Performance Evaluation of High-Frequency Mobile Satellite Communications

Communication satellites have a much longer propagation delay than terrestrial communication networks such as cellular or WiFi. In addition, as the carrier frequency moves up, mobile satellite communications show worse performances than the conventional fixed satellite communications. The mobile satellite service (MSS) has not been actively pursued with long latency at high-frequency bands for future applications. In this paper, the adverse impact of long propagation delay in the conventional satellite system is investigated with various user mobility and Doppler-shifted carrier frequency. The satellite network is modeled as a basic delayed feedback channel system and the communication performance is analyzed under delayed channel state information (CSI) for assessing the system feasibility in mobile conditions. The results of performance analysis are provided at high-frequency bands with high-speed user movement, specifically on the outage probability and the channel capacity exploiting three types of channel models: conventional land mobile satellite (LMS) channel models of E. Lutz and C. Loo, and Nakagami fading model. In the circumstance with various user speeds, system performances are evaluated with different propagation delays in the LMS channel models and for line-of-sight (LOS) components in the Nakagami fading. In addition, the conventional models are compared depending on different altitudes for geostationary orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, as well as high-altitude platforms (HAP). © 2019 IEEE.1

Connectivity Analysis of Mega Constellation Satellite Networks with Optical Inter-Satellite Links

Recently, low earth orbit (LEO) satellite-based systems have attracted tremendous attention and various technologies have been developed for payload miniaturization and optical communications. In addition, mega-constellation architectures are expected to be deployed with LEO satellites for global broadband networks. In this article, we present a thorough analysis of mega-constellation architecture in terms of a change in the number of visible satellites and antenna steering capability to investigate the impact of increase in the constellation size and adoption of optical intersatellite links. The network architecture is evaluated with respect to satellite antenna steering capability and the satellite visibility considering the very narrow beam divergence of optical communications. We analyze the impact of a change in relative positions among the satellites due to continuous satellite movement in the constellation. The results offer guidelines for designing a novel visibility matrix using a time-varying satellite topology. This could defuse the problem of the conventional studies using fixed visibility matrices. The proposed time-varying visibility matrix achieves better performance than the previous preassigned links in terms of end-to-end link distance and hop count of LEO satellite networks. © 1965-2011 IEEE.1

Impact of Optical ISL on Satellite Routing Path Discovery in LEO Satellite Mega-Constellation

The satellite network is one of the key technology of future communications. However, traditional geostationary orbit (GEO) satellite systems have significant problems of a long latency and a heavy launch cost. Hence, low-earth orbit (LEO) satellites have emerged as an alternative system to mitigate the intrinsic problems of GEO satellites. Along with the development of LEO satellite technologies, the inter-satellite link (ISL) has been designed to provide improved system performance and more reliable service. In addition, the optical ISL has been highlighted due to its high data rate and small terminal size. In this study, we consider the mega-constellation architecture, time-varying satellite topology, and multiple visible satellites. The impacts of optical communications are investigated on the routing path discovery procedure. Then, we propose a routing algorithm and compare the system performances of RF and optical systems. © 2021 IEEE

Evaluating competitiveness of air cargo express services

This paper explores the relative importance of factors that influence the adoption of air express delivery service, and evaluates the competitiveness of air cargo express carriers in the Korean market. Our AHP analysis shows that accuracy and promptness are the two most influential factors to competitiveness, and that DHL is most competitive in the Korean market, followed by FedEx, TNT, EMS, and UPS. We further examine both the factor importance and carriers' competitiveness from the perspective of service users. While accuracy and promptness remain as important factors, price becomes the most important factor. Finally, an importance-performance analysis for each carrier is conduced, and managerial implications are drawn.Air cargo express services Integrators Competitiveness Service factors Analytic Hierarchy Process (AHP) Importance-performance analysis

Exploring the Performance of International Airports in the Pre- and Post-COVID-19 Era: Evidence from Incheon International Airport

Considering the socio-economic importance of Incheon International Airport, this study explored the changes in its aeronautical and non-aeronautical efficiency between 2001 and 2021. The study was conducted to measure and observe the changes in efficiency during the pre- and post-pandemic era of COVID-19. We employed a two-stage analytical approach to obtain the results using a set of desirable and undesirable variables. For the first stage, we employed a novel network data envelopment analysis–window analysis model to find the efficiency measures; for the second stage, we applied the Tobit regression analysis to observe the impact of some control variables on efficiency levels. The empirical results from the efficiency analysis stage revealed that, although the pandemic negatively affected the efficiency of this airport, the gain from appropriate strategies mitigated the excessive efficiency decline. Moreover, aeronautical activities showed better efficiency than non-aeronautical activities during the study period. In addition, further investigation of the second-stage analysis implied that an outbreak of pandemic diseases such as COVID-19 would dramatically impact international hubs such as Incheon International Airport; however, focusing on the import and export activities, in addition to increasing the connectivity with other airports, would improve the efficiency

Deep learning of mutation-gene-drug relations from the literature

Abstract Background Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. Results Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively. Conclusion We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers

Additional file 1 of Integrated clinical and genomic models using machine-learning methods to predict the efficacy of paclitaxel-based chemotherapy in patients with advanced gastric cancer

Supplementary Material