dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the dampening and inflating effects We show the results in table 2, which includes the diagnostics mentioned above. This table decomposes the error originated by the dampening and inflating effects. Each diagnostic is performed on the extreme lows and highs (the retrieved vs. ERA5 LST values themselves) and the LST ranges. It is seen that the more pooling is done in the learning database, the higher the inflating becomes. Indeed, on all sets (i. e. extreme lows, extreme highs, and LST ranges) we observe that the Unique MLP model inflates the most. This is due to the compromise such a model must makes. The model has to find similarities between the TBs-to-LST relationship on very different pixels. If one pixel has a much wider range than the other, this larger range will tend to be present in the retrieval of the pixel whose range was smaller, leading to an inflating effect of the range.
Set Variable in each pixel uses the 5 × 5 neighboring pixel filters. Since information is extracted from the neighboring pixels, the model is able to better detect extreme cases. This leads to a drastic reduction of the dampening effect, but re-introduces a slight inflating, mostly in pixels surrounding the coldest pixels (i. e. mountains). In general terms, a difference in performance of the three methods can be observed between extreme highs and extreme lows. Summer and winter months being equally represented in the training database, this difference is not due to the methodology, but rather comes from an inherent physical difficulty for retrieval over the low extremes. This may be due to the cloud contamination, that is more likely to occur in cold situations  [43]. Overall we can see that diminishing the pooling that is done in the database by localizing the retrieval algorithm as much as possible results in smaller errors in the extreme cases, both by limiting the artificial inflating effect and minimizing the dampening effect.
Conclusion Extreme events are essential for weather and climate impact studies. The satellite retrieval of extreme events by statistical models is true challenge. In this study, we introduce two types of errors that occur when estimating extreme events: the dampening and inflating effects. Dampening comes from the fact that, by construction, statistical methods reduce the range of the retrieval, as they hardly can explain 100% of the variance of a database. Contrarily, inflating occurs when pooling is done (i. e. mixing several datasets into one; here the different locations of the domain) so that the behavior over one pixel is transposed into another pixel of the domain. This study shows that the novel concept of localizing the retrieval helps reducing drastically the dampening and inflating effects, and therefore reduces the overall error that is made by the NNs on extreme events.
Acknowledgments The authors would like to thank  EUMETSAT , more specifically  Marie Doutriaux-Boucher , for providing the IASI data and data reading software;  Cathy Boonne  for helping to access IASI data on the AERIS data center;  ECMWF  for providing the ERA5 Skin Temperature data; and especially to  Patricia de Rosnay  and to  Tony McNally  for fruitful discussions. We would also like to thank the  Centre National d'Etudes Spatiales  and  Thalès , in  Clémence Pierangelo ,  Clément Luitot  and  Laure Chaumat  for engaging discussions and for funding this research. The authors declare that they have no conflict of interest.
Data availability statement The data that support the findings of this study are openly available at the following URL/DOI: https://doi. org/10. 5281/zenodo. 6606344. This study used openly available data. The IASI Level 1C Satellite observations  [24]  are available from https:// data. eumetsat. int/product/EO:EUM:DAT:METOP:IA SIL1C-ALL. The ERA5 Land Surface Temperature data  [25]  are available from https://cds. climate. cope rnicus. eu/cdsapp#!/dataset/reanalysis-era5-single-lev els. All processed data including the TELSEI Emissivity climatology are openly available from  [26].
Clinical evidence and guidance on these issues are lacking. Following the EU and US approval (in 2017 and 2019, respectively), CladT has gained marketing authorisation in more than 80 countries. As of July 2022, an estimated 56,300 patients have received CladT, with 95,664 patients-years of exposure since approval  (Giovannoni et al. , 2022). Therefore, publication of real-world-use case studies, cohorts and registry data has become increasingly prevalent, possibly providing answers and guidance to some of these questions. Real-world evidence (RWE) can usually offer more realistic insights into the use, safety and efficacy of a treatment outside of the stringent inclusion criteria (e. g. , age, prior treatments, level of DA) of clinical trials. We performed a systematic literature review (SLR), with a focus on RWE, with the aim of identifying data to help answer six questions relating to the use of CladT: 1 How would you manage a case of reactivation of disease/breakthrough DA within Year 1? 2 How would you manage a patient who has taken the indicated two courses of CladT but has evidence of DA after achieving the full therapeutic effect (end of Year 2, 3 or 4)? 3 How would you manage a patient who has taken the indicated two courses of CladT but has evidence of DA in Year 5 or later? 4 How would you manage a patient who has taken the indicated two courses of CladT and remains stable/no evidence of DA in Year 5 or later? 5 What are the safety considerations for continued treatment with CladT? 6 In the event of continued treatment with CladT, in the context of DA, what are the recommended number of additional courses? A plain language summary video of this review can be found in the supplementary materials.
Study selection and data extraction The hits identified through electronic searches were subjected to screening. A systematic approach was applied to identify publications of real-world data relating to each question. A single independent analyst screened the titles/abstracts using the inclusion and exclusion PICOS criteria listed in Table  1. A second independent reviewer screened ~10% of records selected at random to check criteria were being applied consistently. Discrepancies in the screening were resolved by a senior reviewer. Positive exclusion methods were employed to selected records for full text review. Where it was unclear whether the inclusion criteria were met, the article remained in the review until sufficient information was available to determine eligibility. Full texts of all the citations included in the first-pass screening were obtained.
Results The process of study identification and selection is summarised in Fig. 1. Electronic searches yielded a total of 3163 hits after removal of duplicates (n=470), which were screened in the first pass, based on the predefined criteria. A total of 447 references (including congress search results) were included for the full text review, which resulted in identification of 226 relevant publications then reviewed by an expert. Of 49 publications selected for inclusion, eight were common between the questions and six were further excluded due to insufficient data/relevance. Finally, 35 publications reporting the RWE data were selected for inclusion. Most studies (n=25) were summarised only as abstracts. Studies included those from: Italy (n=9); Germany (n=4); international (n=3); Canada, Norway, Portugal, Spain and Sweden (all n=2); Australia, Argentina, Belgium, Lebanon, Chile, the Czech Republic, Finland, the United Arab Emirates (UAE), and the United Kingdom (UK) (all n=1).
Summary of available data Two studies reported additional courses with CladT. Oreja-Guevara et al. (2022)  reported that an additional course of CladT in six patients was well tolerated, with only very mild adverse events (AEs), no cases of serious or opportunistic infections, and no Grade 4 lymphopenia. Pfeuffer et al. (2022)  also detailed 5/142 patients who received additional courses of CladT due to ongoing DA, but no safety information was provided. An additional study of interest is from  Butzkueven et al. (2021) , reporting outcomes from 16 MS patients in Australia treated with CladT 1. 75 mg/kg in 2010-2011, and again in 2018. These patients have now received three courses, but with a long treatment gap (approx. 9 years) between courses 1 and 2. Of 11 patients with recorded lymphocyte counts post CladT, none recorded Grade 4 lymphopenia, and five patients recorded Grade 3 lymphopenia.
One patient experienced an episode of herpes zoster (shingles). In the absence of real-world data, it may be useful to consider integrated long-term safety data from the clinical development programme for CladT and the PREMIERE registry. The most recent analysis included 923 patients who received CladT monotherapy (3. 5 mg/kg)  (Leist et al. , 2020). This confirmed a low level of serious treatment-emergent AEs. No new major safety findings were identified, and no new/significant AEs emerged during long-term follow up  (Leist et al. , 2020). No increased malignancy risk over time and no clustering of malignancies of a particular type were identified in the monotherapy cohort or in the all exposed cohort (n=1976) (which included patients receiving higher doses than those recommended in the EU SmPC) over >8 years  (Merck, 2022; Cook et al.
, 2022). A recent analysis of clinical trial data from 1935 patients enrolled in CLARITY, CLARITY-EXT and ORACLE-MS applied a machine-learning approach to predict future DA  (Basu et al. , 2022). Five criteria were used to assess DA, of which 3-month sustained EDSS progression was the most informative. In this model, the probability of future DA decreases with the increasing number of treatment weeks, up to 4 weeks, confirming the optimal CladT dose of 3. 5 mg/kg given over 2 years. Similarly, increasing counts of new combined unique active lesions and new T1 hypointense lesions raise the predicted probability of future DA. Indeed,  Giovannoni et al. (2022)  observed that the percentage of patients with NEDA-3 numerically decreases as the bridging interval between CLARTY and CLARITY-EXT exceeded 48 weeks.
The risk of the most frequent AEs (e. g. lymphopenia) is greatest following treatment administration  (Boyko and Boyko, 2018; Giovannoni and Mathews, 2022). From the clinical programme of CladT, it has been observed that lymphopenia does not lead to an increased risk of common or severe infections, with the exception of herpes zoster  (Giovannoni et al. , 2010 (Giovannoni et al. , , 2018)). These data together offer some insights regarding long-term safety of additional courses; however, more real-world cohort and registry data are needed. Studies including MAGNIFY-MS (NCT03364036), CLASSIC-MS (NCT03961204), CAMELOT-MS (NCT04997148), CLARION (EUPAS24484), CLOBAS (ACTRN12619000257167), CLIP-5 (BfArM ID 7623), and country-specific cohorts will provide further data and evidence regarding the use of CladT.
Of particular interest is the phase IV MAGNIFY-MS study, which evaluates whether early changes in immune cell phenotypes correlate with MRI-detectable disease control, possibly providing further evidence to support management decisions in the first 2 years of CladT treatment. CLIP-5 will investigate the efficacy and safety of CladT continuation at Year 5. Additionally, CLOBAS will evaluate CladT safety and efficacy over 6 years, offering continuation of therapy in the third year, based on DA after the initial two courses. The results are keenly anticipated. Limitations of this review include the scarcity of real-world data for some questions, particularly those relating to long-term follow up with CladT, and those where additional courses have been administered. It is 5 years since EU approval of CladT, and more real-world studies with longer follow-up periods are expected to be published in the coming months.
9%) Argentina (NCT03375177) 90. 2% free from Gd+ lesions Rosengren et al. (2021) Swedish post-market surveillance study 140 5/47 (10. 6%) relapsed during first 12 3. 5% discontinued within Year 1 -no Relapse data only available for 47 patients (IMSE) months reasons provided (one-year drug survival rate = 96. 5%) Santos et al. (2021) (Abs) Observational, multicentric, prospective 182 89% relapse free at 12 months; 11% 13 (11%) discontinued Follow-up data available for 121 patients study of five tertiary hospitals relapsed 9 (7. 4%) due to DA at 12 months Thakre and Inshasi (2020) (Abs) UAE real-world cohort 88 1/88 (1. 1%) relapsed after 2 months 0 discontinued due to DA - 1/88 (1. 1%) persistent MRI activity at end 1 discontinuation due to pregnancy of year 1 Viitala et al. (2020) (Abs) Non-interventional cohort analysis of data 126 8/55 (14.
Data In this section, we describe the data adopted in this study, namely the financial and economic variables, the data related to the COVID-19 pandemic and the policy announcements implemented in response to it. We select the top-ten ranked countries per total confirmed cases as of December 31, 2020, namely Brazil, France, Germany, India, Italy, Russia, Spain, Turkey, the United Kingdom and the United States. 12  We also include China, Japan and South Korea to achieve a more comprehensive worldwide comparison. More information about the data and their sources are reported in Appendix A. Tables A. 1 to A. 3 in the Appendix provide the data summary statistics.
This represents an end-of-the-day annualized realized volatility measure computed from daily log-returns (see  Schwert, 1989). We adopt the following formula: 𝑅𝑉 𝑂𝐿 𝑡 = √ 252 𝑛 ∑ 𝑛 𝑖=1 𝑟 2 𝑖 , where 𝑟 𝑖 = 𝑙𝑛( 𝑃 𝑡 𝑃 𝑡-1 ) representing daily log-returns computed from the price difference, with 𝑃 𝑖 representing the financial firms' daily prices with 𝑖 ∈ {1, … , 𝑛}. Among the macroeconomic variables, we collect CPI, GDP, IP, T-Bill and unemployment rate for each country from the World Economic Outlook Database of the International Monetary Fund (IMF) at a monthly frequency.
COVID-19 data and policy interventions Regarding the COVID-19 specific variables, we collect the number of confirmed cases in each of the selected countries from the Johns Hopkins Coronavirus Resource Center. To measure governments' lockdown initiatives, we adopt the SIndex developed by the Oxford University, which tracks travel restrictions, trade patterns, school openings, social distancing and other such measures, by country and day. The WHO declared COVID-19 a public health emergency of international concern on January 30, 2020 and characterized it as a pandemic on March 11, 2020. After that, a fast-paced series of actions and policy interventions aiming to contain the economic contraction and market distress due to the COVID-19 pandemic was announced and implemented worldwide. These policies were announced quite consistently throughout the year, with a clear peak around March and April 2020.
These were only some of the examples of broad monetary policies interventions in some of the countries in our sample. Some other announcements targeted more economies on the same dates. For instance, international central banks such as, within our sample, BoE, BoJ, ECB and Fed announced on March 19 coordinated action to enhance the provision of liquidity via the standing U. S. dollar liquidity swap line arrangements. In our dataset, this date will be marked as a monetary policy announcement in the United Kingdom, Japan, European Member States and the United States at the same time. The second policy category is the macroprudential regulations category: it consists of actions aimed to relax some of the regulatory restrictions of regulated financial entities to free up capital for credit creation or their own needs for liquid assets.
cases 𝑖,𝑡-1 + 𝜌 𝑖 SIndex 𝑖,𝑡-1 + 𝛾 𝑖 X 𝑖,𝑡-1 + 𝑛-1 ∑ 𝑗=1 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑖 + 𝑚-1 ∑ 𝑗=1 𝑇 𝑖𝑚𝑒 𝑗 + 𝜖 𝑖,𝑡 (7) where 𝛥𝐶𝑜𝑉 𝑎𝑅 95 𝑡ℎ ,𝑖,𝑡 is our dependent variable computed as described in Section 2. 1; the daily policy dummies mark 1 in case of at least one policy announcement on that day in one of the selected policy categories and countries, and 0 otherwise. We also include the cumulative number of confirmed cases (Conf. cases) and an indicator for governments' lockdown restrictions (SIndex) in each of the selected countries as control variables strictly specific to the COVID-19 pandemic. We have also replaced the number of cases in regression equation (  7 ) with the number of deaths and hospitalizations. 15  These variables are both collected from the Johns Hopkins Coronavirus Resource Center Database.
Available and Acquired Data The available data for the study site are summarised in Table  1. Surface geological information, described by  Dupret et al. , 1987 , and spatialised information included in a geographic information system  [22] , were used. Vertical geological information is provided by the drilling of observation wells on the site (Figure  1 ). Spatial topographic information is provided by a 1 m and 25 m resolution digital elevation model (DEM), and differential GPS (Global Positioning System) data served to locate boreholes and infiltration ponds in three dimensions. The spatial position of streams was defined with the BDTOPO ® (IGN, France) vector-based database. Daily precipitation and PET data for the period 2006 to 2021 are available from the Gouville-sur-Mer weather station (Météo-France), 10 km south of the study site.
For the period 2010 to 2021, daily STWW flow data were acquired by radar monitoring of the levels in a Venturi channel, and biological oxygen demand (BOD5) was measured 24 times per year. Between 2016 and 2021, Cl -was analysed nine times in the STWW. Eight observation wells screened across the thickness of the dune aquifer (Figure  1 ) provide access to groundwater measurements. Groundwater, STWW quality, and STWW flows have been monitored for over 20 years within the regulatory framework  [18]. From 2016 to 2021, the site was the subject of nine measurement campaigns of groundwater quality and STWW as part of the AQUANES  [19]  and EVIBAN (Water JPI, 2022) research projects. The data used for our study were obtained from regulatory monitoring and these research projects. Cl -concentrations in groundwater were measured every month in wells NP1, AQ1-NP2, AQ2-FRE4, AQ3-NP3, and PZ1 between 2010 and 2021 from September to June, and every two weeks from July to August.
From 2017 to 2021, waterlevel measurements  [23]  (based on pressure measurements corrected for atmospheric pressure) were acquired continuously in wells AQ1-NP2, AQ2-FRE4, AQ3-NP3, AQ4, and AQ5  [23]. In addition, continuous electrical conductivity measurements were performed in well AQ5. The initial measuring frequency was 15 min, but due to difficulties in obtaining complete data (such as remote transmission and equipment maintenance), gaps and frequency changes are present in the acquired time series. From 2017 to 2021, nine manual measurements of potentiometric levels were carried out in wells NP1, AQ1-NP2, AQ2-FRE4, and AQ3-NP3, and three more were performed at point PTC6. From 2020 to 2021, pressure and conductivity measurements were carried out in wells NP1 and PZ1 by CTD-Diver probes (Van Essen ® , Delft, The Netherlands) at 15 min time steps (barometric probe located at NP1).
The riverbed elevation was set at -0. 5 m from the river water table level. The time-varying hydraulic head observed at AQ5 was fixed as a boundary condition on the meshes located at the western seafront boundary of the model (Figure  5 ). The measurements available at AQ5 over a one-year period between April 2017 and May 2018, and their potentiometric dynamics, were reconstructed for the modelling period from 2010 to 2021, based on a diffusivity equation via the computational code CATHERINE (  [34] , Supplementary Materials S2) that relies on the diffusivity equation and temporal sea level rise data reconstructed from the FES2014 harmonic component database (  [35] , Supplementary Materials S2). The progression of the sea in Blainville harbour is shown by the imposed hydraulichead boundary conditions for some of the meshes representing the harbour.
Daily flow was imposed in a homogeneous manner on the meshes of each pond, impounded according to the provisional schedule of alternating pond supply (Figure  6 ) as identified by the conceptual model. For periods when the gates are open without precise knowledge of the discharge position (Figure  6 ) from December to February (before January 2019), the distribution of STWW flow was 16%, 34%, and 49% for ponds 1, 2, and 3, respectively. After the alternation was halted (January 2019 to June 2021), the flow distribution was set at 60%, 40%, and 20% for ponds 1, 2 and 3, respectively. Outside these periods, the meshes of the non-fed ponds were subjected to the flow and transport of solutes without STWW injection constraints. plementary Materials S2) that relies on the diffusivity equation and temporal sea level rise data reconstructed from the FES2014 harmonic component database (  [35] , Supplementary Materials S2).
Acknowledgments: This study has been performed in the frame of the  EU Water JPI  ( Joint Programming Initiative  " Water Challenges for a Changing World ") within the research projects EviBAN (evidence-based assessment of NWRM for sustainable water management- ANR-18-WTW7-0008-ERA-NET Cofund WaterWorks-2018 ) and AquaNES (Demonstrating synergies in combined natural and engineered processes for water treatment system under grant agreement no. 689450 ). We gratefully thank  Aubéry Wissocq  for initiating the first ideas and designs of the conceptual model and the numerical groundwater flow model, and  Marinus Kluijver  for performing English corrections of the manuscript, then  Eric Dufour ,  Mickaël Gosselin ,  Didier Alain  (SAUR), and  Denis Neyens  ( Imageau ) for their field support and sharing their data, as well as  Yan Lefevre  ( BRGM ) and the municipality of  Agon-Coutainville , who facilitated the implementation of the monitoring and data sharing.
Taking this into account, the choice is made to perform experiments on Ta(4)/ Tb(t Tb )/Co(0. 8)/Gd(t Gd )/Co(0. 8)/Tb(t Tb )/TaN x (4) multilayer samples (thicknesses in nm). A sketch of the sample design is shown in Fig. 1(a). Comments on the reproducibility of the samples are provided in the supplementary material. For the first sample, Gd and Tb thicknesses are spatially varied along perpendicular directions to study a wide composition range. In order to determine the composition range where PMA is present, we measure hysteresis loops at different locations along the sample using MOKE. As the MOKE measurement is sensitive to mostly the Co magnetization, a reversal of the sign of the Kerr angle is expected when scanning from Co to RE-dominated regions. We extract the saturation Kerr angle h s from our data and show the resulting phase diagram in Fig.
Furthermore, higher thicknesses of Tb may lead to rougher Tb/Co interfaces, which could also contribute to the observed decrease in PMA toward higher Tb thicknesses. Bringing the values of K eff into perspective with literature data on other samples exhibiting AOS, we find anisotropy constants in a similar range as found in (Gd, Tb)Co alloys,  34  yet slightly lower than in Pt/Co(1 nm)/Gd(3 nm) layers. 38, 39  We argue that it should be possible to increase K eff through stack engineering, such as by adding more Tb/Co repeats. We now turn our attention toward the characterization of AOS in our samples. First, we investigate single-shot AOS at different spots in the double wedge sample, indicated in Fig. 1(b ). To do so, the sample is first brought to saturation by a perpendicular magnetic field.