dataesr/scientific-paragraphs-categorization huggingface dataset:
NOVEL APPROACHES TO LARGE-SCALE ECOSYSTEM MONITORING IN SUPPORT OF MODELING EFFORTS Ecological resources and associated ecosystem services are a key component of the Blue Economy, and ensuring sustainable use requires new and continued monitoring across multiple levels of biological organization. Monitoring biodiversity and ecological patterns improves our understanding of ecosystem dynamics, and supports conservation and management planning. In addition, ecological observations can provide input to physical/biogeochemical models and inspire alternative modeling approaches. Major roles in ecological monitoring are played by observing platforms (e. g. , movebank, OTN, GEO BON, GOOS, OBIS, IMOS, Sea Around Us) that often provide freely accessible data for research purposes. However, such efforts can be spatiotemporally and taxonomically disjointed, while there is need for co-location of the ecological and physical/biogeochemical observations.
Toward Holistic Marine Ecosystems Monitoring for Improving Ocean Models Since 2009 the Tara Oceans international consortium, a partnership between academic scientists and the Tara Ocean Foundation 11  , has developed an eco-systems biology approach for observing and analyzing planktonic ecosystems globally and over a broad range of scales. The Tara Oceans project is based on data derived from two circum-global research expeditions performed between 2009 and 2013 on board of a 36m long schooner (SV Tara) refitted to operate state-of-the-art oceanographic and plankton sampling equipment. The first expedition lasted 32 months, and sampled all of the principal ocean basins with the exception of the Arctic Ocean; the second lasted 6 months and circumnavigated the Arctic Circle. A wide range of contrasting ecosystems were targeted, and the shipboard activities collected environmental data and plankton of different sizes, for later analysis using state-of-the-art DNA sequencing and imaging technologies  (Pesant et al.
, 2015; Lombardi et al. , 2019). The Tara Oceans program was designed to target well-defined mesoscale oceanographic features such as gyres, eddies, currents, frontal zones, upwellings, hotspots of biodiversity and low pH or low oxygen zones. SSH fields from AVISO (Archiving Validation and Interpretation of Satellite Data in Oceanography), as well as maps of SST from Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and satellite-derived ocean color (ACRI-ST GlobColour service) were used to characterize the spatial and temporal variability of key environmental parameters along Tara's track and select well defined sampling stations. In addition, Temperature-Salinity profiles available around sampling stations were compiled from the Argo autonomous network array, and a meteorological station mounted on board Tara continuously measured wind speed and direction, air temperature, pressure, and humidity.
g. , size, shape, and optical density) of plankton and non-living suspended particles. The holistic and systematic sampling implemented during the Tara Oceans expeditions was key for generating a comprehensive dataset that allowed to perform multi-parametric computational analyses similar to those being used in systems biology  (Bork et al. , 2015; Lombardi et al. , 2019). The large number of biological samples and associated environmental data collected during the expedition have highlighted several important aspects of the functioning of plankton communities: (1) There is a clear vertical stratification of epipelagic community composition at the global scale, which appears to be driven primarily by temperature  (Sunagawa et al. , 2015). (  2 ) A core set of prokaryotic gene families were found ubiquitously in the ocean and distinguished variable adaptive functions from stable core functions.
, 2015) , underlying the importance of monitoring specific genes at a global scale in the ocean. (3)  The large-scale nature of the plankton dataset enabled the analysis of the global plankton interactome (i. e. , predicted cross-kingdom ecological interactions,  Lima-Mendez et al. , 2015) , leading to the surprising result that the plankton community structure appears to be shaped primarily by organism-organism interactions rather than by abiotic (e. g. , physical environment) factors, which has important implications for ecosystem modeling where the forcing usually originates from physical (bottomup) processes. (4) The diversity of eukaryotic plankton is likely higher than that of prokaryotes and viruses  (de Vargas et al. , 2015) , as is their functional gene complement  (Carradec et al.
Using Acoustic Data to Understand the Dynamics of Mesopelagic Fish Species Mesopelagic fish refers to the species living in the 200-1000 m depth range. The potentially huge biomass of mesopelagic fish at a global scale has raised interest in the exploitation of this resource as a potential for "blue growth"  (St. John et al. , 2016). However, without a good knowledge of the biomass and dynamics of these species, as well as in-depth understanding of the role they play in the global ecosystem dynamics the development of such exploitation could have devastating impacts on the whole ecosystem structure. These mesopelagic species, or FIGURE 9 | Meta-omics data based biogeochemical modeling. A schematic conceptual framework for global biogeochemical modeling from environmental, imaging, and meta-omics data  (Sunagawa et al.
, 2015). A semi-automatic computational pipeline is schematized for combining biomarkers with biogeochemical data  (Guidi et al. , 2016)  that can be incorporated into classic biogeochemical models  (Guyennon et al. , 2015)  for creating a next generation of biogeochemical trait-based meta-omics models by considering their respective traits. Such novel meta-omics-enabled approaches aim to improve the monitoring and prediction of ocean processes while respecting the complexity of planktonic system. For more information, see  Louca et al. (2016)  and  Coles et al. (2017). The biogeochemical model is taken from https://www. biogeosciences. net/12/7025/2015/under a CC license (https://creativecommons. org/licenses/by/3. 0/). FIGURE 10 | A proposed pipeline for omics-based plankton modeling and its potential implications.
The conceptual-methodological pipeline aims at integrating traditional and omics observations over the long term within a framework exploiting high-standard computational approaches, such as niche and network analyses using artificial intelligence. These latter interpretative tools should stand at a high-level pipeline-position, i. e. , at the intersection between data-providing nodes, and must be mutually calibrated and computationally merged by means of new-generation hybrid models. The latter should be fed by data processed by both taxonomic and functional databases exploiting omics resources and laboratory observations, which allow (i) generation of reference genomes for target organisms (selected for their key role or features) and (ii) developing experimental settings to characterize the growth dependencies of the same target organisms.
MESOPP (Mesopelagic Southern Ocean Prey and Predators), a European International Cooperation project, having just finished its initial 3-year plan, aims to advance international collaboration in observing mesopelagic biota using active acoustics in a Global Sampling Network to support modeling of the Mid-High Trophic Levels of Ocean Ecosystems. MESOPP, initially a collaboration in the Southern Ocean (SO), developed a public e-infrastructure with standardized acoustic data to allow estimation of the biomass of mesopelagic biota, and used these to improve and validate ecosystem models  16. The priority has been to develop the datasets, methods and models needed to simulate and quantify the dynamics and functional roles of these communities. This activity has global value. Thus, the MESOPP consortium would like to expand this work to the Global Oceans.
It has developed methods and data standards that can be easily used elsewhere. MESOPP has found international interest in combining advances in ecosystem modeling with those in acoustic sciences to propose a coupled observation-modeling system to generate new knowledge, enable quantification of ecosystem processes and dynamics, and in the long-term form the basis for ecosystem assessments. Its results suggest that the incorporation of acoustic information into ecosystem models would require the development of acoustic observation models, the definition of eco-regions inhabited by homogeneous species communities (in regard to their responses to acoustic signals), and a limited number of reference stations of those regions providing accurate estimates of absolute biomass. Genomics have also been identified as a promising approach to characterize species communities that characterize eco-acoustic provinces.
While multi-frequency technology is developing, a first global dataset of one single frequency (38 kHz) could be achieved from existing archived research cruise data and planned initiatives. Acoustic observation models are the first key step needed to translate acoustic signals into predicted biomass. This acoustically derived biomass can then be compared with the biomass predicted by ecosystem models, or alternatively used to convert the model biomass into a predicted acoustic signal to be compared directly with acoustic observations. This is particularly important since some organisms contribute relatively more to the total backscattered energy than others. Acoustic energy is not a linear function of biomass, and if these latter groups are not separated in the model, the uncertainty in the conversion using the acoustic observation model will be high.
Important insights on observational needs to sustain and advance these diverse efforts have emerged from this review, and are summarized here below. Global physical models with sufficiently high spatial resolution to resolve coastal features are essential for understanding the connection between regional processes and large-scale climate variations, as they seamlessly simulate the interaction among these different scales. The development and validation of these models at the regional scale relies on high-resolution observations in the area of interest (as seen, e. g. , in the Northwest Atlantic, see section Modeling the Northwest Atlantic). At the same time, high-resolution global-scale fields, like SST and SSH data sets obtained from satellite missions, are also key for the development and validation of the models at the large scale.
ACKNOWLEDGMENTS We would like to thank  J. D. Scott  and  E. Norton  for their help in preparing some of the figures, and  C. Stock  for his precious input on global biogeochemical modeling. We would also like to thank the three reviewers, whose excellent suggestions and very constructive criticism have greatly improved the manuscript. The  Surface Ocean CO 2 Atlas (SOCAT)  is an international effort, endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS)  and the  Integrated Marine Biosphere Research (IMBeR) program , to deliver a uniformly quality-controlled surface ocean CO 2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.
FUNDING This study was supported by the  NOAA's Climate Program Office 's Modeling, Analysis, Predictions, and Projections (MAPP) Program through grants  NA17OAR4310106 ,  NA17OAR4310104 ,  NA17OAR4310108 ,  NA17OAR4310109 ,  NA17OAR4310110 ,  NA17OAR4310111 ,  NA17OAR4310112 , and  NA17OAR4310113. This manuscript is a product of the  NOAA/MAPP Marine Prediction Task Force. The Tara Oceans consortium acknowledges support from the  CNRS Research Federation FR2022 Global Ocean Systems Ecology and Evolution, and OCEANOMICS  (grant agreement ' Investissement d'Avenir'   ANR-11-BTBR-0008 ). This is article number 95 of the  Tara Oceans consortium. MK and SD acknowledge support from  NASA  grant  NNX14AP62A  " National Marine Sanctuaries as Sentinel Sites for a Demonstration Marine Biodiversity Observation Network (MBON) " funded under the  National Ocean Partnership Program  (NOPP RFP NOAA-NOS-IOOS-2014-2003803 in partnership between NOAA, BOEM, and  NASA ), and the  NOAA Integrated Ocean Observing System (IOOS)  Program Office.
Like Taiwan, the orography of Reunion Island may impact tropical cyclone (TC) track and intensity. A Mann-Whitney test is applied on best-track data from the Regional Specialized Meteorological Center (RSMC) La Reunion to demonstrate that this effect is detectable at less than 250 km from the island. A set of idealized experiments is carried out to investigate this effect with the French nonhydrostatic mesoscale numerical model Meso-NH at 12 and 4-km horizontal grid spacing. Results show that the island influences TC track and intensity within two radii of maximum winds, defining a distance of influence. The impact is similar to an aspiration of the vortex by the island, accompanied by vortex weakening. An asymmetry is found between TCs passing north or south of the island and can be explained by the presence of the island in the flow.
A similar behavior was also observed during TC Bejisa  (Pianezze et al. , 2018 ; December 2013-January 2014, Figure  1b ) and TC Dina in January 2002  (Roux et al. , 2004; Jolivet et al. , 2013) , as they passed nearby La Reunion. The aim of this study is to further investigate the potential effect of a small island such as La Reunion on TC behavior as well as to quantify its distance of influence. To achieve this objective, the large observational database of the Regional Specialized Meteorological Center (RSMC) La Reunion is first relied upon to search for the possible signature of TC track and intensity changes in the vicinity of La Reunion. A set of idealized experiments is then carried out to complement and refine this observation-based statistical analysis.
| Database and definitions The database used in this study is the best-track of RSMC La Reunion. In order to ensure good data quality, the dataset spans from August 1981 to July 2015 and covers the entire SWIO basin, which extends from the eastern African coast to 90 E and from the equator to 40 S (Figure  1a ). In the following, TC track changes are quantified by the absolute difference of storm heading and storm speed ΔV at successive times, hereafter labeled, respectively, |ΔD| and |Δ V|. Using absolute values allows to better show the statistical effect of the relief by taking into account compensation resulting from acceleration and deflection of the systems. Using current intensity (CI) to estimate the intensity of tropical systems following the approach of  Dvorak (1975) , the temporal evolution of CI is used to diagnose TC intensity changes (intensification if ΔCI > 0).
As a reminder, CI is a quantity resulting from a subjective analysis of the cloud configuration using visible or Infra-Red satellite data and used to define the intensity of a cyclonic vortex. This is a discrete value ranging from 1 to 8 in steps of 0. 5. The larger the CI, the more intense the vortex is, so stronger winds and lower central pressure. For the intensity, the predominance of the population ΔCI = 0 or |ΔCI| = 0 being true, no signal is gained by using the absolute values. This allows us to keep information on intensification or weakening, using ΔCI. The database statistics are reported in Table  1. Overall, it is found that all parameters (deviation or speed) are associated with large standard deviation. The higher the intensity is, the lower the standard deviation is. For intensity change, Table  1  also shows the predominance of the class ΔCI = 0, that is, no intensity change.
With trade winds of 5 m/s used in this numerical setup, speed and direction changes occur within a 24-hr period. Therefore, in the case of landfall, observations with a sampling period of 6 hr are theoretically sufficient to detect the terrain effect, even if increasing the sampling period would likely reveal more details in the changes. This is not true for systems passing nearby at 200 km distance or embedded in a fast flow: track changes in such cases may go undetected in the best-track database because of its 6 hr resolution.
This super-resolution network with a new loss function produced high fidelity HS spectra reconstruction. Advantages of the proposed approach were demonstrated for the task of land cover classification. The paper "A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching" by T. Ma, J. Ma, and K. Yu considered the task of multispectral image matching in conditions of significant nonlinear intensity variations  [2]. The authors designed a new local feature descriptor and a novel guidance image extractor able to detect highly similar structure information in multispectral data. The advantage the proposed descriptor was its robustness to intensity variations; this was proven by experiments performed for three commonly used datasets named Potsdam, EPFL, and CVC.
The paper "Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery" prepared by J. Choi, H. Park, and D. Seo addressed an important task of improving the spatial clarity of very high resolution (VHR) satellite imagery  [3]. In the proposed approach, guided filtering was employed to generate optimal multispectral images for pansharpening. In this sense, the approach differed from known ones that use guided filtering to extract spatial details. The proposed method and algorithms were successfully tested for KOMPSAT-3A data. In the paper "Enhancement of Component Images of Multispectral Data by Denoising with Reference" by S. Abramov, M. Uss, V. Lukin, B. Vozel, K. Chehdi, and K. Egiazarian, it was shown that there are sub-band images that are quite noisy, and that it was worth carrying out filtering for them  [4].
The neighbors and the associated reconstruction weights were estimated by a resolution based on the alternative direction multiplier method (ADMM) optimization of an appropriate weighted problem to exclude some outliers. The proposed approach was proven to be more effective than other methods in terms of spatial enhancement and spectral preservation on QuickBird and Geoeye-1 satellite image datasets. The second paper "Hyperspectral Image Classification with Multi-Scale Feature Extraction" by B. Tu, N. Li, L. Fang, D. He, and P. Ghamisi investigated the classification of hyperspectral images by means of a Gaussian pyramid based multi-scale feature extraction (MSFE)  [6]. Spatial texture features of different scales were thereby captured. Then, support vector machine (SVM) based probability maps in each layer of the Gaussian pyramid were optimized through edge-preserving filtering (EPF).
The majority voting method was considered for determining the label of pixels, thus producing the final classification map. Better accuracy over other classification methods, especially with a quite limited number of training samples, was demonstrated on Indian Pines and Salinas AVIRIS datasets and the University of Pavia Reflective Optics System Imaging Spectrometer (ROSIS) dataset. The penultimate manuscript "A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing" by J. Zou and J. Lan covered unsupervised multi-scale hierarchical sparsity unmixing  [7]. The large scale spatially regularized unmixing problem was broken into two different problems in different image domains. One approximation domain was obtained by using multi-scale spatial resampling, and thus captured coarse spatial contextual information and the original domain represented fine-scale details.
In the coarse domain, multilayer sparsity based on L1/2 norm was considered to cope with spectral variability and achieve more accurate endmembers and abundance matrix. Then, the abundance matrix was converted back to the original domain and used to regularize the unmixing process so as to promote the spatial dependency between neighboring pixels. The proposed method was shown to outperform previous methods in endmember extraction and abundance fraction estimation with a reduced computational complexity-both on simulation datasets derived from the USGS digital library and real hyperspectral data experiments (Resonon Pika XC2 push broom imager ground-based dataset and HYDICE Urban dataset). The last one, "FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels" by S.
Mukhopadhaya, A. Kumar, and A. Stein concerned the comparative accuracy assessment of alpha-cut embedding into fuzzy c-means classification with using different similarity and dissimilarity measures  [8]. The goal was to highlight the two best measures and parameters whose combination and optimization allowed for better results to be obtained to handle the problem of mixed pixels as well as the effect of noise on the considered datasets. The comparative analysis was performed on images with different resolution and large heterogeneous areas with high complexity in the land cover (Landsat-8 and Formosat-2 images). The third group is composed of three contributions related to object detection and feature extraction. The first one "Diffuse Skylight as a Surrogate for Shadow Detection in High-Resolution Imagery Acquired Under Clear Sky Conditions" by M.
Then, a two-step superpixel classification strategy was used to predict each superpixel as cloud or non-cloud. The bright and thick cloud superpixels, as well as the obvious non-cloud superpixels, are separated from potential cloud superpixels through a threshold function. Then high-level information is extracted using double-branch PCA Network (PCANet) combined with a Support Vector Machine (SVM) classifier. In the last step, a fully connected Conditional Random Field (CRF) model was used to refine the cloud detection results. The method was validated on the Landsat 8 Cloud Cover Assessment (L8 CCA) dataset. The last contribution, "Thermal Airborne Optical Sectioning" by I. Kumi et al. proposes a multispectral (RGB and thermal) camera drone for synthetic aperture imaging  [11]. The aim of this study was to remove the occluding vegetation to reveal hidden objects in applications related to archeology, search-and-rescue, animal inspection, and border control.
2 ±0. 1 K  [9]. The CIMR mission will deliver both high-latitude and global data with near-global coverage every day. CIMR SSTs will therefore improve the observability of ocean dynamics reflected in SST compared to an infra-redonly SST dataset, particularly in areas of persistent cloud: the Arctic, Southern Ocean, north Pacific and Atlantic oceans, marine stratiform regions, and the inter-tropical convergence zone including the tropical warm pool. The targets for the uncertainty components are given in Table  1. Measurements from both the forward and rearward arcs of CIMR's conical scan will be available, enabling averaging of retrievals to reduce noise. This combination of features implies that CIMR SSTs are expected to have simultaneously higher spatial resolution and lower noise uncertainty than present PMW SSTs  [9].
The aim of this study is to examine the effect on SST analyses of adding CIMR to the IR-observing constellation, here represented by the Advanced very-high-resolution radiometer (AVHRR) instrument on-board Met-Op A and by the Sea and Land Surface Temperature Radiometer (SLSTR) carried by Sentinel-3A. Synthesised observations were provided as input to the Operational Sea Surface Temperature and Sea Ice Analysis system (OSTIA)  [10]  in different combinations as set out below. The OSTIA system is in operational near-real time use by the Met Office. It operates by ingesting satellite and in situ SSTs and optimally combining them to produce estimates of daily, global, foundation SST (defined as SST free of diurnal variability  [11, 12] ). A climate configuration of the system generates SST analyses from satellite data only, which are representative of the daily average at 20 cm depth  [7]  and this is used in this study.