Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data

Properly handling satellite data to constrain the inversion of CO2 sources and sinks at the Earth surface is a challenge motivated by the limitations of the current surface observation network. In this paper we present a Bayesian inference scheme to tackle this issue. It is based on the same theoretical principles as most inversions of the flask network but uses a variational formulation rather than a pure matrix-based one in order to cope with the large amount of satellite data. The minimization algorithm iteratively computes the optimum solution to the inference problem as well as an estimation of its error characteristics and some quantitative measures of the observation information content. A global climate model, guided by analyzed winds, provides information about the atmospheric transport to the inversion scheme. A surface flux climatology regularizes the inference problem. This new system has been applied to 1 year's worth of retrievals of vertically integrated CO2 concentrations from the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS). Consistent with a recent study that identified regional biases in the TOVS retrievals, the inferred fluxes are not useful for biogeochemical analyses. In addition to the detrimental impact of these biases, we find a sensitivity of the results to the formulation of the prior uncertainty and to the accuracy of the transport model. Notwithstanding these difficulties, four-dimensional inversion schemes of the type presented here could form the basis of multisensor data assimilation systems for the estimation of the surface fluxes of key atmospheric compounds.
Hierarchical Method of Urban Building Extraction Inspired by Human Perception

In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, a shape analysis is applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation and a Bayesian based approach is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identified urban buildings from high-resolution satellite images.
An analysis of errors in special sensor microwave imager evaporation estimates over the global oceans

Monthly averaged evaporation over the global oceans is estimated from 1 year of special sensor microwave imager (SSM/I) data using the method proposed by Liu (1984). Intercomparisons involving SSM/I and in situ data are made over a wide range of oceanic conditions during August 1987 and February 1988 to determine the source of errors in the evaporation estimates. The most significant spatially coherent evaporation errors are found to come from estimates of near-surface specific humidity q. Systematic discrepancies of over 2 g kg−1 are found in the tropics, as well as in the middle and high latitudes. The q errors are partitioned into contributions from (1) the parameterization of q in terms of the columnar water vapor W (i.e., the Liu q:W relationship) and (2) the retrieval algorithm for W. It is found that the spatial pattern of discrepancies due to the q:W relationship can be interpreted readily in terms of dynamical and physical processes that maintain the vertical profile of atmospheric water vapor content. The effects of W retrieval errors are found to be smaller over most of the global oceans and due primarily to the implicitly assumed vertical structures of temperature and specific humidity on which the physically based SSM/I retrievals of W are based. To improve estimates of q, it will thus be necessary to have supplementary information on both the temperature and water vapor profiles that is able to distinguish the most important dynamical and physical regimes over the global oceans.
Experience gained with texture modeling and classification of 1 meter resolution SAR images

For many years, high resolution SAR (Synthetic Aperture Radar) imaging was limited to airborne instruments. Nowadays, the analysis of spaceborne high resolution SAR images with up to 1 meter spatial resolution has become possible with the advent of German, Italian, and Canadian missions and their subsequent data distribution. For instance, compared to previous missions with much lower resolution, the German TerraSAR-X data allow us to analyze SAR images containing an increased amount of details and information content. As a consequence, a robust detection and recognition of small scale man-made structures representing buildings, roads, harbors, bridges, etc has become a new challenging task. An important property of SAR data is the presence of speckle phenomena which, in most cases, precludes an automated interpretation of SAR images. Therefore, we use a Bayesian approach relying on models and their parameters to fit the data. We suggest an automated method being able to extract and interpret the genuine information contained in high resolution SAR images. Our solutions are provided for optimal processing both for visual and automated data interpretation. The image information content is extracted using model-based methods based on Gibbs Random Fields combined with a Bayesian inference approach. The approach enhances the local adaptation by using a prior model, which learns the image structure; it enables despeckling with minimum loss of resolution and simultaneously estimates the local description of the structures. Form these we may obtain detection, classification, and recognition of the image content. In the following, we present typical texture description and classification examples of 1 meter resolution TerraSAR-X images taken in spotlight mode. In particular, we describe how well speckle can be removed, how well local texture parameters of the data can be estimated using dedicated model-based methods, and what can be expected from automated classification. For our work, we use the Knowledge-based Information Mining system called KIM, which includes a graphical user interface for data handling, image inspection, and semantic image annotation.
Evaluating the impact of coastal land uses on water‐clarity conditions from Landsat TM/ETM+ imagery: Candarli Bay, Aegean Sea

Landsat TM and ETM+ imagery was evaluated as a potential data source for monitoring the coastal degradation of Candarli Bay and its basin on the eastern coast of the Aegean Sea. The bay is under pressure from conflicting uses, and there is an information need for future management plans for the area surrounding the bay. Spatial and temporal distributions of land use/land cover patterns were derived by unsupervised classification with the ISODATA algorithm. The post‐classification comparison technique was applied to evaluate decadal changes in land area. The turbidity patterns of the bay were determined from sea surface reflectances, and at‐satellite temperatures for the sea area were calculated. The processed images show that the water clarity conditions were affected by the adjacent coastal land uses and by river discharges. In accordance with the change‐detection analysis of land‐use patterns, the forested areas were decreased by 21%, whereas the increase in urban areas was determined as 105% within a decade.
Effects of Approximate Residential Distribution System Models on Power Systems Analysis

A secondary feeder model is traditionally not included in power distribution system analysis. A large share of distributed energy resources are located on the secondary side, and most power quality issues in the distribution system are associated with the secondary networks requiring complete models. In the absence of sufficient measurements and appropriate model data, secondary models are often represented using approximate models. This paper evaluates three approximate distribution system models: with no secondary, using single-phase equivalent for balanced split-phase, and a detailed unbalanced split-phase model for residential distribution systems, using IEEE 8500-node test system. The results demonstrate that the case with no secondary and using a single-phase equivalent model cannot correctly reflect the operations of voltage regulators. Any approximate models cannot capture the degree of unbalanced in the secondary network. Approximation of an unbalanced split-phase residential distribution system with the balanced split-phase model can provide reasonably accurate calculations of total losses and primary voltage estimations.
Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods

AbstractThis paper presents a comprehensive review of a fundamental and challenging issue in hydrology: the regionalization of streamflow and its advances over the last two decades, specifically 1990–2011. This includes a discussion of developments in continuous streamflow regionalization, model parameter optimization methods, the application of uncertainty analysis in regionalization procedures, limitations and challenges, and future research directions. Here, regionalization refers to a process of transferring hydrological information from gauged to ungauged or poorly gauged basins to estimate the streamflow. Huge efforts have been devoted to regionalization of flood peaks, low flow, and flow duration curves (FDCs) in the literature, while continuous streamflow regionalization is helpful in deriving each of these variables. Continuous streamflow regionalization can be conducted through rainfall-runoff models or hydrologic model–independent methods. In the former case, model parameters are used as instru.
Examining bacterial transport in intact cores from Oyster, Virginia: Effect of sedimentary facies type on bacterial breakthrough and retention

The effects of the physical7/chemical properties of sediment on the transport of bacteria were examined. An adhesion‐deficient, groundwater isolate was injected into intact cores of three different sedimentary facies. Total effluent recovery of bacteria varied with facies type and appeared to be most influenced by mean grain size and total metal hydroxide content. Bacterial concentrations within the core were highest near the point of injection, but cell distribution exhibited considerable lateral and longitudinal variation. Calculations of the collision efficiency (α) indicated that the bacterial inoculum injected into the cores was composed of a range of subpopulations of cells, each possessing different α values. Although predictions based on previously published research were generally correct, this research highlights the need for further studies of bacterial transport under environmentally relevant conditions and the necessity of examining how the physiological heterogeneity of the injected organisms affects their transport in subsurface sediments.
Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

Simple Summary RNA-binding proteins play crucial roles in essential biological processes, and disruptions in their functionality can lead to various diseases, including cancer. Despite the significant progress that computational deep learning methods have made in identifying their binding sites, obtaining high-quality data in sufficient amounts remains a major challenge, impeding development of accurate predictive models for many proteins. In this work, we present a novel approach to address the limited availability of training samples by leveraging transfer learning for predicting RBP binding sites. Using three input features and a sophisticated network architecture, we demonstrate the substantial advantages of employing transfer learning in a reusable and interpretable manner, as showcased on two prominent benchmark datasets for RNA-binding proteins. Abstract RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein–RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.
Modeling tidal currents beneath Filchner‐Ronne Ice Shelf and on the adjacent continental shelf: Their effect on mixing and transport

A depth-averaged tidal model has been applied to the southern Weddell Sea. The model domain covers the southern continental shelf, including the ocean cavity beneath Filchner-Ronne Ice Shelf. Reasonable agreement with the available current meter data has been achieved. Our results confirm that in areas with shallow water and large topographic gradients, tidal oscillations with peak velocities up to 1 m s−1 play a significant role in the vertical mixing and transport of water masses. The estimated energy dissipation beneath Filchner-Ronne Ice Shelf due to surface friction is 25 GW, approximately 1% of the world's total tidal dissipation. Tidally induced Lagrangian residual currents converging at the ice front, an area of strong mixing, draw together water masses from the continental shelf and sub-ice shelf cavity. The model indicates that Lagrangian residual currents have fluxes of up to 250,000 m3 s−1, and speeds of over 5 cm s−1 along the ice front, with over 350,000 m3 s−1 being exchanged between the sub-ice shelf cavity and adjacent continental shelf. These currents are particularly efficient in ventilating the sub-ice shelf cavity within 150 km of Ronne Ice Front. Such strong tidal mixing will significantly modify the properties of water masses that flow through this region, particularly to the west of Berkner Island. The model predictions indicate that tidal processes strongly influence the oceanographic conditions in the vicinity of Ronne Ice Front. Shipborne observations along the ice front support many of the model predictions concerning the effect of tides on the hydrography.
Day-Ahead Estimation of Renewable Generation Uncertainty Set for More Efficient Market Clearing

Wholesale Day Ahead Markets (DAMs) dominated by renewable generation can improve social surplus by driving unit commitment (UC) and security constrained economic dispatch (SCED) in a novel systematic uncertainty aware manner. A new robust uncertainty aware DAM model can anticipate worst-case output of renewable generation and use a system reliability constraint involving an uncertainty set of renewable capacity realizations. The uncertainty set yields unique worst-case renewable capacities, which can be written as linear functions of a tentative, most-recent, SCED. A few repetitions of the UC-SCED converge to the optimal DAM clearing under the new system reliability constraint, endogenously determining reserve requirements, and potentially prices for energy and reserves. This paper focuses on constructing the uncertainty set essential to this new uncertainty aware Market paradigm and illustrates it on a realistic size Balancing Area with hundreds of wind farms.
Long-term variation in the North Pacific using satellite-derived wind data set/J-OFURO over the last decade and other data sets over a longer record

Time series of gridded data sets of surface winds (from Qscat (J-OFURO)) constructed by satellite microwave sensors covering almost a decade (1999–2009) are used to examine long-term change in surface wind fields over the world’s oceans. Evidence has been provided by most previous studies that wind speeds have a tendency to increase over time in many area, and we verify whether or not this tendency persists. Results reveal that zonal winds tended to be weaker over the study period in the region of the North Pacific where westerly winds prevail. Time series of different types of data sets based on numerical model products and voluntary ship measurements present similar features of weakening westerly winds, even allowing for discrepancies among the data sets. These time series also exhibit a tendency of enhanced westerly winds in periods prior to the start of the twenty-first century, which means that the long-term trend in wind speed has changed from positive in the 1980s/1990s to negative in the 2000s. Examinations of time series for each season reveal that the above feature is found in winter, suggesting that it is related to the strength of the Aleutian Low.
Empirical model of global soil‐biogenic NOχ emissions

The authors construct a global, temperature and precipitation dependent, empirical model of soil-biogenic NO{sub x} emissions using 6-hour general circulation model forcing. New features of this source relative to the latest published ones by Dignon et al. and Muller include synoptic-scale modeling of {open_quotes}pulsing{close_quotes} (the emissions burst following the wetting of a dry soil), a biome dependent scheme to estimate canopy recapture of NO{sub x}, and an explicit linear dependence of emission on N fertilizer rate for agricultural soils. Their best estimate for annual above-canopy emissions is 5.5 Tg N (NO{sub x}) with a range of 3.3-7.7 Tg N. Globally, the strongest emitters are agriculture, grasslands, and tropical rain forests, accounting for 41%, 35%, and 16% of the annual budget, respectively. {open_quotes}Pulsing{close_quotes} contributes 1.3 Tg N annually. In temperate regions, agriculture dominates emission, and in tropical regions, grassland dominates. Canopy recapture is significant, consuming, on average, possibly 50% of soil emissions. In temperate regions, periodic temperature changes associated with synoptic-scale disturbances can cause emission fluctuations of up to 20 ng N m{sup {minus}2}s{sup {minus}1}, indicating a close correlation between emission and warm weather events favorable to O{sub 3}/smog formation. By the year 2025, increasing use of nitrogen fertilizer may raisemore » total annual emissions to 6.9 Tg N with agriculture accounting for more than 50% of the global source. Finally, biomass burning may add up to an additional 0.6 Tg N globally by stimulating emissions for a short period after the burn. 74 refs., 4 figs., 9 tabs. « less
Numerical experiments of internal wave generation by strong tidal flow across a finite amplitude bank edge

Results of some idealized numerical experiments of strong tidal flow of a stratified fluid across a finite amplitude bank edge are presented. These experiments were motivated by a need to develop an understanding of some of the complex internal wave phenomena observed on Georges Bank (Loder et al., 1992) and at other locations where tidal forcing is strong. The numerical model solves the fully nonlinear, nonhydrostatic Boussinesq equations on an ƒ plane. The model is two-dimensional, with spatial variation in the vertical and cross-bank directions only. Model forcings are based on the Georges Bank observations. A horizontally uniform stratification is used. The model successfully reproduces some observed features including the formation of a large depression and a hydraulic jump over the bank edge during off-bank flow and two on-bank propagating depressions every tidal period. An undular bore propagating away from the bank is in agreement with other observations (La Violette et al., 1990). Rotational effects are shown to be responsible for the formation of the second of the on-bank propagating depressions. Sensitivity of the results to the topographic slope, tidal current strength, stratification, and model initialization is explored.
Reduction in Obesity-Related Hepatic Fibrosis by SR1664

Simple Summary Obesity can contribute to the development of liver disease, including the accumulation of excess collagen (fibrosis), which leads to organ dysfunction. Drugs that target peroxisome-proliferator-activated receptor gamma (PPARγ), a protein that controls genes involved in fat storage and fibrosis, have been used to treat diabetes, but can cause unwanted side-effects. Newer drugs have been designed that selectively target PPARγ to retain the antidiabetic effects, but without the side-effects such as weight gain and bone loss, but less is known about whether they reduce fibrosis. We hypothesized that one of these new drugs (SR1664) would improve the liver fibrosis associated with obesity. We used mice fed a diet high in fat and carbohydrates to induce fatty liver with fibrosis, and then determined the effect of SR1664. We found that SR1664 did not improve insulin sensitivity, but effectively reduced liver fibrosis, without causing weight gain. These findings support the use of selective PPARγ drugs for the treatment of liver fibrosis. Abstract Peroxisome-proliferator-activated receptor gamma (PPARγ) is a transcription factor with adipogenic, insulin-sensitizing, and antifibrotic properties. Strong PPARγ activators, such as the thiazolidinediones, can induce unwanted effects such as edema, weight gain, and bone loss, and therefore selective modulators of PPARγ are in development. We previously reported that one selective PPARγ modulator, SR1664, reduced toxin-induced hepatic fibrosis and the activation of hepatic stellate cells (HSCs), the main collagen-producing liver cell in fibrosis. In this study, we used a high fat and high carbohydrate (HFHC) model of hepatic steatosis and fibrosis to determine the effect of SR1664. Mice were placed on a standard chow or HFHC diet for 16 weeks, with SR1664 or control treatment for the final 4 weeks. SR1664 did not alter weight gain or fasting insulin or glucose levels. The size of lipid droplets in the HFHC group was reduced by SR1664, but there was no effect on total liver triglyceride levels. The degree of fibrosis was significantly reduced by SR1664 in mice on the HFHC diet, and this was accompanied by a decrease in activated HSC. In summary, SR1664 improved insulin sensitivity and reduced fibrosis in the HFHC diet, suggesting selective PPARγ modulation is effective in obesity-related liver fibrosis.