1,720,970 research outputs found
Super-resolution techniques for Sentinel-5P products
No full textAir pollution is considered a very critical environmental risk to human health. The World Health Organization reports that it is responsible for almost 7 million deaths. As so, motivation is enough to decrease population exposure. However, several unsolved issues that require additional research remain. In particular, despite global monitoring development, coverage is insufficient to accurately describe the spatial variability for specific pollutants within different areas. The TROPOshperic Monitoring Instrument mounted on Sentinel-5P is one of the satellite instruments that retrieve atmospheric pollutants' concentration with a comparatively high spatial resolution, around 5 km. However, the spatial detail of the available products is often unsuitable for the purpose at hand. Also, physical constraints prevent enhancing the sensor's nominal spatial resolution further. So, there is no alternative way to collect high-resolution information than through processing algorithms. In this research, we investigated the problem of super-resolving Sentinel-5P products by employing traditional and deep learning-based approaches. While the former do not require a training phase because they rely on simple physical models, the latter can attain higher performance by reproducing highly complicated models. However, the lack of high-resolution reference data makes the needed training phase of network parameters extremely challenging. In this paper, we studied different approaches tailored to the imagery at hand and evaluated their accuracy with Sentinel-5P data. This study provides insights into the techniques and how they should be employed to monitor air quality accurately. The results of this work give significant information for the development of suitable super-resolution algorithms
Dolutegravir: A new option for HIV treatment
No full textThe need for new strategies for treating HIV-1 infection has led to the development of a number of new drugs. The aim of this article is to review the latest results of clinical trials of dolutegravir, an integrase inhibitor whose efficacy, tolerability and safety have been confirmed in treatment-naive and treatment-experienced patients. The findings, together with its high genetic barrier and limited interactions with other drugs, indicate that dolutegravir will play an important role in the future treatment of HIV infection
Reduced And Full-Scale Assessment Of Super-Resolution Of Sentinel-5P Radiance Images
No full textThe spatial resolution of TROPOMI, the sensor mounted on board of the satellite Sentinel-5P to monitor air quality, is much higher than its predecessors. Yet, the high variability of pollutants limits the use of the resulting maps in practical applications. Super-resolution approaches can improve the precision of estimates, but their use is heavily reliant on the ability to precisely tune the parameters of the algorithms. For this reason, the employment of a specific image acquisition model is essential for both learning-based and traditional methods. This contribution leverages real full-scale images for validation of a recently proposed model for the degradation introduced by the TROPOMI instrument, which is applied to both classical and learning-based techniques. The model's validity can be evaluated by analysing the quantitative data and visually inspecting the images that have been generated. This contribution proves that the degradation model is an essential basis for the development of novel approaches as well as for the application of all already available techniques
Efficient Hyperspectral Super-Resolution of Sentinel-5P Data via Dynamic Multidirectional Cascade Fine-Tuning
No full textSentinel-5P is a valuable resource for academics and policymakers. The ability of the satellite's equipment to span the electromagnetic spectrum from ultraviolet (UV) to short-wave infrared (SWIR) frequencies is vital in determining the distribution of important gaseous pollutants on a global scale, a significant turning point for air quality monitoring. In technical terms, Sentinel-5P provides an excellent balance between spatial and spectral resolutions; however, physical limitations keep hindering the quality of its products. S5Net is the first deep-learning-based (DL-based) approach designed to super-resolve Sentinel-5P radiance images. Despite its simplicity, this neural network has showed excellent performance when applied to monochromatic images, particularly when compared to more complex deep neural networks. Yet, this groundbreaking study has a significant limitation: the computational inefficiency of the fine-tuning employed, which must be adequately extended to numerous channels. We hence propose a novel dynamic multidirectional cascade fine-tuning procedure, whose routine is fully governed by the correlation between consecutive spectral channels. Our study is accordingly successful in striking a remarkable balance between spectral coherence and spatial resolution improvement, as well as substantially optimizing computing efficiency. The code is available at https://github.com/alcarbone/S5P_SISR_Toolbox
Deep learning processing of remotely sensed multi-spectral images
No full textThis chapter covers the most recent advancements in deep learning approaches tailored to multi-spectral remotely sensed images. Multi-spectral imaging conveys detailed information across several wavelengths, allowing for better environmental monitoring, precision agriculture, urban planning, and disaster management. The ability of deep learning-based approaches to extract complex patterns and features holds prospective in this domain. We specifically explore the challenges that these images give, including disparities in spatial resolution, spectral variability, and a lack of labelled data, while concurrently looking at cutting-edge deep learning-based algorithms and learning techniques specifically designed to deal with them. By summarizing current developments and outlining future research objectives, this chapter serves as a valuable resource for academics and professionals seeking to leverage deep learning for multi-spectral remote sensing image analysis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
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