dataesr/scientific-paragraphs-categorization huggingface dataset:
Road User Detection Ten years ago, the detection of road users and, particularly, pedestrians were mainly based on Histograms of Oriented Gradient (HOG)  [12, 13]. Nowadays, artificial neural networks and, more specifically, Convolutional Neural Networks are used for those tasks. YOLO  [14]  has been widely used for real-time object detection in various applications and specifically for pedestrian detection [15] in video images. In our work, we use YOLOv4, implemented thanks to the TensorFlow library, and we tuned a COCO trained version with transfer learning to adapt it to near-ortho-photographic fisheye images. The YOLOv4 model was fine-tuned using a custom dataset of ortho-photographic fisheye images. The dataset was prepared by recording videos in a parking lot with a vertical angle of view and low illumination.
The calculation of the average displacement vector → d is then carried out only on the pixels selected by the binarization. A diagram for the evaluation of the displacement vector of a bounding box is represented in Figure  8. The procedure is repeated for each bounding box detected by YOLO. proximately 3000 images. Data augmentation techniques, such as rotation, flip, and brightness reduction, were applied to expand the dataset to approximately 8000 images. Training was carried out with a batch size of 16 and 500 epochs.
Data Extraction for I2V Communication 4. 1. Transformation World to Vehicle's Own Referential The objective is to provide, in the reference frame of each vehicle, a dynamic horizontal map of nearby users. To carry out this cartography, knowledge of the position (X u , Y u ) and velocity components V X and V Y of each vehicle in the ground plane (O, X, Y, H) is imperative. V X and V Y are provided by the supervisor. The determination of X u and Y u requires knowledge of the GNSS (Global Navigation Satellite System) positions of the vehicle with respect to the position of the detecting camera. The supervisor must transmit the positions (X u , Y u ) of each detected mobile in GNSS format. This requires an additional transformation in the CameraToWorld, which is a rotation around the Z axis equal to the opposite of the azimuth (relative to geographic North) of the Y axis.
An image extracted from this video sequence is shown in Figure  15. The label (Person, Car), the latitude, and the longitude of the centers of the detected objects are saved in a spreadsheet file. Since July 2022, YOLOv7 has constituted the state of the art in object detection. We tested this version trained on MS COCO only on our videos. Figure  16  gives an overview of the detection results. A comparison with Figure  15  shows the improvement brought by re-training our dataset of ortho-photographic fisheye images. The positioning of detected users on a cadastral map can be easily achieved using the Python function library "plotly. express" (Figure  16 ). As can be seen in Figure  17 , the location of the camera mast (the yellow dot) appears slightly offset (less than a meter) in the northeast direction.
This effect represents the refraction due to the change of medium from vacuum to the Earth's atmosphere. The effect varies as a function of the zenith angle and wavelength. Meyers and Burchat (2015b) performed a study on the impact of the atmospheric chromatic effect on weak lensing for surveys like LSST and DES. Heymans et al. (  2012 ) performed a study on the impact of atmospheric distortions on weak-lensing measurement with real data from CFHT. They characterised the ellipticity contribution of the atmosphere to the PSF for different exposure times. To achieve this, they computed the two-point correlation function of the residual PSF ellipticity between the observations and a PSFExlike PSF model (described in detail in Section 5). Salmon et al. (2009)  studied the image quality and the observing environment at the CFHT  (Xin et al.
, 2012; Chang and Peterson, 2017). The PhoSim parameters can then be fitted or modified to match the simulation output with the observed images. Another known simulation software 7 ,  Rowe et al. (2015) , incorporates options to simulate atmospheric PSFs from 5 github. com/LSSTDESC/psf-weather-station 6 bitbucket. org/phosim/phosim_release/wiki/Home 7 github. com/GalSim-developers/GalSim phase-screen exploiting the photon Monte Carlo approach from  Peterson et al. (2015). To conclude, we have seen that it is possible to develop a physical model of the atmosphere based on the optical understanding that we have from Section 2 and the studies of atmospheric turbulence of Kármán and Kolmogorov. However, this approach has two inconveniences. Firstly, the approach requires physical measurements of the atmosphere at the telescope's site, which is not always available.
In addition to the parametric Tiny Tim model,  Anderson and King (2000)  developed the concept of effective PSF, which is the continuous PSF arriving at the detectors, i. e. , Equation  35 , convolved with the pixel-response function of the detector. They proposed an algorithm to model the effective PSF iteratively from observed 8 github. com/spacetelescope/tinytim stars and continued the work on the effective PSF, adding some improvements and detailed a model for the HST instruments ACS and Wide Field Camera (WFC). Hoffmann and Anderson (2017)  then carried out a comparison between Tiny Tim and the effective PSF approach for ACS/WFC. The study showed that the effective PSF approach consistently outperforms the Tiny Tim PSFs, exposing the limitations of parametric modelling. Anderson (2016)  describes the adoption of the effective PSF approach applied to Wide Field Camera 3 (WFC3/IR) observations, which are undersampled.
The software Photutils 9   (Bradley et al. , 2022)  provides an implementation of the effective PSF approach from  Anderson and King (2000)  with enhancements from  Anderson (2016). Schrabback, T. et al. (2010)  used a data-driven PSF model based on PCA when studying the COSMOS field. The early severe aberrations of HST's optical system were an important driver of phase-retrieval algorithms. Several efforts to characterise HST were published in an Applied Optics special issue  (Breckinridge and Wood, 1993). Solving the phase-retrieval problem provides a reliable approach to characterise the optical system accurately. Fienup (1993)  studied new phase-retrieval algorithms and  Fienup et al. (1993)  presented several results characterising HST's PSF. Regarding recently launched space telescopes, Euclid's VIS parametric model constitutes the primary approach for Euclid's PSF modelling.
When the number of stars in an FOV falls below some threshold, the model built is usually considered unusable for WL purposes. This family of models has been widely used for modelling ground-based telescope PSFs. Nevertheless, they are not yet capable of successfully modelling the chromatic variations in addition to the spatial variations and super-resolution. We proceed by describing several PSF models in chronological order. The first models, described in more detail, were used to process real data from different surveys, except for Resolved Component Analysis  (Ngolè et al. , 2016). The latter models are worth mentioning but have yet to be used to produce a WL shape catalogue with all the validation and testing it implies.
PSFEx PSFEx 11  (Bertin, 2011)  has been widely used in astronomy for weak-lensing surveys, e. g. , DES year 1  (Zuntz et al. , 2018) , HSC  (Mandelbaum et al. , 2017) , and CFIS  (Guinot et al. , 2022). It was designed to work together with the SExtractor  (Bertin and Arnouts, 1996)  software which builds catalogues from astronomical images and measures several properties of the observed stars. The PSFEx models the variability of the PSF in the FOV as a function of these measured properties. It builds independent models for each CCD in the focal plane and works with polychromatic observations. It cannot model the chromatic variations of the PSF field. The model is based on a matrix factorisation scheme, where one matrix represents the PSF features and the other matrix represents the feature weights.
The model's PSF reconstruction is represented by HPSFEx. The first guess can be computed as a function of the median of all the observations. The dimensions p and P are the same if no downsampling operation is included in F PSFEx. The PSF features are learnt in an optimisation problem that aims to minimise the reconstruction error between the PSFEx model and observations, which are given by min S p,q ∀p, q ≥ 0, p + q ≤ d { { { { { { { n obs ∑ i=1 ‖ ‖ Ī(⋅|u i ,v i ) -ĪPSFEx star,(⋅|u i ,v i ) σi ‖ ‖ 2 F + ∑ p,q≥0 p+q≤d ‖T p,q S p,q ‖ 2 F } } } } } } } , ( 45 ) 11 github. com/astromatic/psfex Frontiers in Astronomy and Space Sciences 20 frontiersin. org where σ2 i represents the estimated per-pixel variances, Ī represents the noisy observations, and ‖ ⋅ ‖ F the Frobenius norm of a matrix. The second term in Eq.
The feature weight matrix A is decomposed into a sparse matrix α and a dictionary matrix V ⊤ of graph-based spatial variations; for more information, see  Ngolè et al. (2016). To regularise the inverse problem, the RCA enforces a low-rank solution by fixing N comp , a positivity constraint on the modelled PSF, a denoising strategy based on a sparsity constraint in the starlet  (Starck et al. , 2015)  domain, which is a wavelet representation basis, 12 github. com/CosmoStat/rca 13 In the signal processing community, a dictionary is a collection of templates, or basic elements, used to decompose a signal. and a constraint to learn the useful spatial variations from the graph-harmonics-based dictionary. The optimisation problem that the RCA method targets is min S k ,α k { 1 2 n obs ∑ i=1 ‖ Ī(⋅|u i ,v i ) -F RCA { HRCA (⋅|u i ,v i ) }‖ 2 F + N comp ∑ r=1 ‖w r ⊙ ΦS r ‖ 1 + ι + ( HRCA (⋅|u i ,v i ) ) + ι Ω (α)} s.
lensfit The lensfit  (Miller et al. , 2007; Kitching et al. , 2008; Miller et al. , 2013)  refers to a Bayesian galaxy shape measurement method for WL surveys. It also includes a data-driven PSF model that will also be referenced as lensfit and is sparsely described throughout the different publications involving the shape measurement lensfit  (Miller et al. , 2013; Kuijken et al. , 2015; Giblin et al. , 2021). This method has been used with real data to produce the WL shape catalogues of CFHTLenS  (Erben et al. , 2013; Miller et al. , 2013) , KiDS+VIKING-450  (Wright et al. , 2019) , KiDS-450  (Hildebrandt et al. , 2016; Fenech Conti et al. , 2017) , KiDS-1000  (Giblin et al. , 2021) , and VOICE  (Fu et al. , 2018). However, the code is not publicly available. This PSF model differs from the previous ones.
Let us clarify the difference between the first and last mentioned interpolations. The simple polynomial interpolation first fits the PSF model's parameters p for each observed star. Then, it fits the coefficients of a polynomial of the 2D star positions that will later be used to interpolate. In the basis-function polynomial interpolation, the position polynomial's interpolation coefficients are fitted simultaneously with the model's parameters using all the available stars (from a single CCD or the entire exposure). If this last option is used with the PixelGrid model, it comes closer to the approaches of PSFEx and RCA without the 15 github. com/rmjarvis/Piff specific characteristics of each model. We have only mentioned position polynomials, but, as in the PSFEx, the interpolation polynomial can be built on any parameter of the PSF, e.
An overview of the model is presented in Figure  13. Estimating the model's parameters in the WFE space is a challenging, ill-posed inverse problem known as phase retrieval 16 github. com/CosmoStat/wf-psf  (Fienup, 1993; Fienup et al. , 1993; Shechtman et al. , 2015) , i. e. , estimating a complex signal from intensity-only observations. The optimisation problem is non-convex and non-smooth, the star observations are not very informative, and there is no guarantee that the WFE model's structure can represent the underlying ground truth WFE. Liaudat et al. (2023)  show that under the aforementioned conditions, targeting the estimation of the ground truth WFE is not the best option. The PSF model's objective is to have a good pixel representation of the PSF. It is, therefore, possible to estimate a WFE manifold far away from the underlying WFE but very close in the pixel space.
Therefore, it is necessary to derive different diagnoses or null tests. Jarvis et al. (2016)  proposed a set of null tests for the DES WL shear catalogues Science Verification, which includes the PSF model validation. The most basic rule for any validation of the PSF models is to separate the observations in the FOV into two data sets for estimation and testing, i. e. , validation, which could be 80{%} and 20{%}, respectively. The first one should be used to estimate the PSF model. The second one should help validate its performance and not be used in the PSF model estimation. This rule tests the PSF model's generalisation power to unseen positions in the FOV. Next, we will describe the most used PSF diagnosis that will help us validate the performance of the PSF models.
Further development of these methods is required to define realistic PSF model requirements and study how PSF modelling errors affect the target task. • GalSim  (Rowe et al. , 2015) : github. com/GalSim-developers/ GalSim • WebbPSF  (Perrin et al. , 2014) : github. com/spacetelescope/ webbpsf • PhoSim  (Peterson et al. , 2015) : bitbucket. org/phosim/phosim_ release/wiki/Home • WaveDiff  (Liaudat et al. , 2023) : github. com/CosmoStat/wf-psf • ShapePipe  (Farrens, S. et al. , 2022) : github. com/CosmoStat/ shapepipe • PIFF  (Jarvis et al. , 2020) : github. com/rmjarvis/Piff • MCCD  (Liaudat et al. , 2021a) : github. com/CosmoStat/mccd • RCA  (Ngolè et al. , 2016; Schmitz et al. , 2020) : github. com/ CosmoStat/rca • PSFEX  (Bertin, 2011) : github. com/astromatic/psfex • Tiny Tim  (Krist et al.
, 2011) : github. com/spacetelescope/ tinytim • Photutils  (Bradley et al. , 2022) : github. com/astropy/photutils • ∂Lux  (Desdoigts et al. , 2023) : github. com/LouisDesdoigts/ dLux • psf-weather-station: github. com/LSSTDESC/psf-weatherstation.
Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification as well as their impacts on marine organisms and ecosystems. So far ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim at developing a sustainable production chain of observation-derived global ocean carbonate system datasets at high space-time resolution. As the start of the long-term objective, this study introduces a new global 0. 25 • monthly reconstruction, namely CMEMS-LSCE, for the period 1985-2021.
The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables including surface ocean partial pressure of CO 2 (pCO 2 ), total alkalinity (A T ), total dissolved inorganic carbon (DIC), surface ocean pH, and saturation states with respect to aragonite (Ω ar ) and calcite (Ω ca ). Reconstructing pCO 2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO 2 ATlas (SOCAT). Surface ocean A T is estimated with a multiple linear regression approach, and the remaining carbonate variables are resolved by CO 2 system speciation given the reconstructed pCO 2 and A T. 1σ-uncertainty associated with these estimates is also provided. Here, σ stands for either ensemble standard deviation of pCO 2 estimates or total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty.
Advantages of the combination of pCO 2 and A T over others for the speciation of the carbonate system are: (1) pCO 2 is the most extensively measured parameter, (2) A T can be accurately predicted from salinity, temperature, and nutrient concentrations, and (3) the combination of these two prior variables results in the slightest uncertainty of pH estimates  (Zeebe and Wolf-Gladrow, 2001; Lauvset and Gruber, 2014; Takahashi et al. , 2014; Orr et al. , 2018). The three main successive modules used in the CMEMS-LSCE production chain are summarized as follows. (Chau et al. , 2022b ) is applied to map gridded datasets of SOCATv2022 CO 2 fugacity and predictors in order to reconstruct pCO 2 at a spatial resolution of 0. 25 • for every month in the period 1985-2021 (444 months in total). The CMEMS-LSCE-FFNN works on an ensemble of 100 feed-forward neural networks  (FFNNs).
, 2018; Gregor and Gruber, 2021). Importantly, LIAR allows determining reconstruction uncertainty propagated from multiple sources of input uncertainties at desired model resolutions. iii) Reconstruction of pH, DIC, and saturation states with respect to aragonite (Ω ar ) and calcite (Ω ca ) (Sect. 3. 3): CO2SYS  (Lewis and Wallace, 1998; Van Heuven et al. , 2011 ) is a standard software used for the speciation of carbonate parameters in the marine CO 2 system (see  Olsen et al. , 2016; Bresnahan et al. , 2021; Gregor and Gruber, 2021; Woosley, 2021 , for a few). The CO2SYS speciation is built on a set of equilibrium equations  (Dickson et al. , 2007; Dickson, 2010). Given the reconstructed pCO 2 and A T , non-CO 2 acid-base constituents, physical variables, and equilibrium constants, this method allows solving pH, DIC, Ω ar , and Ω ca at the same input resolutions.
A complementary of the CO2SYS software developed by  Orr et al. (2018)  is used to quantify the uncertainty associated with these carbonate system variables. All the input data uncertainties are propagated through the CO2SYS processing chain. The global monthly, 0. 25 • -resolution datasets of pCO 2 , A T , pH, DIC, Ω ar , and Ω ca are intensively evaluated against different observation-based products independent from our model fitting at a global scale to in situ locations (Table  3 ). In Section 4, multiple metrics are proposed for product analyzes and assessments. Results are presented in section 5 with emphasis on the evaluation of the best reconstruction and associated model uncertainty for each variable (Sect. 5). This section also highlights the advantages obtained with an increase in spatial resolution and presents an application of the CMEMS-LSCE product in tracking ocean acidification over the last three decades.
e. , Chl-a) does not cover the high latitudes of the winter hemisphere. We therefore gap-fill the time series in an ad hoc manner, as in previous studies  (Landschützer et al. , 2016; Gregor et al. , 2019; Chau et al. , 2022b). Monthly climatologies of SSS, Chl-a, and MLD computed on the available data are used for each missing year. Likewise, climatologies plus linear trends of SSH following global warming effects serve for the pre-1993 period. Missing Chl-a data in the high latitudes of the winter hemisphere are replaced by the minimum concentration of Chl-a over the available data for the same grid cell (∼0. 01 mg m -3 ). WOA18 nutrients and LDEO pCO clim   (Jones et al. , 2012). The data distribution of SOCATv2022 CO 2 fugacity before and after combining is shown in Fig. A2  and Table  4.
Product qualification and comparison The monthly, 0. 25 • -resolution reconstructions of carbonate system variables are qualified with gridded observation-based datasets and in-situ time series which are not used in our model fitting (Table  3 ). • The SOCAT data in each reconstruction month are excluded from the model fitting, which avoids overfitting and ensures fairness in the model evaluation  (Chau et al. , 2022b). The global monthly CMEMS-LSCE-FFNN pCO 2 fields at a spatial resolution of 0. 25 • can therefore be evaluated against the pCO 2 data converted from SOCATv2022 CO 2 fugacity (Eq. A2) at the same resolution. Doing this, CMEMS-LSCE-FFNN pCO 2 is assessed with more than 32×10 5 open-ocean data and 4×10 5 coastal-ocean data (Table  4 ). The SOCATv2022 measurements have low random uncertainty (2-5 µatm) but the spatio-temporal sampling bias from the month and grid centers is significant  (Bakker et al.
3. 1). The eight stations are located along the US coast, the Gulf of Mexico, and in a Caribbean coral reef (Fig. A1b  and Table  A2 ). Measurement uncertainty is up to 2 µatm reported for pCO 2 and 0. 02 for pH. For A T and DIC, we consider four time series: (1) Bermuda Atlantic Time Series (BATS,  Michaels and Knap, 1996; Steinberg et al. , 2001)   Dore et al. , 2009). The first three stations are in the North Atlantic while the latter is located in the North Pacific (Fig. A1b ). These long-term time series provide insights into changes in the surface ocean carbonate system over the recent decades  (Bates et al. , 2014; Coppola et al. , 2020; Gregor and Gruber, 2021; Pérez et al. , 2021). The HOT and ESTOC stations provide surface ocean observations of A T and DIC, and BATS and DYFAMED data are extracted at seawater depth shallower than 10 m.
2 ), and the geographical coordinates (latitude and longitude). The datasets of SOCAT f CO 2 and predictors are first reprocessed to match model fitting requirements (Sect. 2. 1). After excluding the data in the reconstruction month, the data within the 3-month window are separated into FFNN training and validation subsets with a ratio of 2 : 1. The excluded SOCATv2022 datasets are used in model evaluation. The CMEMS-LSCE-FFNN approach was originally developed for pCO 2 reconstructions at monthly, 1 • resolutions where pCO 2 is converted from f CO 2 following the formulation (A2) by  Körtzinger (1999). The model best estimate and its uncertainty are defined as the ensemble mean (µ) and ensemble spread (σ) of 100 model outputs of pCO 2. This study slightly modifies the CMEMS-LSCE-FFNN ensemble approach by  Chau et al.
(2022b)  to achieve pCO 2 reconstructions at monthly, 0. 25 • resolutions. Some of the input datasets presented here (  in  Chau et al. (2022b)  (Table  S1 ). The up-to-date input datasets have higher resolutions and a better coverage over the coastal ocean as well as in the high latitudes. Furthermore, the new CMEMS data resources offer space-time varying uncertainty fields which are important in quantifying carbonate system variable uncertainties. For comparable evaluations in this study, we execute 100-member ensembles of FFNN models at spatial resolutions of both 1 • (FFNNr100) and 0. 25 • (FFNNr025) using the same lot of input data resources (Table  2 ). Remind that the training data of 225 f CO 2 is extracted from the SOCATv2022r100 product for FFNNr100 while it comes from the SOCATv2022r025 product (i.
The model which has the smallest propagation uncertainty is chosen to provide the best estimate of A T. Global monthly total alkalinity and 1σ-uncertainty are estimated with given input data from the monthly CMEMS SSS and SST fields and from the WOA18 datasets of nutrient concentrations (Table  2 ). Uncertainty of the A T field is estimated systematically through input uncertainty propagation along the processing chain  (Carter et al. , 2018). Here we define the input uncertainty of predictors in terms of standard deviations (1σ). Input uncertainty fields associated to the monthly CMEMS SSS and SST are products' analysis errors (see e. g. , Fig. A8 ) while uncertainties of the WOA18 NO 3 and SiO 2 climatologies are set to 15% of data values per cell. The 15% quantity refers to the median percentage of standard analysis errors against climatological means of nutrient concentrations (see product standard errors in Table  6 ,  Garcia et al.