Fuzzy Risk Assessment Method for Airborne Network Security Based on AHP-TOPSIS

With the exponential increase in information security risks,ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment.However,experts possess a limited understanding of fundamental security elements,such as assets,threats,and vulnerabilities,due to the confidentiality of airborne networks,resulting in cognitive uncertainty.Therefore,the Pythagorean fuzzy Analytic Hierarchy Process(AHP)Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks.First,Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights,which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks.Second,Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure,which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios.Finally,a comparative analysis was conducted.The proposed method demonstrated the highest Kendall coordination coefficient of 0.952.This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.

Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions

Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.

Eucalyptus carbon stock estimation in subtropical regions with the modeling strategy of sample plots–airborne LiDAR–Landsat time series data

Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area.This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots,airborne LiDAR and Landsat time series data in subtropical regions of China.Specifically,eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data;the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data;and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age(e.g.,months or years).To examine the models’robustness,the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales,and the modeling results were evaluated using validation samples accordingly.The results showed that carbon stock can be successfully estimated using the age-based models(both age variables in months and years as predictor variables),but the month-based models produced better estimates with a root mean square error(RMSE)of 6.51 t⋅ha1 for Yunxiao County,Fujian Province,and 6.33 t⋅ha1 for Gaofeng Forest Farm,Guangxi Zhuang Autonomous Region.Particularly,the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years.The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale,indicating their possibility for analysis of carbon stock change.However,both the month-based and year-based models expressed relatively poor transferability at a spatial scale.This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

Occupation-related airborne diseases: Management and prevention of tuberculosis among nurses working in Ekiti State University Teaching Hospital

Objective:More health workers die from infectious diseases such as tuberculosis(TB)than from any other infectious agent,so the current study is designed to assess TB treatment and preventive measures as they relate to nursing care practice.Materials and Methods:A cross-sectional study was conducted among 82 nurses from December 2022 to January 2023.The data were collected through a self-developed questionnaire and analyzed by frequency table and analysis of variance(ANOVA)statistics.Results:The researchers found that nurses have sufficient knowledge on Infection Prevention and Control(IPC)policy,but not all nurses follow this management protocol,and the reason for their action is addressed in this study.The management and preventive measures used by the participants were identified as:proper cough etiquette/hygiene,working in a well-ventilated area,use of mask,appropriate disposal of used items and so on was reported.Moreover,this study also discovered that there is a positive correlation between nurses who had received training in IPC policy and their use of airborne preventive measures for TB patients(F=1.87,P=0.002)as well as a positive correlation between the availability of resources and their adherence to the proper use of airborne preventive measures when caring for TB patients(F=1.96,P˂0.001).Conclusion:We proposed infection and control training for nurses and adequate equipment supplies to the TB ward which are required in quick TB diagnosis,and must be carried out on a regular basis by health-care personnel,for efficient nursing practice.Ensuring proper safety equipment and isolation units should be available and assessable for nurses or other health workers showing sign or symptoms of TB.Similarly,it is necessary for government to put in place the control and regulations that will safeguard nurses and mandate them to adopt TB prevention protocols,which will aid in lowering the prevalence of TB among nurses.

Determination of Curie Point Depth and Heat Flow Using Airborne Magnetic Data over the Kom-Ombo and Nuqra Basins, Southern Eastern Desert, Egypt

The Kom-Ombo and Nuqra basins in southern Egypt have recently been discovered as potential hydrocarbon basins. The lack of information about the geothermal gradient and heat flow in the study area gives importance to studying the heat flow and the geothermal gradient. Several studies were carried out to investigate the geothermal analyses of the northwestern desert, as well as the west and east of the Nile River, using density, compressive wave velocity, and bottom hole temperature (BHT) measured from deep oil wells. This research relies on spectral analysis of airborne magnetic survey data in the Kom-Ombo and Nuqra basins in order to estimate the geothermal gradient based on calculating the depth to the bottom of the magnetic source that caused the occurrence of these magnetic deviations. This depth is equal to the CPD, at which the material loses its magnetic polarisation. This method is fast and gives satisfactory results. Usually, it can be applied as a reconnaissance technique for geothermal exploration targets due to the abundance of magnetic data. The depth of the top (Zt) and centroid (Z0) of the magnetic source bodies was calculated for the 32 windows representing the study area using spectral analysis of airborne magnetic data. The curie-isotherm depth, geothermal gradient, and heat flow maps were constructed for the study area. The results showed that the CPD in the study area ranges from 13 km to 20 km. The heat flow map values range from 69 to 109 mW/m2, with an average of about 80 mW/m2. The calculated heat flow values in the assigned areas (A, B, C, and D) of the study area are considered to have high heat flow values, reaching 109 mW/m2. On the other hand, the heat flow values in the other parts range from 70 to 85 mW/m2. Since heat flow plays an essential role in the maturation of organic matter, it is recommended that hydrocarbon accumulations be located in places with high heat flow values, while deep drilling of hydrocarbon wells is recommended in places with low to moderate heat flow values.

PC-based artif icial neural network inversion for airborne time-domain electromagnetic data

Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.

Calculation and Analysis of Near Field of Airborne Short Wave Antenna

Aim To calculate and analyze the near field distribution of ariborne short wave antenna. Methods B-spline method was used to get the mathermatital model of a Boeing 707320Baircraft and simulate its short wave antenna . FDTD (finite-difference time-domain) method are ed tO complete the calculation and analysis. Results The near field distributions on aircraft's surface were obtained, the curve and gray figures of the normalized near field value were shown. Conclusion These modeling and calculating methods can provide data foraircraft's EMC analysis and design.

Three-dimensional arbitrarily anisotropic modeling for time-domain airborne electromagnetic surveys

Electrically anisotropic strata are abundant in nature, so their study can help our data interpretation and our understanding of the processes of geodynamics. However, current data processing generally assumes isotropic conditions when surveying anisotropic structures, which may cause discrepancies between reality and electromagnetic data interpretation. Moreover, the anisotropic interpretation of the time-domain airborne electromagnetic （TDAEM） method is still confined to one dimensional （1D） cases, and the corresponding three-dimensional （3D） numerical simulations are still in development. In this study, we expanded the 3D TDAEM modeling of arbitrarily anisotropic media. First, through coordinate rotation of isotropic conductivity, we obtained the conductivity tensor of an arbitrary anisotropic rock. Next, we incorporated this into Maxwell＇s equations, using a regular hexahedral grid of vector finite elements to subdivide the solution area. A direct solver software package provided the solution for the sparse linear equations that resulted. Analytical solutions were used to verify the accuracy and feasibility of the algorithm. The proven model was then applied to analyze the effects of arbitrary anisotropy in 3D TDAEM via the distribution of responses and amplitude changes, which revealed that different anisotropy situations strongly affected the responses of TDAEM.

2.5D forward modeling and inversion of frequency-domain airborne electromagnetic data

Frequency-domain airborne electromagnetics is a proven geophysical exploration method.Presently,the interpretation is mainly based on resistivity-depth imaging and onedimensional layered inversion;nevertheless,it is difficult to obtain satisfactory results for two- or three-dimensional complex earth structures using 1D methods.3D forward modeling and inversion can be used but are hampered by computational limitations because of the large number of data.Thus,we developed a 2.5D frequency-domain airborne electromagnetic forward modeling and inversion algorithm.To eliminate the source singularities in the numerical simulations,we split the fields into primary and secondary fields.The primary fields are calculated using homogeneous or layered models with analytical solutions,and the secondary（scattered） fields are solved by the finite-element method.The linear system of equations is solved by using the large-scale sparse matrix parallel direct solver,which greatly improves the computational efficiency.The inversion algorithm was based on damping leastsquares and singular value decomposition and combined the pseudo forward modeling and reciprocity principle to compute the Jacobian matrix.Synthetic and field data were used to test the effectiveness of the proposed method.

Influence of environmental conditions on airborne infection risk in ward

In order to study the influence of parameters on airborne transmitted diseases in a hospital ward,a pollutant decay equation and a Wells-Riley model are adopted to study the relationship between the airborne infection risk and the parameters such as the ventilation rate,the number of susceptible persons and the volume of the ward.The results show that the airborne infection risk can be reduced by increasing the ventilation rate.For two wards with the same air change per hour（ACH）but different volumes,the ventilation rate in a large room is higher than that in a small one.The number of the infected persons in the ward increases with the increase in the susceptible persons when the ACH is a constant.Therefore,the ventilation rate in the ward is not only estimated by the ACH but also related to the volume of the ward and the number of the susceptible persons.

Development of analytical methods for polycyclic aromatic hydrocarbons (PAHs) in airborne particulates: A review

In the present work, the different sample collection, pretreatment and analytical methods for polycyclic aromatic hydrocarbons （PAHs） in airborne particulates is systematacially reviewed, and the applications of these pretreatment and analytical methods for PAHs are compared in detail. Some comments on the future expectation are also presented.

Effect and Prospect of Basic Geological Survey Based on Airborne Gravimetry in China

The airborne gravimetry was an important leap and innovation in the world＇s history of geophysical exploration. China＇s first test of the airborne gravity geological survey in the onshoreoffshore transitional area of the western and southern part of the Bohai Sea was successful and effective in geology. Based on the airborne gravity data, and combining previous ground gravity, seismic and drilling data etc., we carried out the geological interpretation by forward and inverse methods. The result shows that the airborne Bouguer gravity anomaly was clear, the fracture interpretation was reliable, and the inversion depth of the main geological interfaces was relatively accurate. This airborne gravity geological survey not only filled the exploring gaps in the onshore- offshore transitional area, and realized the geological and tectonic junction between the sea and the land, but also discovered four local gravity anomalies, 11 fractures and three sags or subsags, and so on. The good geological effect of airborne gravimetry not restricted by terrain condition shows that it can be served as a new geophysical method in the exploration of complex terrain physiognomy area such as mountain, jungle, desert, marsh, onshore-offshore transitional area and so on, and has an extensive application prospect in China in the future.

Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China

China Marine Surveillance Force was equipped with modern aerial equipments for marine lawexecute with the advantage of functioning agilely at a large scale of surveillance coverage, providing powerful all-round safeguard, which is of benefit to the harmonious and sustainable development of coastal economy. Onboard the planes, three kinds of remote sensing sensors have been installed, including a marine airborne multi-spectrum scanner （MAMS）, an optical-electronic platform, and an airborne hyper-spectral system AISA＋. The specifications of remote sensing platforms were introduced briefly first, then examples of water quality monitoring by airborne remote sensing were presented, including the monitoring in coastal suspended material, oil-spill and abnormal warm water, etc.

Characteristic Verification and Parameter Optimization of Airbags Cushion System for Airborne Vehicle

Abstract： The major methods to investigate the airbags cushion system are experimental method, thermodynamic method and finite element method （FEM）. Airbags cushion systems are very complicated and very difficult to be investigated thoroughly by such methods For experimental method, it is nearly impossible to completely analyze and optimize the cushion characteristics of airbags of airborne vehicle because of charge issue, safety concern and time constraint. Thermodynamic method fails to take the non-linear effects of large airbag deformation and varied contact conditions into consideration. For finite element method, the FE model is usually complicated and the calculation takes tens of hours of CPU time. As a result, the optimization of the design based on a nonlinear model is very difficult by traditional iterative approach method. In this paper, a model based on FEM and control volume method is proposed to simulate landing cushion process of airborne vehicle with airbags cushion system in order to analyze and optimize the parameters in airbags cushion system. At first, the performance of airbags cushion system model is verified experimentally. In airdrop test, accelerometers are fixed in 4 test points distributed over engine mount, top, bottom and side armor plate of hull to obtain acceleration curves with time. The simulation results are obtained under the same conditions of the airdrop test and the simulation results agree very well with the experimental results, which indicate the established model is valid for further optimization. To optimize the parameters of airbags, equivalent response model based on Latin Hypercube DOE and radial basis function is employed instead of the complex finite element model. Then the optimal results based on equivalent response model are obtained using simulated annealing algorithm. After optimization, the maximal acceleration of airborne vehicle landing reduces 19.83%, while the energy absorption by airbags increases 7.85%. The performance of the airbags cushion system thus is largely improved through optimization, which indicates the proposed method has the capability of solving the parameter optimization problem of airbags cushion system for airborne vehicle.

Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan

Determining forest structural complexity,i.e.,a measure of the number of different attributes of a forest and the relative abundance of each attribute,is important for forest management and conservation.In this study,we examined the structural complexity of mixed conifer–broadleaf forests by integrating multiple forest structural attributes derived from airborne Li DAR data and aerial photography.We sampled 76 plots from an unmanaged mixed conifer–broadleaf forest reserve in northern Japan.Plot-level metrics were computed for all plots using both field and remote sensing data to assess their ability to capture the vertical and horizontal variations of forest structure.A multivariate set of forest structural attributes that included three Li DAR metrics(95 th percentile canopy height,canopy density and surface area ratio) and one image metric(proportion of broadleaf cover),was used to classify forest structure into structural complexity classes.Our results revealed significant correlation between field and remote sensing metrics,indicating that these two sets of measurements captured similar patterns of structure in mixed conifer–broadleaf forests.Further,cluster analysis identified six forest structural complexity classes includingtwo low-complexity classes and four high-complexity classes that were distributed in different elevation ranges.In this study,we could reliably analyze the structural complexity of mixed conifer–broadleaf forests using a simple and easy to calculate set of forest structural attributes derived from airborne Li DAR data and high-resolution aerial photography.This study provides a good example of the use of airborne Li DAR data sets for wider purposes in forest ecology as well as in forest management.

Comprehensive characterization of indoor airborne bacterial profile

This is the first detailed characterization of the airborne bacterial profiles in indoor environments. Two restaurants were selected for this study. Fifteen genera of bacteria were isolated from each restaurant and identified by three different bacterial identification systems including MIDI, Biolog and Riboprinter. The dominant bacteria of both restaurants were Gram-positive bacteria in which Micrococcus and Bacillus species were the most abundant. Most bacteria identified were representative species of skin and respiratory tract of human, and soil. Although the bacterial levels in these two restaurants were below the limit of the Hong Kong Indoor Air Quality Objective （HKIAQO） Level 1 standard （i.e., 〈 500 cfu/m^3）, the majority of these bacteria were opportunistic pathogens. These results suggested that the identity of airborne bacteria should also be included in the IAQ to ensure there is a safety guideline for the public.

Space-time clutter model for airborne bistatic radar with non-Gaussian statistics

To validate the potential space-time adaptive processing （STAP） algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, space-time clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement （MCARM） experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.

Study of Residual Basin and Tectonolayering Based on Airborne Gravity and Magnetic Data

This paper takes the South Yellow Sea as an example to show a new method for comprehensive geological-geophysical research such as residual basin and tectonolayering using airborne gravity and magnetic data in China. Based on airborne gravity and magnetic data, by measuring and analyzing stratigraphic density and susceptibility, the depths to the pre-Sinian magnetic basement top, the pre-Jurassic top and the Cenozoic bottom, are obtained by forward and inverse methods constrained by seismic and drilling data; and furthermore, the residual thicknesses of the Sinian-Triassic, the Jurassic-Cretaceous and the Cenozoic are calculated. Based on airborne gravity and magnetic anomalies, the faults in the pre-Sinian magnetic basement, the Jurassic-Cretaceous and the Cenozoic are respectively interpreted by the qualitative and quantitative methods. On the basis of the above study, and combining regional important tectonic events, four tectonolayers are divided in the vertical succession in South Yellow Sea, namely the pre-Sinian magnetic basement, the Sinian-Triassic, the Jurassic-Cretaceous and the Cenozoic. The result shows that there are thick Cenozoic and Jurassic-Cretaceous strata and thin residual Sinian-Triassic strata in the Suzhong-Huangnan depression area, but there are thin or only sporadic Mesozoic-Cenozoic terrestrial strata and thick Sinian-Triassic marine strata reserved in Subei-Huangzhong uplift area and Sunan-Wunansha uplift area. The four tectonolayers are very different in structures as well as distributions in plane.

Study on Size Distributions of Airborne Particles by Aircraft Observation in Spring over Eastern Coastal Areas of China

The authors studied the size distributions of particles at an altitude of 2000 m by aircraft observation over eastern costal areas of China from Zhuhai, Guangdong to Dalian, Liaoning (0.47-30 μm, 57 channels, including number concentration distribution, surface area concentration distribution and mass concentration distribution). In these cities, the average daily concentrations of PM10 are very high. They are among the most heavily polluted cities in China. The main pollution sources are anthropogenic activities such as wood, coal and oil burning. The observed size distributions show a broad spectrum and unique multi-peak characteristics, indicating no significant impacts of individual sources from urban areas. These results are far different from the distribution type at ground level. It may reflect the comprehensive effect of the regional pollution characteristics. Monitoring results over big cities could to some extent reflect their pollution characteristics.