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Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part- Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English- Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving onetime occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil. [ABSTRACT FROM AUTHOR]

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of eco-consumers purchase decisions through the lens of consumer preferences and patterns of information processing in the course of eco-friendly brands purchase. The main purpose of the research is to bridge the gap in understanding why shoppers in India despite high environmental awareness and positive attitudes practice sustainable consumer behaviour by consuming less than better. Systematization of literary sources and approaches for solving the problem of the low frequency of eco-shopping indicates that positive attitudes and purchase intentions correspond with environmental concerns, however, they are not manifested in more frequent purchase actions. The relevance of the solution of this scientific problem is that it gives orientation to the firms on a better understanding of eco-market in India. Investigation of the topic reveals high ambiguity in buying decisions among young eco-buyers, who are ready to consume more but find it difficult due to high price, poor quality and distribution models of eco-firms. The research established the negligible impact of family members and friends as role models for promoting green sales. Methodological tools of the research methods were comparative studies, regression analysis, Principal Component Analysis and ANOVA to establish the effects of information processing and consumer preferences on the frequency of eco-friendly brands purchasing. The object of research is eco-consumers residing in Delhi and NCR because namely they are more educated and have better access to green products. Convenient sampling covered 194 respondents and was conducted in 2018. The paper presents the results of an empirical analysis of the effects of consumer preferences on the frequency of eco-shopping actions, which showed a significant relationship between purchase frequency and level of environmental concerns. The research empirically confirms and theoretically proves that increasing demand for information about eco-brands does not lead to more often shopping. The results of the research can be useful for eco-firms to enhance marketing-mix capable to offer competitive price, quality, distribution and communication with customers. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment. [ABSTRACT FROM AUTHOR]