dataesr/scientific-paragraphs-categorization huggingface dataset:
A minimum threshold of 50 measurements of the inversion product was used to exclude all sites without a sufficient number of measurements. We also ensure that all seasons are represented in the dataset. As mentioned above, one possible application of our aerosol microphysical model is the validation of satellite products in an operational context, whereby the atmospheric correction is performed when the aerosol loading is not too high. Thus, we decided to limit the dataset to aerosol optical thicknesses at 550 nm lower than 0. 8. Out of 1139 available AERONET sites, we selected 851 globally distributed sites (Fig. 1 ), resulting in ∼ 1. 3 million retrievals of aerosol microphysical properties. To characterize the representativeness of these sites, we analyzed the type of land cover surface around the selected AERONET sites.
There are three different sets of retrievals: 1. The size distribution (see Eq. 1) of C vf , C vc , %C vf , %C vc , r vf , r vc , σ r , and σ c is always available when aerosol microphysical property retrievals are performed by AERONET. For this study, this first block provides Table  1. Description of the database of aerosol microphysical properties for aerosol optical thickness τ at 440 nm and the Ångström coefficient α (440, 870). a little fewer than 1. 3 million sets of retrievals for the whole 851 AERONET sites used. τ 440 α 440-870 %C vf C vf r vf σ f %C vc C vc r vc σ c nr 440 ni 440 %S ph (µm 3 µm -2 ) (µm) (µm 3 µm -2 ) ( 2. The complex refractive index for four wavelengthsnr 440 , nr 650 , nr 850 , nr 1020 , ni 440 , ni 650 , ni 850 , ni 1020 -has a lower occurrence in terms of retrievals, only 0.
The decrease observed in 2020 is because all data have not yet been validated. A technical description of values for all aerosol microphysical properties for the 851 AERONET sites is presented Table  1 , showing the percentile at 1 %, 5 %, 95 %, and 99 % and the median value for each of the properties. This gives a global overview of aerosol microphysical properties over land. AERONET sites do not have the same number of observations (see Fig. 4 ). In the database we developed, one site may contain several thousands of selected retrievals for each aerosol microphysical property. For example, 8 sites provided more than 10 000 sets of retrievals for the size distribution, i. e. , Sede Boker (Israel), Solar Village (Saudi Arabiano longer in the network), GSFC (USA), Burjassot (Spain), El_Arenosillo (Spain), Carpentras (France -no longer in the network), Sevilleta (USA), and Granada (Spain).
Parameterization of the aerosol microphysical properties Two measurements protocols are followed to acquire AERONET data. The aerosol optical thicknesses (AOT) is regularly measured every 15 min following a direct measurement of the sun when cloud-free. For the retrieval of the aerosol model microphysical properties, as specified above, the protocol required an almucantar measurement  (Holben et al. , 1998; Dubovik and King, 2000) , which is performed early in the morning or late afternoon. The main issue is that this AERONET measurement might not be coincident with the Earth observation satellites' overpass times. Moreover, for various reasons (e. g. , inhomogeneous sky, small clouds, calibration procedure) some measurements might be missing. We can obviously interpolate data between two available measurements, but we miss the variability in the considered aerosols.
As an illustration, Fig. 9  shows an example of the impact of changing the aerosol model for size distribution from early morning (07:21:30 local time, LT) to late afternoon (16:28:45 LT). In this example, there is an increase in coarse aerosols between the morning and the evening, but we do not exactly know when that occurred. In 2002,  Dubovik et al. (2002a)  suggested to determine each microphysical parameter with a direct regression (Eq. 5) using the aerosol optical thickness at 440 nm from the AERONET dataset. Aerosol microphysical parameter = a + b • τ 440  (5)  For each AERONET site, this approach has been used so far for the official validation of the MODIS and VIIRS surface reflectance products  (Vermote et al. , 2002 (Vermote et al. , , 2014)) , for the NASA HLS (Harmonized Landsat Sentinel-2) project (https: //earthdata.
, 2007; Russell et al. , 2010; Giles et al. , 2012; Lenoble et al. , 2013; Tsikerdekis et al. , 2021). Therefore, the complexity of the radiative transfer through the atmosphere partially allows mitigation of this phenomenon. Indeed, coupling between the scattering and the absorption of light allows us to indirectly capture the aerosol absorption information. With our AERONET database (over the 400 sites where we have all microphysical properties), we explored several mathematical formulations for a regression between an aerosol microphysical property, called AMP in the following equations, and the two variables τ 440 and α 440-870. We used a similar idea after Dubovik's law (Eq. 5). We first tested Eq. (  5 ). Then, we tested a linear regression with the Ångström coefficient α 440-870. AMP i = a i + b i • α 440-870 , (6) where i represents one of the microphysical properties (e.
In the AERONET processing, the complex refractive index is applied when the AOT is higher than 0. 4 at 440 nm. This limits the variability in terms of AOT and probably artificially increases the occurrence for α 440-870. We applied our approach for the three mathematical formulations given by Eqs. (  5 ), (6), and (7) over the whole selected dataset and present the results in Table  4. In terms of accuracy A (Eq. 2), results show very low values. Except for C vf , C vc , and %C vf , which present an accuracy up to 2 %, accuracies of all other microphysical parameters are below 0. 1 %. For uncertainty U (Eq. 4), the third mathematical formulation gives the overall best results (Table 4). As expected, τ 440 better represents C vf , while in contrast α 440-870 better represents the %C vf. Finally, including both variables, we get a non-negligible improvement for both volume concentrations (absolute and relative).
Retrieved microphysical properties from the whole dataset To expand on Table  4 , Fig. 12  give the A, P , and U of the retrieved microphysical properties over the whole dataset versus τ 440 and α 440-870. The interesting point of these figures is the dependency of uncertainties with τ 440 and α 440-870. Indeed, except for C vf and C vc , uncertainties are quite stable with the aerosol optical thickness. In contrast, most uncertainties present variation with the Ångström coefficient. This confirms the importance of considering α 440-870 in the regression. Another point is the correlation between Tables  4  and 5  and Fig. 12. When the variability in the uncertainty with α 440-870 is important (Fig. 12 ), the variability in the microphysical properties is more important as well (Tables  4  and 5 ).
Impact of the uncertainties on the surface reflectance product over land As previously mentioned, this work is meant to support atmospheric correction validation over land. Thus, one question is how the uncertainty in the retrieved aerosol microphysical property affects the surface reflectance product validation. To address this issue, we decided to define, for each aerosol microphysical property, the impact of its uncertainty (Table  5 ) on the atmospheric correction and the determination of the surface reflectance over land. For that purpose, we defined a synthetic database of TOA reflectances for each AERONET site and for each specific satellite band. To generate this database, we used the 6S code  (Vermote et al. , 1997; Kotchenova et al. , 2006 Kotchenova et al. , , 2008;; Kotchenova and Vermote, 2007)  with the following inputs: (1) a set of 80 viewing conditions (solar angle, view angle, azimuth angle) describing all satellite angular configurations possible, (2) a set of different atmospheres (pressure, temperature, water vapor), (3) a set of surface reflectances (from 0 to 0.
6 depending on the wavelength), and (4) a set of 40 aerosol microphysical properties with associated τ 440 and α 440-870 picked up in the real AERONET database. Then, we applied the atmospheric scheme developed for the land surface reflectance code (LaSRC) algorithm for MODIS, VIIRS, Landsat 8, and Sentinel-2  (Vermote et al. , 2002 (Vermote et al. , , 2014 (Vermote et al. , , 2016;; Claverie et al. , 2018; Doxani et al. , 2018). First, using each set of inputs, we computed the TOA reflectance. Then, inducing 20 cases of random uncertainties for each aerosol microphysical property, we applied an atmospheric correction to get the surface reflectance ρ surf to be compared to the one used as input. Table 6 gives the uncertainties we get for the MODIS red channel (band 1, 620-670 nm). For example, %C vf is generated with an uncertainty of 22.
Acknowledgements. We thank all  AERONET PIs  and their staff for establishing and maintaining all sites used in this investigation. Thanks to  Chris Justice , professor at the  University of Maryland , for his helpful comments on drafts of the paper and for supporting this project. Financial support. This research has been supported by  NASA  (grant nos. NNX17AJ63A  and  80NSSC19M0222 ). Review statement. This paper was edited by  Marloes Penning de Vries  and reviewed by two anonymous referees.
Objectives: Glioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as "lower grades gliomas", while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.
Material and methods: Using WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth. Results: Our first training reached 84. 1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset.
These approaches rely on the radiomic analysis of glioma used for tumor segmentation: the tumor shape and heterogeneity, the length of the first and second major axis, or the presence of necrosis and enhancement in T1 contrast-enhanced (T1ce) sequences can be used in the MR diagnosis  [11]. One of the most popular datasets used for automatic binary grade classification is the BraTS dataset  [12] [13] [14]. This dataset, originally published for a tumor segmentation challenge, has been used extensively for binary glioma grade classification. But it is not providing a WHO-aligned division, as it groups together grades I, II and III under the term "lower grades gliomas" (LGG BraTS ), and has only glioblastoma multiform, or grades IV, in its high grades gliomas category (HGG BraTS ). Therefore, we want to investigate the consistency of the LGG BraTS population, using machine learning and WHO-aligned radiomic features.
In this paper, we present the current state of the art for radiomics analysis and glioma grading using artificial intelligence. We train a SVM classifier with radiomic features on binary grade discrimination. Then we use prediction scores to analyze and identify radiologically high grade patients among the "lower grade" population of the BraTS dataset. We also developed a visual module to allow radiologist experts to discriminate between LGG WHO and HGG WHO and collect their votes. We used those votes to align the BraTS dataset on the WHO classification and create a radiological groundtruth. This section also shows the performances of our classifier on this new groundtruth data. A discussion about the results is given in conclusion.
Glioma grading learning dataset We used the 2018 version of the BraTS dataset, composed of 285 glioma cases. These patients are divided into 210 glioblastoma multiform (HGG BraTS , grade IV) which is the most advanced grade for gliomas, and 75 "lower grade glioma" (LGG BraTS , grades I, II and III). These labels have been established by histological screening. For each patient, 4 registered and skull-stripped anatomical sequences are available: T1, T1ce, T2 and T2 FLAIR.
Features Using the segmentation groundtruth data given with the BraTS dataset, we computed 51 features from the PyRadiomics package  [26]  for each patient: 7 shape features, 6 histogram-based intensity features and 5 texture features. The shape features are the length of major and minor axis, the maximum 3D diameter, elongation, flatness, sphericity and surface area. These 7 features are the same for all 4 sequences available and are only computed once per patient. The remaining features are computed on each 4 sequences. The histogram-based intensity features include mean, skewness, kurtosis, contrast, energy and entropy. For texture analysis we used the correlation of the gray level co-occurrence matrix (GLCM), coarseness, inverse difference moment (IDM), complexity and strength. This set of features (shape, intensity, texture) relates to the criteria used by radiologists during glioma grade assessment and have been selected as intelligible features.
Results and consistencies on radiomics criteria We trained an SVM classifier and analyzed its performances through its prediction score results. We tested a C-Support Vector Classifier with different hyperparameters: the used kernel (linear or Radial Basis Function) and the value of the regularization parameter C (between 0. 1 and 2). The hyperparameters were selected and optimized with a 5-fold cross-validation and a grid search. We applied usual techniques to avoid class imbalance and overfitting, namely the use of balanced class weights in training and the 5-fold cross validation. The best result was obtained with linear kernel and a C value of 1. 0. We reached 84. 1% accuracy, 87. 0% sensitivity and 75. 9% specificity on the BraTS dataset. With this first classifier, we can explore these results on the LGG BraTS population to analyze how it dealt with its unmatching HGG WHO patients.
For our test, we define true positives and true negatives as shown in Table  2. We define the test as positive when the patient is labelled as HGG BraTS by our classifier. We want to discriminate the LGG WHO and HGG WHO population among the BraTS dataset. As some HGG WHO are overlapping into the LGG BraTS group, we must focus on this population. With our classifier, this population is divided between true negatives and false positives. False positive patients are particularly interesting, as they are LGG BraTS classified as HGG BraTS by our classifier. The confusion matrix of our first training is shown in Table  3. In order to analyze our classifier behaviour, we propose to use the prediction score probabilities, as defined by Platt et al. [27]. This score gives a value between 0 and 1 and can be read as the confidence of our classifier when assigning a patient to a class.
Label gathering with expert radiologists We want to see if our radiological criteria match the expert analysis. This is why we created a labelling task for expert radiologists. Using a Python module in Slicer3D  [28] [29], we were able to present each patient in randomized order. We chose to present 100 patients so that the task would last about an hour. This allowed us to present all 75 LGG BraTS patients, with 25 HGG BraTS patients as complement. An odd number of 5 experts analyzed the images and chose to assign each patient in the LGG WHO or HGG WHO category. Each of them then filled a list of criteria to later compare homogeneity of the responses. Using this list shows that our radiologists used necrosis and contrast enhancement as their main criteria, then relied on various details such as intralesional bleeding, FLAIR inhomogeneity or mass effect for a finer classification.
Our experts described these ambiguous cases as patients on the edge of anaplasic transformation and were more inclined to label them with the high grade status in order to start intensive care without delay. Majority voting changed the grade label from LGG BraTS to HGG WHO of 44 patients out of 75. For 29 patients, these labels were changed after a 5/5 consensus; 8 after a 4/5 vote; and 7 after a 3/5 vote. We can note than more than half of LGG BraTS patients changed label, which can raise questions about the quality of classification produced by previous works published with this dataset, as real LGG WHO patients appear to be scarce. LGG WHO patients can be asymptomatic and therefore under-diagnosed, which explains the difficulty of creating a large LGG WHO dataset.
Discussion and conclusion In this paper, we showed how prediction scores on the BraTS dataset can be used to identify groups within the classifier performances. Studying the distribution of each patient along the prediction axis can give more information than usual evaluation based on accuracy, sensitivity and specificity. In our example, we saw a modal bin in the false positives close to 0, expressing the high confidence of our classifier to give the wrong label to these patients. This example suggests that the use of prediction score offers a new insight in understanding the behaviour of classifiers. Thus, the histogram of prediction may be used in a way to "open the black box", as it is a tool of results visualization. Detailed radiological analysis shows that our false positive patients present radiological high grade criteria such as necrosis and contrast enhancement.
We asked 5 expert radiologists to label each patient allowing us to generate a new radiologically WHO-based groundtruth data for the BraTS dataset. A new training on the radiologically coherent groundtruth shows improvement in the false positive distribution and slightly lower general performances, despite a important class imbalance. Due to the important class imbalance, our classifier trained on this groundtruth is more accurate for HGG WHO patients and less accurate for LGG WHO patients. Using data augmentation techniques to generate more LGG WHO data may reduce the class imbalance and improve the LGG WHO accuracy. Again, we used the prediction score histograms and a crossed prediction scores plot to show that, with an individual tracking level, despite these lower performances, our classifier trained on our new groundtruth was more confident in classifying HGG WHO patients.
Thus, we showed how a crossed prediction scores plot can be used as a comparison tool for classifiers analysis. Yet, caution is to be taken for glioma grading when relying on anatomical sequences. Accessing the biological reality of the patient must involve a more complete screening with biopsy and multiparametric MRI scan. Perfusion imaging and MR Spectroscopy give the radiologist more information for grade classification. For example, anatomical imaging has trouble identifying a non-enhancing high grade patient, so our classification can only rely on a limited body of evidence. That's why our groups based on anatomical imaging can only get close to a WHO-defined system, without the certainty and specificity of the real diagnosis. Perspectives include creating a large multimodal dataset, with MR spectroscopy, diffusion and perfusion imaging to improve the classification.
There are several heterogeneity issues to address 1) data format, 2) languages to describe sensor metadata, 3) ontologies to structure sensor datasets, 4) reasoning mechanisms and rule languages to interpret sensor datasets, and 5) applications. The challenge today is finding better ways to reuse more meaningful and more accurate information, to get useful abstractions from sensor data. Innovative methodologies are required to link the data from different domains to improve knowledge discovery. Semantic Web technologies are widely used in applications and in online activities that we perform every day. Google, reused the Knowledge Graph (KG)  [WR4]  term in 2012, which became popular, and demonstrated the impact of semantic web technologies. Everyday, we are using KG technologies without being aware of it.
Disseminating tools for ontology quality There is a real need for the proper tools to improve ontology quality, guide, and educate ontology designers/engineers. A state of the art analysis (Section 3) for best practices, quality, and evaluation of ontologies has been done to fill this gap. Taking inspiration from the Linked Data blog [WR26], we innovatively disseminate state of the art: a website which encourages semantic interoperability by classifying and referencing reusable tools that can assist the improvement of ontologies. Example for dissemination and ontology quality Federated Interoperable Semantic IoT Testbeds and Applications (FIESTA-IoT)  5  is an H2020 EU project focusing on IoT semantic interoperability. To integrate new testbeds (e. g. , smart cities), a set of practices needs to be followed to semantically annotate and link the data, deduce new knowledge from data, and unify models, services, and applications.
FIESTA-IoT ontology  [17] , aims to integrate a set of IoT-related ontologies that have been studied: 19 ontologies for IoT, 28 ontologies for Wireless Sensor Networks (WSNs), 8 ontologies for the Web of Things (WoT), and 16 ontologies for smart cities. Our goal was to design a unified IoT ontology by reusing well-designed and most common ontologies. Furthermore, the FIESTA-IoT dataset validation tool compliant with the FIESTA-IoT ontology has been implemented  [18]  to check interoperability among semantic datasets. When IoTrelated ontologies have been found (e. g. , reading scientific publications), we have suggested the ontology URLs to the LOV ontology catalog. We have learned from the LOV community (through email exchanges) that the required practices are not followed most of the time and ontologies need to be improved with the set of tools presented in Table  3.
Ontology Metrics Ontology metrics have been defined in  [10]   [12]. However, there is a need to classify and prioritize metrics. Ontology metric works are also referenced in Table  1. OntoMetric is a methodology to choose the appropriate ontology for a specific system  [45]. Five dimensions are provided: (1) the content of the ontology and its organization, (2) the language used for the implementation, (3) the development methodology employed to build the ontology, (4) the software tools used to design the ontology, and (5) the costs to develop and maintain the ontology. The OntoMetric framework defines 160 characteristics according to those five dimensions. Burton et al. [44]  define ten metrics for ontology quality: lawfulness, richness, interpretability, consistency, clarity, comprehensiveness, accuracy, relevance, authority, and history.
From our point of view, is also really relevant for doing automation with user interfaces. OntoQA provides three kinds of evaluations: (1) evaluation-based validation, (2) symbolic-based validation, and (3) attribute-based validation. OntoQA has been evaluated with three ontologies: SWETO, TAP and GlycO, and related datasets. As a future work, the need for a web-based tool to automatically measure the quality of the ontologies is explained. However, we did not find such tools available online that we can reuse and integrate with other tools.