Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network

Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation.

Sensitivity Analysis of FAARFIELD Rigid Airport Pavement Thickness Determination

FAARFIELD is a common mechanistic-empirical software that uses a combination of layered elastic and finite element methods for the determination of rigid aircraft pavement thickness. The primary input parameters are the aircraft type, mass and departures, concrete flexural strength, sub-base material and thickness, as well as subgrade support characteristic. A parametric sensitivity analysis, including three common commercial aircraft and four subgrade conditions, determined that concrete thickness was most sensitive to concrete strength and aircraft mass. The concrete thickness was least sensitive to the sub-base material and thickness and was moderately sensitive to the subgrade condition and aircraft departures. These relative sensitivities were consistent when the results were analysed based on average percentage change in concrete thickness, the average slope of lines of best fit for normalised parameter values and the coefficients of a numeric linear regression for concrete thickness. It is recommended that designers focus their attention on accurately estimating realistic concrete strength and aircraft mass values, as these parameters had the greatest influence on concrete thickness.

Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

Detecting aircraft from remote sensing image (RSI) is an important but challenging task due to the variations of aircraft type, size, pose, angle, complex background and small size of aircraft in RSIs. An aircraft detection method is proposed based on multi-scale convolution neural network with attention (MSCNNA), consisting of encoder, decoder, attention and classification. In MSCNNA, the multiple convolutional and pooling kernels with different sizes are utilized to learn the multi-scale discriminant features, and the global attention mechanism (GAM) is employed to capture the spatial and channel dependencies and adaptively preserve the relationships of the entire image. Compared with the standard deep CNN, multi-scale convolution neural networks (CNN) and GAM are integrated to learn the multi-scale features for aircraft detection, especially small aircrafts. Experiment results on the aircraft image dataset of the public EORSSD dataset show that the proposed method outperforms the state-of-the-art method on the same dataset and the obtained multi-size aircraft edge is clearer.

Analysis of Aircraft Operation System Regarding Readiness—Case Study

The aim of the study was to develop a model of the readiness and reliability of an aircraft to perform an air task. The applied research method uses quantitative statistical methods and Markov processes in order to create a mathematical algorithm to exploit a selected aircraft type. The paper presents a case study of the TS-11 “Iskra” aircraft. The results show that even if the probability of being on stand-by is low, the tasks can be completed by operating the entire fleet properly.

Performance-Based Navigation Flight Path Analysis Using Fast-Time Simulation

The growing demand for air transportation has led to an increase in worldwide air traffic inefficiency due to capacity constraints. The impacts associated with this situation can be reduced through operational changes. To better handle the problem, the Single European Sky ATM Research (SESAR) and the Next Generation Air Transportation System (NextGen) program suggest Performance-Based Navigation (PBN) as a solution. The Area Navigation (RNAV) and Required Navigation Performance (RNP) approaches belong to the group of PBN procedures. These procedures allow for a more efficient use of airspace by reducing route distances, fuel consumption and perceived aircraft noise. This article quantifies the benefits of PBN systems for two indicator parameters—fuel burn and flight time—and compares PBN systems to conventional instrument navigation procedures. The case studies use five airports in Brazil. The results of this analysis show that the benefits of the PBN approach vary with aircraft type and individual route characteristics.

Aeromedical Implications of Long-Term COVID-19 Sequelae

BACKGROUND: While many COVID-19 studies focus on acute effects of the infection, few examine the intermediate and long-term sequelae of the illness. Studies have shown that a good portion of patients have chronic effects in several body systems for several months or longer. Such effects can potentially adversely impact pilot performance in flight. We sought to determine the long-term effects of COVID-19 infection, how such effects can affect pilot performance, and how to best evaluate pilots for aeromedical flight clearance.METHODS: We used the PubMed literature search engine to review peer-reviewed articles that focused on the intermediate and long-term effects of COVID-19 infection. Chronic signs and symptoms were subdivided based on the particular body organ system affected. Merging information obtained from case reviews, article reviews, and aeromedical standards, we created a risk stratification guide to assist with the aeromedical disposition of affected pilots.RESULTS: Long-term effects of COVID-19 infection can last for several months or longer. The most common effects are fatigue, weakness, pulmonary diffusion defects, depression, and anxiety.DISCUSSION: This review article focuses on the most common intermediate- and long-term COVID-19 conditions of aeromedical significance and the corresponding course of actions recommended for the aeromedical examiner. Aeromedical evaluation should take into consideration factors related to the pilot, aircraft type, and specific aviation environment. Such evaluation may include diagnostic testing, medical specialist consultation, preflight simulation in an altitude chamber, human centrifuge testing, and/or a flight simulator checkride.Ko SY, Nguyen NK, Lee CL, Lee LA, Nguyen KUT, Lee EC. Aeromedical implications of long-term COVID-19 sequelae. Aerosp Med Hum Perform. 2021; 92(11):898-907.