dataesr/scientific-paragraphs-categorization huggingface dataset:
Configuration and input data The modeling platform has been implemented for the Paris megacity, with 2009 as the base case scenario. There are two reasons for this choice. First, the validation of the OLYMPUS model was conducted for the year 2009 due to emission database availability. Second, the occurrence of notable air pollution episodes during the winter of that year: they provide contrasted situations of exposure to pollutants that are particularly relevant for mitigation studies. The simulation domain encompasses the entirety of the Parisian metropolis and contains 159 × 129 grid cells at a resolution of 1 km 2. This resolution aligns with the input data expected for the CHIMERE model. The administrative division adheres to the National Institute of Statistics and Economic Studies (INSEE) classification based, comprising 1300 zones called IRIS for the entire region.
The detailed procedure is shown in (Appendix C). For biogenic emissions, we retained To enable this simulation to be evaluated, we have simultaneously carried out a simulation that is identical in every respect, but which uses all the tabulated emissions supplied by the AIRPARIF reference inventory in the IDF1 domain, instead of the OLYMPUS data. This referent simulation therefore contains the state of the art in terms of modeling traffic emissions and energy use in buildings: comparing the outputs of these 2 simulations will enable us to assess the quality of the data produced by OLYMPUS-CHIMERE.
Communes in the outer suburbs of Paris have a more spread-out layout and a pronounced rural and agricultural character. Paris and its inner suburbs are served by a high-density road network. This network supports significant commuting activity towards the center every day, with roads being heavily trafficked and even subject to recurrent congestion, as can be seen in the intensity of all road-related emissions. These are well-known features for the Paris region, and their presence in the emission data confirms the comprehensive nature of the OLYMPUS simulation. The main limitations of the model in its current version are the lack of consideration for travels from outside city limits, and the underestimation of the transport of goods. The model's ability to reproduce a relevant emission situation in space and time is more deeply discussed in Elessa Etuman and  Coll (2018).
As we announced above, the validation work is based on the comparison of output data from the OLYMPUS-CHIMERE platform with an AIRPARIF-CHIMERE simulation based on a state-of-the-art inventory used for regulatory air quality missions, and with measurement data. To measure the importance of the model intercomparison work, it should be pointed out that the emission sectors modeled by OLYMPUS represent, according to AIRPARIF estimates for the Paris region, 75% of total nitrogen oxide emissions, and 50% and 60% of total PM 10 and PM 2. 5 emissions respectively. The pollutant emissions derived from the OLYMPUS model were validated in accordance with the methodology established in the study by Elessa Etuman and  Coll (2018). This validation procedure was conducted in several phases, at each step of the production of output data, to verify the model's ability to replicate the reference scenario characteristics.
It is worth noting that, compared to the model's initial version, the emission calculation methodology has been updated, transitioning from COPERT 4 to COPERT 5. Additionally, the traffic modeling within OLYMPUS has been optimized to better capture congestion phenomena. Based on the cadastral data from the reference year, the model estimates increased emissions, notably for NOx, with a rise of 20% for all the transportation sector, 11% for passenger car exhaust emissions for NO x , over 40% for fine particles from exhaust emissions and 8% for NMVOCs. Discrepancies in emission inventories have been described in the litterature. We can refer to the work of  (Timmermans et al. , 2013)  on the sensitivity of urban air quality to emissions inventories, which indicates that although inventories may rely on common approaches such as COPERT, assumptions on the vehicle fleet or parameterizations of cold-road fractions, for example, can justify a minimum of 20% deviation on emission totals.
In our case, we also need to consider assumptions relating to congestion modeling, average speed per road segment and lane occupancy by light and heavy duty vehicles. Fig. 5  has been set up to assess the differences in concentrations For all species and all domains in Fig. 5 , the data from the two simulations appear highly consistent with each other, with concentration levels showing a strong linear correlation. This is quite important, as this linearity confirms that the OLYMPUS-CHIMERE model does indeed reproduce the spatial concentration gradient over the region, and hence the territorial distribution of emission zones. In the specific case of NO 2 , since the share of emissions from OLYMPUS is around 75%, we can consider that the small common fraction of emissions between the two simulations is not the main reason for their very high consistency, which is therefore attributable to a relevant distribution of emitting activities by OLYMPUS over the region.
Several points (in grey) escape this trend. They are mainly visible for NO 2 (although this trend also exists for particulate matter) and are -this time -aligned with the slope 1 line. On the map of model differences, we can see that they correspond to major traffic roads linking the urban center to neighboring regions. We believe this phenomenon in outers suburbs may be due to a lack of volume in suburban highway emissions, which can be at least partly attributed to the share of interregional transport and freight, which is not coded in the motives for mobility in OLYMPUS. However, a lack of assignment of commuting on the road network in the outer suburbs cannot be completely ruled out. The tables with the statistical analysis of simulated versus measured data are presented in Appendix D.
Exposure derived from simulation data To complete the work, we estimated population exposure using outputs from the 2 model configurations. The exposure parameter is called EXPO and it can be estimated from any of the four pollutants we displayed in our study. EXPO was calculated across distinct urban regions, namely the center, inner suburbs, and outer suburbs. This quantification was achieved by intersecting high-resolution population density maps -derived from census data -with annual mean pollutant concentration maps from CHIMERE. The equation for exposure is defined as: EXPO(i) = ∑ agent Exposure(agent, i) n Agents Where. EXPO(i) denotes the mean exposure of the population within area i. Exposure(agent, i) represents the exposure experienced by a specific agent in the area i. n Agents is the total count of modeled agents, assessed within the specified area i.
This observation calls for two comments. Firstly, it appears is essential to refine our spatial representation of air quality in dense urban areas, as it is a key parameter for population exposure. That is why further steps to refine CHIMERE output data need to be considered. Secondly, the use of a tool like OLYMPUS in forcing CHIMERE makes a great deal of sense, since it enables us to better understand the link between urban organization, mobility practices and emissions, which drive most of the population exposure in metropolitan areas.
This inventory is used as input data for the CHIMERE air quality simulation. As OLYMPUS outputs were evaluated in a previous work, we evaluated here the air quality data. Our simulation was carried out for the Greater Paris region, for the year 2009. In this validation work, we demonstrated the ability of the OLYMPUS-CHIMERE platform to produce a comprehensive representation of the concentration gradients of the main primary pollutants over the domain of interest (NO 2 , PM 10 , PM 2. 5 mainly), and of the variability of these concentrations. We were able to compare our results with those of a simulation carried out with an emissions inventory corresponding to the state of the art in bottom-up inventories. We observed two main differences in the output concentration fields. First, higher concentration values for primary pollutants in the dense zone were produced by OLYMPUS, but this difference remains compatible with differences in inventory methodology and it even brings some improvements in the concentration fields.
On the other hand, we saw that road transport on a few major regional routes may be underestimated, mainly because OLYMPUS in this version does not integrate inter-regional mobility and possibly due to an underestimation of freight transport. Changes in the parameterization of the OLYMPUS module for inter-regional transport, FRET, should be considered. Comparison with data from the local air quality network finally showed that our results are fully comparable with simulations carried out with stateof-the-art inventories, and even allowed for NO 2 concentration improvements in the metropolis. Finally, observing the distribution of exposures across the domain of study highlighted the importance of refining our understanding of the links between urban configuration, housing, mobility, transport and emissions in metropolises.
In the measurement network of AIRPARIF there are 3 types of base stations. Urban, peri-urban, and rural bottom stations. The measurement of ozone is made by absorption of ultraviolet radiation, that of NOx by chemiluminescence and the masses of fine particles taken from the air are measured by TEOM-FDMS analyzers. The stations available for the study are presented by site typology in Fig. 9. Our model-measure comparison is supported by standard statistical tools for assessing the gaps between the two datasets. We compared the concentration fields modeled by the OLYMPUS-CHIMERE platform with measurement data from the regulatory air quality monitoring network implemented by AIRPARIF (here named MES-APF). Our objective is the differentiated analysis between the signals of modeled concentrations and the measurements obtained on different types of measurement sites.
The principle consists in positioning a point representative of the scores of a simulation in a plane. The centered Root Mean Square Error (RMSE) of the models can be read on the dotted purple curved lines. Correlation coefficients are displayed on the dotted yellow lines. Normalized standard deviation is the ratio between the variance of the model data versus that of the observations. Its values can be read on the dotted black curved lines. In the Taylor Plot presented here, the comparison between the observed and predicted air pollutant concentrations for the Ile-de-France Region is conducted using hourly concentrations. OLYMPUS-CHIMERE simulation scores are depicted by red dots, while that of AIRPARIF-CHIMERE are shown as blue stars.
Recent literature highlights examples of new modeling platforms that integrate parameters such as transportation networks, employment centers, activities, traffic demand, emission factors and street geometry into air quality modeling. The stated aim is List of acronyms EMEP European Monitoring and Evaluation Programme EURO European emissions standard for vehicles Airparif Ile-de-France air quality monitoring network GTFS General Transit Feed Specification (Data format for public ARENE Regional Agency for the Environment and New Energies transport schedules) ARPEGE Global numerical weather prediction model (Météo HTAP Emission database from the Hemispheric Transport of Air France) Pollution initiative CEREN French Center for Economic studies and Research on INSEE French National Institute for Statistics and Economic ENergy Studies CHIMERE Eulerian Chemical Transport Model IRIS Grouped islands for statistical information CITEPA Interprofessional technical center for the study of MEGAN Model of Emissions of Gases and Aerosols from Nature atmospheric pollution PM Particulate Matter COPERT Computer Program to Calculate Emissions from Road Transport SNAP STIF Standard Nomenclature for Air Pollution Île-de-France public transport authority CTM Chemical Transport Model TNO Independent research body in the Netherlands.
In the following section we provide a formalization of the main problems we will be dealing with, namely the crowdsourced geolocation problem and the active crowdsourced geolocation problem. Later, we define two statistical models, and several estimators and compare them on their quality when solving problems. We end up with a brief overview of the related work and some conclusions. To the best of our knowledge, this is the first time when the crowdsourced image geolocation problem has been mathematically formalized. All experiments and analyses shown in this work can be found and reproduced at https://github. com/IIIA-ML/crowdsourced geolocation.
Now that the model is defined, our objective is to find the values for {v 1 ,. , v w } with maximum likelihood on that model. The problem can be effectively solved through quasi-Newton methods such as LBFGS  [9]  as long as the number of annotators and the number of reference points is not very large. Using MCMC simulation to obtain the mean of a set of posterior distribution samples with the help of the Stan software and by fixing the number of reference points to 15, we can get a proper estimation, as we will see in the following section. When using this method to solve the CGP it will be referred as the Spatial OneShot method, as opposed to the name given to the active strategy.
In addition, PACOME is able to produce a few plausible estimates of the orbital elements of the detected sources and provide their local error bars. Results. We tested the proposed algorithm on several datasets obtained from the VLT/SPHERE instrument with IRDIS and IFS using the pupil tracking mode of the telescope. By resorting to injections of synthetic exoplanets, we show that PACOME is able to detect sources remaining undetectable by the most advanced post-processing of each individual epoch. The gain in detection sensitivity scales as high as the square root of the number of epochs. We also applied PACOME on a set of observations from the HR 8799 star hosting four known exoplanets, which can be detected by our algorithm with very high signal-to-noise ratios. Conclusions. PACOME is an algorithm for combining multi-epoch high-contrast observations of a given star.
In this paper, we focus on angular differential imaging (ADI;  Marois et al. 2006)  in combination with spectral differential imaging (SDI;  Racine et al. 1999). The ADI process brings on a temporal diversity by using the pupil-tracking mode of the telescope, so that speckles remain quasi-static across exposures, while the objects of interest follow a circular rotation with respect to the star. This apparent motion is deterministic and depends solely on the experienced parallactic rotation angles. Then, SDI guarantees spectral diversity by simultaneously recording images in several spectral channels using an integral field spectrograph (IFS) or a dual band imager (IRDIS) with less spectral leverage. A large variety of post-processing algorithms has been developed in the last decade to extract the relevant information from 3D (respectively, 4D) datasets recorded by ADI (respectively, ASDI), see e.
It usually consists of building a finer model of the stellar (on-axis) point spread function (PSF). This model can be built from several datasets, resulting from the observation of different stars, in which the objects of interest are not expected to be colocalized. This is the general principle of approaches based on reference differential imaging  (Ruane et al. 2019; Wahhaj et al. 2021; Sanghi et al. 2022; Xie et al. 2022). The second category of methods targets a better combination of the signal of the objects of interest. It consists of combining several epochs 1  to constructively co-add their signal by taking into account their proper Keplerian orbital motion. In the following, we focus on this second category of approaches, as it is more relevant to the algorithm presented in this paper.
Another important source of limitation is related to the photometric calibration of the datasets. Most of the algorithms assume that the companion's relative photometry (i. e. , contrast) is constant across epochs, while it is well known that this is a critical issue in direct imaging  (Biller et al. 2021). The sources of relative photometric variability are multiple: evolution of the companion's angular separation and its associated biases, instrumental calibration issues, variability of the atmospheric conditions, and (only marginally) the intrinsic variability of the companion brightness. This strong assumption leads to some loss of sensitivity and can even lead to an increase of the false alarm rate in the multi-epoch combination. Most advanced algorithms cope with these different issues only partially by resorting to empirical, and possibly time consuming, correction steps: for instance, by filtering, massive injections of synthetic exoplanets, annular corrections of the estimated detection confidence by the variance of the flux maps, and via small-sample statistics  (Mawet et al.
e. , detection scores interpretable in terms of probability of detection and of probability of false alarm, as well as reliable estimates of the orbits and of the associated error bars) by a end-toend propagation of the uncertainties; (iii) remaining robust to the highly variable quality of each individual epoch and to the lack of absolute calibration of the relative photometric variability across epochs; and (iv) integrating some prior domain knowledge in the form either of additional parameters to be optimized (e. g. , common stellar mass) or of additional constraints (e. g. , known system resonances, when available). The method we propose in this paper 2  , dubbed PACOME (for PACO Multi-Epoch) 3  , specifically addresses these points by capitalizing on the statistical framework of the PACO algorithm dedicated to the post-processing of single-epoch datasets.
Direct model of the observations Classical ASDI datasets are temporal sequences of coronagraphic images, decoupled into spectral channels and recorded at different epochs. The apparent position on the sky θ t (µ) of a (point-like) celestial body following a Keplerian motion depends on a set of orbital elements µ and on the epoch of observation t. We model a N-pixel observed intensity image, r t,ℓ,k ∈ R N , taken at epoch, t, spectral channel, ℓ, and temporal frame, k, via: r t,ℓ,k = α t,ℓ h t,ℓ (θ t (µ)) + f t,ℓ,k , (1) with θ t (µ) as the 2D angular location of the companion at epoch, t, for a given set of orbital elements, µ, h t,ℓ (θ) ∈ R N , the off-axis PSF centered at angular location, θ, and identical for all frames, k, of the same epoch, α t,ℓ as the contrast of the companion 4  at epoch, t, and spectral channel, ℓ, and f t,ℓ,k as a nuisance term representing all other contributions other than the mean signal due to the companion.
Two other classical constrained optimization methods 8  without derivatives were also tested: NEWUOA  (Powell 2006 ) and BOBYQA  (Powell 2009 ). However, VMLM-B showed better performance by refining the solution deeper, thus giving higher multi-epoch detection scores. Optimizing the solution with VMLM-B is also fast. It takes about 50 µs per evaluation of the cost function and its gradient with a cubic spline interpolator and a dataset consisting of T = 10 epochs, L = 2 spectral channels (see Sect. 3. 4. 1 for more details).
Massive code parallelization and execution time The entire algorithm is written in Julia  (Bezanson et al. 2017 ), an open-source and strongly typed language designed for high performance programming. Coupling the intrinsic speed of Julia with massive code parallelization enables the algorithm to process very large numbers of orbits in a reasonable computation time. Even though PACOME can be run on a laptop, we used a local machine equipped with an Intel™ Xeon E5-2620 v3 CPU running at 2. 40 GHz with 12 cores. The computational power achievable by this CPU is estimated at a maximum of 325 Gflops by the Intel™'s Math Kernel Library Benchmarks for Linux. Our implementation of PACOME does not use any GPUs. Testing an orbit is done by selecting the orbit, computing its projected positions by solving Kepler's equations, interpolating the a t,ℓ and b t,ℓ terms 10 , computing the cost function, assessing its value, and saving it in a memory-mapped file.
If a detection is claimed, the local uncertainties associated to the solution are computed via the perturbation method and/or with the CRLBs. A schematic diagram of the PACOME algorithm is represented in Fig. 6  and a pseudo-code is given in Algorithm 1. 10 The 2D maps a t,ℓ and b t,ℓ are pre-computed only once per epoch with PACO, so that PACOME can be launched multiple times, e. g. , with various bound constrains on the orbital parameters, without the need to relaunch the data reduction step with PACO. Similarly, including new available epochs is performed efficiently by reducing the new epochs with PACO and by relaunching PACOME. A38, page 10 of 30.
Datasets description In this section, we evaluate the performance of the proposed algorithm on 23 datasets from both the InfraRed Dual Imaging Spectrograph (IRDIS;  Dohlen et al. 2008a,b)  and from the Integral Field Spectrograph (IFS;  Claudi et al. 2008 Claudi et al. , 2010) )  of the VLT/SPHERE instrument  (Beuzit et al. 2019 ). The observations were conducted with the A(S)DI technique using the pupil tracking mode of the instrument (see Sect. 1). The observations were scheduled so that the star was observed during meridian passage to take benefit, as best as possible, of the apparent rotation of the sought-for objects. All datasets correspond to observations of HR 8799 (HIP 114189), obtained under highly variable observing conditions between 2014 and 2021 (see Table  4  summarizing the main logs parameters).
Pre-reduction Raw observations were pre-processed with the pre-reduction and handling pipeline of the SPHERE consortium  (Pavlov et al. 2008). Background, flat-field, bad pixels, registration, true north, wavelength, and astrometric calibrations are performed during this step. Additional custom steps implemented at the SPHERE Data Center  (Delorme et al. 2017)  were also applied to refine the wavelength calibration, reduce the crosstalk, and improve the identification of bad pixels. We also designed a new strategy to refine the conventional frame centering implemented in the SPHERE Data Center, as described in the following. All the datasets of this work were acquired in pupil-tracking mode, namely, at a given epoch the field rotates around the star from a frame to another (see Sect. 1). In that context, having a precise knowledge of the rotation center is critical to maximizing the detection confidence and deriving accurate astro-photometric estimates of the putative sources in single-epoch datasets.
, et al. : A&A, 679, A38 (2023)   spots, if their positions are precisely estimated. We re-addressed the previous numerical centering strategy that was used on the SPHERE Data Center by developing an algorithm that is faster and less sensitive to outliers (e. g. , bad pixels and large stellar leakages). This leads to a better recentering procedure which improves the S/N of real sources (see Table  D. 1 for more details). Similarly to the work of  Wang et al. (2014)  for GEMINI/ GPI, we implemented a new routine that fits 2D Gaussian functions to the satellite spots via a nonlinear least mean square constrained optimization method to estimate their subpixel locations. Based on these measurements, all the frames are shifted accordingly to match the measured rotation center to an arbitrary position satisfying the SPHERE Data Center and the PACO pipeline requirements.