dataesr/scientific-paragraphs-categorization huggingface dataset:
i 2 Materials and Methods We present our dataset and proposed method to model multi-year SITS, along with several baseline methods to assess the performance of its components. We denote by [1, I] the set of years for which satellite observations are available to us and use the compact pixel-set format to represent the SITS. For a given parcel and a year i ∈ [1, I], we denote the corresponding SITS by a tensor x i of size C × S × T i , with C the number of spectral channels, S the number of pixels within the parcel, and T i the number of temporal observation available for the year i. Likewise, we denote by l i ∈ {0, 1} L the one-hot-encoded label at year i, denoting which kind of crop is cultivated in the considered parcel among a set L of crop types. Note that, in this article, we focus on the prediction of the main culture, ie, only one crop type per year.
Dataset Our proposed dataset, represented in Figure  2 , is based on parcels within the 31TFM Sentinel-2 tile, covering an area of 110 × 110 km 2 in the South East of France (centered around 4. 31N, 46. 44E in WGS84). This area is in the Auvergne-Rhône-Alpes region, a major producer of cereal with over 54 000ha of corn and 30 000ha of wheat. Extensive livestock production makes meadow the most common crop type with over 60% of declared parcels in the LPIS. The most frequent crop rotations are permanent cultures (meadows, vineyards, pasture) and alternating between corn, wheat, and rapeseed. Our satellite time series are constituted of Sentinel-2 level 2A images. We discard the bands B01, B09, and B10 and resample the remaining 10 spectral bands to a spatial resolution of 10m per pixel with bilinear interpolation.
Note that we ignore secondary cultures for parcels with multiple growth cycles. To limit class imbalance, we only keep crop types among a list of 20 of the most cultivated species in the area of interest. In sum, our dataset is composed of 103 602 parcels, each associated with three image time sequences and three crop annotations corresponding to the farmers' declarations for 2018, 2019, and 2020. The Sentinel2Agri dataset  [15] , composed of parcels from the same area, is composed of 191 703 parcels. We can estimate that our selection criteria exclude approximately every other parcel. A more detailed analysis of the evolving parcel partitions across different plots could lead to retaining a higher proportion of the original parcels. N N As represented in Table  1 , the dataset is still imbalanced: more than 60% of declarations correspond to meadows.
Pixel-Set and Temporal Attention Encoders The Pixel Set Encoder (PSE)  [15]  is an efficient spatio-spectral encoder which learns expressive descriptors of the spectral distribution of the observations by randomly sampling pixels within a parcel. Its architecture is inspired by set-encoding deep architecture  [28, 22] , and dispenses us from preprocessing parcels into image patches, saving memory and computation. The Temporal Attention Encoder (TAE)  [15]  and its parsimonious version Lightweight-TAE (LTAE)  [13]  are temporal sequence encoders based on the language processing literature  [37]  and adapted for processing SITS. Both networks can be used sequentially to map the sequence of observations x i at year i to a learned yearly spatio-temporal e i = TAE PSE x i t Ti t=1. ( 1 ) l i observations labels x i-2 l i-2 x i-1 x i year i-2 year i-1 year i target D l i-1 l i observations labels x i-2 l i-2 x i-1 x i year i-2 year i-1 year i target D CRF l i-1 T (a) Single-year model (b) CRF model l i observations labels x i-2 l i-2 x i-1 x i year i-2 year i-1 year i target D l i-1 l i observations labels x i-2 l i-2 x i-1 x i year i-2 year i-1 year i target D l i-1 (c) Observation bypass (d) Proposed model.
Mixed-year Training: We train a single model with parcels from all available years. Our rationale is that exposing the model to data from several years will contribute to learning richer and more resilient descriptors. Indeed, each year has different meteorological conditions influencing the growth profiles of crops. Moreover, by increasing the size of the dataset, mixed-year training mitigates the negative impact of rare classes on the performance. We assess the impact of mixed-year training by considering I = 3 specialized models whose training set is restricted to a given year: M 2018 , M 2019 , and M 2020. In contrast, the model M mixed is trained with all parcels across all years with no information regarding of the year of acquisition. All models share the same PSE+LTAE configuration  [13].
Instead, we use the same setting as the vast majority of work in parcel classification and whose task is to classify parcels after the year is over  [42, 38, 35, 5, 22, 31, 27, 12, 30, 41, 21, 13, 15, 32, 14]. As the Sentinel-2 mission was only operational starting in 2017, we only have access to full-year coverage since 2018. This means that we only have three years' worth of data at the time of writing this paper. In our opinion, this prevents us from a realistic setting in which the last year is withheld from the training set. Indeed, the inter-year meteorological variations between two years are typically too great to test for a third year and reasonably expect good results, as corroborated with preliminary experiments not shown in this paper. As more Sentinel-2 data become available, we will be able to evaluate our approach in a more realistic setting.
Scope of the Study. Given the large amount of data involved and the complexity of data collection, we have limited our analysis and our proposed open-access dataset to a single area of the French Metropolitan territory. While nothing in our method is specific to this area, some of our analysis may be biased by the preponderance of stable cultures such as vineyards in this area. To confirm the generality of our conclusions, we would require a dataset with parcels taken from regions across the world with various meteorological conditions and agricultural practices. This task is complicated by the lack of harmonization between LPIS regarding open-access policy and even nomenclature. We hope that our results will encourage mapping agencies worldwide to release multi-year LPIS in open-source to help constitute a truly global dataset, allowing the community to assess the spatial generalizability of state-of-the-art methods.
Conclusions We explored the impact of using multi-year data to improve the quality of the automatic classification of parcels from satellite image time series. We showed that training a deep learning model from multi-year observations improved its ability to generalize and better precision across the board. We proposed a simple modification to a state-of-the-art network to model both inter-and intra-year dynamics. This resulted in an increase of +6. 3% of mIoU when compared to models operating on single-year data. The effect is most substantial for classes with strong temporal structures, but also impacts other crop types. We also showed how simple post-processing could improve the calibration of the models considered. Finally, we release both our code and our data. We hope that our promising results will encourage the SITS community to develop methods modeling multiple time scales simultaneously and release more datasets spanning several years.
Abstract. The Sierra Nevada Lidar aerOsol Profiling Experiment I and II (SLOPE I and II) campaigns were intended to determine the vertical structure of aerosols by remote sensing instruments and test the various retrieval schemes for obtaining aerosol microphysical and optical properties with in situ measurements. The SLOPE I and II campaigns were developed during the summers of 2016 and 2017, respectively, combining active and passive remote sensing with in situ measurements at stations belonging to the AGORA observatory (Andalusian Global ObseRvatory of the Atmosphere) in the Granada area  (Spain). In this work, we use the in situ measurements of these campaigns to evaluate aerosol properties retrieved by the GRASP code (Generalized Retrieval of Atmosphere and Surface Properties) combining lidar and sun-sky photometer measurements.
, 2015; Ortiz Amezcua et al. , 2020). As an alternative, during the last few years, several synergetic retrievals algorithms have been developed to retrieve aerosol optical and microphysical properties combining data from sun-sky photometers and backscatter lidar measurements, such as LIRIC (LIdar-Radiometer Inversion Code) by  Chaikovsky et al. (2016)  and  Granados-Muñoz et al. (2020)  and GARRLiC (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) by  Lopatin et al. (2013). One of the most popular advanced inversion algorithms is the Generalized Retrieval of Atmosphere and Surface Properties code (GRASP;  Dubovik et al. , 2011 Dubovik et al. , , 2014)). It should be noted here that GARRLiC is a branch of GRASP. The versatility of GRASP allows the retrieval of aerosol vertical and surface properties combining different types of measurements, such as sun photometers, lidar, ceilometers, satellite, sky cameras, and nephelometers (e.
, 2019). It is important to explore the potential of these kinds of algorithms by applying them to different input data and for different atmospheric conditions. In this regard, the extensive measurement dataset obtained during the Sierra Nevada Lidar aerOsol Profiling Experiment I and II (SLOPE I and SLOPE II) campaigns in May, June, and July of 2016 and 2017, respectively, allows an evaluation of the atmospheric aerosol properties retrieved by the GRASP code combining lidar and sun-sky photometer measurements. This database was successfully utilized in several previous studies of the atmospheric aerosol (e. g. de Arruda Moreira et al. , 2018 , 2019; Bedoya-Velásquez et al. , 2018; Horvath et al. , 2018; Casquero-Vera et al. , 2020). The main objective of this work is to provide an overview of the aerosol optical and microphysical properties during the SLOPE I and II campaigns using the GRASP code.
The combination of integrating nephelometer and aethalometer data allows the calculation of the aerosol extinction coefficients (α). The scanning mobility particle sizer (SMPS) composed of an electrostatic classifier (TSI Mod. 3082) and a condensation particle counter (CPC; TSI Mod. 3772) provide the sub-micron particle number size distribution within the 6-307. 5 nm particle mobility radius range with 5 min temporal resolution. SMPS data have been corrected of internal dif-fusion losses and multiple charges by AIM software (version 10. 2. 0, TSI, Inc. , St Paul, MN, USA). The SMPS measurements followed ACTRIS and GAW recommendations  (Wiedensohler et al. , 2012 (Wiedensohler et al. , , 2018) )  and high-quality data were guaranteed after the successful participation of the instrument in the ACTRIS inter-comparisons workshops (TRO-POS, Leipzig, Germany) and in situ inter-comparison (AC-TRIS Round Robin Tour).
GRASP retrievals In this work, we use the GRASP code following the scheme proposed by  Lopatin et al. (2013) , which combines lidar and sun-sky photometer measurements to retrieve the optical and microphysical properties of aerosol particles. This scheme uses normalized backscattered range corrected signal at 355, 532, and 1064 nm from lidar, the AOD, and sky radiance (almucantar scan) both at 440, 675, 870, and 1020 nm from AERONET version 3 level 1. 5. It should be noted that the GRASP retrievals were performed during the daytime with solar zenith angles larger than 40 • and clear-sky conditions. This configuration of GRASP allows the retrieval of aerosol properties for both fine (radii range 0. 05 to 0. 576 µm) and coarse (radii range 0. 33 to 15 µm) modes separately, the complex refractive index, single-scattering albedo (SSA), and lidar ratio (LR).
Aircraft data In order to make comparable the profiles from the aircraft data and the remote sensing retrievals, there are some corrections to consider. Remote sensing data are provided at ambient conditions (temperature and pressure), but the aircraft data are registered at different conditions. Nephelometer data from the aircraft were recorded at cabin temperature and ambient pressure, and aethalometer data were registered at 0 • C and 1013. 25 hPa. The cabin temperature used was the nephelometer sampling temperature (T s ), i. e. the temperature inside the nephelometer, and the profile atmospheric pressure used was the nephelometer pressure sensor (P s ). The cabin on the aircraft was not pressurized so the pressure inside the nephelometer can be considered the outside pressure. The aircraft did not register the outside temperature, so an external source of temperature profile was required.
We used a temperature profile from a microwave radiometer MWR (T mwr ) as described in Sect. 2. 1 using an average profile during the time of the entire aircraft profile and interpolated to the exact altitudes of the aircraft profile. Aircraft profiles show some noise, especially at higher altitudes, so a convolution with a mean filter was applied to the aircraft in situ data in order to smooth the profiles. We observed that using 100 m for the nephelometer and 200 m for the aethalometer data in the vertical profiles reduced noise while preserving the profile features. Finally, Aurora nephelometer wavelengths were converted to the TSI wavelengths using the Ångström exponent law to make the aircraft and ground based in situ data comparable. For the inter-comparison between the GRASP retrievals and the SNS in situ data, we selected the in situ measurements averaged in ± 15 min around the GRASP retrieval time and the 400 m averaged data of the GRASP retrieval profile at 2500 m a.
In general, volume concentrations retrieved by the GRASP code show good correlation with the SNS measurements with correlation coefficients (R) of 0. 58, 0. 83, and 0. 80 for fine, coarse, and total volume concentrations, respectively. The results show that the GRASP retrievals overestimate in situ measurements with a mean difference (± standard deviation) of 4 ± 4 and 6 ± 8 µm 3 cm -3 for fine and total volume concentrations, respectively. In contrast, better correlation is observed for coarse mode volume concentrations (slope equals 1) with a lower mean difference (2 ± 6 µm 3 cm -3 ). In terms of absolute concentrations, 65 % (91 %), 70 % (88 %), and 45 % (71 %) of the differences are observed within ± 5 µm 3 cm -3 (± 10 µm 3 cm -3 ) for fine, coarse, and total volume concentrations, respectively.
Aircraft profiles A total of six flights were carried out on 15, 17, and 18 June 2016 during SLOPE I and 21, 23, and 24 June 2017 during SLOPE II. During the SLOPE I flights, the aerosol conditions were characterized by AOD values at 440 nm (AOD 440 ) lower than 0. 1 and an Ångström exponent (AE), computed with AOD at 440 and 870 nm (AE 440-870 ), between 0. 6 and 1. 3. On the other hand, during the week of flights for SLOPE II, there was a dust intrusion from the Sahara with higher AOD 440 values (ranging from 0. 13 to 0. 36 on 23 and 24 June 2017, respectively) and low AE 440-870 values between 0. 3 and 0. 8. Figure  5  shows the vertical profiles of scattering and absorption coefficients retrieved by the  GRASP code and measured by the on-board instrumentation. This figure also includes the mean value measured at the SNS during the flights.
For the sake of comparison, the GRASP values at 355, 532, and 1064 nm have been interpolated to the nephelometer and aethalometer wavelengths using the Ångström exponent law. For σ sca , both GRASP and airborne measurements follow the same pattern where GRASP overestimates the airborne data with a mean absolute difference of 14 ± 10 Mm -1. During SLOPE I, these mean absolute differences are lower than 8 Mm -1 and there is a good agreement between GRASP and the SNS measurements (differences < 4 Mm -1 ). How-ever, during SLOPE II, the differences between GRASP and in situ measurements (both airborne and SNS) are larger, reaching values of 30 Mm -1. In the case of σ abs , the GRASP and airborne profiles show large differences during SLOPE I with mean absolute differences between 0. 5 and 3 Mm -1 and reaching differences around 6 Mm -1 on 18 June 2016.
Column integrated Figure  6  shows the temporal evolutions of AOD 440 and AE 440-870 daily mean values retrieved by the GRASP code at the UGR station during the SLOPE I and II campaigns. Daily averaged values of AOD 440 retrieved by the GRASP code range from 0. 06 to 1. 0 with a mean (± standard devia-  Figure  7  shows the box-and-whisker diagrams of retrieved aerosol columnar-integrated properties such as SSA, LR, and aerosol absorption optical depth (AAOD) at 355, 440, 532, 675, 870, 1020, and 1064 nm retrieved by the GRASP code during the study period. For aerosol intensive properties, the SSA values are typical for Saharan dust outbreaks at the study region (e. g. Valenzuela et al. , 2012) , ranging from 0. 88 ± 0. 05 at 355 nm to 0. 90 ± 0. 06 at 1064 nm, respectively. These relatively large values of SSA for all wavelengths indicate a small concentration of absorbing aerosol particles (e.
Thus, the GRASP retrievals show the capability of this code to characterize aerosol absorption coefficients with vertical resolution, which is a step towards aerosol characterization. The profiles of intensive properties such as LR, AAE, and SAE can provide information about the predominance of different aerosol particle types. For LR at 532 nm (Fig. 8g ), a constant mean profile is observed with a mean value of ∼ 52 sr. LR at a given wavelength depends mainly on both chemical composition and particle shapes  (Müller et al. , 2007) , which explains the variability in the retrieved values for the different aerosol types with a strong contribution of mineral dust. The LR values obtained are very similar to those observed in other studies (e. g. Guerrero-Rascado et al. , 2009; Navas-Guzmán et al.
Conclusions In this study, we presented an overview of aerosol optical and microphysical properties retrieved with the GRASP code during the SLOPE I and II field campaigns. The measurements from lidar and sun-sky photometer performed on May, June, and July 2016 and 2017 were used as input data in GRASP to retrieve these aerosol properties. The in situ measurements performed at the Sierra Nevada Station during the SLOPE I and II campaigns and the airborne measurement gathered during special periods on both campaigns allowed for the assessment of the aerosol properties retrieved by the GRASP code at 2. 5 km a. s. l. and for the whole profile, respectively. The volume concentration comparison shows better agreement for the coarse mode (R = 0. 83) than for the fine and total modes. The range of values for the fine mode is small due to the few cases (15 % of cases) with predominant fine particles; therefore, we cannot conclude that there is agreement between the GRASP retrievals and in situ measurements for the fine mode.
Acknowledgements. Jose Antonio Benavent-Oltra  is funded by the  University of Granada  through " Plan Propio. Programa 7, Convocatoria 2019 ". Roberto Román  is funded by  MINECO  under the postdoctoral programme  Juan de la Cierva-Incorporación  ( IJCI2016-30007 ). Juan Andrés Casquero-Vera  is funded by  MINECO  under the predoctoral programme  FPI  ( BES-2017-080015 ). Maria J. Granados-Muñoz  received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  796539. Oleg Dubovik  was supported by the  Labex CaPPA project , which is funded by the  French National Research Agency  under contract " ANR-11-LABX0005-01 ". This work was also supported by the  Spanish Ministry of Economy and Competitiveness  (projects  CMT2015-66742-R ,  CGL2016-81092-R ,  CGL2017-85344-R ,  RTI2018-097864-B-I00  and  CGL2017-90884-REDT ), by the  Andalusia Regional Government  through project  P18-RT-3820  and the  Unity of Excellence "María de Maeztu"  (project  MDM-2016-0600 ) financed by the  Spanish State Research Agency (AEI).
The authors thankfully ac-knowledge the  FEDER  programme for the instrumentation used in this work, the  University of Granada , which supported this study through the  Excellence Units Program , and the  Sierra Nevada National Park. We also thank  Dr. Grisa Monick  who provided the Aethalometer AVIO AE-33 installed on the aircraft. Thanks to AERONET and  ACTRIS/AERONET Europe  for the scientific and technical support. Finally, the authors would like to acknowledge the use of the GRASP inversion algorithm software (http://www. grasp-open. com,last access: 1 April 2020), in this work. Financial support. This article is based upon work from  COST Action CA18235 PROBE (PROfiling the atmospheric Boundary layer at European scale ) and supported by  COST (European Cooperation in Science and Technology ; http://www.
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i. e. , compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome.
This approach allows for more accurate classification of the microbial communities (even down to the strain level), and also permits the study of genes and their functions, e. g. , by the construction of Gene Ontology (GO)  (Ashburner et al. , 2000)  tables and Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa and Goto, 2000)  pathways  (Scholz et al. , 2016). Improved data-analytical tools are needed to exploit all the information from these biological datasets, considering the peculiarities of microbiome data, i. e. , compositional data, heterogeneous and sparse nature of the datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between the microbiome, predict various disease states or improve human health is beneficial for personalized medicine.
, 2012; Werner et al. , 2012; Vervier et al. , 2016). Secondly, ML was used for the inference of host phenotypes in disease prediction  (Pasolli et al. , 2016; Flemer et al. , 2017; Asgari et al. , 2019; LaPierre et al. , 2019; Thomas et al. , 2019) , and finally, to support the use of microbial communities to stratify patients by the characterization of state-specific microbial signatures  (Koohi-Moghadam et al. , 2019; Wirbel et al. , 2019; Yachida et al. , 2019). Here, we aim to review the application of the different ML techniques to human microbiome data analysis and the available ML-based software resources currently used in the analysis of human microbiome data. The review is mainly focused on the application of ML in microbiome studies related to causality and clinical use for diagnostics, prognostics, and therapeutics.
In this way, in total 54 papers were collected, and 35 papers are included in the final list. • An automated search of digital libraries of three major publishers (PubMed, Springer and IEEE) using NLP Toolkit  (Zdravevski et al. , 2019)  to automate the literature search, scanning, and eligibility assessment. This automated search was additionally constrained to the period from January 2008 to December 2019 (and including those). In total 5,935 papers were identified using this method, after removal of duplicates that appear as a result of multiple searches using the similar subsets of keywords. From that, 67 papers were selected for a manual check, and 37 papers are included in the final list. • An automated search through the available GitHub resources using NLP algorithms to identify relevant software repositories and extract corresponding scientific papers.