Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Vertical Accuracies of Open-Source Digital Elevation Models over Multiple Sites in China Using GPS Control Points

Digital elevation models (DEMs) are widely used across a range of fields. Several open-source global DEMs have been released, including the advanced land observing satellite world 3D 30 m DEM (AW3D30DEM), advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTMDEM), and TerraSAR-X for digital elevation measurement (TanDEM-X). ASTER and SRTM are the most widely used DEMs, while the newer models AW3D30DEM and TanDEM-X are becoming increasingly popular. Many studies have evaluated the qualities of these DEMs; however, few multi-regional studies have been conducted in China. To comprehensively and systematically evaluate the qualities of these DEMs in China, the vertical accuracies of AW3D, ASTER, STRM (all 30 m), and TanDEM-X (90 m) were tested across 16 regions in China. Using high-precision global positioning system control points for reference, error values were determined by subtracting these reference values from corresponding global DEM elevation values. As the study only covered flat areas (slope < 5°), slope was treated as a controlled variable. After assessing the impacts of the slope aspect and land cover type, variations in vertical accuracy were examined with respect to longitude and latitude. Overall, TanDEM-X exhibited the highest stability and accuracy, AW3D30 and SRTM also performed well, while ASTER exhibited the worst accuracy. The DEMs showed relationships with the slope aspect and land cover type, assuming that slope had no influence on vertical accuracy. In general, vertical accuracy in high latitudes was slightly better than that in low latitudes, and no evident variations were observed with respect to longitude. This study is the first to conduct DEM analysis across many regions in China from open sources. Since most of the users rely on public domain DEM datasets, this work contributes to their analysis in academic and engineering fields.
Ultra-Wideband Antennas for Biomedical Imaging Applications: A Survey

Microwave imaging is an active area of research that has garnered interest over the past few years. The main desired improvements to microwave imaging are related to the performances of radiating systems and identification algorithms. To achieve these improvements, antennas suitable to guarantee demanding requirements are needed. In particular, they must operate in close proximity to the objects under examination, ensure an adequate bandwidth, as well as reduced dimensions and low production costs. In addition, in near-field microwave imaging systems, the antenna should provide an ultra-wideband (UWB) response. Given the relevance of the foreseen applications, many UWB antenna designs for microwave imaging applications have been proposed in the literature. In this paper, a comprehensive review of different UWB antenna designs for near-field microwave imaging is presented. The antennas are classified according to the manufacturing technology and radiative performances. Particular attention is also paid to the radiation mechanisms as well as the techniques used to reduce the size and improve the bandwidth.
Operational experience of HVDC light

1. SUMMARY HVDC Light is an electric power transmission technology based on Voltage Source Converters with Pulse Width Modulation and modem HVDC cables. This type of converters with modem power electronics has interesting characteristics based on separate and fast-acting controls of active and reactive powers. The technology is well suited for connection of networks that otherwise are difficult or impossible to interconnect. It's suitability is based on accurate control of the transmitted active power and independent control of the reactive power in the connected AC networks. HVDC Light is designed at standard units between 10 and 300 MW and are built in transportable housings. This together with the above technical characteristics make them suitable for various applications of power transmission, such as exchange of power between networks, infeed of wind power to a network or as a feeder to an isolated load. There are four HVDC Light transmissions in operation in the world: one each in the countries of Sweden, Denmark, Australia and US. The HVDC Light links so far in operation or under construction have been justified for network interconnection or infeed of windpower. The speed and accuracy of both active and reactive controls have been used in customised ways in each of the above projects and is a key to success for HVDC Light projects. As the first project, the Gotland Light in Sweden started operation in November 1999 and the others came along during 2000. The operational experiences for the Gotland Light, Tjaereborg, Directlink and Eagle Pass projects regarding the use of the controllability of HVDC Light projects is presented.
Application of advanced algorithms and statistical techniques for weed-plant discrimination

II COPYRIGHT DECLARATION . IV ACKNOWLEDGMENT . V DEDICATION . VI TABLE OF CONTENT . VII LIST OF FIGURES . IX TABLE OF TABLES . XI LIST OF PUBLICATION . XII CHAPTER 1: INTRODUCTION . 13 1.1 BACKGROUND MOTIVATION . 13 1.2 LITERATURE REVIEW . 14 1.3 THESIS ORGANIZATION . 17 CHAPTER 2: PLANT DISCRIMINATION BY SUPPORT VECTOR MACHINE CLASSIFIER BASED ON SPECTRAL REFLECTANCE . 19 2.1 ABSTRACT . 19 2.2 METHODOLOGY . 19 2.2.1 System description . 19 2.2.2 Data description . 23 2.2.3 Data Analysis . 29 2.2.4 Algorithms . 30 2.3 RESULTS . 33 2.3.1 Comparison of dual-NDVI-based plant discrimination and SVM algorithms with NDVI inputs . 33 2.3.2 SVM classifier performance comparison using NDVI and raw input data . 35 2.4 DISCUSSION . 37 CHAPTER 3: A STATISTICAL APPROACH TO PROVIDE EXPLAINABLE CONVOLUTIONAL NEURAL NETWORK PARAMETER OPTIMIZATION . 38 3.1 ABSTRACT . 38 3.2 EXPERIMENTAL METHODS . 39 3.2.1 The convolutional neural network primitives . 39 3.2.2 Taguchi method . 41 3.2.3 Design of experiment . 43
Addressing pitch Mismatch for Children's Automatic Speech Recognition

This thesis addresses the acoustic mismatch due to pitch differences between the adults’ and the children’s speech for children’s automatic speech recognition (ASR) on adults’ speech trained models. The motivation for the work is obtained through the study done on exploring various acoustic sources of mismatch: pitch, speaking rate, formant frequencies and glottal flow parameters (open quotient, return quotient and speed quotient) for children’s ASR on adults’ speech trained models. The effect of variations in each of these acoustic correlates across speech signals is studied on Mel frequency cepstral coefficient (MFCC) features and ASR models. Following that, their relative significance is explored for children’s speech recognition on the adults’ speech trained models in a consistent setup. It is found that apart from the formant frequencies, the pitch is the other major source of acoustic mismatch between the adults’ and the children’s speech. The increase in the pitch of the signals is found to significantly increase the dynamic range and in turn the variances of the higher order coefficients of MFCC (C0−C12) features. Motivated by that, the pitch-robustness of perceptual linear prediction cepstral coefficient (PLPCC) and perceptual minimum variance distortionless response (PMVDR) cepstral coefficient features is studied to explore their efficacy for children’s ASR on adults’ speech trained models in comparison to MFCC features. It is found that MFCC features outperform PLPCC features while with suitable optimization of model order PMVDR features are more pitchrobust than MFCC features. However, the children’s ASR performance obtained with MFCC features after explicit pitch normalization of children’s speech is found to be comparable to that obtained with PMVDR features after optimization of its model order for children’s speech. Following the observations, a pitch normalization algorithm is proposed which modifies the Mel filterbank during MFCC test feature extraction based on the average pitch of the test signal for children’s ASR on adults’ speech trained models. Also, a Mel cepstral truncation based method is proposed for reducing the pitch mismatch beTH-1042_06610209 tween the training and the test data. The proposed algorithm automatically selects the appropriate length of the base MFCC features for each test signal without prior knowledge about the speaker of the test utterance. Significant improvements are obtained in the children’s speech recognition performances using the proposed algorithms on the adults’ speech trained models. Using the proposed adaptive MFCC feature truncation algorithm significant improvements are found in the children’s and adults’ ASR performances on children’s speech trained models as well. The improvements obtained in the ASR performances with the proposed algorithms are also found to be additive to those obtained with the existing speaker normalization and model adaptation techniques viz., VTLN, MLLR and CMLLR.
From Low to High Inflation: Implications for Emerging Market and Developing Economies

Policy frameworks . Over the past four decades, many advanced economies and EMDEs implemented macroeconomic stabilisation programmes and structural reforms, improved fiscal frameworks and gave their central banks clear mandates to control inflation. A shift from a strong mandate for price stability to objectives related to the financing of government would undermine the credibility of monetary policy frameworks and raise inflation expectations. Mounting public and private debt in EMDEs in the past decade could weaken commitment to disciplined fiscal and monetary policy frameworks. EMDE sovereign credit ratings have continued to deteriorate, with some falling below investment grade, reflecting concerns about rising debt and deteriorating growth prospects (Kose et al. 2019, Ha and Kindberg-Hanlon 2021). Populist sentiment could inspire a move away from prudent fiscal policies and disciplined monetary policy frameworks.
Preformulation Studies of Ezetimibe-Simvastatin Solid Dispersions in the Development of Fixed-Dose Combinations

Two active pharmaceutical ingredients (APIs) with limited solubility, simvastatin and ezetimibe, prepared as a drug-drug solid dispersion (SD) was evaluated for physicochemical, microstructural, and aqueous dissolution properties. The simvastatin-ezetimibe SD was prepared using the co-grinding method in a wide range of weight fractions and differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD) were used to perform the phase composition analysis. DSC studies confirmed that simvastatin and ezetimibe form a simple eutectic phase equilibrium diagram. Analysis of Fourier transform infrared spectroscopy (FTIR) studies excluded strong interactions between the APIs. Our investigations have revealed that all studied dispersions are characterized by substantially improved ezetimibe dissolution regardless of simvastatin content, and are best when the composition oscillates near the eutectic point. Data obtained in our studies provide an opportunity for the development of well-formulated, ezetimibe-simvastatin fixed-dose combinations (for hypercholesterolemia treatment) with reduced ezetimibe dosages based on its dissolution improvement.
Application of Adaptive Optics in Ophthalmology

The eye, the photoreceptive organ used to perceive the external environment, is of great importance to humans. It has been proven that some diseases in humans are accompanied by fundus changes; therefore, the health status of people may be interpreted from retinal images. However, the human eye is not a perfect refractive system for the existence of ocular aberrations. These aberrations not only affect the ability of human visual discrimination and recognition, but restrict the observation of the fine structures of human eye and reduce the possibility of exploring the mechanisms of eye disease. Adaptive optics (AO) is a technique that corrects optical wavefront aberrations. Once integrated into ophthalmoscopes, AO enables retinal imaging at the cellular level. This paper illustrates the principle of AO in correcting wavefront aberrations in human eyes, and then reviews the applications and advances of AO in ophthalmology, including the adaptive optics fundus camera (AO-FC), the adaptive optics scanning laser ophthalmoscope (AO-SLO), the adaptive optics optical coherence tomography (AO-OCT), and their combined multimodal imaging technologies. The future development trend of AO in ophthalmology is also prospected.
A Randomised, Double-Blind, Placebo-Controlled Crossover Trial of Resveratrol Supplementation for Prophylaxis of Hormonal Migraine

Resveratrol, a vasoactive phytoestrogen, has beneficial effects on cerebrovascular function. Previous research has shown that hormonal migraineurs have poorer cerebrovascular function than non-migraineur women. We aimed to investigate if resveratrol supplementation for three months could reduce the hormonal migraine burden index (HMBI: the number of days with menstrual migraine per month), reduce migraine-related disability and improve migraine-related quality of life. A randomised, double-blind, placebo-controlled, crossover, intervention trial was conducted in 62 hormonal migraineurs (mean age: 37.5 ± 0.8 years). Participants consumed 75 mg of resveratrol or matching placebo capsules twice daily for three months before crossing over to the other treatment arm. Participants completed a daily diary and the Headache Impact Test-6™, Migraine Disability Assessment and Migraine-Specific Quality of Life questionnaires at months 0, 3 and 6. The HMBI was the primary outcome and was calculated using data extracted from the participant’s diary. No differences in the HMBI (p = 0.895), the Headache Impact Test-6™, the Migraine Disability Assessment and Migraine-Specific Quality of Life were found between the resveratrol and placebo treatments. Resveratrol supplementation for three months did not affect the HMBI, the migraine-related disability or quality of life measures in our cohort of hormonal migraineurs.
Sentiment Analysis of Cybersecurity Content on Twitter and Reddit

Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.
Generating megacity-scale building height maps without DGNSS surveyed GCPs: An open-source approach

This paper describes an open-source method for generating megacity-scale building height maps without proprietary software or Differential Global Navigation Satellite System surveyed ground control points (GCPs). We use the open-source Satellite Stereo Pipeline (S2P) software along with four scenes of 2.5m resolution Cartosat-1 data for Bengaluru to demonstrate this. Digital Surface Models (DSMs) of 5 m resolution are generated using S2P, and terrain removal is achieved using 30m SRTM data resampled to 5m. The resulting normalized DSM is calibrated and validated using 1270 GCPs. These were generated by counting the number of occupiable floors of buildings and marking zero elevation ground points. The final building height map of Bengaluru covers a total area of about 1420 km2, and across the four scenes, has RMSE values ranging from 2.8m to 3.9m—an error of approximately one floor. Furthermore, we implemented this workflow using stereo imagery for Mumbai, and the RMSE values obtained were comparable to those for Bengaluru. Hence the method we describe is a very cost-effective way of generating megacity-scale building height maps. The height maps generated using this method can be used to better understand numerous urban characteristics including land use intensity and population distribution and can play a crucial role in urban planning and policy making.
China’s Public Firms’ Attitudes towards Environmental Protection Based on Sentiment Analysis and Random Forest Models

In this article, we investigated changes in public firms’ attitudes towards environmental protection in 2018–2021 in China. We crawled the firm–investor Q&A record on the website of East Money, extracted the carbon- and environment-related corpus, and then applied the sentiment analysis method of NLP (natural language processing) to calculate the sentiment weight of each firm-level record to estimate the attitude before and after towards carbon reduction. We found that there were significant changes in firms’ attitudes towards carbon reduction and environmental protection after the COVID-19 pandemic and the implementation of environment-related policies. We also found a heterogeneous effect of the attitude in different industries. In addition, we built several models to examine the relationship between a firm’s carbon reduction attitude and its financial performance. We found that: A goal with consequent specific policies can raise the positive attitudes of firms toward carbon reduction topics; firms’ attitudes toward ecological topics are different from industry to industry, which means that there are different needs and situations in the trend of carbon reduction from industry to industry. COVID-19 influenced firms’ attitudes toward carbon reduction and environmental protection, calling back the classic dilemma or trilemma of economic growth, carbon reduction, and energy consumption or, perhaps, epidemic control today. The stock situation also influenced the attitude toward environmental protection.
Bioremediation of Copper- and Chromium-Contaminated Soils Using Agrostis capillaris L., Festuca pratensis Huds., and Poa pratensis L. Mixture of Lawn Grasses

Environmental pollution by toxic metals is a common ecological problem. Chromium and copper compounds released into the environment as a result of human-made stress pose a serious threat to living organisms. Phytoremediation is a promising method of toxic metals removal from contaminated sites. The concentration of metals in grass biomass—in the roots and aerial parts—was determined by X-ray fluorescence analysis. The estimation of numbers of microorganisms was conducted by a tenfold dilution and spread-plating method. It was shown that lawn grass accumulated from 69.1 ± 13.2 to 497.7 ± 74.1 mg/kg Cu and Cr during the growth in the contaminated soil with 50, 100, and 200 mg/kg of metals. In general, there was a pattern of accumulation of copper in the aerial part of the grass and chromium in the roots. Thus, the total copper concentration in the aerial part ranged from 105.2 ± 23.8 to 497.7 ± 74.1 mg/kg of plant biomass. The total chromium concentration in the roots ranged from 156.4 ± 47.9 to 426.8 ± 62.5 mg/kg. The viability of the soil microbiome was not inhibited at such metal concentrations. The obtained data allow lawn grass to be considered as promising for the phytoremediation of contaminated areas.
A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market

The integration of renewable energy sources with active thermal power plants contributes to the green environment all over the globe. To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system.
Privacy Preservation in IoT systems via Blockchains: A Brief Review

—Modern-day Internet of Things (IoT) techniques are paving their path for a revolutionized world in which most of the everyday items that we use will be interconnected. These entities will be able to connect and share information to mechanize the majority of our tasks. This interconnection of edge devices needs security, impeccable authentication, robustness, and effortless maintenance services. To provide such relevant features, blockchain appears as a sustainable solution. The decentralized nature of blockchain has solved numerous security, maintenance, and authentication issues of IoT systems. Consequently, enormous growth in applications of blockchain-based IoT systems can be witnessed in recent years. However, a blockchain-based IoT network is public, so transactional details and encrypted keys are open and visible to everyone in that network. Thus, any foe can infer critical information of users from this public infrastructure. In this study, we discuss the privacy issues caused due to the integration of blockchain in IoT applications by focusing on the applications of our daily use. Furthermore, we highlighted the implementation of privacy preservation strategies in blockchain-based IoT systems.