dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study was supported by funds from the  European Union 's  Horizon2020 framework  ( LANDMARC ; Grant agreement  ID 869367 ) the  Swiss National Science Foundation (SNSF ; grant number  172940 ) and by the  DSCATT project  " Agricultural Intensification and Dynamics of Soil Carbon Sequestration in Tropical and Temperate Farming Systems"  ( N°AF 1802-001 ,  N°FT C002181 ), supported by the  Agropolis Foundation  (" Programme d'Investissement d'Avenir"   Labex Agro ,  ANR-10-LABX-0001-01 ) and by the  TOTAL Foundation  within a patronage agreement. We further acknowledge funding and technical support from the  Tropical Soil Biology and Fertility Institute   of CIAT (TSBF-CIAT) , the  International Institute of Tropical Agriculture (ITTA) , and  ETH Zurich  in maintaining the experiments throughout many years.
Code availability. An earlier version of DayCent is freely available under https://www. nrel. colostate. edu/projects/century/century-downloads. php. For the latest version, we suggest to contact DayCent developers directly, who in our case kindly provided the latest DayCent version. Data availability. The data sets used for the calibration of this study are available under the IITA data repository. For SOC: https://doi. org/ 10. 25502/wdh5-6c13/d. For yields and biomass: https://doi. org/10. 25502/be9y-xh75/d.
Context. Orbital motion in binary and planetary systems is the main source of precise stellar and planetary mass measurements, and the joint analysis of data obtained using multiple observational methods can both lift degeneracies and improve precision. Aims. We set out to measure the masses of individual stars in binary systems using all the information brought by the HIPPARCOS and Gaia absolute astrometric missions. Methods. We present BINARYS, a tool that uses the HIPPARCOS and Gaia absolute astrometric data and combines them with relative astrometry and/or radial velocity measurements to determine the orbit of a binary system. This tool rigorously combines the HIPPAR-COS and Gaia data (here EDR3) and can use the HIPPARCOS Transit Data as needed for binaries where HIPPARCOS detects significant flux from the secondary component.
2016 ) is an open-source R package designed to quickly and robustly adjust non-linear models with a large number of parameters. The R code calls functions from a user-provided C++ file that compute the likelihoods, which we make available 7. To briefly explore the uncertainties and degeneracies of the parameters, we postprocess the TMB results with a short MCMC run using the companion R package tmbstan  (Monnahan & Kristensen 2018 ). In the present paper, the orbits are well constrained and a single short MCMC chain of 3000 iterations -of which we discard the first 1500 as warm-up iterations -has been found to be enough to reach convergence. To help TMB converge, we adopt starting values from the literature whenever available. For previously unstudied systems, we explore a large range of starting values and often initially fix some parameters to plausible values (e.
The solution published in the original reduction of HIPPARCOS    2 ) is the green dotted line and sample orbits from the MCMC are in black. The red dots represent the radial velocity observations with their associated error bars. The systemic velocity of 0. 59 km s -1 was derived from a TMB adjustment of the radial velocities and the direct imaging data for the sole purpose of this visualisation. includes an acceleration (seven-parameter solution), and the F2 goodness of fit for the five-parameter solution in the new reduction of HIPPARCOS is 2. 32, meaning that the reflex motion was already detected by HIPPARCOS itself. Kervella et al. (2019)  also detected a proper-motion anomaly, therefore finding signal in the HIPPARCOS-Gaia difference. We adjusted the orbital and astrometric parameters to the relative astrometric data, to the five Gaia astrometric parameters, and to the HIPPARCOS residual abscissae extracted from the IAD of the new reduction using the orbital parameters of  Mann et al.
HIP 88745: HIPPARCOS transit data and resolved Gaia observation HIP 88745 is known to be a binary system with a main sequence F star  (Hutter et al. 2019 ) and a circumbinary polarised debris disk  (Kennedy et al. 2012). Direct imaging studies of the system include  Heintz (1972) ,  Abt & Willmarth (2006)  (1999) studied the system using the TD of the original HIPPAR-COS reduction, and it appears in his Table  4 , which lists stars for which only the total mass of the system could be derived. We chose to analyse this system because it has a component solution in HIPPARCOS and was analysed as resolved in Gaia EDR3. We therefore adjusted to the HIPPARCOS TD as well as to the Gaia EDR3 astrometric parameters for the primary (5AP A ) and secondary (2AP B ) components. The HIPPARCOS new reduction considered the secondary flux and an astrometric acceleration, but still contains a strong remaining signal (F2 = 9.
77). The ruwe value of the 5AP A Gaia EDR3 solution is 1. 39. Direct imaging and radial velocity data are also available for this star: 17 relative positions from the Fourth Catalog of Interferometric Measurements of Binary Stars  (Hartkopf et al. 2004 ) and 19 radial velocities of the primary from SB9 Pourbaix et al. (  2004 ) covering a small fraction of the orbital period. To complete those radial velocities, we also consider the Gaia DR3 radial velocity  Katz et al. (2023)  with the error inflation described in  Babusiaux et al. (2023)  and two observations available in the ELODIE archive 8  (Baranne et al. 1996 ) -for which we consider a quite arbitrary 0. 3 km s -1 offset uncertainty -in our validation plots. For this star, we performed two adjustments, one (A) to the absolute astrometry data from HIPPARCOS and Gaia, and one (AR) that additionally uses the available relative astrometry.
BINARYS is also being extended for the study of triple systems  (Lagrange et al. 2020)  and to take into account the new non-single solutions (NSSs) that are provided by Gaia DR3 (Gaia Collaboration 2023). Additionally, this tool prepares us for Gaia DR4, which will provide epoch observations. When DR4 is released, we will be able to combine HIPPARCOS and the Gaia equivalent of the TD. By that point, the tool will run mostly without HIPPARCOS constraints because of the huge sample size difference. Although non-single solutions will be provided by the Gaia-DPAC consortium, the combination of Gaia with external data will have to be done using the epoch data for an optimised solution, but also in order to derive solutions for systems with an insufficient Gaia signal to obtain a full NSS solution alone, and to handle specific cases such as multiple systems.
Acknowledgements. We thank the referee for his detailed comments that helped to improve the clarity of the manuscript. This work is supported by the  French National Research Agency  in the frame-work of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), in particular through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called superemitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit providing daily global coverage of methane mixing ratios at a resolution of up to 7x5. 5 km 2 , enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a Convolutional Neural Network to detect plume-like structures in the methane data and subsequently apply a Support Vector Classifier to distinguish emission plumes from retrieval artefacts.
5 km 2 and daily global coverage  (Hu et al. , 2018; Lorente et al. , 2021) , resulting in a point-source detection limit down to ∼5 t h -1 under favorable conditions  (Jacob et al. , 2016). TROPOMI data have been used to quantify global  (Qu et al. , 2021)  and country-level  (Chen et al. , 2022)  distributions of methane emissions as well as large area sources, such as oil and gas basins like the Permian Basin  (Zhang et al. , 2020; de Gouw et al. , 2020; Schneising et al. , 2020; Shen et al. , 2022). Lauvaux et al. (2022)  performed a study into oil and gas related methane super-emitters using TROPOMI data. Several individual super-emitters have been studied in detail using TROPOMI CH 4 data; e. g. natural gas well blowouts  (Pandey et al. , 2019; Cusworth et al. , 2021; Maasakkers et al.
, 2022b; Maasakkers et al. , 2022b). TROPOMI has collected over 5 years of methane data, which include numerous methane emission plume signals that cannot feasibly be identified manually. To monitor the growing volume of data for super-emitters, an automated approach is needed. The vast amount of data provides an opportunity for machine learning techniques that require a substantial amount of representative training data. Applications of machine learning in satellite remote sensing have mostly focused on studying the Earth's surface, but also include monitoring anomalous atmospheric conditions and identify plume signatures in large datasets  (Valade et al. , 2019; Finch et al. , 2022). Detecting methane plumes in TROPOMI data is particularly challenging because not every retrieved methane enhancement is a genuine methane emission plume as retrieval artefacts and natural variability can appear like methane plumes.
We therefore use a two-step machine learning method to identify methane emission plumes. We first use a Convolutional Neural Network (CNN) to detect plume-like structures in TROPOMI XCH 4 data and then use a Support Vector Classifier (SVC) to evaluate these potential plumes using additional information to distinguish real plumes from artefacts. We train the machine learning models on verified TROPOMI methane plumes from 2018-2020, and then apply the trained models We use two machine learning models in sequence to detect plumes in the TROPOMI methane data. First we apply a Convolutional Neural Network to detect plume-like structures in TROPOMI methane atmospheric mixing ratio data, then we use additional atmospheric parameters and supporting data to further distinguish between genuine methane plumes and retrieval artefacts.
TROPOMI We use data over land from the TROPOMI XCH 4 Level 2 scientific data product version 18_17. This product version is consistent with operational version 02. 03. 01  (Lorente et al. , 2021; Hasekamp et al. , 2022) , but re-processed for the full timespan of the mission resulting in a homogeneous data product  (SRON CH4 L2 team, 2022). We use albedo-bias corrected data with: QA≥0. 4, methane precision < 10 ppb, SWIR aerosol optical depth < 0. 13, NIR aerosol optical depth < 0. 30, SWIR surface albedo > 0. 02, mixed albedo (2. 4 * NIR surface albedo -1. 13 * SWIR surface albedo) < 0. 95, and SWIR cloud fraction < 0. 02. The loosened filtering compared to the recommended QA=1 filter provides more coverage but also retains more biased retrievals, especially at the borders of clouds or along coasts.
The methane data is de-striped following the approach introduced by  Borsdorff et al. (2018). To train a machine learning model to recognise methane plumes in TROPOMI data, we created a dataset of scenes consisting of 32x32 pixels, both with and without methane plumes. 32x32 pixels correspond roughly to an area of ∼176x232 km 2 at nadir, up to ∼176x448 km 2 for larger viewing angles. For scenes with plumes, we use data over 60 persistently emitting locations identified using long-term wind-rotated averages  (Maasakkers et al. , 2022b). By manual inspection, we compile a dataset of 828 positive scenes from 2018-2020 with plumes, of which 195 originate from coal mines, 203 from landfills/urban areas, and 430 from oil&gas infrastructure. An example scene is shown in Figure  1a , including the local windfield at the time of observation.
A set of (negative) scenes without an emission signal was obtained through manual inspection of six full orbits in different sections of the orbital repeat cycle, covering a diverse set of surfaces and (meteorological) conditions. We obtain 32x32 pixel scenes using a moving window algorithm with 50% overlap, resulting in a dataset of 2242 scenes without a plume signal. The moving window algorithm later ensures that if a plume is cut in half in a particular scene, it will be in the center of the adjacent -and partially overlapping-scene. Scenes with <20% valid XCH 4 pixels are discarded. This processing is applied to full orbits, the scenes with plumes originating from known locations were processed to match the same format. Combined, the dataset contains 3070 scenes used for training, the difference in the number of positive (828) and negative (2242) scenes is corrected for later on using class weights.
Numerical values show input dimensions, layer dimensions and optimized hyperparameter values. Visualization generated using PlotNeuralNet  (Iqbal, 2018). For the training process, the class weight parameter is set to the ratio between the number of plumes (828 positives) and negatives (2242), such that both categories have equal impact. These scenes mostly contain clear positives and clear negatives to effectively learn distinguishing features. We randomly split the data into a training (80%) and test (20%) set. 20% of the training scenes are then used as validation subset (Table  A1 ). The validation dataset is used to infer whether there is a generalized performance increase during the training of the CNN to prevent overfitting. We then augment the data in order to obtain larger training and test sets; all scenes are rotated 90 • thrice and flipped, enlarging the datasets by a factor 8.
We use the training dataset of 19,648 (augmented) scenes (Table  A1 ) to train the CNN. The CNN was designed and trained using Keras  (Chollet et al. , 2015) , at first we selected the default values for the hyperparameters, which control the details of the high-level architecture and training process (i. e. number of layers, dimensions of layers, number of filters, learning rate etc. ). The model is trained for a maximum of 100 epochs, optimizing the validation loss, using binary cross-entropy as the loss function and ADAM as the optimizer. We use a 0. 4 dropout layer in the first fully-connected layer during training to make the model more robust. We apply the ReLU (rectified linear unit) the activation function in all layers except for the final layer where we apply softmax as the activation function.
To force the model to focus on plume-like signatures, the loss weight of plume scenes is set to double that of negatives scenes. Training is halted after the validation loss does not improve for several epochs and the best model weights are restored. After training, the model performance is inferred by classifying the labeled testset. After training this initial "default" model version, the hyperparameters were further optimized using Keras Tuner  (O'Malley et al. , 2019)  and Hyperband  (Li et al. , 2018). The optimal hyperparameters depend on the size of the training dataset, architecture of the CNN, number of classes and problem type. The search space for the optimization was defined using insights from the initial training, theoretical foundation, and design constraints. We inspected the hyperparameters of the top 10 performing models and selected the optimal hyperparameters by combining this optimization with expert judgement on this particular problem.
We train the SVC to find the optimal classification boundary within this 41 dimensional space based on the 843 labeled feature vectors. Each feature is standardized by subtracting the mean, and scaling the value to the unit variance of that feature in the entire trainingset. We use a Radial Basis Function (RBF) kernel and set the regularization parameter to 1. 2, this hyperparameter was optimized using a simple grid search optimization. The gamma value is scaled inversely to the number of features (41 in this case) multiplied with the variance of the training dataset, as is common practice and helps to homogenize the features which have different units and ranges of values (Table  C1 ). We randomly split the labeled dataset into a training set (80%) and a test set (20%). Contrary to the CNN, when training an SVC no validation set is used.
Correctly detecting plumes is of predominant interest, therefore we combine non-plume scenes (artefact & empty classes) when evaluating the performance. We train the model 2000 times for different splits of the dataset. The distribution of these different realizations shows convergence with binary kappa = 0. 78 ± 0. 04 and recall = 0. 88 ± 0. 03 (standard deviations), indicating the model setup is not too dependent on the data split. We select a model with relatively high kappa and recall, where performance is similar between the training and test sets. To evaluate which features are important for the SVC to classify a scene, we performed a permutation importance analysis perturbing each feature 40 times  (Breiman, 2001). Based on the resulting mean feature importance, the most important features are: correlation of XCH 4 with χ 2 , the CNN score, the albedo correlation, the enhancement of the plume, the fraction of valid pixels, the angle with the local wind, and the average quality flag of plume pixels (the top 10 ranking feature importance metrics are presented in Table  C1 ).
1x0. 1 degree grid, removing duplicate coverage from overlapping scenes in the same orbit. We then correct for local variations in coverage (such as persistent areas without data) by convoluting this field with the summed footprints of all 2021 TROPOMI data at 0. 1x0. 1 degrees. We finally aggregate our detected emissions to a 1x1 degree grid and divide those by the fraction of days in 2021 with coverage resulting from the coverage map average to 1x1 degrees. We find a scaled-up annual emission flux of 10. 3 Tg, which is approximately 2. 7% of total bottom-up 2017 anthropogenic emissions (380 Tg yr -1 )  (Saunois et al. , 2020). Super-emitter plumes from landfills account for 4. 1 Tg yr -1 , those from coal 2. 1 Tg yr -1 , those from oil 2. 2 Tg yr -1 and those from gas 1. 9 Tg yr -1 each.
These estimates are only small fractions of total anthropogenic emissions as our conservative upsaling approach only takes large TROPOMIdetected super-emitter plumes into account. Emissions from smaller point sources and area sources make a large contribution to the total and are better captured by an atmospheric inversion. and data analysis (PRISMA archive, Sentinel-2) following  (Maasakkers et al. , 2022b). Furthermore, we investigate large transient emissions with data from non-targeted instruments such as Sentinel-2. Figure  7  shows a TROPOMI plume (14. 6 ± 8. 8 th -1 ) detected near Casablanca in Morocco on June 12th, 2021. We detected 23 plumes in the area in 2021 with source rates ranging from 8. 9 ± 5. 1 t h -1 to 40. 5 ± 18. 0 t h -1 with a mean of 18. 8 t h -1 , indicating a persistent source (Figure  6f ).
0 and 2. 5 ± 0. 8 th -1 ), the sources are only had a single TROPOMI detection in 2021. The emission therefore appeared to be transient at first, but with Sentinel-2 we find three emission plumes ranging from 1. 3 ± 0. 4 to 10. 0 ± 3. 0 t h -1 originating from the same oil facility in a timespan of two months. Extensive monitoring of a location of interest over a longer timespan is feasible using Sentinel-2  (Varon et al. , 2021; Irakulis-Loitxate et al. , 2022b). We detected methane emission plumes in 2021 TROPOMI data using an automated, machine-learning-based pipeline. We have trained a Convolutional Neural Network with a relatively small set of known plumes in pre-2021 TROPOMI methane data to detect plume-like morphological structures (kappa score of 0. 97 and recall of 0. 98 on the test set).
We then used a Support Vector Classifier to distinguish real plumes from retrieval artefacts using additional information from the scene and supporting data (kappa score of 0. 81 and recall of 0. 93 on the test set). This two-step approach can also be applied to other instruments in the future. We tested our detection, source localization, and emission quantification estimate for a specific well-characterized natural gas well blowout and found it was accurately captured by our monitoring system. After application of our pipeline to 2021 data, we targeted high-resolution observations and analyses to find the facilities responsible for twelve (clusters of) plumes seen in TROPOMI. Using our automated machine learning pipeline we scan all 794. 395 scenes of 2021 in three hours on a single core.
https://doi. org/10. 5194/acp-2022-862 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License. Code and data availability. TROPOMI data are publicly available at https://ftp. sron. nl/open-access-data-2/TROPOMI/tropomi/ch4/18_17/. GHGSat-C1/C2 methane plume data used in this study are available at https://doi. org/10. 7910/DVN/QQQ9IU. Sentinel-2 data 560 are publicly available at the Copernicus Open Access Hub https://scihub. copernicus. eu/. PRISMA data are publicly available at https://prisma. asi. it/, after registering at https://prismauserregistration. asi. it. GEOS-FP wind data can be downloaded at https://gmao. gsfc. nasa. gov/GMAO_products/. ERA5 wind data are available at https://cds. climate. copernicus. eu. WRF-CHEM code is available at https://github.
SVCs in general perform better with datasets of limited size compared to deep learning algorithms and are in general less prone to erroneous influence from outliers. We use 843 labeled scenes from 2020 classified by the CNN to contain plume-like structures as a training dataset for the Support Vector Classifier (SVC). About half of the scenes are randomly selected from within 7 geographical zones with specific types of predominant artefacts and the other half is selected randomly. Scenes are labeled as 'plume' (444 scenes), 'artefact' (341 scenes) or 'empty' (58 scenes, indicating there is not a clear plume or artefact). We have only included scenes with unambiguous labels. The fact that there are relatively few 'empty' scenes in this subset indicates the CNN performs well. We use balanced class weights to correct the imbalance in the number of training samples per class, the weights are inversely proportional to the number of scenes in each class.
One of the reasons behind this issue is the poor quality of the models being used, partly due to the lack of high-quality datasets. Indeed, these datasets are generally biased towards including mostly regular traffic at the expense of malicious traffic. This work addresses the problem of detecting cyber-attacks at the network edge using an anomaly-based intrusion detection approach. The detection consists in classifying the communication flows into normal and anomalous. We propose an efficient network representation that provides relevant information about cyber-attacks, enabling Artificial Intelligence (AI) algorithms to detect malicious patterns. Our structure provides important structural information about attackers' behavior, such as the iterative malicious routines, connections between several malicious activities, and the existence of multi-step attacks or distributed attacks.