dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature permutation analysis Feature permutation is a technique used to determine the importance of input channels used in a model. The principle is to i) permute the values of a feature (e. g. exchange the u wind field corresponding to an XCO 2 image with another random u wind field), and ii) use the model to predict emissions for the given input, which includes the XCO 2 field, other associated inputs and the random u wind field. By comparing the performance of the model on the original dataset with the performance on the permuted dataset, we can measure the impact of each feature on the performance of the model. The more the permutation of a feature affects the performance of the model, the more important that feature is. In table 6, we present the outcomes of the permutation of the features for nine ensembles of models (for each PP and each ensemble of inputs considered).
Discussions and limitations The approach developed in this paper carries certain limitations: firstly, it focuses exclusively on European PPs, which questions its generalisability to PPs in other regions with different climatic conditions. Secondly, the study emphasises the importance of a balanced dataset, as highlighted by the 5. 3. 2 section, and the need to be able to identify and address potential overfitting issues. It should also be noted that the study did not specifically investigate outliers such as PPs with exceptionally high emission rates. Despite incorporating plume scaling strategies, the model may struggle to generalise to such outliers. In terms of future research, several areas should be explored such as the challenge posed by clouds. In this respect, CNNs can be trained to ignore missing data caused by cloud cover and to make effective use of the available data.
Another aspect to consider is the presence of noise in CO 2 M data. While Gaussian noise may not pose significant issues, if the satellite noise exhibits structured patterns, it would becomes crucial to develop robust noise modelling techniques to enable CNNs to accurately distinguish and remove such noise. Finally, in real-world applications, training on synthetic datasets and applying the trained models to real datasets may encounter distributional differences. Strategies such as importance weighting, specific data augmentation techniques, transfer learning, or active learning methods may be necessary to account for these differences and ensure reliable performance.
The fields used to train and evaluate the CNNs are based on the SMARTCARB dataset and possess the same resolution, satellite noise level and ancillary data availability to the future CO 2 M images. Each synthetic XCO 2 image encompasses the anthropogenic plume from at least one power plant, a background arising from other biogenic and anthropogenic fluxes, and a random Gaussian noise intended to simulate the errors inherent in satellite instruments. But the model evaluation was conducted using simulated data. This approach does not account for all the challenges that real satellite images present, specifically issues related to cloud cover and systematic error patterns due to surface reflectance and the aerosol dependency of retrievals. Our source emission estimation model is an image-to-scalar CNN model, which infers from the full XCO 2 field a flux rate, estimation of the anthropogenic emission corresponding to the plume.
The first layers of the model consist of preprocessing steps which transform the input data at training time, in particular by adding noise and by scaling the plume and emission rate. The core model is a CNN composed of approximately 200,000 neurons divided into convolutional, max pooling, dropout and batch normalisation layers. We demonstrated that the design of a "universal" CNN, trained on a small power plant subset and highly accurate on all of them, is possible. To do so, we evaluated the model's ability to generalise to unobserved images from another region. Explicitly, three CNN models were tested on plumes from three sources: Boxberg, Lippendorf and Turow. The training dataset for each CNN is restricted to a dataset consisting of all other power plants except their target. The CNNs are highly accurate in each case and the addition of NO 2 fields often improves the results slightly.
Data availability. The datasets used in this paper are available on a compliant repository on https://zenodo. org/record/8096616 and originate from https://zenodo. org/record/4048228. The weights of the CNNs are available on https://zenodo. org/record/8095487. The algorithms are available on Zenodo (https://zenodo. org/record/8096760) and Github at https://github. com/cerea-daml/co2-images-inv-dl.
To solve the problem of the fixed threshold method, Ren et al. [26]  leveraged a hidden Markov model (HMM) to adaptively adjust the ZVI detection threshold according to pedestrian movement modes. Yang et al. [27]  utilized a Mahony filtering algorithm to merge and de-noise the original foot acceleration and angular velocity information to provide high-precision pitch angle information. Then, they utilized the pitch angle sliding variance for zero-velocity detection. Wahlstrom et al. [28]  proposed a threshold-adaptive ZVI detection algorithm, which selects the optimal threshold online according to the user's speed, gait frequency, or movement pattern. However, this algorithm requires collecting large quantity datasets at different walking velocities and different movement modes. With the rapid development of deep learning technology, some researchers try to use a data-driven approach to perform ZVI detection, which can be applied to more general motion models without any manual feature extraction  [29].
Acknowledgements This work was supported in part by  National Key Research and Development Program  under Grant No. 2020YFB1708800 ,  China Postdoctoral Science Foundation  under Grant No. 2021M700385 ,  Guang Dong Basic  and  Applied Basic Research Foundation  under Grant No. 2021A1515110577 ,  Guangdong Key Research and Development Program  under Grant No. 2020B0101130007 ,  Central Guidance on Local Science and Technology Development Fund of Shanxi Province  under Grant No. YDZJSX2022B019 ,  Fundamental Research Funds for Central Universities  under Grant No. FRF-MP-20-37 ,  Interdisciplinary Research Project   for Young Teachers of USTB (Fundamental Research Funds for the Central Universities)  under Grant No. FRF-IDRY-21-005 ,  National Natural Science Foundation of China  under Grant No.
The sealing must be excellent to avoid evaporation, which drives flows that hinder the particle diffusivity measurement. The motion of the particles in a 2d plane is then recorded under the microscope with a 60x water objective. The recording framerate was adjusted between 5 fps and 0. 5 fps depending on the sample viscosity. Particle tracking is done with a custom Matlab code available at https://doi. org/10. 5281/zenodo. 4884194. The particle diffusivity is extracted from the tracks using the optimal estimator from Ref  56  cor-rected for a constant background flow: 623 D = (∆x n ) 2 2∆t + ∆x n ∆x n+1 ∆t - 3 2 ∆x n+1 ∆x n-1 ∆t. Here, (•) denotes an average over all tracks (all particles, all 624 time points, both x and y direction), ∆x n the displacements 625 of a tracked particle between frames n + 1 and n and ∆t the 626 time between two frames.
For studies relying on data, we excluded those using exclusively human-manipulated or simulated data. Figure  1A  summaries this selection process and shows the number of articles excluded on the basis of their failure to meet the above criteria. In addition, we included four reports from the 'Grey literature'  (IPBES, 2019; IUCN, 2022; Pörtner et al. , 2021; WWF, 2020) , produced by organisations among the best known that provide assessments of temporal changes of biodiversity globally. 91 references constituted the final database, that we classified into four different categories: (i) biodiversity empirical analysis (n=48) ; (ii) reviews (n=20) ; (iii) methodological papers (n=19) ; (iv) reports (n=4).
e. there were as many increasing trends as decreasing, or a majority of no trends) and factor-dependent trends (i. e. directions that varied according to certain factors, such as location). While retrieving those information, we identified two main assessment approaches adopted in the corresponding studies to produce a global picture of biodiversity changes. First, a bottom-up approach, which correspond to reviews or meta-analyses aggregating results of studies analyzing individual datasets with varying methodologies from one study to the other (e. g. Pereira et al. , 2012; Pievani, 2014). Second, a top-down approach that produces a global result analyzing heterogeneous data aggregated in large databases within the same methodological framework (e. g. Dornelas et al. , 2019; Wilson & Fox, 2021).
We recorded the assessment approach for each reference and decided to investigate to what extent these could explain part of the observed heterogeneous results. When relevant and available, we also retrieved information on the data used, the ecological level targeted and the methodological processes used to quantify the changes. We recorded the databases used, temporal and spatial scope, taxonomic scope, and number of species. There are many measures of biodiversity, but in the papers selected we found measures of biodiversity at the population scale (abundance, density or biomass), at the species scale (mostly species richness), and at the community scale (assemblage composition, studied through similarity indices). We categorised the methods used to quantify the changes into three main categories.
Global overview Interestingly, we found only 91 papers that analyze temporal changes of biodiversity globally, while a plethora of examples accumulate at more local scales (e. g. Donald et al. , 2001; Koleček et al. , 2021). These were published from 1991 to 2021, with a majority published after 2010 (Appendix S2). We identified an increasing interest in this question over time, although this tendency also reflects an overall increase in the number of papers published during the same period. Of the 91 papers, 48 directly relied on empirical datasets, 20 were reviews, 19 were methodological papers and 4 were reports that we added from grey literature. Out of the 44 papers assessing temporal trends in biodiversity changes, 57% (n=25) concluded that biodiversity is globally in decline. More than a third of the papers concluded that trends are mixed or factor-dependent (36%, n=16).
Only 3 of the reviewed papers showed evidence of increasing biodiversity trends. Out of the 34 studies focusing on drivers, 65% (n=22) concluded negative effects on biodiversity, 26% (n=9) concluded that the effects were factor-dependent, and only 3 papers concluded that there was no significant effect or that the effect was positive (the latter referring to the effect of conservation plans only). (ncalc=3) 2,905 (ncalc=21) Description of the five types of encountered databases providing time series data on different biodiversity levels. n tot represents the total number of articles considering each of the data source. n calc represents the number of articles used to calculate the averages (without those whose information is unavailable). * Single non-aggregated but marginal data sets (used in articles only once).
** Aggregation of several locally available data sets in individual studies. Papers relying on empirical data used in majority aggregations of several data sets from individual studies (62,5%, n=30). Still, we found that three main global databases gathering biodiversity data were used: BioTIME  (Dornelas et al. , 2018) , the Living Planet Database (LPD)  (Loh et al. , 2005) , and the Global Population Dynamics Database (GPDD)  (Inchausti & Halley, 2001). The remaining papers relied on other unique but more sporadically used databases that were not open access. Table  1  highlights the characteristics and properties of the different data sources. The overwhelming majority of papers were based on terrestrial data covering all continents (67%, n=32), which is not surprising since our selection process was based in part on the choice to have global studies.
Still, we accepted articles covering at least two continents, and we note that the only continents that were studied in pair were America and Europe. The temporal coverage of the data was quite heterogeneous, but mainly covered the last 70 years (Fig. 2A ). This also confirms the strength of BioTIME's temporal coverage compared to other databases  (Dornelas et al. , 2018). The taxonomic coverage shows that the papers in our corpus mainly relied on data referring to several groups (Fig. 2B ). As well as for the geographical extent, this is not surprising considering our selection process. Still, there is a preponderance of data on mammals and birds, more generally on terrestrial vertebrates, and an under-representation of insects and invertebrates. The empirical data were based on different levels of biodiversity (Fig.
Potential sources of heterogeneities in the assessment of biodiversity trends We investigated three potential sources of heterogeneities. First, we examined the effect of using different assessment approaches. When papers used a bottom-up approach (i. e. aggregating local or regional results with varying methodologies into meta-analyses or reviews), they mostly concluded a decline in global biodiversity (69%, Fig. 3 ). In contrast, when papers used a top-down approach (i. e. performing analyses through a single methodology on global datasets), their conclusions were much more balanced: a bit more than 50% concluded that biodiversity was decreasing and almost 50% that trends were mixed or increasing (Fig. 3 ).
As our analysis is based on a single bibliographic database and additions from the grey literature, we recognize that the coverage of the literature is certainly not complete. Moreover, with the explosion of open databases worldwide, the literature on this subject is flourishing and articles are accumulating very quickly (Appendix S2). There is therefore a gap between the representativity of the current knowledge compared to the one from the established corpus. However, the robustness of our methodology to target a broad range of papers within the initial query allows a certain degree of confidence regarding the approximations we may draw in the present paper.
, 2023). They showed that not only short time series are less reliable than longer ones  (Wauchope et al. , 2019) , but also that the assessment of temporal changes in biodiversity at the global scale depends more on the number of time series (considered here at population scale) than on the representativeness of the number of species present within each taxonomic group in the data  (Dove et al. , 2023). To overcome these issues, a short-term solution would be to merge existing aggregated databases that are complimentary in order to increase the amount of data; although it should be noted that such synthesis requires caution regarding possible scale mismatches among datasets (temporal and/or spatial) and diversity in the metrics used  (Record et al. , 2021). Additionally, sensitivity analyses regarding the length of the time series used should be implemented systematically.
, 2018b). Currently, more than 40 stations around the world operate within this network, providing long-term aerosol observations, with 12 of them designated as core GAW-PFR sites by the WMO Scientific Advisory Group for aerosols. There are other associated stations currently providing PFR data which are not part of GAW, as is the case of Valladolid data used in the present study. There is overlapping between some GAW and AERONET/SKYNET stations, which allows PMOD/WRC not only to provide reference observations with their uncertainty estimation to contribute to GAW and GCOS but also to develop a strategy to ensure the traceability of AOD between different networks and merge their aerosol observations into a global dataset. GAW-PFR calibration chain, described in  Kazadzis et al. (2018b) , consists of a triad of instruments that continuously measure at PMOD/WRC, in addition to three portable transfer radiometers to guarantee traceability of AERONET with the world reference of the WORCC.
010 and 0. 020, with higher values for ultraviolet (UV) spectral bands  (Holben et al. , 1998; Eck et al. , 1999). The CE318-TV12-OC is another version of the CE318-T photometer specifically designed for ocean color applications. Equipped with 12 optical filters centred at  400, 412. 5, 442. 5, 490, 510, 560, 620, 665, 779, 865, 937, and 1020 nm, this  photometer is utilized by AERONET Ocean Color (AERONET-OC) global network, installed on offshore platforms to measure the radiance emerging from the sea, a critical parameter for satellite ocean colour validation  (Zibordi et al. , 2021). Cimel data used in this study has been screened for clouds by means of matching our dataset with AERONET version 3 level 2. 0 quality-assured data. This technique ensures that our data are free of clouds, are filtered by instrumental problems and the final post-field instrument calibration has been applied.
Calibration transfer from GAW-PFR to AERONET-Cimel In this section, we present the results of transferring calibration from the GAW-PFR, which is considered the reference instrument for AOD measurements by the WMO, to an AERONET-Cimel (CE318-TS) photometer. Although the GAW-PFR and AERONET-Cimel have distinct characteristics such as different optics, sun tracking systems, relatively different field of view and spectral bands with varying FWHMs and λ c , several comparison studies have been carried out in the past to make these different datasets comparable  (Kazadzis et al. , 2014 (Kazadzis et al. , , 2018a;; Cuevas et al. , 2019, among others). For example, Cuevas et al. (2019) conducted an exhaustive, long-term comparison in terms of AOD, which showed excellent traceability of AERONET-Cimel AOD with the GAW-PFR AOD reference at 440, 500, and 870 nm channels, with poorer traceability results in the UV range.
Acknowledgements. This work has been developed within the framework of the activities of the  World Meteorological Organization (WMO) Commission for Instruments and Methods of Observations (CIMO) Izaña Testbed for Aerosols and Water Vapour Remote Sensing Instruments. AERONET sun photometers at Izaña were calibrated through the  AEROSPAIN Central Facility  (https://aerospain. aemet. es/) supported by the  European Community Research Infrastructure Action  under the  ACTRIS  grant, agreement no. 871115. This work has been supported by the  European Metrology Program for Innovation and Research (EMPIR)  within the joint research project  EMPIR 19ENV04 MAPP. The EMPIR is jointly funded by the  EMPIR  participating countries within EURAMET and the  European Union. The authors also acknowledge the support from the  Ministerio de Economía y Competitividad from Spain  through the project  SYNERA  ( PID2020-521-118793GA-I00 ),  Ministerio de Ciencia e Innovación  (grant no.
For the raw comparison, while the correlation was poor, the model showed relatively small biases for O 3 and captures the overall ranges seen in observational data (NAPS mean values were within the model mean ± 1 model standard deviation for most sites). MDA8 O 3 comparison fared better, with a much higher correlation across most sites. For all species, there was considerable variability in correlation coefficients from site to site, and resampling (e. g. , 6 hour average) the dataset did not lead to improved correlation (up to 48 hour averages were tried in this analysis), although as noted above, for O 3 , comparison using MDA8 did result in better correlation. NAPS data are reported to the nearest integer values, meaning the data is quite coarse, leading to "discretization" artifacts that can clearly be seen in Figure  6a.
Foudhil, H. : Technical Note: The air quality modeling system Polyphemus, Atmos. Chem. Phys. , 7, 5,479-5,487, 2007. Minet, L. , Wang, A. , and Hatzopoulou, M. : Health and Climate Incentives for the Deployment of Cleaner On-Road Vehicle Technologies, Environmental Science & Technology, 55, 6602-6612, https://doi. org/10. 1021/acs. est. 0c07639, 2021. Ministry of the  Environment and Climate Change: Air Quality in Ontario : 2016 Report, Distributed by the  Ministry of the Environment, Conservation and Parks  (current name), Toronto, Ontario, Canada, 2016. NAPS:  National Air Pollution Surveillance   (NAPS)   Program , https://open. canada. ca/data/en/dataset/ 1b36a356-defd-4813-acea-47bc3abd859b, accessed on March 7, 2023, 2016. NCAR: Weather Research & Forecasting Model (WRF). Pourchet, A.
NEO-MAPP selected as primarily reference scenario the ESA Hera mission, which has recently been approved by the ESA Council at Ministerial Level, Space19+, in November 2019 for launch in 2024. The main goal of NEO-MAPP is to support the development and data analysis of NEO missions, as Hera and provide significant advances in both our understanding of the response of NEOs to external forces (in particular a kinetic impact or a close planetary approach), and in the associated measurements by a spacecraft (including those necessary for the physical and dynamical characterization in general). The NEO-MAPP objectives, include: (1) Pushing the limits of numerical modelling of the response of NEOs to a kinetic impact, as well as of their physical and dynamical properties while maturing European modelling capabilities linked to planetary defence and NEO exploration; (2) Increasing the maturity of multiple spaceborn and landed European instruments directly related to planetary defence, while focusing on measurements of surface, shallow sub-surface and interior properties of NEOs; (3) Developing algorithms and simulators to prepare for closeproximity operations and payload data analyses and exploitation; (4) Developing innovative and synergetic measurement and data-analysis strategies that combine multiple payloads, to ensure optimal data exploitation for NEO missions; (5) Developing and validating robust GNC strategies and technologies enabling surface interaction and direct response measurements performed by CubeSat or small/micro-lander architectures.
Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, Laboratoire Lagrange, Nice, France (michelp@oca. eu) * A full list of authors appears at the end of the abstract NEO-MAPP stands for Near Earth Object Modelling And Payload for Protection. This project is funded by the H2020 program of the European Commission and addresses the topic "Advanced research in Near Earth Objects (NEOs) and new payload technologies for planetary defence" (SU-SPACE-23-SEC-2019).
Acknowledgement: This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870377  (project  NEO-MAPP ). NEO-MAPP Team:  Karl Atkinson ,  Paula Benavidez ,  Jens Biele ,  Mélanie Drilleau ,  Colleen Fiaschetti ,  Sarah Fontaine ,  Paulo Gordo ,  Alain Hérique ,  Martin Jutzi ,  Ozgur Karatekin ,  Julia de Leon ,  Javier Licandro ,  Naomi Murdoch ,  Danica Rémy ,  Grig Richter ,  Francisco da Silva Pais Cabral ,  Paolo Tortora ,  Kleomenis Tsiganis ,  Jean-Baptiste Vincent ,  George Voyatzis ,  Kai Wuennemann ,  Marco Zannoni.
1 Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, Laboratoire Lagrange, Nice, France (michelp@oca. eu) * A full list of authors appears at the end of the abstract NEO-MAPP stands for Near Earth Object Modelling And Payload for Protection. This project is funded by the H2020 program of the European Commission and addresses the topic "Advanced research in Near Earth Objects (NEOs) and new payload technologies for planetary defence" (SU-SPACE-23-SEC-2019).