dataesr/scientific-paragraphs-categorization huggingface dataset:
The source term data with a sharp drop-off above 284 GeV dominates at high energies, as seen in Fig. 42. The experimental data on cosmic ray positrons show that, at high energies, positrons predominantly originate either from dark matter annihilation or from other astrophysical sources. As an example, Fig. 43  shows the comparison of AMS data with a dark matter model based on Ref. [62]  with a mass of 1. 2 TeV together with the contribution from cosmic ray collisions  [72]. This good agreement needs to be verified with more statistics at high energies. The study of the rate at which the positron spectrum falls beyond the turning point continues. Fig. 44  shows the current and projected results for the positron spectrum from AMS through 2028. By then we will have collected 5 million positron events.
As discussed in Section 2, AMS has measured the excess in the positron spectrum to 1000 GeV. These data generated many interesting theoretical models including collisions of dark matter particles, astrophysical sources, and collisions of cosmic rays (see Section 2). Some of these models also include specific predictions on the antiproton flux. In this Section we present a comparison of the AMS antiproton results with several theoretical models. We had reported on the precise measurement of the antiproton flux in primary cosmic rays up to 450 GV using the data sample of 3. 5 ×10 5 antiproton events collected by AMS  [44]  including extensive studies of the systematic errors. Our latest measurements of the antiproton flux from 1 to 525 GV are based on 5. 6 × 10 5 events collected by AMS (Table  6 ).
We have, therefore, additionally modified the geant4 Glauber-Gribov model to match the AMS data over the entire rigidity range  [119]. Fig. 72  shows the AMS result of the He+C cross section as a function of rigidity together with the original geant4 Glauber-Gribov model and measurements from ground-based accelerators. Similarly, we have measured the survival probabilities for Li, Be, B, C, N, O, Ne, Mg, and Si nuclei. Measurements for other nuclei are in progress. Below 30 GV, the rigidity dependence of the measured He inelastic cross section is used to modify the Glauber-Gribov model for the description of the heavy nuclei inelastic cross sections. Fig. 73  shows the comparison of the survival probabilities for carbon and oxygen between the simulation and the data. Most importantly, AMS is able to measure interaction cross sections that could not be measured in accelerators.
Acknowledgments We thank former  NASA  Administrator  Daniel S. Goldin  for his dedication to the legacy of the  ISS as a scientific laboratory and his decision for NASA  to fly AMS as a DOE payload. We particularly want to acknowledge the continuous strong support and guidance of the former  NASA, USA Associate Administrator William H. Gerstenmaier , whose enthusiasm to use the ISS to explore frontier science has made AMS a reality. We also want to acknowledge the support, dedication and guidance of the  AMS Program Office at JSC, USA  as well as the  MSFC, USA  flight control teams which has allowed AMS to operate continuously and optimally on the ISS. We are grateful for the support of  Jim Siegrist  and  Glen Crawford  and their staff of the  DOE, USA  including resources from the  National Energy Research Scientific Computing Center  under Contract No.
AMS research is supported by: the  Academy of Finland , Finland, Project No. 321882 , Finland;  CNRS/IN2P3 ,  France  and  CNES, France, France ;  Pascale Ehrenfreund ,  DLR ,  Germany  under Grants No. 50OO1403  and No. 50OO1803  and  JARA-HPC, Germany  under Project No. JARA0052 ,  Germany ;  INFN, Italy and ASI, Italy  under  ASI-INFN  Agreements No. 2019-19-HH. 0  and No. 2014-037-R. 0 , and  ASI-University of Perugia , Italy Agreement No. 2019-2-HH. 0 ,Italy; CHEP,  Republic of Korea and NRF, Republic of Korea  under Grant No. NRF-2018R1A6A1A06024970  at  Kyungpook National University , Korea; the  Consejo Nacional de Ciencia y Tecnología ,  Mexico  and  UNAM , Mexico, Mexico;  NWO ,  Netherlands  under Grant. No. 680-1-004 , the Netherlands; FCT, Portugal under Grant No. CERN/FIS-PAR/0013/2019 , Portugal;  CIEMAT, Spain, IAC, Spain, CDTI, Spain , and  SEIDI-MINECO , Spain under Grants No.
ESP2017-87055-C2-1-P , No. SEV-2015-0548 , No. MDM-2015-0509 , and No. RyC-2013-14660, Spain; the  Swiss National Science Foundation (SNSF) ,  Switzerland , federal and cantonal authorities, and the  Fondation Dr. Manfred Steuer, Switzerland ;  Academia Sinica, Taiwan  and the  Ministry of Science and Technology (MOST) ,  Taiwan  under Grants No. 103-2112-M-006-018-MY3 , No. 105-2112-M-001-003 , and No. CDA-105-M06 , former Presidents of  Academia Sinica Yuan-Tseh Lee and Chi-Huey Wong and former Ministers of MOST Maw-Kuen Wu  and  Luo-Chuan Lee , Taiwan; the  Turkish Atomic Energy Authority ,  Turkey  under Grants No. 2017TAEK ( CERN ) A5. H6. F2-15 , Turkey; and  NSF , United States Grants No. 14255202  and No. 1551980 ,  Wyle Laboratories ,  United States  Grant No. 2014/T72497 , and  NASA NESSF, United States  Grant No.
Thus, the GRASP package can be considered as a platform for developing, testing, and refining novel retrieval concepts and their utilization in operational processing environment. In addition, GRASP is designed as a practically efficient, transparent, and accessible community open source algorithm that can be used as an advanced tool for verifying different retrieval concepts and realizing those concepts in highperformance operational software. At present, GRASP is being adapted to reprocess the observations provided by several satellite instruments, ground-based networks, single instruments, aircraft and for various synergetic data sets that combine coordinated passive and active remote sensing observations. For example, GRASP has been adapted for operational reprocessing of observations from POLDER-1, -2, -3, MERIS, and AATSR/ Envisat satellite missions.
The overall idea of such algorithm and many specific features are aimed to address the existing challenges of modern remote sensing data interpretation  (Dubovik et al. , 2021). Thus, GRASP unified algorithm and software was developed as a tool applicable to many different observations that is alternative to development of several retrieval codes dedicated to interpretation of different remote sensing observations. GRASP has been developed as freely assessable software from a platform for GRASP open source code (https://www. grasp-open. com/), introductory video is available at (http://www. youtube. com/ watch?v PcDeqwDF15A). Moreover, recently GRASP-CLOUD services have been offered to remote sensing community described in details by  Aspetsberger et al. (2019). GRASP is based on several generalization principles with the idea to develop scientifically rigorous, versatile, practically efficient, transparent and accessible algorithm.
Two main modules of GRASP numerical inversion and forward model are independent, can be changed separately and even fully replaced if needed for a particular application. Indeed, numerical inversion includes general mathematical operations and forward model module is developed for simulating various atmospheric remote sensing observations. Both modules are described below in details. As it will be shown many specific features in both modules can be adapted to a particular application. Following the multi-term LSM strategy the numerical inversion was implemented as a statistically optimized fitting of observations that unites the advantages of a variety of known approaches  (Dubovik, 2004). For example, in aerosol retrievals, different smoothness constraints were applied simultaneously on aerosol size distributions and spectral dependencies of aerosol refractive index (see  Dubovik and King, 2000; King and Dubovik, 2013).
, 2006, etc. )  for inverting AERONET data  (Holben et al. , 1998)  it can be naturally applied to the observations of ground-based sun/skyradiometers. It should give very close results to AERONET operational code if applied in identical way as done in the network processing. At the same time, GRASP includes some additional features that can be of interest for obtaining some new results from ground-based radiometers. For example, GRASP can be applied to aerosol size information from direct Sun observations only  (Torres et al. , 2017) , or it can implement simultaneous inversion of sequence of Sun and/or Sun/sky radiometric observations under a priori constraints on aerosol variability using multi-pixel approach. This will be demonstrated below. Schuster et al. (2019)  used the code with Polarized Imaging Nephelometer measurements to mimic the AERONET retrieval algorithm and validated the results with simultaneous laboratory measurements of scattering, absorption, and particle size.
THEORY OF MULTI-TERM LSM INVERSION The inversion module is one the key parts of the GRASP algorithm and software. It is implemented based on rather general principles that are not directly related with any specific application. This is why, this module can be used with diverse forward models that make the GRASP concept fundamentally generalizable. It is important to note that the inversion module is based on a rather extensive and positive heritage of inversion algorithm developments for atmospheric remote sensing  (Dubovik et al. , 1995; Dubovik et al. , 2000; Dubovik and King, 2000; Dubovik, 2004; Dubovik et al. , 2006; Dubovik et al. , 2008; Dubovik et al. , 2011, etc. ). As a result, the GRASP inversion strategy and software module has a generalized character and, at the same time, it is well tuned for interpretation of ground-based and satellite observations.
( 2 2Ψ(a) min → K k 1 N k -n, (2. 1. 7) where N K is a number of measurements (inputs) in the k-th data set and n is the number of retrieved unknowns (parameters). Similarly, the a priori data sets are included in the K data sets. They are statistically independent of the measurements, i. e. they have errors with a different level of accuracy uncorrelated with the remote-sensing errors. It should be noted that in practical application it is often convenient to use weighting matrices W k instead of covariance matrices C k (e. g. ,  Linnik 1962; Twomey 1977). When several data sets inverted together the use of weighting matrices add convenient transparency into approach because it makes it evident the relative contribution of the data from different data sources (e. g. ,  Dubovik et al. , 1995; Dubovik and King, 2000; Dubovik 2004, etc.
1 K. Thus, this realized RT development opened opportunity to apply GRASP approach for realizing atmospheric retrieval using both photometric and hyperspectral observations in visible and thermal IR spectral range. The total surface reflectance matrix BRDM in GRASP code is modelled using Surface reflectance BRDF and BPDF. Specifically, as described in detail by  Dubovik et al. (2011) , 4 by 4 BRDM matrix is modelled using two terms M M diff + M spec , where diffuse unpolarized term M diff has only one nonzero element {M diff } 11 modelled using semi-empirical BRDF function and specular reflection term is represented by Freshnel reflection function scaled by selected BPDF coefficient. Nevertheless, GRASP can operate also with more physically based models of the BPDF  (Litvinov et al. , 2012)  when physical constraints can be imposed by the surface structure and composition.
The number of the retrieved parameters is significantly reduced in this approach, which is helpful for the observations with limited sensitivity to the size, shape and refractive index of the aerosol particles. For example, as will be discussed below this approach was successfully employed in processing MERIS and POLDER satellite data by GRASP. Also, as discussed by  Lopatin et al. (2021)  this approach is rather suitable for processing vertically resolved observations by lidar or airborne radiosonde data. In applications to MERIS and POLDER satellite data, the vertical aerosol distribution was assumed the same for all the components and modeled as Gaussian or exponential function, while in processing of vertically resolved observations a detailed separate vertical distribution can be retrieved for each aerosol component.
In general, the aerosol components are associated with optically distinct types of aerosol based on particle sizes, scattering and absorption capabilities, etc. For example, in GRASP applications to MERIS, POLDER, lidar and radiosonde data, the aerosol components were defined based on AERONET climatology by  Dubovik et al. (2002)  and  Smirnov et al. (2002) , the exact definitions are discussed by  Lopatin et al. (2021). At the same time, these components can be easily redefined and modified in frame of GRASP software. Thus, aerosol in GRASP retrieval can be represented as rather sophisticated mixture of one or several aerosol components that can differ by particle size distribution, shape distribution, vertical profile and complex index of refraction. All these characteristics can be either assumed and fixed or retrieved.
For example, in studies by  Dubovik et al. (2006)  such properties as detailed size and shape (axis or aspects ratios) distributions were retrieved together with spectral complex index of refraction from laboratory measurements of aerosol samples phase matrices. In the framework of GRASP software, the inversion of full scattering matrix (6 elements) at any wavelengths and for any angular range selection together with the spectral measurements of σ scat/ext (λ, h) is realized. In addition, the measurements of any simple functions of P ii΄ (λ; Θ; h) and σ scat/ext (λ, h) can be easily added if required. For example, the measurements of total absorption σ abs (λ, h), backscattering β(λ, 180 °, h) or total scattering σ scat (λ, Θ min , Θ max , h) in the selected range of scattering angles: Θ min ≤ Θ ≤ Θ max can be included in the set of inverted single scattering properties with GRASP.
Ground-Based Passive Observations GRASP approach is developed based on the heritage of AERONET retrieval developments by  Dubovik and King (2000) ,  Dubovik et al. (2000)  and  Dubovik et al. (2006). Therefore, GRASP can be used for inversion of AERONETlike radiometric ground-based observation data in a similar way as it is done in AERONET operational aerosol retrieval. Therefore, applying GRASP to such data should provide essentially similar retrieval results. At the same time, even though GRASP has adapted the main conceptual elements from the AERONET retrieval, the actual algorithm is different, therefore some minor differences can be observed even in the identical application. The potential of applying GRASP to radiometric groundbased observations lies in the possibilities of trying and evaluating new retrieval concepts.
(2003)  and  Perez-Ramirez et al. (2015). In addition, GRASP-AOD provides information about aerosol size distribution including concentrations, standard deviations, median and effective radii of total, fine and coarse modes of aerosol size distributions. These retrieved aerosol properties agree well with the results of full AERONET retrieval performed for the same AOD observations. GRASP also can be used for inverting diverse non-AERONET observations from ground or AERONET observations that generally included in AERONET processing. For example, Torres and Fuertes (2020) demonstrated aerosol retrieval using direct Sun AOD observation combined with the sky-radiances measured in solar aureole. Such retrieval showed to provide superior about aerosol size distribution compare to GRASP-AOD only retrieval and can be applied to the AERONET data acquired in partially cloudy environment.
actris. eu/). The standard GaRRLiC approach by  Lopatin et al. (2013)  inverts spectral lidar data together with sun/sky scanning AERONET like radiometer, and the detailed columnar size distributions, and spectral complex index of refraction can be retrieved for two fine and coarse aerosol components together with two vertical profiles of their concentration can be derived as illustrated by Figure  10. While the original GARRLiC scheme was limited to the specific observational set of multi-wavelength elastic scattering lidar together with AERONET-like sun/sky-radiometer observations, the area of GRASP application was significantly extended during last years and now covers a variety of active and vertically resolved observations that can be processed. For example, at present GRASP can be applied for interpretation of such vertical observations of atmosphere as profiles of extinction, backscatter, normalized elastic and inelastic lidar signals, profiles of volume and particle depolarization.
Indeed, polarimetry is highly sensitive to a large number of atmospheric parameters, and accounting adequately for all these sensitivities in the retrieval algorithm is very demanding, especially in the satellite applications where large volumes of data need to be processed with a minimal delay, and for quite a long time there were no available operational MAP product demonstrating the practical advantages of MAP retrieval. In these regards, GRASP was adapting AERONET retrieval methodology and, in difference with conventional look-uptable (LUT approaches) attempted highly advanced statistically optimized fitting of satellite data implementing rigorous search for the solution in a continuous space of solutions. Such approaches are considered as the most promising for satellite retrieval and with some technical differences are deployed in other algorithms of new generations developed for interpretation of MAP observations  (Hasekamp and Landgraf, 2007; Hasekamp et al.
, 2019; Fu et al. , 2020; Dubovik et al. , 2011; Xu et al. , 2016; Xu et al. , 2017, etc. ). The challenges of realizing such state-of-the-art algorithms lie in the fundamental difficulties to adapt them optimally to the high sensitivities of MAP instruments and in specific technical issues, such as, a need of significantly more computational time than LUT algorithms to simulate the satellite signal and the Jacobian derivatives matrices online. In these regards, the developed structure of GRASP algorithm allows realizing rigorous inversion of MAP satellite observations. In addition, the GRASP software was extensively optimized to reduce time of calculation and adapted to practical application to real satellite observations. At present, 18 months of POLDER-1 and -2 and 9 years of POLDER-3 observations have been processed and several versions of the retrieval product have been archived at the AERIS/ICARE Data and Services Center (http://www.
icare. univ-lille1. fr) and GRASP-OPEN site (https://www. graspopen. com). For POLDER, GRASP utilizes radiance and polarization observations from all available spectral channels with minor gaseous absorption: 5 and 6 channels for POLDER-1 and -2, and POLDER-3, correspondingly and 3 polarized radiances spectral channels for all instruments. The retrieval uses a unique global set of constraints (no locationspecific assumptions) and a single initial guess globally. The radiative transfer computations accounting for multiple interactions of the scattered solar light in the atmosphere are performed on-line. All GRASP retrievals were performed at POLDER native resolution POLDER-1 and -2 at ∼7 km and POLDER-3 at ∼6 km. In the saved data archives, the original POLDER/GRASP retrievals are stored at Level-1, Level-2 and -3 products and are publicly available in the form of daily, monthly, seasonal, yearly and climatological datasets.
Independently of which aerosol model approach all retrieval data products contain the aerosol main aerosol characteristics including spectral aerosol optical depth (AOD), aerosol absorption optical depth (AAOD) and single scattering albedo (SSA) as well as Ångström exponent (AE), spectral fine mode AOD (AODF) and coarse mode AOD (AODC). Figure  12  illustrate the climatology of these parameters. The main aerosol POLDER-3/GRASP products including AOD, AE, AODF, AODC, SSA and AAOD were extensively evaluated using validations against AERONET and comparisons with the original POLDER algorithm (PARASOL/Operational), and MODIS Collection 6 aerosol products. For example,  Schutgens et al. (2021)  have evaluated GRASP/Models Level3 1-degree SSA against AERONET and compared with other satellite SSA products.
For example, Figure  17  illustrates the application of the GRASP algorithm for processing AirHARP (airborne Hyper Angular Rainbow Polarimeter) observations made during the NASA ACEPOL 2017 campaign  (Puthukkudy et al. , 2020a). GRASP retrieved AOD using the AirHARP data is validated using the collocated AERONET and HSRL2 measurements. Figure  17C  shows a good agreement of GRASP AirHARP retrieved AOD with the collocated AERONET AOD observation with a maximum bias of -0. 009 and mean absolute error of 0. 015 at 870 nm band. Comparison of GRASP retrieved AOD with measurement of HSRL2 lidar for a forest fire smoke plume shows good correlation and agreement with a correlation coefficient of 0. 940 at 532 nm (see Figure  17D ). Furthermore, preliminary aerosol retrievals based on the HARP CubeSat data using the GRASP was presented at the AGU Fall meeting 2020  (Puthukkudy et al.
The data were inverted at resolution of 10 km in cloud-free conditions as determined by original MERIS cloud-mask algorithm at latitudes below 60 degrees north and south. The retrieval of aerosol and surface properties is conducted simultaneously. An external mixture (see Eq. 4. 3. 3) of four aerosol components was used for modeling aerosol. The surface BRDF as well as general retrieval approach was similar to the POLDER/GRASP processing e. g. , multi-pixel concept was applied. The full list of the retrieved parameters, as well as, Level 2 and Level 3 products of all derived parameters are available at GRASP-OPEN website (https://www. grasp-open. com). The MERIS/GRASP products were validated against AERONET and compared to other satellite products. The analysis has shown that MERIS/GRASP retrievals provided robust and reasonably accurate results.
ACKNOWLEDGMENTS The authors are grateful for the continuing support of GRASP developments: -from  ESA  in frame of multiple projects including IDEAS, CAWA, DIVA,  QA4EO , aerosol retrieval developments for Sentinel -4,  5P  and -7, etc. , -from  EUMETSAT  in frame of multiple projects OLCI/Sentinel-3,  3MI/EPS-SG  and  MAP/S-7 , -from  CNES  for POLDER-3/PARASOL, 3MI/EPS-SG, ACCP/EECLAT, etc. ,from  European Commission in frame of GRASP-ACE  grant. The authors acknowledge the support from the  Chemical and Physical Properties  of the  Atmosphere  Project funded by the  French National Research Agency  through the  Programme d'Investissement d'Avenir  under contract  ANR-11-LABX-0 0 05-01 , the  Regional Council "Hauts-de-France" , and the " European Funds for Regional Economic Development ", from Research grant  (no.
Furthermore, it predicts at genome-scale the gene expression levels required to ensure pre-defined levels of those metabolic functions. The set of methods included in our pipeline are summarized in Figure  1. The code is freely available as a MATLAB toolbox at https://github. com/claudioangione/PGA and C-EDGE. Our methods can be used for mechanistic prediction of promising high-order sets of genes, thus potentially complementing the trial and error overexpression task usually performed by experimentalists. Additionally, our high-order study of the combined effect of many genes highlights, among all redundancies in the model, those that actually affect the biomass. As a side-effect, our method also predicts hidden genetic interactions, where the combined effect of two or more genes cannot be measured or predicted from the effects of the genes alone.