Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of consistently disconnected soil water pools over
(pore)space and time

Abstract. Storage and release of water in the soils is critical for sustaining plant transpiration and groundwater recharge. However, the subsurface mixing of water available for plants or quickly flowing to streams and groundwater is not yet understood. Moreover, while water infiltrating into soils was shown to bypass older pore water, the mechanisms leading to a separation between water routed to the streams and water held tightly in smaller pores are unclear. Here we present an extensive data set, for which we sampled fortnightly the isotopic composition (2H and 18O) of mobile and bulk soil water in parallel with groundwater, stream water and rainfall in the Mediterranean long-term research catchment, Vallcebre, in Spain. The data revealed that mobile and tightly bound water of a silty loam soil in a Scots pine forest do not mix, but they constitute two separate subsurface water pools; despite intense rainfall events leading to high soil wetness. We show that the isotopic compartmentation results from rewetting of small soil pores with isotopically depleted winter/spring rain. Thus, stable isotopes, and therefore water residence times too, do not only vary across soil depth, but also across soil pores. Our findings have important implications for stable isotope applications in ecohydrological studies assessing water uptake by plants or process realism of hydrological models, as the observed processes are currently rarely implemented in the simulation of water partitioning into evapotranspiration and recharge in the critical zone.
A Nonlinear Dynamical Systems‐Based Modeling Approach for Stochastic Simulation of Streamflow and Understanding Predictability

We propose a time series modeling approach based on nonlinear dynamical systems to recover the underlying dynamics and predictability of streamflow and to produce projections with identifiable skill. First, a wavelet spectral analysis is performed on the time series to identify the dominant quasiperiodic bands. The time series is then reconstructed across these bands and summed to obtain a signal time series. This signal is embedded in a D‐dimensional space with an appropriate lag τ to reconstruct the phase space in which the dynamics unfolds. Time‐varying predictability is assessed by quantifying the divergence of trajectories in the phase space with time, using Local Lyapunov Exponents. Ensembles of projections from a current time are generated by block resampling trajectories of desired projection length, from the K‐nearest neighbors of the current vector in the phase space. This modeling approach was applied to the naturalized historical and paleoreconstructed streamflow at Lees Ferry gauge on the Colorado River, which offered three interesting insights. (i) The flows exhibited significant epochal variations in predictability. (ii) The predictability of the flow quantified by Local Lyapunov Exponent is related to the variance of the flow signal and selected climate indices. (iii) Blind projections of flow during epochs identified as highly predictable showed good skill in capturing the distributional and threshold exceedance statistics and poor performance during low predictability epochs. The ability to assess the potential skill of these long lead projections opens opportunities to perceive hydrologic predictability and consequently water management in a new paradigm.
RAIM Detector and Estimator Design to Minimize the Integrity Risk

Future multi-constellation GNSS open the possibility to fulfill stringent navigation integrity requirements specified in safety-critical applications using receiver autonomous integrity monitoring (RAIM). In this paper, both the RAIM detector and its estimator are analyzed to develop a new algorithm. In the first part of this paper, the detector is selected by rigorously comparing two of the most widely implemented methods. In particular, the paper reveals fundamental differences between solution separation (SS) and residual-based (RB) RAIM. SS provides higher fault-detection performance than RB RAIM because the SS test statistic is tailored to the fault hypothesis, and to the state of interest. To prove these results in presence of multi-measurement faults, which occur in multi-constellation GNSS, analytical expressions of the worst-case fault direction are derived for both SS and RB RAIM. In the second part of the paper, a non-least-squares estimator is designed to reduce the integrity risk at the cost of lower accuracy performance for applications where integrity requirements are more demanding than accuracy requirements. The new estimator is numerically determined by solving an integrity risk minimization problem that includes multiple simultaneous fault hypotheses. Performance analyses show a substantial drop in integrity risk using the new RAIM algorithm as compared to a SS method that uses a least-squares estimator. In parallel, the decrease in accuracy performance is quantified. Combined availability of accuracy and integrity is evaluated at an example location for a GPS/Galileo navigation system.
Diversity of wood-inhabiting fungi in woodpecker nest cavities in southern Poland

Globally, tree-holes are important ecological component of forest and woodlands. Numerous microorganisms rely on cavities, both natural and those excavated by primary cavity nesting birds, mainly by woodpeckers, for their survival and reproduction. However, the fungi occurring in cavities are not well characterized. Specifically, very little is known about the fungal communities inhabiting the woodpecker nest cavities. Therefore, in this study, we investigated the fungal diversity of cavities in southern Poland. The samples were collected from freshly excavated woodpecker nest cavities using a nondestructive method (ND). The spatial distribution of fungal communities within the cavities was evaluated by sampling different parts of a single cavity using a destructive method (D). We detected 598 fungal isolates that included 64 species in three phyla and 16 orders using the ND method. Most of the fungi isolated from the cavities represented the phylum Ascomycota (73.9% of the isolates) with 11 orders, and Microascales was the predominant order (30% of the isolates). The most common species detected was Petriella musispora, which was isolated from 65% of the cavities. A total of 150 isolates (25%) were members of Basidiomycota, with Hymenochaetales being the dominant order (16% of the isolates). The basidiomycetous fungi were isolated from 55% of the cavities. Several taxa closely related to the pathogenic fungi and associated with secondary animal infections were detected in the wood of cavities. We identified different fungal communities in the three cavity parts using the D method. The cavity entrance had more number of species than the middle and bottom parts. The results of this study advanced our current knowledge on the mycobiota in woodpecker nest cavities and provided preliminary evidence for tree cavities being the hotspot for fungal diversity.
Rapid Electron Acceleration in Low‐Density Regions of Saturn's Radiation Belt by Whistler Mode Chorus Waves

Electron acceleration at Saturn due to whistler mode chorus waves has previously been assumed to be ineffective; new data closer to the planet show it can be very rapid (factor of 104 flux increase at 1 MeV in 10 days compared to factor of 2). A full survey of chorus waves at Saturn is combined with an improved plasma density model to show that where the plasma frequency falls below the gyrofrequency additional strong resonances are observed favoring electron acceleration. This results in strong chorus acceleration between approximately 2.5 RS and 5.5 RS outside which adiabatic transport may dominate. Strong pitch angle dependence results in butterfly pitch angle distributions that flatten over a few days at 100s keV, tens of days at MeV energies which may explain observations of butterfly distributions of MeV electrons near L=3. Including cross terms in the simulations increases the tendency toward butterfly distributions.
Reliability Analysis of European ERA5 Water Vapor Content Based on Ground-based GPS in China

On the basis of GPS data at stations of different climatic types in China, precipitable water vapor (PWV) products in year 2016 were obtained through precision data processing and water vapor inversion. On the basis of these products, the reliability of accuracy, annual variation, and diurnal variation of water vapor content for the first hourly resolution global reanalysis data ERA5 was systematically evaluated in China and compared with the previous generation of ERA-Interim analysis data. Results show that the annual and diurnal variation characteristics of PWV in different regions are mainly affected by monsoon and sunlight, respectively. The PWV accuracy provided by ERA5 is better than that of ERA-Interim. The two kinds of reanalysis data adequately reflect the annual variation characteristics of PWV, and the diurnal variation deviation of ERA5 is smaller than that of ERA-Interim in general, especially in the Qinghai–Tibet Plateau area. Keywords—GPS; ERA5; Precipitable Water Content (PWV); annual variation; diurnal variation
Modeling and analysis of extracellular field potentials in the brain

In order to model processes occuring in the brain it is necessary to have reliable measures of neural activity, with a clear intepretation rooted in the biophysics of the neural tissue. One of the most important probes of neural activity is the measurement of extracellular field potentials. The potential picked up by an electrode placed inside the brain is typically filtered in to two distinct frequency bands: the high-frequency part (! 500 Hz) captures the spiking output of nearby cells (termed multi-unit activity or MUA), while the low-frequency part (" 200-500 Hz) called local field potential (LFP) is thought to mainly reflect synaptic input of the cells in a local volume surrounding the electrode. The ability of the LFP to sample activity in whole populations of neurons makes it an important tool for understand neural network dynamics. Despite its wide use, there is however only limited knowledge about the relation between the measured LFP signal and the underlying neural activity. This thesis addresses different aspects of the relation beween activity at the single-cell or population level and the measured extracellular potential. First, we characterize the salient features of extracellular potentials generated by a single synaptic input. We use a forward-modeling approach where we simulate the LFP generated by neurons with realistic morphologies. We describe how dendrites, due to passive cable properties, filter the signal between input current and measured potential, and propose simplified models of LFP generation. Second, we show how distributions of synapses on to single cells give power-law scaling in power spectra of soma currents, soma potentials and current dipole moments. Third, we investigate the size of the cortical region that contributes to LFP recordings. By a combination of a simplified model and numerical simulations of populations of neurons, we identify the main factors determining the size of this region. Specifically, we highlight the role of correlations in the underlying synaptic activity. Finally, we analyze data from monkey motor cortex and relate oscillations in the LFP to temporally precise synchronized spiking activity. In summary, the findings presented in this thesis provide building blocks for further modeling and interpretation of the LFP, as well as other extracellular potential measures such as the electroencephalogram (EEG).
On the Metaphors of“Ельцин”

In modern Russian newspapers the precedent proper names have been realizing their cultural connotations and developing their culturally changed meanings. In the recent years such usages are increasingly and have drawn linguists’special attention.In the given article, the author takes“ПётрПервый”as examples to analyze anthroponomic metaphors in detail and further prove that vivid metaphor of proper names will enhance the emotion expressing power in newspaper articles. (The language materials of this article come from pe- riodicals in Russia and Belarus)
Contrastive Influence of ENSO and PNA on Variability and Predictability of North American Winter Precipitation

In this work, the roles of El Niño–Southern Oscillation (ENSO) in the variability and predictability of the Pacific–North American (PNA) pattern and precipitation in North America in winter are examined. It is noted that statistically about 29% of the variance of PNA is linearly linked to ENSO, while the remaining 71% of the variance of PNA might be explained by other processes, including atmospheric internal dynamics and sea surface temperature variations in the North Pacific. The ENSO impact is mainly meridional from the tropics to the mid–high latitudes, while a major fraction of the non-ENSO variability associated with PNA is confined in the zonal direction from the North Pacific to the North American continent. Such interferential connection on PNA as well as on North American climate variability may reflect a competition between local internal dynamical processes (unpredictable fraction) and remote forcing (predictable fraction). Model responses to observed sea surface temperature and model forecasts confirm that the remote forcing is mainly associated with ENSO and it is the major source of predictability of PNA and winter precipitation in North America.
Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).
Methane Emissions From Wetland Trees: Controls and Variability

Methane (CH4) produced in wetland soil generally is thought to be released to the atmosphere primarily via diffusion, ebullition and transport through aerenchyma of herbaceous plants adapted to waterlogged soils. The role of trees as a conduit for CH4 export from soil to the atmosphere has received limited attention despite laboratory studies of saplings demonstrating that wetland trees have a significant capacity to transport soil-produced CH4 to the atmosphere. In order to investigate the role of trees in transporting soil-produced CH4 to the atmosphere and assess its ecosystem contributions, tree-mediated CH4 flux was measured in situ from a temperate forested wetland (Flitwick Moor, UK) dominated by Alnus glutinosa and Betula pubescens and from a tropical forested wetland (Borneo, Indonesia). Mesocosm experiments complemented in situ data, in which CH4 emissions were measured from Alnus glutinosa saplings subjected to two water-table treatments. In both the in situ and mesocosm studies, CH4 emissions from trees were compared to CH4 emissions from the soil surfaces. Both temperate and tropical tree species released significant quantities of CH4 from stem surfaces throughout the observation period. In Alnus glutinosa, CH4 emissions from leaf surfaces were not detected and stem surfaces were the principle point of CH4 egress. Stem-CU4 emissions from both Alnus glutinosa and Betula pubescens were less sensitive to small changes in water-table variations when compared to CH4 emissions from soil surfaces, however, the quantity, temporal variability and CH4 transport mechanisms differed between the two tree species. Stem-CH4 emissions were controlled by a number of factors including tree physiology, abiotic factors and gas transport mechanisms. Wetland trees contributed significantly to ecosystem CH4 flux (6-87%), with tropical trees dominating ecosystem level CH4 fluxes. The results demonstrate that exclusion of tree-mediated CH4 emissions from flux measurement campaigns conducted in forested wetlands can significantly underestimate ecosystem-wide CH4 flux.
Identifying the hill torrents and Groundwater Resources for a remote area of District Jamshoro using Remote Sensing and Geographical Information System

Water resources are the major segments of the sustainable development in any region of the world.The current study identifies the hill torrents and groundwater resourcesin the Kohistan region of district Jamshoro, Sindh province of Pakistan. The study area is a hilly, mountainous range with arid to semi-arid climatic conditions, the high altitude do not allow the source of perennial/canal system, and depending on source of precipitation. Remote sensing data of Alos DEM of 30-meter resolution were obtained throughUSGS earth explorer official website. Shape filewas developed through the Google Earth Pro and ArcGIStechnique. HEC-GeoHMS was used to obtain hill torrents through processing tools and the stream definition was automated fixed to delineate the watershed area covering >= 75 km2.IdentifiedThe Groundwater resource into two categories of dug well and deep well. The study showed various ranges in the length of hill torrents 0.12 to 43.30 km, the depth of dug well aquifer 08.23-33.50 and deep well 91.50 to 164 meters in the region. Due to some research limitations the number 52 hill torrents, 33 dug wells and 11 deep wells were identified. Hill torrents carry runoff for the least period of rainy days, the torrential beds percolate the runoff and recharge shallow aquifers in the region, while deep aquifers recharges through adjoining watersheds. Water resourcepotential in the study area could be established by efficient heavy machinery to strengthening the torrential beds, and farms periphery bund structures, channelize watercourses, developed different ranges of ponds and reservoirs to capture floodwater, which enhance the recharge capacity of groundwater and introduce a high efficiency irrigation system in the study area. The spatial variation of water resources through modeling assessment has a major importance in the planning and development. Remote Sensing and GIS techniques enable the analysis with accurate results by consuming short time.
Radar-derived convective storms' climatology for the Prut River basin: 2003–2017

Abstract. Weather radar measurements are used to study the climatology of convective storms and their characteristics in the transboundary Prut River basin. The Storm Cell Identification and Tracking (SCIT) algorithm was used to process the volumetric reflectivity measurements, in order to identify, characterize, and track the convective storm cells. The storm attribute table output of the algorithm was used to separate the convective from the stratiform storm cells, by applying a simple selection criterion based on the average vertically integrated liquid (VIL) values. The radar-derived characteristics of convective storms were used to document the spatial and temporal distributions and storm properties in terms of duration, distance travelled, movement direction, and intensity. The results show that 94.3 % of all convective storm cells were detected during May–August, with the peak in July. The peak time for convective storm cells' occurrence was in the afternoon and evening hours between 10:00 and 18:00 UTC. The median duration of a convective storm was 42 min, the median distance travelled was 23 km, and the median movement speed was 7.7 m s−1. The average movement of storms varied with months, but overall most convective storms move from the south-west and south–south-east. Also, the analysis shows that the longer-lasting convective storms were the most intense. The spatial distribution of the convective cells reveals yearly variation patterns and hotspots but also highlights the limitations of radar measurement at longer distances. Reanalysis data suggest that low values of sea level pressure over the Black Sea can act as a dynamical driver of convective storms in the analysed area.
A Syntactic Errors Analysis in the Malaysian ESL Learners' Written Composition

This study aims to examine the English syntactic errors occurred persistently in the Malaysian ESL learners' written composition in the Politeknik Kota Kinabalu, Sabah, Malaysia. The subjects were 50 multilingual students who speak their own dialect, Malay as their second language and English as their third or foreign language. Data were collected from the written discourse in the form of descriptive essays. The subjects were asked to write in the classroom within 45 minutes. Fifty-one categories of errors were classified to find out the causes of syntactic error, types of frequent errors, weakness areas and problems tend to occur in written composition. The findings of the study showed that the syntactic errors were due to learners' mother tongue interference, lack of grammatical knowledge, lack of vocabulary knowledge, repetition, redundant lexical choice, bad sentence formation and developmental errors.
Transfer Learning for BioImaging and Bilingual Applications

Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution, but we may have plenty of labeled data from one or multiple related sources with different distributions. Due to its capability of migrating knowledge from related domains, transfer learning has shown to be effective for cross-domain learning problems. In this dissertation, I carry out research along this direction with a particular focus on designing efficient and effective algorithms for BioImaging and Bilingual applications. Specifically, I propose deep transfer learning algorithms which combine transfer learning and deep learning to improve image annotation performance. Firstly, I propose to generate the deep features for the Drosophila embryo images via pretrained deep models and build linear classifiers on top of the deep features. Secondly, I propose to fine-tune the pretrained model with a small amount of labeled images. The time complexity and performance of deep transfer learning methodologies are investigated. Promising results have demonstrated the knowledge transfer ability of proposed deep transfer algorithms. Moreover, I propose a novel Robust Principal Component Analysis (RPCA) approach to process the noisy images in advance. In addition, I also present a two-stage re-weighting framework for general domain adaptation problems. The distribution of source domain is mapped towards the target domain in the first stage, and an adaptive learning model is proposed in the second stage to incorporate label information from the target domain if it is available. Then the proposed model is applied to tackle cross-lingual spam detection problem at LinkedIn’s website. Our experimental results on real data demonstrate the efficiency and effectiveness of the proposed algorithms.