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As the complexity and criticality of software increase and projects are pressed to develop software faster and more cheaply, it becomes even more important to ensure that software-intensive systems are reliable and safe. This book helps you develop, manage, and approve safety-critical software more efficiently and effectively. Although the focus is on aviation software and compliance with RTCA/DO-178C and its supplements, the principles also apply to other safety-critical software. Written by an international authority on the subject, this book brings you a wealth of best practices, real-world examples, and concrete recommendations.

The security of the U.S.commercial aviation system has been a growing concern since the 1970's when the hijacking of aircraft became a serious problem.Over that period, federal aviation officials have been searching for more effective ways for non-invasive screening of passengers, luggage, and cargo to detect concealed explosives and weapons.

Five reanalysis datasets—National Centers for Environmental Prediction reanalysis II (NCEP-2), NCEP Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and National Aeronautics and Space Administration (NASA) Modern Era Reanalysis for Research and Applications Version-2 (MERRA-2)—are selected to estimate meteorological droughts of China using three drought indices—the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). Drought indices, drought areas and drought severity estimated for China from these reanalysis datasets are assessed against corresponding results obtained from observed climate dataset of China using Nash–Sutcliffe efficiency (NSE), correlation coefficient, and the analysis of time series. Further, temperature, precipitation and potential evapotranspiration data of the five reanalysis datasets are also compared against the observed dataset. Drought indices and drought areas estimated from reanalysis datasets are generally more representative of historical droughts that had occurred in eastern China than in western China. However, the performance of these five reanalysis datasets in representing the drought severity is unsatisfactory in both western China and eastern China. SPEI is generally more representative than PDSI and SPI partly because temperature and potential evapotranspiration data of reanalysis datasets are generally better than precipitation data. PDSI is also based on a supply-and-demand model of soil moisture but estimating the demand of soil moisture is complicated. Therefore, SPEI is preferred over PDSI and SPI as the drought index to characterize the meteorological droughts of China. Climate data and meteorological drought characteristics of eastern China are best represented by JRA-55, while that of western China are best represented by MERRA-2. From 1980 to 2014, statistically significant increasing trends in annual drought areas and drought severity are detected from JRA-55 and observed climate datasets in eastern China, but they are only detected from observed dataset in western China.

Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

The rapid development of data and artificial intelligence technology has introduced new opportunities and challenges to aeronautical information intelligence. However, there are many obstacles in the sharing, reasoning and reusing aeronautical data due to the disunity of norms, the opacity of sharing and semantic ambiguity. To a large extent, as a basic method for processing, storing and deducing aeronautical data in the future, NER provides a new idea for the natural language processing of aeronautical information intelligence. In this paper, the problem with NER for aeronautical information is deeply analyzed, the relationship among the data model, the knowledge system and the named entity (NE) is combed, and the main characteristics of NE are summarized. At the same time, the resources that are useful to NER involving thematic databases, aviation domain ontology and evaluation indicators are described. Finally, two main directions of NER are suggested for further research, which is helpful in aviation development. This paper first provides a comprehensive survey of the approaches and directions of NER in a specific domain: aeronautical intelligence information.

Ferromagnetic materials are considered to have the applications in data storage, data processing and telecommunication. A Kraenkel-Manna-Merle system, which describes the nonlinear electromagnetic short waves in a ferromagnetic saturator, is investigated in this paper. With respect to the magnetization related to the saturated ferromagnetic material and external magnetic field, a generalized Darboux transformation (GDT) is constructed and utilized to derive the solitons, multi-pole solitons and their interactions. Analytic expressions of the double-pole solitons are offered and analyzed via the asymptotic analysis. Then, amplitudes, characteristic lines, slopes and phase shifts of the asymptotic solitons are presented. With the multiple spectral parameters involved in the GDT, interactions among the solitons and multi-pole solitons are illustrated.

This book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life.

The purpose of hydrologic modeling of a watershed is to gain valuable information about the processes occurring within watershed. With increasing temperature of the earth atmosphere, the snow fed mountainous river basins are going to get impacted severely. Lack of adequate weather station limits the scope of researches in these mountainous basins which are critical source of water resource for the country. However, improvement of satellite-based weather products has been able to nullify this barrier to great extent. In this study, a semi distributed hydrologic model of Upper Alaknanda river basin has been developed using gridded meteorological input data sourced from India Meteorological Department (IMD), National Aeronautics and Space Administration (NASA) Power, and The SWAT (Soil and water Assessment Tool) model. The calibration and validation of the model reflected satisfactory performance with the validation period (2013–2017) showing better match between simulated and observed flow than calibration period (2005–2012). The values of Nash-Sutcliffe efficiency, coefficient of determination, and Percent of bias for calibration period are 0.65, 0.67, and 14% respectively. Adoption of semi distributed approach for modeling enables to analyze the basin while preserving the heterogeneous nature of the basin. The spatiotemporal evaluation of snowmelt reveals that highest snowmelt was generated during month of April which also causes highest snowmelt contribution to runoff for April (59.76 %). The outcomes of this study reveals that satellite-based meteorological product can be adopted satisfactorily with SWAT model for estimation of snowmelt in upper Himalayan regions which gives a new direction of research in SWAT diaspora.

Advanced digital data-driven applications have evolved and significantly impacted the transportation sector in recent years. This systematic review examines natural language processing (NLP) approaches applied to aviation safety-related domains. The authors use Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct this review, and three databases (Web of Science, Scopus, and Transportation Research International Documentation) are screened. Academic articles from the period 2010–2022 are reviewed after applying two rounds of filtering criteria. The sub-domains, including aviation incident/accident reports analysis and air traffic control (ATC) communications, are investigated. The specific NLP approaches, related machine learning algorithms, additional causality models, and the corresponding performance are identified and summarized. In addition, the challenges and limitations of current NLP applications in aviation, such as ambiguity, limited training data, lack of multilingual support, are discussed. Finally, this review uncovers future opportunities to leverage NLP models to facilitate the safety and efficiency of the aviation system.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.