dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation by RCF Optimization In this section, we analyze the enhancement in robustness allowed by our proposal, according to the RCF. Comparisons with torus automorphism in  [11]  and Rombaut's SA  [14]  are also given. Different strategies where implemented in the Java language (Java 8 Update 91) and executed in an HP Envy 4-1152la computer with an Intel Core i5-3317U 64-bit processor, 8 GB RAM, 128GB SSD and Windows 8. 1 PRO as the operating system. For evaluation purposes, we select a number of different scenarios that represent various possible situations reported in the application field of Wireless Visual Sensor Networks (WVSN)  [29, 30]. We must note that, even if we restrict the number of scenarios to some particular cases due to the problem's complexity, the number of scenarios simulated in this paper exceeds most of the related works found in the literature.
Acknowledgments: This works was supported by  Conicyt-Chile  under Fondecyt Grant No. 11121657  and the  University of the Bío-Bío  under grants  DIUBB 161610 2/R  and  GI 160210/EF. Furthermore, the authors thank  María Angélica Pinninghoff Junemann  for her invaluable advice, which allowed improving the quality of the obtained results, and  Eric Orellana-Romero  for providing useful technical information at the first stages of this work.
The TALYS Evaluated Nuclear Data Library (TENDL) has now 8 releases since 2008. Considerable experience has been acquired for the production of such general-purpose nuclear data library based on the feedback from users, evaluators and processing experts. The backbone of this achievement is simple and robust: completeness, quality and reproducibility. If TENDL is extensively used in many fields of applications, it is necessary to understand its strong points and remaining weaknesses. Alternatively, the essential knowledge is not the TENDL library itself, but rather the necessary method and tools, making the library a side product and focusing the efforts on the evaluation knowledge. The future of such approach will be discussed with the hope of nearby greater success.
Introduction The TALYS Evaluated Nuclear Data Library project (TENDL) started in 2008. Since then, a new version was released every year, including further improvements and increasing its relevance for diverse applications. Such project includes nuclear data evaluations for neutron, proton, deuteron, triton, helium-3, alpha, and photon induced reactions. To illustrate the evolution, the TENDL-2008 library included 350 isotopic evaluations from 19 F to 209 Po for neutron induced reactions, whereas the TENDL-2015 library includes more than 2800 evaluations for neutron (all isotopes from 1 H to 289 Fl leaving longer than 1 sec. ) up to 200 MeV with covariance files, fission yields, thermal scattering data and so-called random files for uncertainty propagation. Other outputs, such as processed files ready to be used in simulation codes are also available.
An overview of the TENDL evolution in terms of file number is presented in Fig. 1. The process of producing such library is presented in Ref. [1]. Over the years, TENDL is being used in many subjects, among which fission, fusion, medical isotope production, astrophysics, high-energy physics, damage calculations, non proliferation, etc. For more references on the use of TENDL in different application fields, see the latest TENDL webpage. As specific examples, one can cite the inclusion of TENDL in the projected JEFF-3. 3 library, in the GEANT simulation program, as well as in the CASMO library. One of the difference with other existing nuclear data libraries is that TENDL itself is "only" the output of series a e-mail: dimitri-alexandre. rochman@psi. ch of codes, referred to as T6. T6 stands for six codes, such as TALYS, TEFAL, TASMAN, TARES, TAFIS and TANES, with specific tasks for each one in order to produce an ENDF-6 file as complete as possible.
Therefore the development of TENDL goes through the improvements of T6, assuring reproducibility. Once such a system is in place, the developers, the evaluators, theoreticians and other specialists can focus on the essentials: improving the methods, theories, models and parameters. With this approach, the improvement of the quality is almost guaranteed, with better agreement to observables, better physics and better tracability. The T6 approach also helped to produce other interesting methods, such as the Total Monte Carlo idea (TMC)  [2] , fast TMC  [3]  and recently the Bayesian Monte Carlo (BMC)  [4, 5]. These methods are now in use for both the development of TENDL, but also in the uncertainty propagation in reactor physics related subjects. Other projects, aiming at a global improvement of the nuclear data and their applications were also proposed, such as in Ref.
T6 As mentioned, T6 is made of a suite of programs, each of them with specific tasks. T6 was originally created as TALYS does not produce (yet) all the necessary quantities which need to be calculated for a complete ENDF-6 file. Ultimately, T6 should be come T5, T4 until T1 as all parts will be integrated into a single software. Currently, efforts are being made to merge TALYS, TASMAN and TEFAL into a single code. If successful, the result of this unification will change T6 in T4. This would allow an easier maintenance and more practical use. But for the time being, the T6 package is mainly composed of six codes, plus some other utility packages. A simplified structure is presented in Fig. 2. TENDL can be produced every year with improved performances because of the robustness and constant improvements of the T6 package.
Related projects: TMC, BMC, HFR Different methods step in during the elaboration of the TENDL library. As presented many times, the random variation of all model parameters leads to random cross sections and other quantities, which can also be formatted into ENDF-6 files. This allows to loop over simulation code using nuclear data, thus applying the TMC method. As an improvement on TMC to include differential information, the BMC method allows to sample from a updated parameter distributions, based on simple weighting functions. Finally the use of statistical resonances for all isotopes (the HFR method) brings the TENDL evaluations for short-lived isotopes to "the next level". Recent progress in these three methods, strongly linked to TENDL, and again possible thanks to the robustness of T6, are shortly presented in the following.
[20]  with specific applications for 239 Pu and the Cu isotopes in Refs. [21, 22]. The idea is again relatively simple once the T6 programs are in place. First a set of experimental data needs to be selected. It should be both differential and integral data, including as much sources as possible. The starting point library can be any library, such as TENDL, JEFF, ENDF/B or JENDL. The most sensitive isotopes are likely to be 235 U and 238 U, therefore these isotopes (at the same time) needs to be optimized. To do this, random evaluations for these isotopes can be produced with T6. The other ones included in the starting point library stay unique. Each combination of the random 235 U and 238 U evaluations with the library is then used to be compared with the set of experimental data and χ 2 values are calculated for each random library.
They often concern specific parts of the ENDF-6 file which can be interpreted differently by processing codes. This ambiguous interpretation of the ENDF-6 rules can only be solved with the developers of such processing codes. A more obvious source of limitation in the TENDL library is the lack of parameter adjustments for some specific reactions. This leads for instance to cross sections with poor agreement to experimental data and thus degrades the quality of the evaluation. Such disagreement can also come from the limitation of the theoretical models used in T6. Theoretical models always include some degrees of simplifications, which is ultimately limiting the quality of TENDL. Other libraries can bypass this issue by manually correct the evaluations at a final stage. In the case of TENDL, this action is not permitted and other solutions need to be found.
Also, the interaction with the fusion community made a number of advances possible  [26]. To be complete, one needs to acknowledge that the current state of knowledge for resonance parameters as well as for model parameters in the fast neutron range is reliable in TENDL (and other libraries) thanks to various past efforts, such as experimental compilations  [27, 28] , and the RIPL model parameter database  [29]. Finally, a more general limitation for TENDL and for some other nuclear data libraries is the lack of coordinated effort and funding. Both limit the number of specialists working on a given problem and lead to ill-defined priorities. Such an issue can only be solved once the importance of nuclear data is recognized by the industry and safety bodies.
e. , roughly onethird of the hypothetical patients would be categorized into each of the three possible outcomes), which meant that the study would require 300 responses (10 events × 10 variables ÷ [1/3]). Since the same physician was expected to evaluate many different scenarios, observations in the dataset were not independent, causing some statistical power to be lost. As an attempt to adjust for this potential loss in statistical power, the number of observations was doubled, resulting in a dataset with a minimum of 600 observations. Given that 21 physicians were available to evaluate the scenarios, the study required each physician to evaluate a minimum of 29 scenarios (600 ÷ 21).
Statistical analyses Survey results were analyzed using SAS ® 9. 3 (SAS Institute Inc, Cary, NC, USA). The Fleiss' kappa was calculated to provide a summary statistical measure for assessing the reliability of agreement between endocrinologists in rating the common scenarios. For algorithm development to predict disease activity categorization based on values of the five health status parameters, a combination of the Classification and Regression Tree (CART) method and multivariable logistic regression was implemented. Because the purpose of this analysis was not to test any specific hypothesis, no p-values were presented, no significance testing was performed, and no adjustments for multiple comparisons were made.
THE GASSP AEROSOL MEASUREMENT DATABASE. A synthesis of multiple measurements is a vital component of GASSP. As we describe in the  "Model uncertainty and observational constraint" section, a diversity of measurements is a critical factor in constraining a complex model with many compensating sources of uncertainty. The community's considerable investment in measurements of aerosol microphysical and chemical properties is significantly underexploited to evaluate and constrain climate models, mainly because many datasets are not available in a harmonized form (i. e. , standardized format, time units, variable names, etc. ) in common repositories. A key objective of GASSP was therefore to harmonize a large fraction of the world's in situ aerosol measurements and make them "user ready. " The number and sophistication of aerosol measurements has increased dramatically over the last 20 years, providing essential data to evaluate the latest model simulations of aerosol microphysical properties (see sidebar "Developments in aerosol microphysical measurements").
Long-term measurement programs have been established, such as the Global Atmosphere Watch (GAW) program; the Aerosols, Clouds, and Trace Gases Research Infrastructure (ACTRIS); and the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. These data are generally readily available (see Table  1 ) and have been used to evaluate multiple models  (Mann et al. 2014). However, many measurements are made in short campaigns of typically a month duration. While some campaign data have been synthesized  (Heintzenberg et al. 2000; Clarke and Kapustin 2002, 2010; Asmi et al. 2013 ) and used to evaluate multiple models  (Mann et al. 2014) , most data have been used to evaluate specific aspects of particular models under specific conditions, often called "golden days. " We collected measurements related to six broad aerosol properties important to the effects of aerosols on climate: particle number concentration, cloud condensation nucleus concentration, particle number size distribution, particle mass concentration less than 2.
5 µm in diameter (PM 2. 5 ), chemical composition, and black carbon (BC) mass concentration (Table  2 ). The measurements were made with 18 different aerosol instruments (Table  3 ), and within the six aerosol property classes there are about 70 different measured variables related to different particle size thresholds, cloud condensation nucleus supersaturations, etc. It is this diversity of measurements, compared to gas-phase chemistry  (Sofen et al. 2016) , that makes it so challenging to use a wide array of aerosol data in model evaluation. The GASSP database currently contains measurements from 86 field campaigns (of a total of 119 collected) and long-term measurements from over 350 ground-based monitoring stations spanning 1990-2015 (see Table  ES1  in the online supplement). It includes 20 ship campaigns, 16 ground station campaigns, 29 aircraft campaigns, and 21 campaigns involving multiple measurement platforms.
2013). Considerable effort was put into merging files and correcting or standardizing units, variable names, error flags, missing value indicators, and time dimensions. We also collated metadata (as well as missing metadata from published papers) such as the location of the measurement, the sampling conditions (e. g. , standard or ambient pressure), and information on the instrument type, detection limits, particle size definition, occurrence of clouds, and whether the particles were dried or not. The data were converted to a standardized Network Common Data Form (netCDF) format  (Pringle 2017). We included a large number of data fields or "tags" within the global attributes of the file, with many adopted from the National Center for Atmospheric Research (NCAR) Research Aviation Facility (RAF) file format  (NCAR 2009) , such as the bounding box and time coverage of the measurements.
Gridded "level 3" data formats (commonly provided with remote sensing datasets) and aerosol climatologies  (Heintzenberg et al. 2000)  allow for easy data handling. However, because requirements differ for specific applications, GASSP has instead supported the development of the Community Intercomparison Suite (CIS; www. cistools. net; Watson-Parris et al. 2016 ), an open-source command-line tool and Python library designed to read, aggregate, collocate, analyze, and visualize a wide range of ungridded and gridded datasets. CIS has a plug-in for NCAR-RAF and GASSP format data so the full GASSP level 2 database is accessible via CIS, allowing the in situ measurements to be collocated with model and many satellite products.
In the last 20 years many measurement techniques have been developed that are capable of measuring aerosol particle number, size, composition, and other properties with high time resolution and on a variety of platforms. A full list of instruments in GASSP is given in Table  3. Mobility particle size spectrometers to measure size-resolved particle number concentrations have existed for a long time. International standards have been developed so that instruments can be operated consistently and those data used with confidence  (Wiedensohler et al. 2012). The GASSP database includes measurements from scanning and differential mobility particle sizers (SMPS/DMPS), aerodynamic particle sizers (APS), differential mobility analyzers (DMA), ultra-high-sensitivity aerosol spectrometers (UHSAS), passive cavity aerosol spectrometer probes (PCASP), and optical particle counters (OPC).
SUMMARY AND FUTURE PROSPECTS. GASSP set out to understand and reduce uncertainty in model simulations of aerosol radiative forcing caused by the uncertainties in the aerosol component of the model. What have we learned about the nature and availability of aerosol in situ measurements in the context of model constraint? GASSP has created a harmonized dataset of nearly a quarter of a century of aerosol in situ measurements comprising over 46,000 measurement hours from aircraft and ships and from over 300 surface sites (Fig. 2 ). We intend to make these data readily accessible for wider use with an appropriate data protocol and with the permission of the data providers and other data centers. The database can, and should, be added to in the future. Aerosol measurements are highly diverse and difficult to harmonize (the GASSP database includes 70 measured aerosol variables).
Measurement standards and quality control are vital  (Wiedensohler et al. 2012) , but equally important is data harmonization to reduce the very large number of data formats that have proliferated (see "The GASSP aerosol measurement database" section). We should prioritize new measurements in a targeted way with the specific objective of reducing model uncertainty. GASSP has shown that we can define representative "uncertainty environments" (Fig. 8 ), which help to establish the effect that specific measurements will have on model uncertainty (Fig. 9 ). Such information from models could help guide measurement strategies and priorities. A greater number of longer-term measurements with greater global coverage, particularly in undersampled environments, would help to constrain global aerosol models.
GASSP was funded by the Natural Environment Research Council (NERC) under Grants NE/J024252/1, NE/J022624/1, and NE/J023515/1; ACID-PRUF under Grants NE/I020059/1 and NE/I020148/1; the European Union BACCHUS project under Grant 603445-BACCHUS; ACTRIS under Grants 262254 and 654109; and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. We made use of the N8 HPC facility funded from the N8 consortium and an Engineering and Physical Sciences Research Council Grant to use ARCHER (EP/K000225/1) and the JASMIN facility (www. jasmin. ac. uk/) via the Centre for Environmental Data Analysis funded by NERC and the UK Space Agency and delivered by the Science and Technology Facilities Council.
We acknowledge the following additional funding: the Royal Society Wolfson Merit Award (Carslaw); a doctoral training grant from the Natural Environment Research Council and a CASE studentship with the Met Office Hadley Centre (Regayre); the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement FP7-280025 (Stier); the Department of Energy under DE-SC0007178 (Zhang); the U. S. National Science Foundation under ATM-745986 (Snider); the NOAA Global Change Program (Nenes); NASA Global Tropospheric Experiment Program, the NASA Tropospheric Composition Program, the NASA Radiation Sciences Program, and the NASA Earth Venture Suborbital Project (Anderson); the NOAA Climate Program Office (Quinn); NSF Atmospheric Chemistry Program, the NASA Global Tropospheric Experiment, and NASA Earth Science Project Office (Clarke); German Federal Ministry of Education and Research (BMBF) CLOUD12 project Grant 01LK1222B (Kristensen); Swedish Research Council (VR), the Knut and Alice Wallenberg Foundation and the Swedish Polar Research Secretariat (SPRS) for access to the icebreaker Oden and logistical support (Leck); the Department of Energy (DE-SC0007178) and the Max Planck Society (Andreae, Poeschl); the global environment research fund of the Ministry of the Environment in Japan (2-1403), the Arctic Challenge for Sustainability (ArCS) project of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) in Japan, and the Japan Society for the Promotion of Science (JSPS) KA-KENHI (Grants JP16H01770, JP26701004, and JP26241003) (Kondo, Oshima); Lufthansa for enabling CARIBIC and the German Federal Ministry of Education and Research (BMBF) for financing the CARIBIC instruments operation as part of the Joint Project IAGOS-D (Hermann); the Collaborative Innovation Center of Climate Change supported by the Jiangsu provincial government and the JirLATEST supported by the Ministry of Education, China (Ding and Chi); the Max Planck Institute for Chemistry, Mainz, Germany (Schmale); the NOAA Atmospheric Composition and Climate Program, the NASA Radiation Sciences Program, and the NASA Upper Atmosphere Research Program supporting the NOAA SP2 BC data acquisition and analysis (Schwarz); DOE (BER/ ASR) DE-SC0016559 and EPA STAR 83587701-0 (the EPA has not reviewed this manuscript and no endorsement should be inferred) (Jimenez); and Environment and Climate Change Canada (Leaitch).
ACKNOWLEDGMENTS. GASSP was funded by the Natural Environment Research Council (NERC) under Grants NE/J024252/1, NE/J022624/1, and NE/J023515/1; ACID-PRUF under Grants NE/I020059/1 and NE/I020148/1; the European Union BACCHUS project under Grant 603445-BACCHUS; ACTRIS under Grants 262254 and 654109; and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. We made use of the N8 HPC facility funded from the N8 consortium and an Engineering and Physical Sciences Research Council Grant to use ARCHER (EP/K000225/1) and the JASMIN facility (www. jasmin. ac. uk/) via the Centre for Environmental Data Analysis funded by NERC and the UK Space Agency and delivered by the Science and Technology Facilities Council.
(8) The H 0 relic density Ωh 2 | IDM has been calculated using micrOMEGAs  [77] , thanks to an implementation of the model in the FeynRules package  [78]. Our computations take into account 3-body final state contributions to the total DM annihilation cross section, which can be extremely important in some regions of parameter space. We compute the χ 2 contribution for the relic density via χ 2 Ω = Ωh 2 | DM, total -Ωh 2 | Planck 2 (σ rel × Ωh 2 | DM, total ) 2 , (9) where we assume that the dominant uncertainty originates from the theoretical prediction of the relic density, in particular from the uncertainty on the annihilation cross section. We estimate σ rel = 10% (cf. e. g. [79]  for a discussion of the one-loop corrections to the Higgs portal type annihilation and  [80]  for a relevant discussion within the IDM).
Direct detection In the IDM at tree-level spin-independent WIMP-nucleon scattering arises from Z-and hexchange  [4]. The former is, however, only significant for extremely small mass splittings between H 0 and A 0 , which are not in the focus of this study. The cross section for WIMP-nucleon scattering via h-exchange reads σ SI = λ 2 L f 2 N 4π µ 2 r m 2 N m 4 h 0 m 2 H 0 , (11) where f N ∼ 0. 30  [81]  denotes the strength of the effective Higgs-nucleon interaction, and µ r = m N m H 0 /(m N + m H 0 ) is the DM-nucleon reduced mass. In this study we compute the total spin-independent scattering cross section at tree-level (including an effective vertex for the Higgs interaction with gluons) with micrOMEGAs  [77]. We take into account the most recent constraints from LUX  [37]. In order to estimate the respective log-likelihood we utilize the program LUXCalc  [82].
Comparison with the known NMSSM corrections The next check is to compare with existing results in literature. For the MSSM with CP violation the codes FeynHiggs  [84]  and CPsuperH  [85]  exist. However, both codes use another renormalisation scheme compared to SPheno. Therefore, already differences in the real case are present which are often larger than the expected effects from CP phases. Therefore, a quantitative comparison is not possible. The only other public code which supports CP violation is NMSSMCALC for the (scale-invariant) NMSSM. NMSSMCALC makes use of mixed DR-OS renormalization conditions for the computation of the Higgs masses, but the OS effects in the (s)top sector can be turned off. This option together with some modifications described in the following allow for a very precise comparison.