dataesr/scientific-paragraphs-categorization huggingface dataset:
From the HARPS data, we obtain a  ★ value of 21. 2 km s -1 that agrees with the Gaia value of 20. 7 km s -1 (Gaia Collaboration et al. 2021). To assess the robustness of the fitted eccentricities we conducted two further analyses of the data. Firstly, we carry out a fit of the complete transit photometry and RV dataset setting the eccentricities of both planets to 0 whilst using the same priors as detailed above for the remaining parameters. We find a difference in log evidences between this fit and our global analysis of Δln = 17. 8 in favour of the non-zero eccentricity solution, indicating a decisive preference for this result  (Kass & Raftery 1995; Gordon & Trotta 2007). Second, we fit the transit photometry using the same priors as previously set on the appropriate parameters to evaluate the contribution of the RVs.
RV Model Comparison To give confidence in the subtle RV signal of TOI-1064 c found by the global analysis, we conduct two additional fits of the complete transit photometry and RV dataset. In these supplementary analyses we set the priors of the majority of the parameters as detailed above, and either remove planet c from the RV model or fix the semi-amplitude of TOI-1064 c to 0. By comparing the log Bayesian evidences between these analyses and our previous fit, we find Δlns of 55. 6 and 26. 1, respectively, in favour of our global analysis presented in Section 4. 3. 2. This indicates decisive preferences of using a non-massless prior to model the RV signal of TOI-1064 c  (Kass & Raftery 1995; Gordon & Trotta 2007) , and provides evidence for the validity of the tenuous signal seen in the HARPS RVs.
The values on top of each column are the mean and 5% and 95% quantiles. Next to each corner plot, we also show illustrations of both planets representing the radius fractions of the high-Z components (inner core and mantle; brown), water layer (blue), and gas envelope (grey), corresponding to the medians of the posterior distributions, with the dashed line outer rings representing the uncertainty on the total radius. preference. However, given the more comprehensive datasets analysed in our global fit, we adopt the values presented in Table  4  as the nominal values for this system.
SUMMARY In this study, we present the multi-planet TOI-1064 system identified by TESS. We report the discovery of the pair of sub-Neptunes; TOI-1064 b and c, and characterise both planets using high-precision space-based photometry from TESS and CHEOPS, and ground-based photometry from LCOGT, NGTS, and ASTEP, and HARPS RVs. We also present a novel method for detrending photometric data via the monitoring of PSF shape changes and successfully apply it to the CHEOPS photometry. From our analyses find that TOI-1064 is a bright K-dwarf with a stellar rotation period of ∼26 d, that hosts two warm sub-Neptunes, TOI-1064 b and c, on orbital periods of ∼6. 44 d and ∼12. 23 d. Interestingly, although this pair of sub-Neptunes has similar radii, the masses are substantially different resulting in a density disparity.
The mass fractions of the component layers and the total refractory mass of TOI-1064 b may help shed light on the formation mechanisms for such dense bodies. Whilst there are similarities in the mass fractions for the high-Z components of both planets, TOI-1064 c likely has an extended gaseous envelope. Our atmospheric escape evolution modelling shows that TOI-1064 b likely lost its primordial atmosphere after migration through the protoplanetary disc. TOI-1064 c may have also lost some of its envelope subsequent to migration, however understanding the origin and evolution of this planetary system requires first the acquisition of additional RV data to better measure the mass of planet c. Future atmospheric studies may shed light on whether the atmosphere of TOI-1064 b is primordial or secondary in nature, and may potentially constrain formation pathways of TOI-1064 c after the acquisition of further RV data.
ACKNOWLEDGEMENTS The authors would like to thank the anonymous referee for helpful comments that improved the manuscript. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. Funding for the TESS mission is provided by  NASA's Science Mission Directorate. We acknowledge the use of public TESS data from pipelines at the  TESS Science Office and at the TESS Science Processing Operations Center.
MSIP is funded by  NSF. Based on data collected under the  NGTS  project at the  ESO La Silla Paranal Observatory. The NGTS facility is operated by the consortium institutes with support from the  UK Science and Technology Facilities Council (STFC)  projects  ST/M001962/1  and  ST/S002642/1. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work makes use of observations from the ASTEP telescope. ASTEP benefited from the support of the  French and Italian polar agencies IPEV  and PNRA in the framework of the  Concordia station program  and from  Idex UCAJEDI  ( ANR-15-IDEX-01 ).
T. G. W. , A. C. C. , and K. H. acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant  ST/R003203/1. Y. A. and M. J. H. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. D. G. and L. M. S. gratefully acknowledge financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseous or rocky?Unveiling the nature of small worlds". D. G. , M. F. , X. B. , S. C. , and J. L. acknowledge their roles as ESA-appointed CHEOPS science team members. C. M. P. , M. F. , J. K. , and A. J. M. gratefully acknowledge the support of the  Swedish National Space Agency (SNSA. ; DNR 65/19 ,  174/18 ,  2020-00104 , and Career grant  120/19C ). A. De. and D. E. acknowledge support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project F A ; grant agreement No  724427 ).
2018/0098 ). This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. M. L. acknowledges support from the  Swiss National Science Foundation  under grant no. PCEFP2_194576. P. F. L. M. acknowledges support from  STFC  research grant number  ST/M001040/1. L. D. N. thanks the  Swiss National Science Foundation  for support under  Early Postdoc. Mobility  grant  P2GEP2_200044. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). I. R. cknowledges support from the  Spanish Ministry of Sci-ence and Innovation  and the  European Regional Development Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
2014 ) , EAGLE  (Schaye et al. 2015 )  and IllustrisTNG  (Pillepich et al. 2018  ) simulations, has been to produce a 'random' representative patch of the Universe that can be statistically compared to the one we observe, in terms of the properties of the large-scale structure, clustering statistics, the E-mail: stuart. mcalpine@helsinki. fi galaxy abundance and di versity, etc. Ho we ver, whilst these simulated universes might reflect the observed Universe statistically, they do not contain the specific objects (such as the Local Group, or the Virgo and Coma clusters), embedded within the correct large-scale structure, that we actually observe. There remains an underlying tension between an observational data set, which is subject to cosmic variance, and the ensemble mean predictions from cosmological simulations.
SIBELIUS-DARK was run on 160 compute nodes (using 320 MPI ranks for a total of 4480 compute cores) of the COSMA-7 DiRAC facility 4  hosted by Durham University, for a total of 3. 5 million CPU hours o v er 14 845 time-steps. We note, ho we v er, that the v ersion of SWIFT used did not exploit a domain decomposition algorithm tailored for zoom simulations. Up-coming code impro v ements in this direction are expected to reduce the required CPU time for such a simulation by at least 25 per cent.
A P P E N D I X A : P U B L I C DATA R E L E A S E With the publication of this study comes the public data release of the SIBELIUS-DARK galaxy and halo properties at z = 0. As with previous Virgo Consortium projects, the data will be made available through an SQL data base. Users familiar with the Millennium data base  (Lemson & Virgo Consortium 2006 )  and the EAGLE data base  (McAlpine et al. 2016 )  will recognize the main features of the interface and should be able to adapt their scripts easily to the SIBELIUS-DARK data base. Therefore here we do not provide an indepth description of the data base layout, but we do describe the unique considerations when using the SIBELIUS-DARK data, along with some examples. In the future we plan to append the SIBELIUS-DARK data base with additional simulation outputs, which will allow users to track the evolution of particular objects through cosmic time.
The SIBELIUS-DARK data can be accessed through the VirgoDB portal ( https:// virgodb. dur. ac. uk/ ), either via the Millennium or EAGLE interface. The data base containing the SIBELIUS-DARK data is named 'McAlpine2022a', and is divided between two tables: a Halo table (properties listed in Table  A1  ), which stores the mass and size for each dark matter halo, and a Galaxy table (properties listed in Table  A2  ), which stores various properties for each galaxy/subhalo that populate these haloes. The objects from the Galaxy table can be linked to the Halo table using the hosthaloid (see the examples). There are al w ays unique considerations to be aware of when using any particular data set. When interpreting the results from SIBELIUS-DARK data, users should consider the following: MNRAS 512, 5823-5847 (2022) (iv) Simulation resolution : as with all simulations there is a minimum halo mass we can resolve.
18  ). By traditional conventions, this classifies the Milky Way as a 'satellite' of the Andromeda in the simulation, as the more massive Andromeda subhalo/galaxy is assigned to be the 'central'. This is why the Milky Way has no reported halo mass in Table  B3  (as the structure finder, HBT + , only computes o v erdensities for central subhaloes), and why it shares the same hosthaloid /halo mass as Andromeda in the public data base. (vi) The data are not perfect: whilst we believe SIBELIUS-DARK to be the most comprehensive constrained realization simulation to date, we understand it is not a perfect representation of our Local Volume. We would therefore urge caution as to not o v erinterperate the results from the simulation, and instead use them as plausible predictions for how structures in the Local Volume may have come to pass.
A1 SQL examples Here we list three example SQL queries that return the necessary data to reproduce  Figs 3 , 9 , and 16. Barring Figs 1 , 4 , 14 , 15, and 18  , which make use of the raw particle data, all the figures of this paper are reproducible with similar queries. Fig. 3  : the halo mass function in spherical apertures. This example returns the masses and distances of all SIBELIUS-DARK haloes with a mass in excess of M 200c ≥ 10 9 M. We must link the Halo table to the Galaxy table via the hosthaloid in order to retrieve the distance information. The distance to a halo is assumed as the distance to the haloes most massive subhalo/galaxy, i. e. the one with rank = 0. Fig. 9  : r -band counts in the North Galactic Cap. This example returns the redshift and apparent r -band magnitude of SIBELIUS-DARK galaxies within the North Galactic Cap region of the SDSS surv e y.
version 0. 9. 0. This w ork w as supported by the Academy of Finland: grant numbers 314238 and 335607. PHJ acknowledges the support by the European Research Council via ERC Consolidator Grant KETJU (no. 818930). MS is supported by the Netherlands Organisation for Scientific Research (NWO) through VENI grant 639. 041. 749. CSF acknowledges support by the European Research Council (ERC) through Advanced Investigator DMIDAS (GA 786910). GL acknowledges financial support by the French National Research Agency for the project BIG4, under reference ANR-16-CE23-0002, and MMUniverse, under reference ANR-19-CE31-0020.
Many other methods can be used, but the principal purpose behind this choice is to find global solutions and to evaluate or approximate the solution of the cost function, i. e. , the FSC. PSO is implemented by the particlewarm() function in the MATLAB global optimization toolbox and is adequate for solving the minimizing problem. The application of the FSC algorithm with PSO directly reveals solutions as parameters of the damage assessment steps as if the initial damage has already been detected. There are several parameters in PSO, i. e. , the swarm size, number of iterations, inertia weight, learning factors, etc. We use default values for most of the PSO parameters because it allows us to obtain the correct results. In FSC minimization, only the swarm size and number of maximum iterations with values of 20 and 100 are explicitly specified.
Numerical Beam Model The Euler-Bernoulli cantilever beam is discretized using 100 elements, as described in Section 2. 1, and its material and physical properties are given in Table  1. Modal parameters for healthy and damaged beams are generated using MATLAB. First, it is important to update Young's modulus of the 2D FE model in order to closely match the natural frequencies of the 2D FE healthy model to a healthy real experimental beam. To determine the updated Young's modulus, a maximization problem is formulated that uses the inverse of the statistical error on implementing the natural frequencies of the 2D numerical and experimental healthy beam. The function of Young's modulus updating (E) is as follows: E = Argmax   1 1 m | ∑ m i=1 f 2D i -f h i 2 |   (9) where f 2D i represents natural frequency of updating 2D FE beam model and f h i represents identified natural frequency of healthy experimental beam.
The proposed technique (see Section 3. 2) and algorithm (see Section 3. 3) are implemented to classify the position and severity of the damage. The technique is evaluated using the FE updating strategy of bending stiffness within 2D vs. 2D FE models. Based on FSC, the 2D healthy and damaged case natural frequencies are extracted, performed with other healthy 2D and stiffness updating damaged model natural frequencies in order to minimize FSC. At the same time, the PSO (as implemented by Particleswarm() from MATLAB global optimization toolbox) with the FSC algorithm is then used to locate and estimate the severity of the damage using the FSC objective function, as defined in Equation  (7). FSC value arrives zero or close to zero. Here, Particleswarm() function works as part of the algorithm to find optimal global solutions (position and severity).
During the experimental beam tests, two accelerometers are mounted at different beam positions (0. 35 m and 0. 75 m). Experimental beam configurations and setup are shown in Figures  10  and 11. A hammer impact test was conducted, and signals with LMS multi-analyzer (Siemens LMS software, Plano, TX, USA) were used to record the vibration measurements. The input excitation was produced by the hammer and output data was recorded by the monoaxial accelerometer (Brüel and Kjaer-DeltaTron Type 4507). The beam was clamped on one side with a strong mechanical hinge, while the other side was free. The experimental setup (see Figure  10 ) was then designed in such a way as to extract natural frequencies from the beams (healthy and damaged). FRFs (see Figure  12 ) of the healthy and damaged cantilever beam, were used to acquire natural frequencies by implementing the circle fit method with EasyMod Module  [47]  using MATLAB.
PDR Performance In the previous subsection, we have identified the number of interfering VLC links. Therefore, in order to have a successful communication the signal quality has to be sufficiently high compared to the total interference power. In the following, using Matlab, we evaluate the impact of multi-user interference on the packet delivery ratio between a given transmitter and receiver on a seven-lane straight road as illustrated in Figure  5. The Matlab simulation generates a random packet of 256 bits for each vehicle. Then, those packets are encoded with the code and vehicle lane, corresponding to the ongoing simulation, except for the No-MAC simulations. Finally, the data encoded is modulated in Manchester and sent to a Simulink model. We add the channel effects to the data on the Simulink program to recover it after an analog filter.
• To ensure vehicles in adjacent lanes use different codes so that interference from adjacent lanes is greatly reduced if not completely suppressed (depending on the code type), • To reduce the probability of using the same code for vehicles in the same lane or vehicles in non-adjacent parallel lanes. We assume that individual vehicles, particularly automated vehicles, are aware of their positions and the road lane (i. e. , the lane id)  [27]. As illustrated in Figure  7 , individual lanes have distinct identifications. In the proposed scheme, we have divided the total number of available spreading codes, N, into n code groups where each code group is allocated to vehicles in particular lanes. When N is 24 and n is 4, each code group contains six codes. Each code group is assigned to vehicles in particular lanes, e.
Performance Evaluation In this section, we evaluate the performance of the proposed MAC protocol for a seven-lane highway scenario illustrated in Figure  5. We implemented the proposed VLC MAC and VLC physical layer in a Matlab simulator and evaluated the PDR performance of the proposed MAC with OOC and PN codes. The results of the proposed MAC were compared with the case where no MAC was implemented (hereinafter no-MAC solution). The simulation parameters are listed in Table  1  and the Simulink model is shown in Figure  11. The Matlab script updates the position of the interfering vehicle in 10 cm increments, randomly encodes the data generated using the PN or OOC codes depending on the case, and transmits the packet using Manchester modulation. Finally, after passing through the free-space optical (FSO) channel, the receiver filters the signal, demodulates, decodes, and stores the data received for subsequent comparison with the transmitted data.
Conclusions VLC V2V is characterised by LOS and direct links, therefore, multi-user interference becomes an issue. In this paper, we provided an overview of existing MAC protocols and proposed a CDMA-based MAC protocol. In the proposed MAC protocol, nodes individually select CDMA codes based on their position and the current lane ID. We implemented the proposed MAC protocol in the Matlab simulator and evaluated the PDR performances when OOC and PN codes were used. The simulation results show that the proposed scheme, regardless of the code type, can provide a PDR greater than 90%, with an improvement in performance of up to 40% compared to the no-MAC solution. Moreover, the simulation study on asynchronous communications revealed that the PN codes performed similar to the OOC codes, and the impact of the interference is reduced for both codes.
Analyses of distribution and habitat After the analyses of descriptions and reference materials showed that the four species under study could not be distinguished on a morphological basis, a geographical database was constituted, including the localizations of 78 herbarium specimens, photographs and observations listed above. These sites were mapped and a global distribution model was developed, using the MAXENT 3. 4. 1 software and 19 bioclimatic variable layers from the Worldclim 2. 1 database at a 2'30" grid resolution (corresponding roughly to 4. 4 × 4. 6 km at Equator; https://www. worldclim. org/data/worldclim21. html;  Fick and Hijmans 2017). MAXENT identifies potential distribution areas based on their similarity in climatic conditions compared to those at the sites where the species has already been observed, hence modeling where conditions are suitable for their development.
acuminata with a pubescent ovary. The analysis of the MAXENT model derived from our global dataset (all geolocalized specimens irrespective of their previous assignment) shows that the most determinant bioclimatic variables are the temperature diurnal and annual ranges, the maximum temperature of the warmest month, the mean temperature of the warmest quarter, the precipitation seasonality, with the precipitations of the driest month and those of the driest and coldest quarters. These variables were thence selected for the Principal Component Analysis. The principal plane, accounting for two thirds of the total variance, allowed a fairly good representation of the bioclimatic space of the geolocalized specimens (Fig. 1 ). The first and main observation is the absence of any appreciable differentiation in climatic adaptation between the four taxa under revision.
acuminata. Indeed, P. nitida and P. ambigua have been repeatedly collected there, but no P. acuminata. The MAXENT bioclimatic model also points to very favorable areas along the Central Andes, southward to Bolivia and southern Brazil, but they are not met by any observations beyond Peru. The lack of observations of P. acuminata in several extensive areas that combine highly favorable climates and a relatively good level of botanical prospection suggests that the species distribution is significantly constrained by non-climatic factors, justifying further ecological analyses. Before such studies are carried out, our MAXENT distribution model can be considered as a guide for collecting more data. The ecological information associated with herbarium specimens and the precise geolocalization associated with iNaturalist photograph databases allow inquiring on habitat parameters at a smaller scale, either from label information or from available geographic information (e.
Acknowledgements This research has been carried out with support from the  Herbarium of the Pontificia Universidad Católica del Ecuador  and the  Ecuadorian Ministry of the Environment. We especially thank  Daniela Cevallos  and  Katya Romoleroux  for their help during our expedition in Ecuador. The first author also thanks the  Missouri Botanical Garden (MO) ,  Jim Solomon , and  John MacDougal  for their help during the week he spent in the herbarium at  St. Louis ,  Luiz Otavio Adão Teixeira ,  David Scherberich  for their stunning pictures of P. acuminata, and the different herbaria with which we have collaborated for this work:  Paris herbarium (P), Lyon Botanical Garden (LYBG, Frédéric Danet), Cayenne herbarium (CAY) ,  New York Botanical Garden (NY) ,  Kew Garden  (K),  Museu Goeldi (MG) ,  Instituto Nacional de Pesquisas da Amazônia (INPA) , the  Smithsonian herbarium (US) , the herbaria of the  Universidad Nacional de Colombia  (COL, ME-DEL and VALLE), the  Medellín Botanical Garden (JAUM) , the  University of Antioquia (HUA) , and the  Universidad del Valle (CUVC).