dataesr/scientific-paragraphs-categorization huggingface dataset:
Coastal effects At some coastal sites, such as Mace Head, coastal sources can play a large role in NPF (e. g. Dall'Osto et al. , 2011)  because when coastal macroalgae are exposed to air, they can emit particle-producing iodine species. To see if that is the case at Baring Head, we studied the relationship of negative 2-4 nm ions and tide height (Fig. 9 ). We decided to use this ion concentration since it marks the first steps of particle for-mation. We used only data between sunrise and sunset since photochemistry would likely be important. We coloured the data in Fig. 9  with wind direction to see if more particles are produced from some direction, for example, if there is more macroalgae that produce particle precursors on one side of the station. No correlation was observed between ion concentration and tide height (R = 0.
Data availability. The meteorological data can be downloaded from https://cliflo. niwa. co. nz/ (CliFlo, 2022) and tide height data from https://www. linz. govt. nz/sea/tides/tide-predictions (Land information New Zealand, 2021). Wave height data are available from the Greater Wellington Regional Council (http://graphs. gw. govt. nz/, Greater Wellington Regional Council, 2021). The aerosol data are available on the AERIS database (https://sea2cloud. data-terra. org/en/catalogue/, Sea2Cloud, 2022), the CPC data are found at https://doi. org/10. 25326/354 (Sellegri, 2022a), the SMPS data are found at https://doi. org/10. 25326/356 (Sellegri, 2022b), and PSM data are found at https://doi. org/10. 25326/355 (Sellegri, 2022c). Author contributions. MP, JVT, and KS performed the aerosol measurements.
Over the last decades, high-mass star formation research has concentrated on either the large-scale molecular cloud environments or on the smallscale hot dense molecular cores surrounding massive proto-stars/clusters and young stellar objects. However, questions regarding the gas flow from large to small scales, and how the gas is transferred via intermediate-scale filaments and clumps have so far been largely neglected. With "Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE)", a part of the Max Planck IRAM Observatory Program (MIOP), we want to overcome this missing gap via studying one well-known molecular cloud complex with the IRAM NOEMA and 30m facilities from small to large scales, thereby connecting these important physical processes. In the nearby (1. 4 kpc) luminous Cygnus X region, recent and on-going star formation combine to present a rich Northern hemisphere laboratory in which star formation and feedback processes can be studied globally as well as locally.
2 K for the synthesized beam of 3", sufficient to probe even optically thin lines. First results from the 30 m mapping of NOEMA fields in Cygnus X. The contours show emission in the HNC, HCO + , and HCN J = 1 -0 line, respectively, in some observed sources. The color background shows the 5 cm free-free continuum emission from GLOSTAR, combining VLA and E↵elsberg data  [4, 5]. The position of a methanol maser discovered in the DR20 field is indicated with a red cross. The individual spectra of selected molecules are from a clump in DR20 close to the (0,0) position. Note the velocity shifts of HCN and HCO + compared to optically thin lines.
NOEMA results Three mosaics of the DR20 region were observed in winter 2020/21. All data in the C+D array were taken. For the data processing an imaging pipeline has been setup. Many spectral lines and the continuum emission are well detected in all fields. Figure  3  shows an example of the small scale HCO + line emission morphology and kinematics, resolving the parcsec scale clumps probed with the IRAM 30m into filaments and cores. In particular in the North-West, close to bright HII regions, the velocity structure of the emission becomes complex. The.
Outlook The survey with NOEMA is progressing very well with a potential completion of the survey in 2023. This unique new dataset, in particular through the combination of NOEMA and IRAM 30m data, will allow us to investigate the physical and chemical processes during star formation from large cloud scales down to our spatial resolution limit of ⇡ 4000 au. Furthermore, covering a broad plethora of spectral lines and continuum emission, we can study a diverse set of phenomena covering accretion flows from large to small scales, cloud collapse, filamentary structures, as well as the physical and chemical properties of the gas associated with this prototypical large star formation complex. A first overview paper about the DR20 region has been submitted  [6]  and detailed studies of the large scale deuteration and kinematics of the DR21 Main outflow are ongoing.
Figure2. First results from the 30 m mapping of NOEMA fields in Cygnus X. The contours show emission in the HNC, HCO + , and HCN J = 1 -0 line, respectively, in some observed sources. The color background shows the 5 cm free-free continuum emission from GLOSTAR, combining VLA and E↵elsberg data [4, 5]. The position of a methanol maser discovered in the DR20 field is indicated with a red cross. The individual spectra of selected molecules are from a clump in DR20 close to the (0,0) position. Note the velocity shifts of HCN and HCO + compared to optically thin lines.
2018 ) but also in the classification of UF using high resolution Sentinel-2 RGB imagery and pretrained ImageNet  (Russakovsky et al. 2015)  weights  (El Mendili et al. 2020). Sefrin, Riese, and Keller (2021)  used an encoder/decoder like network combined with a LSTM (Long Short-Term Memory) to classify land cover into 8 classes from Sentinel-2 images and obtained high classification score in several land use classes. Transfer learning  (Lu et al. 2015)  is also used in many semantic segmentation works. This technique consists in assigning the weights of a pre-trained network on a data source to the target network that we intend to train  (Oquab et al. 2014 ). This result is a time saving in the training of the target network and also allows to counterbalance a dataset containing very few entries  (Xie et al.
For instance, Audebert, Le Saux, and Lefèvre (2018) trained a modified SegNet using feature fusion method to detect UF classes from aerial images. They fuse the encoder phase from two SegNet networks, one using exogenous indexes and the other IRRG (Infra-Red, Red and Green) bands using pretrained ImageNet weights. They obtained better results using this new method. Network fusion also applies when using CNN and ConvGRU (Convolutional Gated Recurrent Unit). Ienco et al. (2019)  combined these two networks and two data sources for land cover mapping of Reunion Island and Koumbia and shows the efficiency of networks fusion from different sources. Hu et al. (2017)  developed a two stream CNN for LULC classification using radar and hyperspectral images. In addition, many datasets have been developed to perform research on multi-modal fusion, whether between optical and radar imagery  (Schmitt et al.
2019; Sumbul et al. 2021) , aerial imagery and a Digital Surface Model (Vaihingen and Potsdam datasets developed for ISPRS 2D Semantic Labeling Challenge 1  ) or hyperspectral and LiDAR imagery (Houston2013 dataset 2  ). The use of spectral and textural indexes when applying neural networks are increasingly used as an external addition of exogenous indexes calculated with the spectral bands of the sensor chosen for the study. These hand-crafted features allow for differentiation of different types of spatially structuring objects to accelerate network learning and improve classification results  (Liu et al. 2017 ). Campos-Taberner et al. (2020)  developed a method to determine the importance of Sentinel-2 bands and spectral and textural indices in a neural network. They noted that NDVI (Normalized Difference Vegetation Index), NIR (Near Infra-Red) and red bands, and entropy calculated on NDVI (eNDVI ) are the features providing the most relevant information to the network.
Sentinel-2 Data Sentinel-2 mission  (Drusch et al. 2012 ) is composed of two satellites, Sentinel-2A and 2B respectively launched in June 2015 and in March 2017. They have a high revisit frequency of 5 days over the equator, and 2/3 days near mid-latitudes which is important to map land cover dynamics. Each sensor owns 13 spectral bands with different wavelengths, from the visible to the shortwave infrared at different spatial resolutions. For this research paper, satellite data used come from the Theia/Muscate database (https://www. theia-land. fr/) and 10 spectral bands are available for each sensor (Table  1 ). This product is available on their dissemination platform or by automatic download by requesting their server. For this work, cloudless single-date Sentinel-2 images, from 24 th July 2019 and 21 th July 2020, are respectively chosen for tiles 32ULU and 31UGQ.
UF Typology and Reference Dataset From High Spatial Resolution Imagery (10m), many research papers map UF in western cities in 4 classes (Table  2 ): dense (class 1) and sparse built-up (class 2) areas where the main difference consists in their relative density and the importance of vegetated areas and bare surfaces, specialized areas (class 3) characterizing industrial activities or waste land or open areas with a majority of artificial or bare surfaces and large scale road or rail network (class 5). This is the case for the well-known product, OSO (Occupation des Sols Opérationnellehttp://osr-cesbio. ups-tlse. fr/oso/) available at the national scale and produced from times series of Sentinel-2 or other research works  (El Mendili et al. 2020). In order to improve this classification of UF, a fifth class has been added and describes specialized areas where green surfaces are dominant (more than 80%) (class 4) such as urban parks, cemeteries or vegetated sports or leisure complexes (outdoor sports fields).
In this paper, we assume that a spatial resolution of 10m make it possible to map these five UF classes. A sixth class for non-urban areas (class 6) corresponds to any other non built-up areas as agricultural lands, forests or water surfaces. To produce this reference datastet adapted to any type of city in France to map UF at 10m spatial resolution, two existing topographic databases are used : (1) the regional landuse/cover vector database (BDOCSGE2➞GeoGrandEst 3 , 2019) and (2) the national topographic database with networks (BDTOPO➞IGN 4 , 2014). The Minimum Mapping Unit (MMU) is less than 50m 2 for buildings and urban areas. The first database is produced by visual interpretation of aerial images (2018-2019) and maps each department of the region into 53 classes at level 4. The thematic classes are close to landuse that landcover classes.
This database is a freely available at large scale (1:10,000) from the GeoGrandEst Data Infrastructure (www. geograndest. org) and the production is ongoing for the whole region. At the time of this research, only two departments were available (Bas-Rhin and Moselle) in relation with our tests sites. Since the satellite images and reference data are less than two years distant, we make the Table  2. List of the five UF classes (after pre-processing) used for this research (1/6000 scale).
Class Subset on Sentinel-2 Reference data Description (1) Dense Built-Up Surfaces mainly occupied by buildings and impervious surfaces. Vegetation and bare soil are scarce. ( The legend of BDOCSGE2 is organized into four levels of nomenclature where the first level categorizes land cover into four classes (1) artificial surfaces, (2) agricultural areas, (3) forest areas, and (4) water surfaces. Artificial surfaces (Level 1) are further sub-divided into 16 classes at Level 3 and in 29 classes in level 4. In the BDOCSGE2 layer, all the roads have the same degree of importance which makes it impossible to remove the smallest polygons in the inner city that cannot be distinguished at 10 m spatial resolution (Table  2 ). In order to produce an adapted road network thematic class, the second database (BDTopo), produced by IGN describing lines in vector format with the degree of importance, is pre-processed.
A buffer of 30 meters for the highways and 10 meters for the major roads is calculated to correspond to the real size of these networks which the only ones to be visible at 10 m. These data are then added to other classes of the vector reference datasets layer. We then summarize the fifth classes into a unique vector layer and rasterize alls polygons at a 10 m spatial resolution. The UF classes are not evenly distributed on the reference data and represent 8. 4% of the total area. Indeed, Dense Built-Up represents about 8. 4% of the total UF area of the dataset, Sparse Built-Up 59. 5%, Specialized Built-Up Areas 19%, Specialized butVegetative Areas 8. 3% and Large Scale Networks 4. 8%. For the image processing test based on four classes, the class (4) describing specialized but vegetative areas has been removed and all these areas have been included in the last class (6).
2008). The reference data is pre-process in order to produce training and validation dataset for the different Networks used. N DV I = Red -N IR Red + N IR (1) N DBI = SW IR1 -N IR SW IR1 + N IR (2) where Red is the red band of the Sentinel-2 image (band number 4), NIR is the near infrared band (band number 8) and SWIR1 is the Short-Wave Infrared 1 band (band number 11). SWIR1 is resampled at 10m spatial resolution to match spatial resolution with other spectral bands. Haralick, Shanmugam, and Dinstein (1973)  introduced the concept of Grey Level Co-occurence Matrix (GLCM) in 1973. This technique of feature extraction is widely used in the field of image analysis. GLCM represents a histogram of co-occurring greyscale values at a given offset. Then in urban areas where the heterogeneity is high, the Entropy index representing the randomness of disorder present in the image is calculated.
Several data augmentation methods were applied to the training patches to enrich the dataset. Each patch is thus kept in its initial state and then augmented randomly either by rotations (90, 180, and 270 degrees) or flipping (from left to right or from top to bottom) using numpy library 5. These data augmentation methods doubled the size of the initial training sets of each tile. We also add Dropout and L2 regularization during decoder process  (Srivastava et al. 2014)  to reduce the chance of overfitting due to the use of an imbalanced dataset and a low number of patches. L2 was prefered to L1 as it more stable by putting half of the weights on each input  (Li et al. 2021). Every image is normalized by dividing the standard deviation of the reflectance of the spectral band by the difference of this one with the average of the reflectances.
Loss function To take into account the low representativeness of certain classes in the dataset (imbalanced dataset), a weighted categorical cross-entropy loss is used by assigning higher weights to the classes with the least surface area. These are the inverse of the class frequency  (Audebert, Le Saux, and Lefèvre 2018). This loss is commonly used in remote sensing multi-class supervised classification tasks  (Ienco et al. 2017; Zhu et al. 2017). The loss has been taken from segmentation models framework  (Yakubovskiy 2019). On the other hand, the "Large Scale Networks" and "Specialized But Vegetative Areas" classes (rare objects composing the urban structures) occupy such small areas that we have decided to assign the lowest weights to another poorly represented "Dense Built-Up" class. Indeed, using too high weights can bias the loss and distort the learning process which would lead to prediction errors (Sefrin, Riese, and Keller 2021; Audebert, Le Saux, and Lefèvre 2018).
Semantic segmentation results for Strasbourg, Grand Est, 32ULU Tile Table  7  summarizes the three evaluation metrics for the four different methods and for each UF (Table  2 ) based on Strasbourg test area. We notice an advantage for both fusion methods (U-Net-Encoder and U-Net-Decoder) where the best statistical results are found for both methods. More precisely, these fusion methods improve the results of the specialized but vegetated area (Class 4) and the large scale networks (Class 5), where the F 1 Score is respectively 0. 4716 and 0. 5038 for U-Net-Encoder and 0. 4716 and 0. 5781 for U-Net-Decoder. To complete these quantitive results, Figure  8  presents some subsets in the Strasbourg test area. Compared to the reference dataset, qualitative analysis shows that the methods U-Net-Encoder (subfigure e) and U-Net-Decoder (subfigure f) provide a better detection of large scale networks (class 5) than the reference data (Figure  8 b ).
This is the case for Large Scale Networks (class 5) where roads and railroads, not yet or barely built at the time of the creation of the reference data, are well detected. Indeed, the selection of the Large Scale Networks was made according to a level of road in a French national database. Thus, there may be some roads of a  lower level that are still visible at 10m spatial resolution. But for most of the roads available at this level, they are not visible at this resolution. On the other hand, Specialized But Vegetative Areas (class 4) offers rather low scores rarely exceeding 0. 40 of F 1 Score. This is due to the complexity of this class at 10m where vegetated areas are dominant. The reflectance of these surfaces is quite similar to other vegetated areas such as crops, grasslands or small forests summarized in the Other non-urban areas (class 6).
This opens several perspectives for the use of optical times series imagery in order to take into account the spatio-temporal dynamic of UF. An other issue would be to integrate the multivariate properties of UF from Sentinel-1 imagery which has already demonstrated its interest for mapping urban footprint. Indeed, many works show the interest of using the radar amplitude for the detection of several UF classes. Our next research will focus on the addition of these multi-temporal optical and radar data for the mapping of these UF classes. series' (ANR-17-CE23-0015) and by the French TOSCA project AIMCEE  (CNES, 2019 (CNES, -2022)).
Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C-and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition (∆t). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI ≤ 0.
While optical remote sensing provides the difference in the spectral signature between irrigated and rain-fed crops caused by higher levels of photosynthesis and biomass for irrigated plots, SAR (Synthetic Aperture Radar) data provides the wetness information of the soil (soil moisture), which varies between irrigated and rain-fed plots. SAR data has been recently exploited to detect irrigation events, especially with the availability of the free and open-access C-band SAR images offered by the Sentinel-1 (S1) constellation. The SAR backscattering signal is known to be sensitive to the soil water content  [15] [16] [17] [18] [19] [20] , which makes it possible to detect the wetness information of the soil following an irrigation episode. The increase in the soil moisture caused by an irrigation episode is the key element that links the use of SAR data for irrigation mapping and the detection of irrigation events.
In addition to their low revisit time, reaching sometimes, at best, one image per month, the actual L-band SAR satellites (e. g. ,  are not yet in free and open-access modes, which limits their use for the detection of irrigation events. This low revisit time makes current L-band operating satellites inadequate for monitoring the change in soil moisture values caused by irrigation events. With future planned L-band satellites, such as the NISAR (NASA-ISRO SAR, planned to be launched in 2024) and Tandem-L (German Aerospace Center, planned to be launched in the end of 2022), some of which are planned to be open access, the use of L-band data for irrigation event detection may be more achievable. With the aim of detecting irrigation events at the plot scale using SAR data, the objective of this paper was to compare the sensitivity of the S1 C-band and the ALOS-2 L-band images for irrigation detection over well-developed vegetation cover.
A sensitivity analysis of both the C and L bands for irrigation detection was performed over irrigated grassland plots during two growth cycles. The first growth cycle is rich in grasses (coarse hay) and resembles wheat crops. The second grass cycle is richer in legumes with a lesser percentage of coarse hay. Section 2 presents a description of the study site, dataset, and the analysis approach. Section 3 compares the potential of the C and L bands to detect irrigation events. Finally, Section 4 presents the main conclusions.
Sentinel-2 Images In total, 30 Sentinel-2 (S2) images captured by S2A and S2B satellites were downloaded for the period between March and July 2019 over the study site via the French Land Data Center (Theia). The downloaded S2 images are ortho-rectified images corrected for atmospheric effects using the MAJA (MACCS ATCOR Joint Algorithm) processor for atmospheric correction. The frequency of the S2 images varies between 5 to 10 days according to the cloud cover. The S2 images were used to calculate the NDVI images. The NDVI was used as a vegetation descriptor in the analysis of the σ 0 C and σ 0 L sensitivities' for the detection of irrigation events. 5a ), A2 at 35° (Figure  5b ), D2 at 42° (Figure  5c ), and D1 at 32° (Figure  5d ). In addition, the four plots were selected, with different ∆t values calculated as the difference between the first acquisition date of the ALOS-2 image of 21/05/2019 and the last irrigation date before 21/05/2019 (∆t equals 0, 3, 6, and 11 days for Figure  5a, 3b, 3c,  and 3d , respectively).
Acknowledgments: The authors wish to thank the  French Space Study Center  ( TOSCA 2022 ) and the  National Research Institute for Agriculture, Food and Environment (INRAE)  for supporting this work. Authors would like to thank also the  European Space Agency (ESA)  for providing the S1 images, the  Japan Aerospace Exploration Agency (JAXA)  for the  PALSAR-2/ALOS-2  images and the  French Land Data Center (Theia)  for providing the S2 images.
, 2014; Fu et al. , 2021). The assessment recognized the importance of the unique long-term quality-controlled database in Africa The INDAAF activity is based on a regional long term monitoring network of 10 stations representative of three major African ecosystems covering dry savanna, wet savanna and equatorial forest (https://indaaf. obsmip. fr  (Laouali et al. , 2021). High quality measurements of atmospheric chemistry (rainwater, aerosol and gaseous chemical composition) are performed on a multi-year scale. Many synthesis studies which are representative of ecosystem rural sites, or which rely on the comparison of the eco-systemic transect dry savannas, wet savannas-forests, have been published  (Laouali et al. , 2012; Yoboué et al. , 2005a; Akpo et al. , 2015; Galy-Lacaux et al. , 2009; Galy-Lacaux and Modi, 1998).