Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing the signature of heavy rainfall events using the 2-d-/4-d
water vapour information derived from GNSS measurement in Hong Kong

Abstract. Apart from the well-known applications like positioning, navigation and timing (PNT), Global Navigation Satellite System (GNSS) has manifested its ability in many other areas that are vital to society largely. With the dense setting of the regional continuously operating reference station (CORS) networks, monitoring the variations in atmospheric water vapour using a GNSS technique has become the focus in the field of GNSS meteorology. Most previous studies mainly concentrate on the analysis of relationship between the two-dimensional (2-d) Precipitable Water Vapour (PWV) and rainfall while the four-dimensional (4-d) variations of atmospheric water vapour derived from the GNSS tomographic technique during rainfall events are rarely discussed. This becomes the focus of this work, which investigates the emerging field of GNSS technology for monitoring changes in atmospheric water vapour during rainfall, especially in the vertical direction. This paper includes an analysis of both 2-d, and 4-d, precipitable water vapour profiles. A period with heavy rainfall events in this study was selected to capture the signature of atmospheric water vapour variation using the ground-based GNSS tomographic technique. GNSS observations from the CORS network of Hong Kong were used. Analysed results of the 2-d PWV/4-d water vapour profiles change during the arrival, occurrence, and depression of heavy rainfall show that: (i) the PWV time series shows an increasing trend before the arrival of heavy rainfall and decreases to its average value after the depression of rainfall; (ii) rainfall leads to an anomalous variation in relative humidity and temperature while their trends are totally opposite and show daily periodicity for periods without rain (this is highly correlated with the changes in solar radiation); (iii) atmospheric water vapour presents unstable conditions with intense vertical convective motion and hydrometeors are formed before the arrival of rainfall while returning to relatively stable conditions during heavy rainfall. This study indicates the potential for using GNSS-derived 2-d PWV and 4-d profiles to monitor spatio-temporal variations in atmospheric water vapour during rainfall, which provides a better understanding of the mechanism of convection and rainfall induced by the extreme weather events.
Energy Efficient Routing in Wireless Sensor Networks based on Ant Colony Optimization

Wireless Sensor Networks (WSN’s) have become an important and challenging research area in recent years. Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, has been successfully used in network routing. In this paper, we introduce a heuristic way to reduce energy consumption in WSNs routing process using Ant Colony Optimization. We introduce three Ant Colony Optimization algorithms, the Ant System, Ant Colony System and improved Ant System and their application in WSN routing process. The simulation results show that Ant Colony Optimization is an effective way to reduce energy consumption and maximize WSN lifetime. Keywords :- Ant colony optimization , Delay , Energy consumption, Routing , Routing protocols, Sensors , Wireless sensor networks.
Monitoring the World's Savanna Biomass by Earth Observation

Abstract Savannas constitute approximately 40% of the tropics and occur primarily in developing countries. Their net primary productivity rates are third only to tropical and temperate forests. Due to their high carbon sequestration potential savannas are likely to become important areas under Kyoto Protocol initiatives for removing CO2 from the atmosphere. They are, however, prone to human pressure. It is therefore important that accurate methods for the estimation of savanna biomass are developed which can be repeated for monitoring purposes. The paper examines research conducted to evaluate the use of Synthetic Aperture Radar (SAR) for estimating the biomass of the woody vegetation within a savanna in Belize, Central America. Results show that established SAR methods for estimating biomass for closed canopy forest are not directly transferable to the non-continuous cover woodlands that frequently occur in savanna areas. This has important implications for Earth observation satellites launched for the purpose of global biomass estimation.
Comparison of Potential Evapotranspiration (PET) using Three Methods for a Grass Reference and a Natural Forest in Coastal Plain of South Carolina

Potential evapotranspiration (PET) is a driving factor behind the estimates of ecosystem evapotranspiration (ET). Most of the PET methods with varying levels of complexity have been developed for a standard grass reference with unlimited soil moisture. There is only limited information examining the difference between the PET for a standard grass reference (REF-ET) and for a forest vegetation and their potential effects in water balance. Data being measured at three long-term complete weather stations located within < 10 km distance from each other on the USDA Forest Service Santee Experimental Forest (SEF) in coastal South Carolina are used in this study. The first two stations on grass reference are located at SEF headquarters (SHQ) and on Turkey Creek watershed (TC), and a third one is on a 27-m tall tower above the canopy of a pine/mixed hardwood forest on a control watershed (WS 80). In this study we evaluated (a) the observed micro-climatic conditions at those three stations and (b) the monthly and annual PET estimated by three methods with varying complexities (Penman-Monteith (P-M), Turc, and Thornthwaite (THORN)) using daily climatic data for a recent two-year (2011-2012) period. Average daily wind speed was observed to vary substantially (as much as 3 times) among the three locations, and average daily net radiation (Rn) on the WS 80 forest canopy was ~ 14% higher than the nearby SEF grass site. The effects of these differences were reflected in the PET results by the P-M method with much higher PET for the forest than the nearby grass site where the Turc method provided similar results with another grass site. Where possible and data were available, the results from these three methods were compared with pan evaporation estimates at SHQ. Results indicated that the PET estimates derived by these three methods for a single site and/or the estimates for nearby sites using a single method can vary greatly because of differences in their complexity of describing PET process, climatic factors and their interaction with site vegetation types. These differences should be considered when selecting a PET method and interpreting the results in hydrologic and water balance studies, especially for forested sites with much taller vegetation than the grass reference assumed in most PET methods in the literature.
Wavelet-based active sensing for delamination detection in composite structures

In this paper a signal processing technique is developed to detect delamination on composite structures. In particular, a wavelet-based signal processing technique is developed and combined with an active sensing system to produce a near-real-time, online monitoring system for composite structures. A layer of piezoelectric patches is used to generate an input signal with a specific wavelet waveform and to measure response signals. Then, the response signals are processed by a wavelet transform to extract damage-sensitive features from the original signals. The applicability of the proposed method to delamination identification has been demonstrated by experimental studies of a composite plate under varying temperature and boundary conditions.
Methodology for the development of bridge‐specific fragility curves

A new methodology for the development of bridge‐specific fragility curves is proposed with a view to improving the reliability of loss assessment in road networks and prioritising retrofit of the bridge stock. The key features of the proposed methodology are the explicit definition of critical limit state thresholds for individual bridge components, with consideration of the effect of varying geometry, material properties, reinforcement and loading patterns on the component capacity; the methodology also includes the quantification of uncertainty in capacity, demand and damage state definition. Advanced analysis methods and tools (nonlinear static analysis and incremental dynamic response history analysis) are used for bridge component capacity and demand estimation, while reduced sampling techniques are used for uncertainty treatment. Whereas uncertainty in both capacity and demand is estimated from nonlinear analysis of detailed inelastic models, in practical application to bridge stocks, the demand is estimated through a standard response spectrum analysis of a simplified elastic model of the bridge. The simplified methodology can be efficiently applied to a large number of bridges (with different characteristics) within a road network, by means of an ad hoc developed software involving the use of a generic (elastic) bridge model, which derives bridge‐specific fragility curves. Copyright © 2016 John Wiley & Sons, Ltd.
Experimental Evidences on Synergy of Gas Discharge Agents in Bactericidal Activity of Nonthermal Plasma

Nonthermal plasmas (NTPs) represent a new class of sterilizing agents. A full set of NTP bioactive factors includes electric components (E, charged particles and electric field), neutral active particles (R), and UV. A specific construction of the direct current (DC) corona source was used that allowed dissection of NTP bioactive factors and quantitative evaluation of their individual and combined action on bacterial pathogens Pseudomonas aeruginosa and Staphylococcus aureus. 10 and 120 s were required for the positive and negative coronas, respectively, to kill 105 colony-forming units (CFU) P. aeruginosa. 10 s was required for both positive and negative coronas to kill 105 CFU S. aureus. For the positive corona, the bactericidal activity of components decreased as R+UV>E+UV>>UV and E+UV>R+UV>>UV for P. aeruginosa and S. aureus, respectively. For the negative corona, the bactericidal activity decreased as R+UV>>E+UV=UV=0 and R+UV>>E+UV>UV≈0 for P. aeruginosa and S. aureus, respectively. Despite low, if any, activity of electric components in the negative corona, the whole plasma effect was much higher than the effect of neutral particles and UV alone. The obtained results demonstrated that different combinations of bioactive plasma components exerted diverse species-specific bactericidal effects. It is synergy among plasma bioactive factors that supplies high bactericidal activity of NTP.
An Objective Technique for Verifying Sea Breezes in High-Resolution Numerical Weather Prediction Models

An ongoing challenge in mesoscale numerical weather prediction (NWP) is to determine the ideal method for verifying the performance of high-resolution, detailed forecasts. Traditional objective techniques that evaluate NWP model performance based on point error statistics may not be positively correlated with the value of forecast information for certain applications of mesoscale NWP, and subjective evaluation techniques are often costly and time consuming. As a result, objective event-based verification methodologies are required in order to determine the added value of high-resolution NWP models. This paper presents a new objective technique to verify predictions of the sea-breeze phenomenon over eastcentral Florida by the Regional Atmospheric Modeling System (RAMS) NWP model. The contour error map (CEM) technique identifies sea-breeze transition times in objectively analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean postsea-breeze wind direction and wind speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique improves upon traditional objective verification techniques and previously used subjective verification methodologies because it is automated, accounts for both spatial and temporal variations, correctly identifies and verifies the sea-breeze transition times, and provides verification contour maps and simple statistical parameters for easy interpretation. The CEM algorithm details are presented and validated against independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation.
Canadian Labour Market and Skills Researcher Network Working

We estimate the elasticity of reported income with respect to tax rates for high earners using subnational variation across Canadian provinces. We argue this allows for better identification of tax elasticities than the existing literature. We find that elasticities of reported income at the provincial level are large for incomes in the top one percent, but small for lower earners. There are strong indications that the response happens both through earned and capital income. While our estimated elasticities are large, changes in tax rates cannot explain much of the overall long-run trend of higher income concentration in Canada. ∗We thank seminar participants at Carleton University, Exeter University and University of Ottawa for comments on this project. †University of British Columbia, kevin.milligan@ubc.ca ‡University of Toronto, msmart@chass.utoronto. ca
Study on Zero-Doppler Centroid Control for GEO SAR Ground Observation

In geosynchronous Earth orbit SAR (GEO SAR), Doppler centroid compensation is a key step for imaging process, which could be performed by the attitude steering of a satellite platform. However, this zero-Doppler centroid control method does not work well when the look angle of radar is out of an expected range. This paper primarily analyzes the Doppler properties of GEO SAR in the Earth rectangular coordinate. Then, according to the actual conditions of the GEO SAR ground observation, the effective range is presented by the minimum and maximum possible look angles which are directly related to the orbital parameters. Based on the vector analysis, a new approach for zero-Doppler centroid control in GEO SAR, performing the attitude steering by a combination of pitch and roll rotation, is put forward. This approach, considering the Earth’s rotation and elliptical orbit effects, can accurately reduce the residual Doppler centroid. All the simulation results verify the correctness of the range of look angle and the proposed steering method.
Detecting Silent Data Corruptions in Aerospace-Based Computing Using Program Invariants

Soft error caused by single event upset has been a severe challenge to aerospace-based computing. Silent data corruption (SDC) is one of the results incurred by soft error. SDC occurs when a program generates erroneous output with no indications. SDC is the most insidious type of results and very difficult to detect. To address this problem, we design and implement an invariant-based system called Radish. Invariants describe certain properties of a program; for example, the value of a variable equals a constant. Radish first extracts invariants at key program points and converts invariants into assertions. It then hardens the program by inserting the assertions into the source code. When a soft error occurs, assertions will be found to be false at run time and warn the users of soft error. To increase the coverage of SDC, we further propose an extension of Radish, named Radish_D, which applies software-based instruction duplication mechanism to protect the uncovered code sections. Experiments using architectural fault injections show that Radish achieves high SDC coverage with very low overhead. Furthermore, Radish_D provides higher SDC coverage than that of either Radish or pure instruction duplication.
Corporate Bond Use in Asia and the United States

We examine the determinants of Asian firms' decisions to participate in the corporate bond market and the determinants of the magnitude of bond debt for firms that do issue bonds. We compare the behaviour of Asian firms with US firms and investigate what drives differences in their financing decisions. We also analyse how firms alter their mix of bank and bond debt as their demand for cash varies. Our results show that firm characteristics account for only a very small portion of the difference in corporate bond use between Asian and US firms. Asian firms are simply less likely to issue bonds than US firms. The wedge in bond use between US and Asian firms is primarily driven by the likelihood that a firm issues bonds, rather than the magnitude of bond debt conditional on issuance. These results point to weak infrastructure, ie markets and institutions, as an important factor behind much of the lower use of bond financing in Asia. However, while policies aimed at facilitating the use of bond debt by Asian firms may result in increased bond issuance, it may not necessarily give firms more flexibility in meeting their financing needs. Even firms with a substantial presence in the corporate bond market primarily adjust their bank debt rather than their bond debt as demand for cash varies.
The influence of Organizational Citizenship Behavior on Information Security Behaviors ( A Case Study on Meteorological Organizational of Iran)

The purposes of the study were to examine the relationship between organizational citizenship behaviors and information security behaviors. We distributed 120 questionnaires to Meteorological Organization of Iran. The study used SEM and analyzes them using LISREL software package. there are eight secondary hypotheses and one main hypothesis about organizational citizenship behaviors and information security behavior. The results of the research were that information security behaviors can be influenced positively by organizational citizenship behavior.
Musical interpretation: case study in musical instrument practice learning vocational high school

This study was intended to reveal: (1) the dimensions of musical interpretation taught; (2) the strategy used by teachers to introduce musical interpretation in instrument practice class; and (3) teacher’s ability or competence to teach musical interpretation. This research is case study, with the subjects were 12 teachers responsible for instrument practice class. They were selected using purposive sampling method. The data collection method used were in-depth interviews, passive participation observation, documentation and focus group discussion (FGD). The instrument of study was the researcher herself. Data validity was conduct triangulation by reviewing data acquired from a source. All data then were analyzed using interactive model which included data reduction, data presentation, and conclusion/verification. The results show that: (1) the dimensions of musical interpretation taught in every instrument practice class, were only dimensions of knowledge; (2) the strategy used by teachers when teaching musical interpretation was a direct strategy, teacher-centered learning approach, and used individual approach; and (3) teachers’ competence in teaching musical interpretation must be improved, both pedagogical and professional competences.
Developing second language oral ability in foreign language classrooms: The role of the length and focus of instruction and individual differences

ABSTRACT The current study aimed to examine how instruction can impact the global, segmental, prosodic, and temporal qualities of second language (L2) oral ability in foreign language (FL) settings (i.e., a few hours of target language input per week). Spontaneous speech was elicited via a timed picture description task from 56 Japanese freshman college students who had studied English through FL instruction from Grades 7 to 12 without any experience abroad. The tokens were rated for global accentedness and then submitted to segmental, prosodic, and temporal analyses. According to statistical analyses, (a) the participants' oral performance widely varied in relation to the length and focus of FL instruction, the frequency of their conversations in the L2, and aptitude; and (b) their diverse proficiency levels were predicted in particular by the amount of extra FL activities inside (i.e., pronunciation training) and outside (i.e., cram school) of high school (but not junior high) classrooms. The results in turn suggest that whereas extensive FL instruction (>875 hr) itself does make some difference in L2 oral ability development, its pedagogical potential can be increased by how students optimize their most immediate FL experience beyond the regular syllabus.
A PV Based High Boost Step-Up DCDC Converter

The renewable energy sources such as PV modules, fuel cells or energy storage devices such as super capacitors or batteries deliver output voltage at the range of around 12 to 70 VDC. In order to connect them to the grid the voltage level should be adjusted according to the electrical network standards in the countries. First of all the voltage should be stepped up to sufficient level at which the DC/AC conversion can be performed to AC mains voltage requirements. Overall performance of the renewable energy system is then affected by the efficiency of step-up DC/DC converters, which are the key parts in the system power chain. This review is focused on high efficiency step-up DC/DC converters with high voltage gain. high step-up ratio dc–dc converters are preferred in renewable energy systems The differentiation is based on the presence or lack of galvanic isolation. A comparison and discussion of different DC/DC step-up topologies will be performed across number of parameters and presented in this paper
Mobile Behaviometrics: Behavior Modeling from Heterogeneous Sensor Time-Series

In the era of ubiquitous and mobile computing, the penetration of mobile devices equipped with various embedded sensors makes it possible to capture the physical and virtual context of the user and his/her surrounding environment. There is also an increasing demand to model human behaviors based on those data to enable context-aware computing and people-centric applications, which utilize users’ behavior pattern to improve the existing services or develop new services. Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data. We have three main challenges: statistical modeling of the sensor data, difficulty of multidimensional sensor fusion and structural activity recognition. Based on our observation of the similarity between human behavior and natural language, we apply natural language processing (NLP) techniques to statistically model heterogeneous sensor data and investigate various applications based on the derived models. We also investigate adapting conventional machine learning techniques to human behavioral modeling for non time-sensitive behavior sequences. To overcome the difficulty of multi-dimensional sensor fusion, we develop a hybrid approach to first learn behavioral factors for feature dimension reduction and then infer optimal way of fusing models from different factors. We study a novel Helix algorithm to induce the underlying grammar of human activities and ultimately solve the structural activity recognition. Our analytical work has been incorporated into a mobile security application SenSec, which