dataesr/scientific-paragraphs-categorization huggingface dataset:
More precisely, Gaia DR3 was built upon 34 months of Gaia operations extending from July 2014 to May 2017. A A37, page 2 of 24 Gaia Collaboration: A&A, 680, A37  (2023)  well-observed Solar System body came into the Gaia fields of view about 25-30 times on average, resulting in up to nine accurate astrometric positions at every passage 1. Tanga et al. (2023)  ranked the orbits from excellent to very approximate, with a clear difference in reliability as a function of the fraction of the orbital period covered over 34 months. For the majority of the MBAs, this time was too short to remove strong constraints between orbital elements. In the processing reported in this paper, which yielded the Focused Product Release, the data arc can be as long as 66 months. This typically is a little longer than the orbital period of an average asteroid that orbits in the main belt between Mars and Jupiter.
Therefore, the situation considerably improved as soon as we had a few pairs of data points that lie one orbital period apart from each other. This is sufficient to remove the partial degeneracy that is observed when the arc was too short. Although we have almost twice as many observations as in Gaia DR3, the main source of improvement remains the complete coverage of the orbit in the data set, which is in sharp contrast with the previous solution in Gaia DR3, which was based on only 34 months of data. The length of arc that is used is the primary factor in the improved uncertainty of the orbit. In this paper, we aim to present and discuss the data that are released in the Gaia FPR, together with the results of the orbital fitting obtained in an extended time span, using a refined version of the fitting algorithm, with better weighting and an improved outlier rejection.
Several shortcomings in the code were found and corrected in this new exploitation, and the orbits we provide now are the best we can achieve at the moment with the extremely high astrometric precision of Gaia. It is important to mention at this stage that the orbital solutions are derived from the Gaia data alone, meaning that the arc is still relatively short in comparison with the fits performed at MPC or JPL using all the available observations that so conveniently archived by the MPC, which were collected over decades or even centuries. As shown by the results, the relatively short time-span is somewhat balanced by the homogeneity of the data and the unparalleled single-observation accuracy and precision. As mentioned above, no new photometric data in G band or reflectance spectra are released in the FPR.
5, along with some recommendation for users. 1 We adopted the following terminology for the data sequencing. A passage or a transit is the crossing of a Gaia field of view. During a passage, up to nine position measurements at the CCD level are possible (primarily in the along-scan direction), which are referred to as observations, or elementary observations to remove ambiguity. An epoch or a visibility period is a set of a few successive transits within one or two days, followed by a gap of several weeks before the start of a new period. Blue shows the first 100 000 numbered asteroids with a median of 330 individual observations (91 785 orbits), and red shows the numbered asteroids with numbers ≥100 000 and a median of 190 observations (64 979 orbits).
Properties of the data set Gaia scans the sky throughout the year in a continuous way, without virtually any significant interruption beyond unavoidable but short dead times. The principles have been described in several papers from the Gaia Collaboration, and we refer to the reliable information therein (Gaia Collaboration 2016a). The relevant facts for the orbital solutions we use here are the repeated observations of the same source, with an interval of 6-10 weeks between two sequences. A typical sequence consists of a small set of successive passages in the Preceding Field of View (pfov) and Following Field of View (ffov). The most common sequence is just a simple sequence of two passages pfov to ffov (PF) or ffov to pfov (FP) within 6 h, and then a few weeks before a new similar sequence.
2 orbital periods for the first group, and it is about 0. 5 orbital periods for the Trojans. The typical coverage is shorter at about one orbital period for the fainter asteroids, with a marked tail of short arcs (less than one orbital period) that is caused not by the periods themselves, but by a systematic of the Gaia sensitivity threshold: the faint asteroids are not observed at all elongations that are theoretically allowed by the Gaia scan, but only when their distance to the Earth is small enough to have an apparent magnitude <20. 7 in the Gaia white band. This on-board brightness cut is imposed by the instrument sensitivity and the ground-link data rate. An orbit is better determined when the observation arc covers a complete orbit or more, and the solution is less constrained for short arcs.
The Solar System objects are well sampled, however, just below the sky average, because their ecliptic latitude is predominantly low. This sky area is less frequently visited by Gaia than the mid latitudes. Each passage of a source in the field of view nominally includes nine crossings of the astrometric CCDs either in the preceding or the following field of view. This provides as many 1D astrometric locations in the instrument frame (pixel number) that are perfectly time tagged. These repeated measurements form the basic astrometric data from Gaia. Based on the instrument calibration and the orientation parameters (attitude), these instrument-tied positions can be transformed into nine astrometric positions on the sky, with an excellent accuracy (≈ sub-milliarcsecond to milliarcsecond level) in the scan direction and much poorer accuracy (up to 0.
These are also called observations in other contexts of the Gaia data processing. Consistency checks between the multiple 1D positions in the same transit were used to accept or reject observations for inclusion in the data release, taking the expected linear motion of the Solar System object during the ≈ 40 s of the field crossing into account. All these published observations are considered in the subsequent fits, and normal points for position and velocity were not computed. We implemented an updated version for the FPR reported in this paper for the astrometric calibration used in DR3. The global transformation that produces astrometric positions is also more accurate because it is now based on the full duration of the nominal Gaia mission of 66 months. Moreover, the FPR processing is mostly independent of the previous data releases.
This makes Gaia data outstanding and unusual in comparison with the core of the historical observations: they are extremely accurate, rather dense, homogeneous, but over a limited time range that is not too short to preclude orbit fitting. The orbit can be fit either with observation equations expressed in the along-scan and across-scan directions, or in A37, page 4 of 24 the more usual way in RA, Dec coordinates. The use of either coordinate set is rigorously equivalent theoretically in any case provided that the 2 × 2 variance or covariance matrix is well implemented in the global weight matrix. Tests have shown that identical results are obtained with both approaches, as expected, but great care must be exercised in using the statistical information in the covariance matrix in the right manner.
Units of time and length Gaia time tagging is the Barycentric coordinate time (TCB) internally for the data collection and processing, and externally in the public releases. This means that, for example, the Solar System ephemerides have TCB as an independent variable and that all epoch data are given with a timing in TCB. For stellar astrometry, and even for epoch photometry, the difference between TCB and TDB (or TT), which is about 20 s, is not an issue for the user. This is no longer true for Solar System objects moving at speed of several 10 mas per second, however. This has consequences for the orbit fitting. Observations are timed in TCB, and the equations of motions must be consistent with this timescale. More specifically, the solar mass parameter must be TCB compatible. Unfortunately, a small glitch has remained in the software between the TCB and our using of an outdated Solar System GM ⊙ equal to the square of the Gauss constant k = 0.
017 202 098 95. This is equivalent to our using a unit of length that is not strictly equal to the IAU astronomical unit. Because of the constraints of the complex integrated processing of the Gaia data and the huge validation pipeline, it was not possible to correct for the scale factor in the FPR results. It seems to be a benign correction, and it is, but nothing is simple in a complex system. Fundamentally, this is a mistake, and this issue has been fixed in the software for the planned Gaia DR4. The positive side of this unfortunate bug is that noticing a systematic effect as small as 0. 5 × 10 -8 in the results already means something good about the quality of the solution. Mixing the Gauss constant and the TCB timescale results in an au FPR = 149 597 871 473. 216 m instead of the IAU fundamental constant for the scale length in SI equal to 149 597 870 700.
Principles and algorithm The Gaia-centric astrometric data in right ascension and declination at CCD-level are adjusted using a standard weighted linear least-squares (LLS) fit. The error model with non-diagonal covariance matrix as derived in  Tanga et al. (2023)  provides the weights to be applied for each observation equation. The same set of asteroids as in Gaia DR3 is considered for this new release, but over an observational time span that is roughly twice as long. For the LLS method, the theoretical expectation is required. To spell out this function, the equations of motion are integrated simultaneously with the variational equations. In the ICRF reference frame and on the TCB timescale, the heliocentric equation of motion is expressed as ri = -(GM ⊙ + Gm i ) r i r 3 i + p f p , (1) where r i = |r i | and r i is the heliocentric position vector of asteroid i, GM ⊙ is the mass parameter of the Sun, and Gm i is the mass parameter of asteroid i, which can generally be neglected.
2019). No perturbation of asteroids is included yet in Eq. (  3 ). This is still a limitation compared to other databases of asteroid orbits, which consider systematic perturbations by several massive asteroids (e. g. Ceres, Pallas, or Vesta) together with the major planets, or for consistency with INPOP19a. This approximation on the dynamical model introduces a small bias on the orbital solution (quantified in internal validations; see Sect. 5. 3. 1), but almost no effect on the uncertainty of the orbits themselves. Thus we will compare and analyse the formal uncertainties of the orbital solution, not its accuracy. These systematic perturbations will be included in subsequent data releases (starting from DR4), together with other mutual perturbations, that should also allow the mass determination from a global inversion.
Because mutual interactions between asteroids are neglected, Eqs. (  1 ) and (  3 ) can be integrated simultaneously with the variational equations for each asteroid separately. The small corrections relative to the reference orbit are then computed at a given reference epoch by an LLS method that minimises the residuals on the astrometric position. The correction is determined for the six-dimension initial state-vector in Cartesian coordinates taken from an auxiliary file of orbital elements. In our case, it was derived from the astorb database 4  and was propagated to the reference epoch. The reference epoch for the orbital fit was taken to be the mid-point of the total time span of the observations available for the given asteroid as a proxy for the weighted mean time of the observational arc.
2005 , http:// healpix. sourceforge. net/); -Vaex  (Breddels & Veljanoski 2018) ; -the Hipparcos-2 catalogue (van Leeuwen 2007). The Hipparcos and Tycho catalogues were constructed under the responsibility of large scientific teams collaborating with ESA. The Consortia Leaders were Lennart Lindegren (Lund, Sweden: NDAC) and Jean Kovalevsky (Grasse, France: FAST), together responsible for the Hipparcos Catalogue; Erik Høg (Copenhagen, Denmark: TDAC) responsible for the Tycho Catalogue; and Catherine Turon (Meudon, France: INCA) responsible for the Hipparcos Input Catalogue (HIC); -the Tycho-2 catalogue  (Høg et al. 2000) , the construction of which was supported by the Velux Foundation of 1981 and the Danish Space Board; -The Tycho double star catalogue (TDSC,  Fabricius et al. 2002) , based on observations made with the ESA Hipparcos astrometry satellite, as supported by the Danish Space Board and the United States Naval Observatory through their double-star programme; -data products from the Two Micron All Sky Survey (2MASS,  Skrutskie et al.
2006) , which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center (IPAC) / California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA) and the National Science Foundation (NSF) of the USA; -the ninth data release of the AAVSO Photometric All-Sky Survey (APASS,  Henden et al. 2016) , funded by the Robert Martin Ayers Sciences Fund; -the first data release of the Pan-STARRS survey  (Chambers et al. 2016; Magnier et al. 2020a; Waters et al. 2020; Magnier et al. 2020c,b; Flewelling et al. 2020). The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for.
Anders), the Severo Ochoa Centre of Excellence Programme (SEV2015-0493) and MCIN/AEI/10. 13039/501100011033/ EU FEDER and Next Generation EU/PRTR (PRTR-C17. I1); the European Union through European Regional Development Fund 'A way of making Europe' through grants PID2021-122842OB-C21, PID2021-125451NA-I00, CNS2022-13523 and RTI2018-095076-B-C21, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu') through grant CEX2019-000918-M, the University of Barcelona's official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory project funded by MCIN/AEI/10. 13039/501100011033/ through grant PID2020-112949GB-I00; the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), funded by the Xunta de Galicia through the collaboration agreement to reinforce CIGUS research centers, research consolidation grant ED431B 2021/36 and scholarships from Xunta de Galicia and the EU -ESF ED481A-2019/155 and ED481A 2021/296; the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2017-2-0002, AECT-2017-3-0006, AECT-2018-1-0017, AECT-2018-2-0013, AECT-2018-3-0011, AECT-2019-1-0010, AECT-2019-2-0014, AECT-2019-3-0003, AECT-2020-1-0004, and DATA-2020-1-0010, the Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project 'Models de Programació i Entorns d'Execució Parallels' (MPEXPAR), and Ramon y Cajal Fellowships RYC2018-025968-I, RYC2021-031683-I and RYC2021-033762-I, funded by MICIN/AEI/10.
In this paper, we explore the ability of distributional semantic models (DSMs) to capture synonymy between MWTs in the environmental domain in English and French. Our study focuses on nominal MWTs composed of two lexical words (i. e. , biterms). Two methods are proposed. The first is based on lexical substitution using a masked language model (MLM). The second captures synonymy through analogy between STs and MWTs representations in a Fast-Text  (Bojanowski et al. , 2017)  model. Both methods are tested on two datasets of English and French MWT synonyms of the environment domain extracted from the IATE translation dictionary. Section 2. presents related works on the acquisition of relations between words and terms. Our methods are introduced in Section 3. Section 4. presents the resources we used to create our data set and to perform the experiments.
Data Corpus. We used the English and French monolingual PANACEA Environment corpora (ELRA-W00653 and ELRA-W0065) which were built in the framework of the PANACEA project 1. These corpora are more heterogeneous than typical specialized ones which normally contain specialized texts only because the environmental domain is heterogeneous in nature Bernier-Colborne (2016). DicoEnviro. DiCoEnviro 2  is a multilingual dictionary of environmental terms developed by the Observatoire de linguistique Sens-Texte (OLST) 3. It describes the meaning and linguistic properties (especially the lexical-semantic ones) of terms belonging to various sub-domains of the environment domain. IATE. IATE 4  (Interactive Terminology for Europe) is an EU translation terminology resource that contains synonymy relations between terms.
It is a rich resource from which datasets can easily be extracted. Data sets. Our datasets are created from IATE. We extracted 786 pairs of synonymous English biterms (we will call this set Data_en in the following) like climate conference : climate summit and 928 pairs in French (we will call this second set Data_fr in what follows) like analyse du risque: risk study. We manually validated the synonymy relation between the biterms in the extracted pairs. In order to further select our data, we performed an analysis of the pairs extracted from IATE. We observed that more than 85% of the pairs of synonymous biterms share one lexical word. We used the following subsets of Data_en and Data_fr in our experiments: Data_MLM_en (510 pairs) and Data_MLM_fr (563 pairs) are made up of MWT pairs that have the same pattern and share one lexical element while Data_FastText_en (431 pairs) and Data_FastText_fr (599 pairs) contain MWT pairs of frequency higher than 5 in PANACEA.
Lexical substitution Data sets. The lexical substitution experiments were performed using Data_MLM_en and Data_MLM_fr. We removed the pairs of biterms in which one of the lexical items is not included in the vocabulary of the model because outof-vocabulary words are divided into several wordpieces, which makes the identification of the possible substitutes for the target word difficult. Moreover, we need contexts to build the queries submitted to the MLM model. For each MWT in the dataset, we extracted 100 contexts from the corresponding PANACEA corpus. Only contexts that meet some of the quality criteria of good contexts proposed by  Kilgarriff et al. (2008)  were selected. Note that some MWTs appear less than 100 times in PANACEA. Overall, the data used for the lexical substitution experiment in English consists of 317 term pairs and 24,265 contexts (referred to as Test_MLM_en).
Analogy Data sets. The data used for the analogy experiments are quadruplets W 1 : W 2 :: MW T 1 : MW T 2 such that MW T 1 contains W 1 ; MW T 2 contains W 2 ; MW T 1 and MW T 2 share a word W 3 ; W 1 and W 2 are synonyms. For each of the two languages, two datasets were created using the biterms pairs in Data_Fasttext_en and Data_Fasttext_fr and the synonymy relations in DiCoEnviro and IATE. These datasets will be referred to as Quad_IATE_en (9 quadruplets) and Quad_Envi_en (33 quadruplets) for English and Quad_IATE_fr (20 quadruplets) and Quad_Envi_fr for French (63 quadruplets).
Results and discussions The results of the lexical substitution and analogy experiments are presented in Tables  1  and 2. Overall, the two methods perform similarly on the English and French datasets. 1  shows that lexical substitution results are improved by query conditioning. The MRR scores increase from 0. 304 to 0. 443 for English biterms and from 0. 302 to 0. 374 for French biterms. Precision is also improved. Query conditioning improves synonymy acquisition in English more than in French. This could be due to the fact that we used different MLMs for the two languages. A second possible reason could be that MWT contexts in English are less informative than those in French, which could be roughly estimated by the length of the contexts: on average, English queries contain 30 words while French ones contain 35 words.
Method Table  2  shows that analogy captures synonymy between MWTs effectively. The best MRR score of 0. 744 is obtained for Quad_IATE. We can also see that the quadruplets constructed using relations between simple terms from IATE give the best results. These good numbers could be explained by the fact that the synonymy relations between MWTs and the simple terms that compose these quadruplets come from the same source. Remember that IATE is a translation dictionary while DiCoEnvio is a terminology database. We conducted a qualitative analysis similar to the one we did for lexical substitution. We examined the first 5 predictions of all queries whose unknown is MW T 2. We observed that MW T 2 is present in the first five candidates for more than 70% of the quadruplets. When MW T 2 does not appear among the first five candidates, we found in most cases one of its synonyms among the candidates.
Moreover, FastText representations are built on a small specialized corpus, while BERT models are pre-trained on a large variety of corpora. The differences also arise from the fact that BERT is queried at the occurrence level, whereas FastText representations are based on a set of occurrences. Our analogy-based method is better suited for synonymy identification. This observation is consistent with that of Peters et al. (  2018 ) who show that contextual language models underperform compared to static models on analogy-based semantic relation identification tasks. It should also be noted that the analogy method gets more information than the lexical substitution method because it is provided with the two related simple terms, which is not the case for the latter. The better results obtained with analogy also suggest that semantic composition in MWTs is better captured by FastText models than it is by MLMs.
Conclusion In this paper, we explored the capacity of DSMs to capture synonymy between biterms of the environment domain in English and French. We performed experiments using MLMs for the lexical substitution method and using static FastText models for the analogy method. The results of the experiments show that overall, both methods perform well in both languages. However, the analogy methods outperform the lexical substitution methods. The analogy method obtains an MRR score of 0. 744 on the French dataset extracted from IATE. Our results also suggest that semantic composition is better grasped by static dives; conversely, the level of compositional generalization of Transformer models seems to be lower. Overall, this study is one of the first attempts to identify synonymy between MWTs in a specialized domain, the environment, by exploring DSMs.
Figure3shows the relative covariate contribution to the individual SOC stock observations of the calibration dataset as a function of covariate values. The four most important covariates of Fig. 2contributing to the predictions have a well-defined pattern. The covariate contribution to the SOC stocks prediction tend to increase with elevation and temperature for values up to 1800 m and 5°C, respectively, but sharply decreases for higher annual mean temperature or levels off for higher elevation. For 235.