dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work is mainly funded by  INTERREG Upper Rhine (European Regional Development Fund) ,  Germany  and the  Ministries for Research of Baden-Württemberg, Rheinland-Pfalz (Germany)  and the  Grand Est French Region  in the framework of the  Science Offensive Upper Rhine HALFBACK project. Q. Cao  and  A. Beckmann  (in part) are also supported by the  Engineering and Physical Sciences Research Council (EPSRC) ,  United Kingdom  [grant number  EPSRC EP/S018107/1 ]. C. Giannetti 's work is supported by the  EPSRC ,  United Kingdom  project  [EP/S001387/1 ].
Role of Equatorial Pacific Ocean changes ENSO dynamics is dominated by equatorial processes, which are influenced by the background state  27. Although the PlioMIP models simulate an amplified eastern Pacific warming (Fig. 2a ), there are large inter-model differences in this pattern, as indicated by not consistent changes in the zonal SST difference  28  (Extended Data Figure  3 ). Of particular importance for ENSO dynamics are changes in equatorial thermal gradients in the mixed-layer  5, 6, 29. Firstly, we evaluate changes in the thermocline slope which plays an important role in ENSO dynamics. Stronger (weaker) westward equatorial currents are associated with increased (decreased) east-west thermocline slope  6. The thermocline slope better represents the resultant effect of changes in zonal equatorial ocean dynamics than the zonal SST gradient in the PlioMIP models, as reflected in a higher inter-model correlation with ENSO amplitude change (Figure  2b ; Extended Data Figure  3 ).
PlioMIP2 models in panels b and c:  participants to use dynamic vegetation. CO2 concentrations were set to 405 and 400 ppm in phases 1 and 2, respectively. For a detailed description of each model's implementation see  [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73]  (Extended Data Table  1 ). Beyond differences in boundary conditions, there are also fundamental differences in the conception of PlioMIP2 vs. PlioMIP1. Although both phases have not applied changes in orbital parameters, phase 1 was designed to simulate a time averaged global SST reconstruction between 3 and 3. 3 Ma BP, while phase 2 focuses on a narrower time slice (Marine Isotope Stage KM5c at 3. 205 Ma BP) with almost identical orbital parameters to modern. Figure Legends a -CCSM4-UofT; b - CCSM4-2deg; c -CESM2; d -COSMOS; e -EC-EARTH3.
2) OCSP-Stapling Survey In 2013, Netcraft [5] performed a survey that indicated that around 22% of certificates were served with a stapled OCSP response. However, to the best of our knowledge there has been no survey since then. So we decided to undertake our own to see whether there has been any change since 2013. a) Dataset and methodology We implemented a Java program in order to detect whether a web server supports OCSP Stapling or not. The program doesn't check all available web domains. Instead it checks the top one million websites that we obtained from alexa. com. For each domain, the program establishes a TLS connection and notifies the server that an OCSP Stapling response is needed (by adding the TLS certificate status request extension during the Handshake phase). Our objective is to know whether the server supports: OCSP-Stapling, Must-Staple via a certificate extension or Must-Staple via the HTTP header.
In this paper data of a European strategic alliance (KAAT project) is used to demonstrate the application of the proposed partner selection framework. This paper is aimed at (1) illustrating the approach for measuring similarity, complementarity, and coverage of knowledge, (2) illustrating the approach for evaluating the partners' history of collaboration, (3) demonstrating the proposed framework using the actual data, and (4) investigating the position of this framework in the forthcoming research as well as in the selection of partners in complex projects in practice.
Secondly, the steps of performing the framework on the project data are clarified. Thirdly, all the required input data is represented as well as the steps to extract them from the project information. The required inputs are the project complexity degree, partners' history of collaboration, project required knowledge, and the knowledge that each partner has to bring to the project. Then, the steps of the partner selection algorithm are discussed and applied to KAAT project in the case study section. In addition, programming the algorithm in MATLAB software is illustrated. Afterward, the results of the partner selection algorithm are discussed as well as its limitations and shortcomings. The outcomes of this paper highlight the potential of future research. This research strongly encourages scholars to study the following suggestions: first, in order to examine the effectiveness of the proposed framework, we recommend calculating the partnership score for several finished projects and comparing their partnership score with their success level.
g. ,  Henry et al. 2015 Henry et al. , 2018;; Jaskot et al. 2019). We present several basic galaxy properties in Table  2. To get robust estimates of the LyC flux from the HST/COS G140L spectra, we follow the reduction methods in previous publications  (Wang et al. 2019; Flury et al. 2022a ). We use the standard CALCOS pipeline (v3. 3. 9) and further estimate the dark current and scattered geocoronal Lyα background adopting the custom software FaintCOS  (Makan et al. 2021). For HST/COS G160M data, we directly download the reduced  data from the HST/MAST archive. For each galaxy, we have also checked the 2D G160M spectra and find that Lyα is completely covered in the COS extraction aperture. We then correct both G140L and G160M spectra for Milky Way (MW) extinction using the Galactic Dust Reddening and Extinction Map  (Schlafly & Finkbeiner 2011)  at the NASA/ IPAC Infrared Science Archive assuming the extinction law from  Cardelli et al.
Determining f esc LyC from SED Fitting To fit the SEDs, we adopt the Binary Population and Spectral Synthesis (BPASS) models (version 2. 2. 1; Stanway & Eldridge 2018), which include binary star populations. Comparing to other standard models such as Starburst99  (Leitherer et al. 1999) , BPASS has been suggested to better represent both the local and high-z galaxies (e. g. ,  Steidel et al. 2016). We use the Prospector spectra fitting code, which can take spectroscopic data from the UV to far-IR rigorously using a flexible spectroscopic calibration model  (Johnson et al. 2021). Currently, the only BPASS model available within Prospector is the "-bin-imf135all_100" model, i. e. , assuming a Salpeter initial mass function 21 (IMF; Salpeter 1955) with lower and upper mass cutoffs of 0. 1 and 100 M e , respectively, and the slope α = -2.
Calculating Mg II Escape Fractions from CLOUDY Models Previous studies have shown that photoionization models can be used to infer the escape fraction of Mg II ( f esc MgII ; e. g. , We have resampled the data to bins of ∼2. 5 Å to match the modelʼs resolution. Guseva et al. 2013; Henry et al. 2018; Chisholm et al. 2020). By using multiple strong optical emission lines that are set by the ionization parameter, metallicity, density, and ionizing spectra, one can predict the intrinsic flux of Mg II (F int (Mg II)), which, combined with the observed flux of Mg II (F obs (Mg II)), gives the corresponding f esc MgII. However, previously published photoionization models for Mg II are only based on ionizationbounded (IB) cases, while models for density-bounded (DB) and mixed cases have not been studied in the literature.
Mg II Photon Productions in CLOUDY Models In Figure  7 , we illustrate these two limiting cases of DB and IB by showing the physical structure of an ionized cloud that is at the transition between H I and H II zones. The vertical black line indicates the position of the hydrogen Strömgren radius, i. e. , log(N H ) = log(U H ) + 23. 1 (see Equation (3) in de  Kool et al. 2002). We adopt the photoionization code CLOUDY (version c17. 01; Ferland et al. 2017)  with input parameters that are typical for galaxies in our sample. These include a BPASS SED with constant SFR and an age of 5 Myr, a stellar metallicity of log(Z stars /Z e ) = -1. 5 (assuming Z e = 0. 02), a gas-phase metallicity of log(Z gas /Z e ) = -1. 5, electron number density log(n e ) = 2. 4 (cm -3 ), and ionization parameter log(U H ) = -3.
dorsalis. They showed that tropical and subtropical climates, except the desert, as well as warm temperate areas, are favorable for B. dorsalis. To predict the potential distribution of B. dorsalis,  Liu et al. , (2011) ,  Magagula et al. , (2015)  and  Qin et al. , (2019)  used the MAXENT software. For the same purpose  Gutierrez et al. , (2021)  used weather-driven physiologically-based demographic models. The SIT has been tested to control B. dorsalis with other monitoring and control methods in Thailand. This study was conducted in pilot programs at the orchard level in Ratchaburi province from 1999 to 2000  (Sutantawong et al. , 2002)  and from 2000 to 2004  (Orankanok et al. , 2007) , and in the Phichit province from 2003 to 2004  (Orankanok et al. , 2007). From 2008 to 2013, an AW-IPM program using the SIT was implemented in the Chantaburi province  (Chinvinijkul et al.
, 1996). POM is useful for all types of modelling. In the case of agent-based models (also called individual-based models in ecology  (Grimm et al. , 2005) , patterns can guide model structure by showing what kinds of entities need to be considered and what state variables are needed  (Grimm and Railsback, 2013). Using POM for development, the model was designed to integrate strong knowledge on the biology and ecology of B. dorsalis, their dependence on climatic conditions and the phenology of different host plants in the Niayes landscape. The model was evaluated using field data collected over 4 years in the Niayes area. We then compared sterile male release scenarios to study their effect on the B. dorsalis population dynamics.
Model description The BOOSTIT (BactrOcera dOrsaliS boosTed sIT) model is described according to the Overview, Design concepts, Details (ODD) protocol  (Grimm et al. , 2020 (Grimm et al. , , 2010 (Grimm et al. , , 2006) )  for describing individual-and agent-based models. Implementation was performed using the Netlogo 6. 1. 1 platform (https://ccl. northwestern. edu/netlogo/6. 1. 1/). Our source code is freely available on Cirad Dataverse (https://doi. org/10. 1 8167/DVN1/5WELPP).
Process overview and scheduling A simulation of the BOOSTIT model began by selecting a type of landscape and by reading a. csv file containing the daily temperature and the ripening period of the fruits of different mango-trees cultivars (see initialization). At each time step, processes were run in the following order (Table  1 ): (1) reading input data and calculation of the ripening period of the host plants, (2) the growth and survival processes of the fly, (3) adults that received the minimum dose of infecting spores became infected, (4) the healthy sterile or infected sterile males were released at a chosen date, (5) adults that had not mated had a random movement, (6) lek areas were determined, (7) adult males first increased their attractiveness and then moved to the lek area; if there were males carrying pathogens in leks, they could transmit some of them to some healthy males, (8) some females that had already mated may mate again after a refractory period, (9) some flies died from the infection, (10) a part of female visited the leks in turn and each one chose a male among the most competitive ones and mated with him; if the male carried pathogens, he transmitted some of them to the female.
Inputs BOOSTIT took as input a file that contained time series representing the evolution of the following data over a year: -The date in Julian day, -The mean daily temperature, -The ripening period of different mango cultivars and citrus. As infestation increased as fruits ripened  (Grechi et al. , 2021)  this period was considered the mango ripening period (ripe and over-ripened stages). These different fruits were grouped under the following categories: the mango cultivars Kent, Keitt, BDH (bouko diekhal), the other mango trees and the citrus. The daily temperature came from the average of three years of data of a weather station (Hobo U30, Onset corp. , USA equipped with a temperature and relative humidity S-THB-M002 sensor) located in Sangalkam, in the Niayes area of Senegal (14 • 47.
Validation experiment The data for validation came from three studied orchards located in the Niayes area of Senegal. For each orchard, the host plant composition of surrounding orchards (recorded in a database of orchard monitored in the Niayes area in 2011), within a radius of 275m around the centre, was considered to define the landscape cropping system type to which the orchards belong. The mango cultivars and other tree species of each of these landscapes matched with the composition of a BOOSTIT landscape: an intensive landscape with only Kent cultivar, a low-diversified landscape with mainly Kent, some other mango cultivars, citrus and other tree and a high-diversified landscape with many mango cultivars, citrus and other trees. The B. dorsalis males captured with methyleugenol traps were recorded weekly from January 2011 to December 2014 in the three chosen orchards.
The high-diversified orchard had 2 traps, the two others had three traps. We first computed the mean daily number of trapped males per trap for each orchard. Then, we computed the relative proportions of the number of males captured to the maximum number of annual captured males during each of the four years. A running average over a 7 days window was computed across the 4 years of data to have a mean, minimum and maximum relative number of captured flies per Julian day. The BOOSTIT simulations were done on 365 Julian days from January the 1 st for the three landscapes. Each run was replicated 100 times with input data as described in section 2. 1. 6 and parameters in table 4. We assume that the traps capture a proportion of the males present in the real orchards. These data cannot be directly compared to the fly data simulated by the model.
We used time-series of daily temperature and maturity probability of the mango cultivars and citrus measured in the Niayes area. The maps used to build landscapes were simplified from three orchards mapped in the same area with realistic and various host tree spatial distributions and composition. An interesting aspect of our POM approach is that we used spatial and temporal information to design our model but only temporal information for validation. Adjusting complex agent-based models on time series is challenging (e. g. ,  Wiegand et al. , 1998; Piou and Prévost 2012). However, in our case, the population dynamics following field data emerge from the interactions between individual fly and the fruit resource phenology. Relying on observed patterns optimize the structure and reduce parameter uncertainty of agent-based models  (Grimm et al.
For these two objectives, deeper explorations of the SIT and boosted SIT will be conducted. Our model validation based on comparisons with captured males (pheromone traps) in different landscapes was the result of the interaction of several processes represented in BOOSTIT. We used a visual method of comparison of our model dynamics with real-world data as adopted by  Yonow et al. , (2004) and García Adeva and al. , (2012)  on capture data of B. tryoni, because we were interested in the general pattern of the dynamic of fruit fly abundance. Yonow and al. , (2004)  and García  Adeva and al. , (2012)  observed discrepancies between the predicted and observed numbers of flies but their host plants phenological patterns used in their models were approximately correct. This same situation was observed in the validation of our model, though male capture data and the results of our simulation were expressed as a percentage of the maximum to compare them.
, 2013). This situation is reproduced by BOOSTIT: the mortality of contaminated sterile males being low during the first three days after their release, they actively transmit the pathogen. It was proven that M. anisopliae is pathogenic for B. dorsalis  (Ekesi et al. , 2011; Tora and Azerefegn, 2021). However, for modelling purposes, some lacking pieces of knowledge could have been very helpful. For example, we did not find any published data on the minimal spore dose of the entomopathogen that can kill the fly. Knowledge on the effects of temperature or relative humidity on the efficacy of the entomopathogen on flies in a natural environment would be very useful too. This information could provide more precision on our contamination process. By modifying the values of some parameters, BOOSTIT could also simulate outbreaks caused by other entomopathogenic fungi such as Beauveria bassiana.
Acknowledgements This work was carried out with the financial support of a Long-term  EU-Africa research and innovation Partnership on food and nutrition security and sustainable Agriculture (LEAP-Agri ), project  Pest-Free Fruit , in the framework of the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  727715. It was also supported by the 2019  France-Senegal Research collaboration program  of the French Embassy in Senegal and by  CIRAD   AI1. We wish to sincerely thank  N. C. Manoukis  and an anonymous reviewer whose comments greatly improved an earlier version of the manuscript.
Context. The store of data collected in public astronomical archives across the world is continuously expanding and, thus, providing a convenient interface for accessing this information is a major concern for ensuring a second life for the data. In this context, Solar System objects (SSOs) are often difficult or even impossible to query, owing to their ever-changing sky coordinates. Aims. Our study is aimed at providing the scientific community with a search service for all potential detections of SSOs among the ESA astronomy archival imaging data, called the Solar System Object Search Service (SSOSS). We illustrate its functionalities using the case of asteroid (16) Psyche, for which no information in the far-IR (70-500 µm) has previously been reported, to derive its thermal properties in preparation for the upcoming NASA Psyche mission.
We also present a case study: the analysis of the thermal properties of Psyche from four serendipitous Herschel detections, combined with previously published thermal IR measurements. We see strong evidence for an unusual drop in (hemispherical spectral) emissivity, from 0. 9 at 100 µm down to about 0. 6 at 350 µm, followed by a possible but not well-constrained increase towards 500 µm, comparable to what was found for Vesta. The combined thermal data set puts a strong constraint on Psyche's thermal inertia (between 20 to 80 J m -2 s -1/2 K -1 ) and favors an intermediate to low level surface roughness (below 0. 4 for the rms of surface slopes). Conclusions. Using the example of Psyche, we show how the SSOSS provides fast access to observations of SSOs from the ESA astronomical archives, regardless of whether the particular object was the actual target.
Introduction Providing the Solar System research community with swift and easy access to the astronomical data archives is a long-standing The catalog is also available at the CDS via anonymous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/cat/J/A+A/659/A38 issue. Moreover, the consistently increasing store of archival data coming from a variety of facilities, both from ground-based telescopes and space missions, has led to the need for single points of entry for exploration purposes. However, moving targets seen at different sky positions and under very different observing geometries are not easy to aggregate within a single tool. In general, multi-epoch observations over several oppositions are required to compute the orbit to a sufficient level of accuracy required for targeted studies.
Released in May 2016, ESASky 2  is a Web application that sits on top of ESAC hosted archives, with the goal of serving as an interface to all high-level science products generated by ESA astronomy missions. The data spans from radio to gamma-ray regimes, and includes the Planck, Herschel, ISO, HST, XMM-Newton and INTEGRAL missions. In addition, ESASky provides access to data from other international space agency missions (e. g. , Chandra from NASA and AKARI and Suzaku from JAXA) and provides access to data from major astronomical data centers and observatories, such as the European Southern Observatory (ESO), the Canadian Astronomy Data Center (CADC), and the Mikulski Archive for Space Telescopes (MAST). ESASky is designed to be exceptionally visual  (Baines et al. 2017) , allowing users to: see where in the sky all missions and observatories have observed; find all available data for their targets of interest; overlay catalog data; visualize which objects have associated publications; perform initial planning of James Webb Space Telescope observations; and change the background all-sky images (HiPS;  Fernique et al.
2015)  from many different missions and observatories. However, a clear interface with the Solar System community in terms of the scientific exploitation of these astronomical data holdings is not typically accessible. Efforts to tackle this issue are already in place, such as the Solar System Object Image Search by the Canadian Astronomy Data Centre (CADC) 3   (Gwyn et al. 2012)  and SkyBoT 4  (IMCCE,  Berthier et al. 2006 Berthier et al. , 2008 Berthier et al. , 2009 Berthier et al. , 2016)) , plus a number of ephemeris services, such as Horizons 5  (NASA/JPL), Miriade  6  , and the Minor Planet & Comet Ephemeris Service 7  (MPC). In this first integration of the Solar System Object Search Service (SSOSS), we enable users to discover all targeted and serendipitous observations of a given SSO present in the ESA Herschel, HST, and XMM-Newton archives.
Upon user input, the official designation of the target is first resolved using the SsODNet 8  service. Then the search engine retrieves all the precomputed results for all the observations matching the input SSO provided. These results are pre-computed as a geometrical cross-match between the observation footprints and the ephemerides of the SSOs within the exposure time frame of the observations and stored in the service-dedicated database schema. To showcase the capabilities of this tool, the flux values at 70 µm and 160 µm of asteroid (  16 ) Psyche are reported from four serendipitous detections in the Herschel Space Observatory archival data. These are the first detections of this object in this energy regime, making these results of significant importance for the upcoming NASA Discovery Psyche mission, expected to be launched in 2022 to visit the asteroid in 2026.
Samples of Solar System objects The input asteroid catalog was retrieved from the Lowell Observatory Asteroid Orbital Parameter database (astorb 9 ). This database is continuously changing and growing, so this work is based on the snapshot taken on July 5, 2019, containing 795 673 objects. This catalog was selected based on the availability of the current position uncertainty (CEU) parameter, allowing for the propagation of uncertainties in time and thus permitting us to provide positional uncertainties for each potential detection, while also taking them into account for the cross-match computation, as explained in Sect. 3. 3. The distribution of asteroids in the Solar System is illustrated in Fig. 1 , where the bottom part depicts the semi-major axes and eccentricities of all asteroids in the astorb database.