ (p, q) (4. 34) whose definition, by the nature of (P g ) * as a projection on PT(Γ u,ℵ ), is independent of the choice of the particular Lagrangian extension Ψ ⊃ Λ PT. Further write, for λ(q) locally near b ± i , K Toda 0,2 (p, q) 1 2 q q =q B Toda (p, q ) log µ(q) -log µ(q) , (4. 35) where locally around each ramification point λ -1 (b ± i ), q is the local deck transformation µ(q) = α • l → α • l + α i , α α i • l. We call B Toda and K Toda respectively the symmetrised Bergmann kernel and recursion kernel for the DK data (3. 57). Remark 4. 4. In terms of the Dubrovin-Krichever data (3. 57), notice that the family of differentials B Toda is determined by S g and Λ L PT ⊂ Λ PT alone -that is, by the curves themselves, the invariant periods Λ PT , and the specific marking of the "A" cycles in Λ L PT to be those with vanishing periods for B Toda.
In the case of our packaged VCSEL, the main contributions are the TO-56 can leads and the wire bondings. The VCSEL bandwidth is acquired by measuring the parameter S 21 for bias currents from 2 to 7 mA. Figure  4 (a) shows the S 21 parameter measurements after extracting the photodetector frequency response and normalizing the data to the magnitude at low frequencies. There is a 3-dB bandwidth improvement of 2 GHz (from 2. 7 to 4. 7 GHz) when the current ranges from 2 to 7 mA. In all cases, the frequency response presents a bump after reaching the resonance frequency that decreases with a slope greater than -60 dB/dec. This phenomenon is produced by the driver circuits, connections, and packaging employed during the measurements. Despite the frequency response degradation, the resonance frequency f R for each current is certainly identifiable.
The non-linear effects of the optical fiber are neglected. The photodetection process is performed with a PIN type photodetector, which includes thermal and shot noise. The amplification stage is divided into two: a first low noise amplifier (LNA) responsible for establishing the white noise level and the second amplifier of higher gain for establishing the power level required to guarantee the GS condition. Finally, the filter is modeled with a fourth-order Bessel filter centered at the desired fundamental frequency. for each simulation run. The aperiodic condition guarantees update of visualizers for each data block. To achieve the GS regime when FSR = 200 kHz, it is necessary to compensate for the additional optical losses caused by the FP filter. These losses are induced by the change of the passband width (directly related to FSR) and the temporal analysis of the optical signal using a windowed finite impulse response filter (FIR) for aperiodic conditions.
Additionally, a detailed analysis of two orthogonally polarized OFCs must be carried out to determine the electrical conditions that allow a wider combined OFC. Through some technical modifications, the SVOFC architecture can be applied to the generation of low-noise microwave harmonics at frequencies higher than 10 GHz. Some results on this matter are in the process of publication. Similarly, the width and spacing of OFC tones can be improved and used in sensing applications with fiber Bragg grating sensors, and the optical pulses generated through the GS regime at higher repetition rates can be exploited for optical data pulse shaping. We also showed that the phase noise far-from-the-carrier of the electrical signal generated at f 0 depends strongly on the optical fiber length and the white noise contributed by the microwave amplifier.
Separation was conducted with a Rtx-1ms capillary column (Crossbond 100% dimethyl polysiloxane, 30 m × 0. 53 mm × 0. 5 µm). The detector temperature was 250 • C, and the column was heated using the following program: 40 • C for three minutes at an increment of 8 • C/min to 230 • C, then maintaining a constant temperature for 9 min. The carrier gas was helium at a 1. 0 mL/min constant flow. EIMS electron energy 70 eV; ion source temperature and connection parts: 250 • C. Analyte transfer was performed in splitless mode; the MSD (mass spectrometer detector) was set to scan mode from m/z = 40 to m/z = 400. Volatiles were identified using mass spectral libraries and linear retention indices, calculated from a series of n-alkanes from C6 to C30. The amount of components was determined semi-quantitatively by measuring the relative peak area of each identified compound, according to the NIST database, in relation to that of the internal standard (ethyl nonanoate for esters, 4-methyl-2-pentanol for other components).
05, n = 5, ns-not significant; 0. 001 '***'; 0. 01 '**'; 0. 05 '*"; 1 LRI-linear retention index; the amount of components was determined semi-quantitatively by measuring the relative peak area of each identified compound, according to the NIST (National Institute of Standards and Technology) database, in relation to that of the internal standard. Abbreviations: SF-spontaneous fermentation, SC-fermentation with Saccharomyces cerevisiae (Ethanol Red), SB-fermentation with Saccharomyces bayanus (cider yeast).
Inclusion Criteria and Data Collection An IGASI was defined by the isolation of GAS from blood cultures or from other sites that would be normally sterile. In France, microbiology units of the whole country are encouraged to send the invasive GAS isolates to the French National Reference Centre for streptococci (CNR-Strep). The records of the CNR-Strep were retrospectively queried to identify all the neonates and infants <90 days of life who were declared to have an IGASI between January 1, 2007, and December 31, 2016. Then, each center was contacted and asked to send the hospitalization report to the first author, who collected the initial clinical and laboratory data and the clinical outcome (length of stay, complication, and transfer to intensive care unit) on a standardized anonymized paper case report form.
Our study has several limitations. Data collection was retrospective, and only 26 patients have been included despite a 10-year period in the whole country. Moreover, we could not describe the characteristics of 11 patients identified by the CNR-Strep due to the impossibility to obtain the medical records. Thus, our results may not be generalizable. In conclusion, IGASI in <3-month-old infants remains uncommon, but 50% of patients are severely ill. The very high proportion of GAS infection in the household of these neonates suggests a need to assess whether a systematic prophylaxis and close follow-up allow avoiding these severe infections.
ACKNOWLEDGMENTS Authors gratefully acknowledge  Pr. De Pontual ,  Pr. Labrune ,  Pr. Mercier ,  Pr. Collet ,  Pr. Claris ,  Pr. Picaud ,  Pr. Chabrol ,  Pr. Nicolino ,  Pr. Roze ,  Dr. Piechaud ,  Dr. Pietrement ,  Dr. Leguen ,  Dr. Astruc ,  Dr. Werner ,  Dr. Bourgoin ,  Dr. Chamouine , and  Dr. Françoise  for sending the patients' hospitalization medical records.
NaOH solubility measurements For each temperature, we measured NaOH solubility values using an inflow conductivity probe  (25) (26) (27) , confirmed by ICP and pH analyses. We performed ICP on the total sodium content, with an ICP-optical emission spectrometry 720 ES from Varian, whereas for pH measurements, we used a Hanna Instruments probe. We prepared NaOH solutions from solid NaOH pellets, a reagent grade of ≥98%, anhydrous, from Sigma-Aldrich with ultrapure water. Our operating conditions and equipment of the continuous conductivity probe are described in details in a previous work  (10). Molar conductivity values of sodium hydroxide are taken from literature data  (28). We carried out all analyses at an ambient temperature of 20°C.
Acknowledgments Funding: We acknowledge financial support from the  INNOVEOX Company  and the  French Environment and Energy Management Agency (ADEME). Author contributions: T. V. , A. E. , and C. A. contributed equally to this work. Competing interests: C. A. , T. V. , and A. E. have a priority patent no. FR 1760895  filed on 17 November 2017, published on 24 May 2019 (number  307354 ) and extended by  PCT , application no. EP2018081652  on 16 November 2018. Ecole Nationale Supérieure des Arts et Métiers ,  Université de Bordeaux ,  Institut Polytechnique de Bordeaux. The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the corresponding author.
Acknowledgments Open Access funding provided by Projekt DEAL. Author contributions OE: drafting/revising the manuscript, planning of clinical diagnostics/diagnostic plan, interpretation of results, making diagnosis and starting treatment. FS: revision of manuscript, interpretation of results, diagnosis confirmation, review of MRI. MC: revision of manuscript, patient treatment, psychiatric assessment. MH: review of MRI, revision of manuscript. JC: neurological re-examination of the patient, revision of manuscript.