1,720,956 research outputs found
Deep Learning Driven Detection of Tsunami Related Internal Gravity Waves: a path towards open-ocean natural hazards detection
Get PDFTsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The GNSS are constellations of satellites providing signals from Earth orbit - Europe's Galileo, the United States' Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and China's Bei-Dou. The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Gramian Angular Difference Fields (from Computer Vision) and Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd. Our work represents a new frontier in detecting tsunami-driven IGWs in open-ocean, dramatically improving the potential for natural hazards detection for coastal communities
A Deep Learning Approach for Detection of Internal Gravity Waves in Earth’s Ionosphere
No full textTsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd
Exploring AI Progress in GNSS Remote Sensing: A Deep Learning Based Framework for Real‐Time Detection of Earthquake and Tsunami Induced Ionospheric Perturbations
Get PDFGlobal Navigation Satellite System Ionospheric Seismology investigates the ionospheric response to earthquakes and tsunamis. These events are known to generate Traveling Ionospheric Disturbances (TIDs) that can be detected through GNSS-derived Total Electron Content (TEC) observations. Real-time TID identification provides a method for tsunami detection, improving tsunami early warning systems (TEWS) by extending coverage to open-ocean regions where buoy-based warning systems are impractical. Scalable and automated TID detection is, hence, essential for TEWS augmentation. In this work, we present an innovative approach to perform automatic real-time TID monitoring and detection, using deep learning insights. We utilize Gramian Angular Difference Fields (GADFs), a technique that transforms time-series into images, in combination with Convolutional Neural Networks (CNNs), starting from VARION (Variometric Approach for Real-time Ionosphere Observation) real-time TEC estimates. We select four tsunamigenic earthquakes that occurred in the Pacific Ocean: the 2010 Maule earthquake, the 2011 Tohoku earthquake, the 2012 Haida-Gwaii, the 2015 Illapel earthquake. The first three events are used for model training, whereas the out-of-sample validation is performed on the last one. The presented framework, being perfectly suitable for real-time applications, achieves 91.7% of F1 score and 84.6% of recall, highlighting its potential. Our approach to improve false positive detection, based on the likelihood of a TID at each time step, ensures robust and high performance as the system scales up, integrating more data for model training. This research lays the foundation for incorporating deep learning into real-time GNSS-TEC analysis, offering a joint and substantial contribution to TEWS progression.Global Navigation Satellite System Ionospheric Seismology investigates how the ionosphere responds to earthquakes and tsunamis, detecting TIDs through GNSS-derived TEC observations. Real-time TID identification aids tsunami detection, enhancing early warning systems by extending coverage to open-ocean regions. Automated TID detection is crucial for early warning system improvement. In this study, we propose an innovative approach using deep learning insights to perform automatic real-time TID monitoring and detection. We leverage GADFs and CNNs with VARION real-time TEC estimates. We train the model on four tsunamigenic earthquakes in the Pacific Ocean and validate it on an out-of-sample event. The framework achieves promising performance metrics, highlighting its potential for real-time applications. Our approach improves false positive detection, ensuring robustness and scalability as the system integrates more data for training. This research paves the way for integrating deep learning into real-time GNSS-TEC analysis, contributing significantly to the advancement of early warning systems.The proposed deep learning-based framework can detect earthquake and tsunami induced ionospheric perturbations in Total Electron Content observations We used a multi-event approach, focusing on the Pacific area, to train and to test the framework, achieving 91% of F1 score and 84% of recall The framework is well-suited for real-time applications, making it readily deployable to enhance tsunami early warning system
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
