dataesr/scientific-paragraphs-categorization huggingface dataset:
Predator foraging success: long-term macaroni penguin arrival body mass data Fieldwork was conducted at Marion Island (hereafter MI; 37. 8 o E, 46. 9 o S) where arrival body mass of adult macaroni penguins at breeding colonies has been collected annually since 1994  (Crawford et al. , 2006). Measurements of adult males and females (up to 100 each, per year) were taken each October during the separate peaks in arrival of each sex. Sexes were separated using bill measurements, and timing of arrival, with males arriving approximately-two weeks earlier than females. Upon arrival from the sea, individual birds were caught and weighed in a harness suspended from a spring balance accurate to 10 g  (Crawford et al. , 2006). For our purpose, we used masses collected from 1998 to 2018, matching the temporal coverage of available modelled epipelagic micronekton biomass (detailed below).
Macaroni penguins are size dimorphic, with males capable of performing slightly deeper and longer dives  (Green et al. , 2005). However, both sexes share similar foraging areas  (Thiebot et al. , 2011) , and trends in their masses are highly correlated  (Crawford et al. , 2006) , suggesting sexes likely respond similarly to changes in prey. We, therefore, combined data from both sexes to create a population-level metric of mean annual arrival body mass. Before this, we used stratified random sampling to equalise within-year sample sizes between sexes, eliminating bias towards the more sampled sex. We transformed our time series of mean annual mass into a z-score by subtracting the 21-year mean and dividing by the standard deviation. All data preparation and analyses were conducted in the R programming environment (R Core Team, 2020).
During this time, their latitudinal distribution can be practically constrained within an SST envelope using the SST range of 2-5 • C (Fig. 3a. and  Bost et al. , 2009; Thiebot et al. , 2011). We obtained SST estimates from the GLORYS12V1 Global Ocean Physical Multi Year Reanalysis Product, covering the same date range as modelled prey biomass. SST model output was available at a 1/12 • spatial resolution and daily timestep. To allow comparison between datasets, we first resampled SST to match the spatiotemporal resolution of the prey data (0. 25 • and seasonal average). Given that the position of Southern Ocean fronts varies seasonally (e. g. ,  Pauthenet et al. , 2018)  and likely the corresponding distribution of penguins, we computed our SST envelope as the proportion of time (weeks) each year that each cell fell within the 2-5 • C isotherm band (step 2, Fig.
4. for examples of correlations). We re-scaled our individual time series to a z-score following the same approach for penguin arrival body mass. For each window, we examined the performance of modelled prey biomass in explaining penguin arrival body mass by fitting a generalised least squares model (GLS; package nlme;  Pinheiro et al. , 2020)  incorporating an AR1 temporal autocorrelation structure for year (step 5, Fig. 2. ). Windows for which the GLS model estimated a prey coefficient with a p-value (F-statistic) of < 0. 05 were considered significant. Through this process we obtained a subset of longitudinal windows (within the domain latitudinally constrained via temperature) for which mean prey biomass best-explained variation in penguin arrival body mass (see Fig. 4. for an illustration of correlations within three separate longitudinal windows).
This manner of spatial segregation is well documented across different populations of macaroni penguins (e. g. ,  Thiebot et al. , 2014; Thiebot et al. , 2011). When populations are of unequal size, the smaller should be displaced from shared space  (Bolton et al. , 2019). As such, we defined our realised foraging area as the combined probability of suitable and accessible habitat, given the likelihood that the same area is not also accessible to a larger population. To represent this, we used our SST envelope to denote latitudinal distribution for macaroni penguins. We calculated the minimum distance between each cell and the colony using R package raster  (Hijmans, 2021). We then modelled accessibility from the colony as a Gaussian cumulative distribution function with a mean and standard deviation of Fig.
5b. ). Based on our SST envelope, and distance-and competition-related availability of habitat (Fig. 5. ), we expected that important foraging areas would extend south and westwards from MI, with little extension into the region between MI and the CI. ed. Trends in arrival body mass generally mirrored those of our modelled prey field, particularly in directional change between years (Fig. 6. ). A GLS fit using the weighted mean of prey biomass from our emergent foraging area indicated a significant correlation (coefficient: 0. 62; p = 0. 02; residual SE: 1. 06) with mean arrival body mass of macaroni penguins. Furthermore, the estimated correlation coefficient and p-value were respectively significantly higher and lower than the mean test scores derived from randomly sampling the dataset (p < 0.
This is further demonstrated in our study by the superior performance of modelled prey fields over primary production at predicting penguin foraging success. This work has focused on predators that primarily target micronekton. It is likely that the links between foraging success and SEAPODYM-modelled prey field biomass will not hold for predators that primarily target other prey. For example, the same modelled prey fields do not perform better than environmental covariates in explaining the Fig. 7. Location of prey-informed emergent (orange) and theoretically expected (green; see section 2. 3 for methods) foraging areas computed during this study, overlayed on track-derived observed (blue) distributions (95% kernel utilisation distribution, computed using package adehabitatMA  (Calenge, 2006) ) of winter migrating macaroni penguins, modified from  Reisinger et al.
, 2008). This foraging strategy probably decouples their distribution from mid-trophic prey. In contrast to this, recent work on zooplankton-feeding cetaceans (fin whales Balaenoptera physalus) has found a positive relationship between foraging behaviour (call frequency) and modelled prey biomass  (Romagosa et al. , 2021). Nonetheless, it is worth noting that some model components and inputs require further development. Estimates of primary production used as a forcing for SEAPODYM are derived from a global empirical model (VGPM, case 1: OCEAN) using satellite data (ocean colour, SST, and Photosynthetically Active Radiation). This product is known to carry uncertainty, particularly in the high latitudes  (Johnson et al. , 2013). In addition, SEAPODYM is unable to resolve features in shallow waters associated with coastal or island environments.
Finally, vertical structuring of depth layers within SEAPODYM is based on euphotic depth, due to the governing role of light intensity on vertical migrations. However, defining euphotic depth in winter at high latitudes (such as used in this study) is challenging and potential biases in the thickness of layers might have important consequences for the dynamics and transport of prey. Further studies in this region combining bioacoustics, profilers and animal tracking data could greatly improve model calibration at high latitudes.
Correlation-derived emergent foraging areas to approximate observed foraging areas The overlap between our emergent and observed penguin foraging ranges leads to interesting questions about the potential for deriving predator foraging areas using the relationship between modelled prey biomass and predator foraging success, in the absence of tracking information. This could be useful in identifying potential foraging areas of populations where foraging success metrics are collected, but for which tracking data do not exist or are constrained to a specific annual stage (e. g. , breeding). Nonetheless, care should be taken in extrapolating these methods to other species and regions. We constrained our potential foraging habitat latitudinally using a priori knowledge of the temperature range within which macaroni penguins forage.
Acknowledgements This study received support from the  European H2020 International Cooperation  project  Mesopelagic Southern Ocean Prey and Predators  No  692173. D. B. Green was funded through a  Tasmania Graduate Research Scholarship. S. Bestley  was supported by the  Australian Research Council  under  DECRA  award  DE180100828. Penguin arrival body mass data were collected through a research programme funded by the  South African Department of Forestry, Fisheries, Environment , which also provided logistical support. We thank all field personnel who helped with the collection of data. This research was supported by the  Australian Research Council Special Research Initiative, Australian Centre for Excellence in Antarctic Science  (Project Number  SR200100008 ).
Introduction Fossil groups (FGs) were discovered by  Ponman et al. (1994). They are particular groups of galaxies with high X-ray luminosities but with fewer bright galaxies than most groups or clusters of galaxies. Jones et al. (2003)  later provided a com-Data of the two samples of 87 fossil groups and 100 non fossil groups are only available at the CDS via anonymous ftp to cdsarc. cds. unistra. fr (130. 79. 128. 5)  or via https:// cdsarc. cds. unistra. fr/viz-bin/cat/J/A+A/673/A100 Based on observations obtained with CFHT, SDSS, CAHA, and OHP observatories (see acknowledgements for more details). monly accepted definition of FGs based on the satisfaction of three conditions: they are extended X-ray sources with an X-ray luminosity of at least L X = 10 42 h -2 50 erg s -1 ; with a brightest group galaxy (BGG) at least two magnitudes brighter than other group members; and the distance between the two brightest galaxies must be smaller than half the group virial radius.
Their interpretation is that FGs represent undisturbed, early-forming systems in which large galaxies have merged to form a single dominant elliptical. The role of the BGG is therefore crucial in explaining the lack of bright galaxies in FGs. The aim of the present paper is to analyse the physical properties of the BGGs of FGs and compare them to those of non-FGs and clusters. For this, we gathered as large a sample of FGs as possible from the sample of groups detected by Tinker 1 from SDSS data (Sect. 2). In 1 https://www. galaxygroupfinder. net/catalogs each FG candidate, we detected the BGG and measured its morphological properties. We then compared the properties of FG BGGs to those of a control sample of non-FGs, as well as to the brightest galaxies of clusters and massive groups of galaxies previously studied by  Chu et al.
Basic properties of fossil groups The We searched for images of these 87 FG candidates in the UNIONS image database in the u and r bands. These images were retrieved from the DR3, which includes observations made before January of 2021, and covers 7000 deg 2 in u and 3600 deg 2 in r. We found images for 35 FGs in the u band and for 25 FGs in the r band, among which 12 FGs having both u and r band images. We indicate in Table  A. 1 (last column) the FGs with UNIONS images available. Chu et al. (2022)  show that subtracting the contribution of intracluster light (ICL) could modify the properties derived for BCGs. Therefore, with the aim of estimating how subtracting the ICL could modify the properties derived for the BGG, we also exploited the r band images processed to preserve all low-surface-brightness (LSB) features (hereafter referred to as rLSB).
As explained by  Žemaitis et al. (2023) , for example, the images were obtained using an observing technique that is optimised for LSB surveys at CFHT (e. g. Ferrarese et al. 2012; Duc et al. 2015). There were 19 FGs available in rLSB. For all these objects, we extracted images of 4000 × 4000 pixels 2 centred on the BGG, either by cropping the tiled UNIONS rLSB images, or by first assembling two to four tiled images with the SExtractor and SWarp softwares  (Bertin & Arnouts 1996; Bertin et al. 2002; Bertin 2006)   3  and then cropping them to this size. In the latter case, special care was taken to assemble tiles that had been background subtracted in order to avoid losing faint signal in the outskirts of BGGs.
These objects are listed in Table  A. 2. This subsample of non-FGs is comparable in number to that of the FG candidates for which UNIONS data are available. The BGGs of this control sample were chosen to match the absolute magnitude and colour histograms of our FG sample as closely as possible. This can be checked by looking at Figs. 1 2 3 4. A Kolmogorov-Smirnov test on the FG and non-FG samples shows that these two samples have only a 40% probability of being statistically different. For each FG and non-FG, we extracted all the galaxies belonging to the same halo within 1 arcmin around the BGG from the Tinker catalogue (which gives absolute magnitudes in the g and r bands). The colour-magnitude diagram shown in Fig. 5  shows that all FG BGGs follow a very well-defined sequence. With a colour of gr = -1.
Method We now consider the 25 and 30 BGGs of FGs and non-FGs for which r band data are available in UNIONS. Following  Chu et al. (2021)  and  Chu et al. (2022) , the 2D profile of each BGG was fit with GALFIT  (Peng et al. 2002)  with a single Sérsic model and with a double Sérsic model. GALFIT initial parameters were obtained with SExtractor using a bulge + disk model. A mask and a point spread function were created following the method described in  Chu et al. (2021). The choice between a single and double Sérsic model was made based on the statistical F-test  (Margalef-Bentabol et al. 2016 ) and the computed p-value, which indicates whether or not complexifying the model is necessary by increasing the number of degrees of freedom. As the current sample has the same resolution as that of  Chu et al.
Data We retrieved the spectra of the FG and non-FG BGGs of our sample in the SDSS. Among the 87 FGs, 9 spectra were not available, leaving us with 78 BGGs. Among the 100 non-FGs, 4 spectra were not available, and so we analysed 96 spectra. We fit these spectra with Firefly  (Wilkinson et al. 2017 ) and eliminated the spectra corresponding to AGN as classified by the SDSS: 13 in FGs and 14 in non-FGs. This was done because it is difficult to extract stellar populations from spectra dominated by an AGN. The AGN fractions are very similar between BGGs of FGs and non-FGs, namely 20% and 17%, respectively. We were therefore left with 66 FG BGG and 82 non-FG BGG spectra. As  Wilkinson et al. (2017)  showed that Kroupa and Salpeter IMFs gave comparable results, we limited our analysis to a Kroupa IMF.
Results We find that the stellar populations of FG and non-FG BGGs cannot be distinguished with this relatively straightforward analysis. As an illustration, we show the fraction of stellar mass created as a function of age in Fig. 9. Firefly fits the galaxy observed spectra using multiple star-formation bursts with various luminosities, masses, metallicities, and ages. The software returns the number of bursts, and for each burst gives its age, metallicity, and the fraction it contributed to the luminosity and stellar mass of the galaxy. From these, we computed the fraction of stellar mass formed in a range of galaxy age by summing the fractions of total stellar mass of all the star-formation bursts that occurred in the considered age range. We find that 76% and 71% of the total stellar mass of FG and non-FG BGGs, respectively, was already formed 8 Gyr ago based on the STELIB library.
Discussion and conclusions The aim of this paper is to increase the number of known FGs to shed light on their formation process. Here, we increase the sample by adding 87 spectroscopically confirmed FG candidates with a high probability of being real FGs in view of their estimated large halo mass; though confirmation of the X-ray condition of their X-ray luminosity (L X > 10 42 erg s -1 ) is still missing. For the FGs with UNIONS data available (35 in the u band, 25 in the r band and 19 in the rLSB bandr band treated with the Elixir-LSB software), we analysed the morphological properties of their BGG, and compared these properties with those of a control sample of 30 non-FG BGGs. The 2D photometric fits of the BGGs made with GALFIT with one or two Sérsic components show that a single Sérsic component is sufficient in most objects in the u band, while two Sérsics are needed in the r band, both for FGs and non-FGs.
c6 on Fig. B. 1) show [NII], [SII], and sometimes weak-to-bright Hα emission, very similarly to what is visible in the SDSS spectrum. Figure B. 1 shows a zoom onto the [6700, 6940] Å wavelength interval. The table GalSpecInfo of the SDSS DR17 database lists NGC 4104 as an AGN, with an old stellar population of the order of 13 Gyr. In order to investigate the nature of this galaxy more precisely, we first applied the pipes_vis visualisation tool (Leung et al. 2021)which is based on the BAGPIPES tool (Carnall et al. 2018) -to the normalised SDSS spectrum. We selected a Wild et al. (2020) model, adding dust (Charlot & Fall 2000) and a nebular component (Leung et al. 2021). First, based on Lima Neto et al. (2020), we assumed a stellar mass for NGC 4104 of 10 11. 3 M and a redshift of 0. 03. We also fixed the α and β slopes of the Wild et al.
Acknowledgements. We thank the referee,  Rémi Cabanac , for his careful and constructive reading of the manuscript. We are grateful to  Nicolas Clerc  for matching our FG and non-FG catalogues with his XCLASS X-ray catalogue. F. D. acknowledges support from  CNES. I. M. acknowledges financial support from the  State Agency for Research of the Spanish MCIU, through the "Center of Excellence Severo Ochoa " award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ), and through  PID2019-106027GB-C41. F. S. acknowledges support from a  CNES   Postdoctoral Fellowship. This work is based on data obtained as part of  UNIONS (initially CFIS ), using data from a  CFHT large program  of the  National Research Council of Canada  and the  French Centre National de la Recherche Scientifique.
Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA Saclay, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers (INSU)  of the  Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. Pan-STARRS is a project of the  Institute for Astronomy of the University of Hawaii , and is supported by the  NASA   SSO Near Earth Observation Program  under grants  80NSSC18K0971 ,  NNX14AM74G ,  NNX12AR65G ,  NNX13AQ47G ,  NNX08AR22G , and by the  State of Hawaii. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions.
SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. SDSS-IV is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics. Based in part on observations made at  Observatoire de Haute-Provence (CNRS), France. This research has made use of the MISTRAL database, based on observations made at  Observatoire de Haute Provence (CNRS), France , with the MISTRAL spectro-imager, and operated at  CeSAM (LAM), Marseille, France.
To improve the transient performance and ensure the normal path-tracking maneuver, a novel approach based on a fuzzy-observerbased composite nonlinear feedback (CNF) controller was proposed  [21]. This controller was designed to effectively deal with system disturbances and uncertainties that may affect the vehicle's performance. By including a fuzzy observer, the CNF controller is able to estimate the vehicle's lateral states accurately. The proposed technique was formulated using linear matrix inequalities (LMIs) to implement an observer-based controller aimed at enhancing the tracking performance under challenging conditions such as input saturation and disturbances in GPS-denied environments. To validate the effectiveness of the approach, high-fidelity simulations were conducted using CarSim-Matlab/Simulink, and the results demonstrate the validity of the proposed methodology.
Simulation studies were carried out using CarSim software and Matlab/Simulink, and the proposed controller showed superior performance compared to the backstepping controller in low road friction scenarios. A 3 (DOF) nonlinear vehicle model was developed in  [22] , which accounts for yaw, lateral, and roll motions, and chaotic behavior was then simulated using the Lyapunov exponent method. To control this chaotic behavior, a sliding mode variable structure control (SM-VSC) was designed. To further improve lateral vehicle stability and reduce chattering under maximum operating conditions, a fuzzy control technique was used to realize an adaptive power-reaching law. The SM-VSC system performance was simulated using Matlab/Simulink. The results of the simulation showed that the adaptive-reaching SM-VSC control approach was effective in quelling the chaotic phase of the vehicle's lateral motion.
The QP was solved by employing rule-based control strategies that leverage control barrier functions and control Lyapunov functions (CBF-CLF). The system's safety was ensured by using a convex quadratic program to perform high-frequency updates, ensuring that the system operates in a safe and collision-free manner, especially during lane-change maneuvers. A novel approach utilizing a fractional-order PID control algorithm based on datadriven control was proposed to enhance tracking precision in autonomous driving  [32]. The algorithm's parameters were optimized using the particle swarm optimization (PSO) algorithm. Comparative analysis against PID control and linear quadratic regulator (LQR) was performed using MATLAB/CarSim. Experimental results showed that the fractionalorder PID control effectively reduces tracking error caused by path curvature changes while maintaining comfort, stability, and safety.
In  [33] , a control algorithm based on a lateral dynamic model was introduced for path tracking in autonomous vehicles. To enhance stability at high speeds, an improved model predictive control (MPC) controller was proposed. By combining the steady-state response and MPC, the lateral motion of the vehicle can be controlled smoothly, ensuring accurate path tracking at high speeds. Simulation results obtained using MATLAB were presented to validate the effectiveness of this approach. An efficient model predictive control (MPC) approach for lateral control (LC) in autonomous vehicles was presented in  [34]. To address computational time limitations, a proposed approximate explicit model predictive control (AEMPC) scheme utilized precomputed control gains and eliminated the need for online quadratic programming.