Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-temporal variability of surface chlorophyll-a in the Halmahera Sea and its relation to ENSO and the Indian Ocean Dipole

ABSTRACT Long-term satellite data are used to investigate the variability of ocean surface chlorophyll-a (chl-a) concentration in the Halmahera Sea (HS) under influence of the Australian-Indonesian Monsoon (AIM), the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). In this study, we first analysed the seasonal variability of chl-a, and then examine the relationship between surface chl-a, sea surface temperature (SST), and sea surface wind stress in the area. Our results suggest that prevailing southeasterly winds play a fundamental role in generating chl-a blooms in the HS. Particularly on a seasonal timescale, through the mechanism of Ekman mass transport, strengthening of southeasterly wind stress during the Southeast Monsoon season (June – August) produces enhanced chl-a concentrations associated with ocean surface cooling in the area of study. On the other hand, the chl-a bloom completely diminishes during the Northwest Monsoon season (December – February) due to weakening of wind stress and Ekman transport. On an interannual timescale, sea level pressure and wind stress are coherent with ENSO and IOD phases. During El Niño and positive IOD events (La Niña and negative IOD events), both sea level pressure and wind stress greatly increase (decrease) over the HS. These conditions cause an anomaly in southerly (northerly) wind stress, which is favourable to an enhancement (reduction) of the chl-a concentration in the region. This study demonstrates that sea level pressure and wind stress are the critical factors in determining the magnitude of chl-a bloom in the HS.
Density distribution across the Alpine lithosphere constrained by 3-D gravity modelling and relation to seismicity and deformation

Abstract. The Alpine orogen formed as a result of the collision between the Adriatic and European plates. Significant crustal heterogeneity exists within the region due to the long history of interplay between these plates, other continental and oceanic blocks in the region, and inherited crustal features from earlier orogenies. Deformation relating to the collision continues to the present day. Here, a seismically constrained, 3-D structural and density model of the lithosphere of the Alps and their respective forelands, derived from integrating numerous geoscientific datasets, was adjusted to match the observed gravity field. It is shown that the distribution of seismicity and deformation within the region correlates well to thickness and density changes within the crust, and that the present-day Adriatic crust is both thinner and denser (22.5 km, 2800 kg m −3 ) than the European crust (27.5 km, 2750 kg m −3 ). Alpine crust derived from each respective plate is found to show the same trend, with zones of Adriatic provenance (Austro-Alpine unit and Southern Alps) found to be denser and those of European provenance (Helvetic zone and Tauern Window) to be less dense. This suggests that the respective plates and related terranes had similar crustal properties to the present-day ones prior to orogenesis. The model generated here is available for open-access use to further discussions about the crust in the region.
Environmental features of cultural voters in Sarnggou Bay

The water environmental features in Sanggou Bay are discussed and compared with data obtained 10 years ago.The vertical distribution of determined chemical and biological indices in the waters is well-distributed . Being affected by unlimitedly increasing aquaculture, the water dynomic con dition has changed obviously in comparation with that of 10 years ago.Carrying capacity and self-purification of sea water decreases . The horizontal distribution and seasonal changes of chemical indices, nutritive situation and types, pre mary productivity differ greatly from those of 10 years ago.IN contents and IN/ IP value are lower during kalp cultivation. Their seasonal changes are restricted by primary productivity . So, IN has become a limiting factor to the primary productivity . Aquaculture has also accelerated the circle rate of IN in the waters.
Bayesian performance evaluation of evapotranspiration models based on eddy covariance systems in an arid region

Abstract. Evapotranspiration (ET) is a major component of the land surface process involved in energy fluxes and energy balance, especially in the hydrological cycle of agricultural ecosystems. While many models have been developed as powerful tools to simulate ET, there is no agreement on which model best describes the loss of water to the atmosphere. This study focuses on two aspects, evaluating the performance of four widely used ET models and identifying parameters, and the physical mechanisms that have significant impacts on the model performance. The four tested models are the Shuttleworth–Wallace (SW) model, Penman–Monteith (PM) model, Priestley–Taylor and Flint–Childs (PT–FC) model, and advection–aridity (AA) model. By incorporating the mathematically rigorous thermodynamic integration algorithm, the Bayesian model evidence (BME) approach is adopted to select the optimal model with half-hourly ET observations obtained at a spring maize field in an arid region. Our results reveal that the SW model has the best performance, and the extinction coefficient is not merely partitioning the total available energy into the canopy and surface but also including the energy imbalance correction. The extinction coefficient is well constrained in the SW model and poorly constrained in the PM model but not considered in PT–FC and AA models. This is one of the main reasons that the SW model outperforms the other models. Meanwhile, the good fitting of SW model to observations can counterbalance its higher complexity. In addition, the detailed analysis of the discrepancies between observations and model simulations during the crop growth season indicate that explicit treatment of energy imbalance and energy interaction will be the primary way of further improving ET model performance.
Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.
Rupture Process of the Mw 3.3 Earthquake in the St. Gallen 2013 Geothermal Reservoir, Switzerland

We analyze slip distribution and rupture kinematics of a Mw3.3 induced event that occurred in the St. Gallen geothermal reservoir (NE Switzerland) in 2013. We carry out a two‐step procedure: (1) path effects are deconvolved from the seismograms using an empirical Green's function, resulting in relative source time functions at all seismic stations; (2) the relative source time functions are back‐projected to the corresponding isochrones on the fault plane. Results reveal that the mainshock rupture propagates toward NNE from the hypocenter with an average velocity of 2,000 m/s. Spatiotemporal organization of foreshocks and aftershocks shows that the mainshock broke a previously less active portion of the fault and suggests that the aftershock sequence could be mainly driven by stress transfer. Applying this method in an operational environment could enable fast retrieval of seismic slip, allowing assessment of fault asperities and structures involved in the reservoir creation process.
Monitoring Groundwater Storage Depletion Using Gravity Recovery and Climate Experiment (GRACE) Data in Bakhtegan Catchment, Iran

The Bakhtegan catchment, an important agricultural region in south-western Iran, has suffered groundwater depletion in recent years. As groundwater is considered the main source of fresh water in the catchment, especially for agriculture, monitoring groundwater responses to irrigation is important. Gravity Recovery and Climate Experiment (GRACE) satellite data can help determine water mass changes in catchments and assess water volume changes. In this study, we compared GRACE-derived water mass data against groundwater volume variations measured in situ. We also assessed the efficiency of GRACE-derived data in catchments smaller than the 200,000 km2 recommended area when using GRACE. For the study period (January 2002 through December 2011), the GRACE data showed a 7.6 mm annual decline in groundwater level, with a total volume loss of 2.6 km3 during the period. The in situ monthly measurements of groundwater level showed an average depletion of 10 m in catchment aquifers during the study period. This depletion rate was supported by the recorded decrease in precipitation volume, especially in the post-drought period after 2007. These results demonstrate that GRACE can be useful tool for monitoring groundwater depletion in arid catchments.
Convolutional Neural Networks for Classification and Segmentation of Medical Images (Convolutional Neural Networks zur Klassifizierung und Segmentierung von medizinischen Bilddaten)

Automatic Detection, segmentation and classification of cancer plays an important role in the development of computer-aided diagnosis systems (CADs). This thesis investigates the application of convolutional and fully convolutional neural networks CNN/FCN for automatic detection, segmentation of liver and liver tumor in CT and MRI and classification of tumor malignancy of HCC in DW-MRI.
Effect of short-term sulphate contamination on lime-stabilized expansive soil

ABSTRACT Lime stabilization is an age-old technique for controlling the swell-shrink characteristics of the expansive soil. However, the presence of sulphate in the expansive soils renders the lime stabilization ineffective due to the formation of detrimental compounds – ettringite and thaumasite. The formation of these detrimental compounds can be counteracted by treatment with low-calcium or non-calcium based stabilizers. However, there is a possibility of sulphate contamination post lime stabilization through external sources. Therefore, the present study brings out the effect of short-term sulphate contamination on lime-stabilized expansive soil using sodium sulphate solution. To achieve this objective, the lime-stabilized expansive soil was cured for 1, 7 and 28 days, and subsequently mixed with sulphate solutions of 5000–20,000 ppm concentration and allowed to equilibrate for 1 day. The experimental results showed that the sulphate contamination had a significant effect on the soil structure, physico-chemical and index properties of lime-stabilized soil. The mechanism governing the deterioration of the stabilized soil depended on the curing period of lime-stabilized soil prior to contamination with sulphate. Short-term sulphate contamination of lime-stabilized soil cured for 1 and 7 days resulted in highly flocculated structure, whereas the lime-stabilized soil cured for 28 days resulted in the formation of ettringite. This is evidenced with the aid of Scanning Electron Microscopy (SEM) images, X-ray diffraction (XRD) and Energy Dispersive X-Ray Analysis (EDAX). It can be concluded from this study that the ettringite formation occurs even after the formation of pozzolanic compounds in lime-stabilized soil.
Surveillance Opportunities and the Need for Intersectoral Collaboration on Rabies in Sri Lanka

Sri Lanka is progressing towards its goal of eliminating human rabies. This goal rests on programs designed to limit canine rabies, which in turn requires a combination of targeted dog rabies control and a better understanding of the movement of the virus between domestic animals, people, and wildlife. Coordinated and integrated surveillance of the disease between human and animal health sectors underpins successful rabies elimination. Our objective was to review surveillance data from 2005 to 2014 to assemble the first multispecies synthesis of rabies information in Sri Lanka and, in doing so, assess needs and opportunities for a One Health approach to rabies surveillance in the country. Our descriptive epidemiological findings were consistent with other studies showing a decline in human cases, endemic and unchanging numbers of dog cases, a relationship between human density and the occurrence of human and animal cases, and significant gaps in understanding trends in rabies incidences in livestock and wildlife. Assessing the trends in the data from the three government organizations responsible for rabies surveillance was difficult due to lack of information on animal population sizes, unquantified sampling biases due to inequities in access to diagnostic capacities, regulatory and administrative barriers, and a continued reliance on clinical means to establish a diagnosis. The information required for a comprehensive rabies control programme was not standardized or consistent, was not in one place, showed significant gaps in completeness, and was not amenable to routine and rapid analysis. Achieving rabies elimination in Sri Lanka would benefit from harmonization of diagnostic and information management standards across animal and human health sectors as well as equitable access to diagnostic capacity for all regions and species.
An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties

Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of which are various sorts of Earth observations, such as map making, crop growth assessment, and disaster surveillance. However, the success rates of observation tasks are influenced considerably by uncertainties in local weather conditions, inadequate sunlight, observation dip angle, and other practical factors. The available time windows (ATWs) suitable for observing given types of targets and for transmitting data back to ground receiver stations are relatively narrow. In order to utilize limited satellite resources efficiently and maximize their commercial benefits, it is necessary to evaluate the overall effectiveness of satellites and planned tasks considering various factors. In this paper, we propose a method for determining the ATWs considering the influence of sunlight angle, elevation angle, and the type of sensor equipped on the satellite. After that, we develop a satellite effectiveness evaluation (SEE) model for satellite observation and data-downlink scheduling (SODS) based on the Availability–Capacity–Profitability (ACP) framework, which is designed to evaluate the overall performance of satellites from the perspective of time resource utilization, the success rate of tasks, and profit return. The effects of weather uncertainties on the tasks’ success are considered in the SEE model, and the model can be applied to support the decision-makers on optimizing and improving task arrangements for EOSs. Finally, a case study is presented to demonstrate the effectiveness of the proposed method and verify the ACP-based SEE model. The obtained ATWs by the proposed method are compared with those by the Systems Tool Kit (STK), and the correctness of the method is thus validated.
Strengthening of the Indian Ocean Dipole With Increasing Seasonal Cycle in the Mid‐Holocene

This study presents modeling evidence that the Indian Ocean Dipole (IOD) is enhanced in its frequency and amplitude under mid‐Holocene (8‐ka) conditions. This enhancement is identified in a global climate model simulation driven by the 8‐ka insolation, in which negative sea surface temperature anomalies grow more than the present‐day simulation in the eastern Indian Ocean. The mechanism of IOD amplification is explained in terms of a positive feedback among sea surface temperature, precipitation, and wind speeds over the eastern Indian Ocean. A series of idealized experiments in which the present‐day insolation seasonal cycle over the tropics is systematically perturbed in the model demonstrates that the IOD frequency and amplitude are proportional to the seasonal cycle strength of the atmospheric circulation off Sumatra‐Java, consistent with proxy records in the mid‐Holocene. Greater surface warming over Eurasia due to increased insolation in boreal summer is not critical to the amplification of the IOD.
Spatio-temporal quality distribution of MODIS vegetation collections 5 and 6: implications for forest-non-forest separability

ABSTRACT Moderate Resolution Imaging Spectroradiometer (MODIS) has been employed for continuous monitoring of land surface dynamics to facilitate the examination of spatial aspects of the environment. Periodical generation of MODIS products enables temporal analysis, and the interpretation of temporal patterns requires information about image quality. The MODIS Scientific Data Set (SDS) provides information on image properties. Some research has utilized the SDS to assist in analysis and interpretation, particularly in supporting time series forecasting and estimating ‘invalid’ data from near-dates observation. Our research compares the usability and reliability information provided in the MODIS SDS for collections 5 and 6 to describe the spatio-temporal distribution of image quality. This research compared the ability of the MODIS collections to identify the extent of water and to differentiate forest from non-forest. Four sites representing tropical and temperate regions were selected in Brazil, Congo, Colorado (United States of America), and the European Alps. The robustness of usability and reliability information for assessing MODIS vegetation collections 5 and 6 was compared over these sites by using 16-day composite products over a year of observations (2015). The spatio-temporal distribution of invalid pixels and gaps derived from usability and reliability information were assessed by using TiSeG (Time series Generator) and GeoDa. Moran’s I indicated a large number of invalid pixels and temporal gaps were clustered in a few areas. Collection 6 appears more consistent in the identification of waterbodies, either for inland water or ocean, but the error detection of ice fractions in two tropical sites tends to increase. Masking data by using Quality Assurance (QA)-SDS information improved the separability between forest and non-forest. This research demonstrated that evaluating the quality of image products using the SDS assisted the selection of period and location to better differentiate forest and non-forest. The seasonal fluctuation of separability metrics showed the importance of exploring temporal pattern for better understanding of the dynamics of forest cover.
Cryptoeconomics: Data Application for Token Sales Analysis

Initial coin offering, which is called ICO, is rapidly raised its volume as the bypassed crowdfunding. It reached $3.5B in 2017. This is mainly due to five of successful ICOs, Filecoin $257M, Tezos $232M, EOS $185M, Paragon $183M and Bancor $153M. Since these great successes, many startups are jumping into ICO. However, there is no standard criteria to analyze this investment market. Currently, most of token sales rely on their white papers and SNS channels to attract investors, and also we can find its popularity on Google Trends. The goal of this paper is thoughtfully exploring these data sources using big data analysis tools, then building the visualized analytics on this market. Our main approach is based on domainknowledge to find related criteria. There are several criteria which are widely accepted in this investment markets. We tested the linear correlation between these features and ICO result to validate these criteria. We collected 500 ICO startups to build our database from the ICO-trackers. It consists of founder, white papers, likes and followers in Twitter, ICO results so on. We also collected USD exchange rates of cryptocurrencies to see the price effect of ICO investments. In this data, we considered top 10 ICO startups in 2017 as the successful cases. Using selected criteria, we modeled the prediction of success by the logistic regression method, which may solve our classification problem, successful or not. Our final result is presented as a visual marketing analytics application which is designed on Elasticsearch and Kibana. Kibana is web-based visualization system which connected to the full-search NoSQL database, Elasticsearch. This system analyzes current ICOs status in real-time then visualize whole analysis on the Kibana dashboard. This dashboard provides summarized criteria analysis and the prediction result to monitor ICOs.
Characterization of Volatile Organic Compound Emissions from Anthropogenic and Emerging Biogenic Sources

Non-methane hydrocarbons such as ethane are important precursors to tropospheric ozone and aerosols. Using data from a global surface network and atmospheric column observations we show that the steady decline in the ethane mole fraction that began in the 1970s [Aydin et al., 2011; Helmig et al., 2014; Worton et al., 2012] halted between 2005 and 2010 in