Analysis of Background Ionospheric Effects on Geosynchronous SAR Imaging

Background ionospheric propagation effects are adverse to the performance of Geosynchronous Synthetic Aperture Radar (GEO SAR) system. This paper focuses on the background ionospheric phase advance, which can be modelled as a function of Slant Total Electron Content (STEC). The dispersive feature of the phase advance caused by the background ionosphere could be able to distort the GEO SAR range-imaging. Furthermore, for GEO SAR, the integration time is ultra-long and the coverage is ultra-large, thus temporal and spatial distributions of the background ionosphere have to be taken into account. The resultant ionospheric phase variations might decorrelate the azimuth signal and then lead to azimuth-imaging deteriorations. In this paper, the theoretical model of the background ionospheric effects on GEO SAR imaging is established and in-depth analyses are presented. Finally, theoretical analyses are validated by the signal-level simulation

A high-energy liquid-jet hammer with specially designed backward stroke end buffer structure

A high-energy liquid-jet hammer with specially designed backward stroke end buffer structure was investigated computationally. Computational Fluid Dynamics (CFD) with the technique of dynamic and sliding meshes method was employed in this study. Results indicated that each of the geometric parameter of the buffer structure had a significant effect on the backward impacting energy of the impact body and brought a maximum of 49.8 % of backward impacting energy reduction. Experimental tests based on the non-contact measuring method were conducted to verify the simulation results, by which the accuracy and reliability of this CFD simulation method was proved. In addition, the high-energy liquid-jet hammer worked well with the optimal parameters of the buffer structure in bench testing and reached high penetration rate in a drilled borehole

Induction of heartwood formation in young Indian sandalwood (Santalum album L.) by gas elicitors

Induction of heartwood formation in 6-year-old Indian sandalwood (Santalum album L.) trees by treatment with carbon dioxide, ethylene, nitrogen, and wounding was investigated. All treatments induced fragrant heartwood formation upward and downward from the drill hole. The amount of heartwood formed above and below the drill hole depended on the treatment in the order nitrogen>carbon dioxide>ethylene>wounding, whereas the radial extension proportion was, in order, nitrogen>carbon dioxide>ethylene=wounding. Based on the chemical analysis (GC–MS) and evaluation of the essential oil quality and heartwood properties, heartwood induced by carbon dioxide showed the maximum similarities to naturally formed heartwood, which included the same color, similar chemical composition, reasonable oil content, and quality essential oil, whereas ethylene, nitrogen, and wounding treatment showed fewer similarities to natural heartwood. The results suggest that carbon dioxide is a promising candidate gas elicitor for inducing heartwood formation in young S. album

Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic

Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a sufficient amount of encrypted malicious data with correct labels. When ML models are trained with low-quality training data, they suffer degraded performance. In this paper, we aim at addressing a real-world low-quality training dataset problem, namely, detecting encrypted malicious traffic generated by continuously evolving malware. We develop RAPIER that fully utilizes different distributions of normal and malicious traffic data in the feature space, where normal data is tightly distributed in a certain area and the malicious data is scattered over the entire feature space to augment training data for model training. RAPIER includes two pre-processing modules to convert traffic into feature vectors and correct label noises. We evaluate our system on two public datasets and one combined dataset. With 1000 samples and 45% noises from each dataset, our system achieves the F1 scores of 0.770, 0.776, and 0.855, respectively, achieving average improvements of 352.6%, 284.3%, and 214.9% over the existing methods, respectively. Furthermore, We evaluate RAPIER with a real-world dataset obtained from a security enterprise. RAPIER effectively achieves encrypted malicious traffic detection with the best F1 score of 0.773 and improves the F1 score of existing methods by an average of 272.5%

Optical properties of atmospheric fine particles near Beijing during the HOPE-J3A campaign

The optical properties and chemical composition of PM1.0 particles in a suburban environment (Huairou) near the megacity of Beijing were measured during the HOPE-J3A (Haze Observation Project Especially for Jing–Jin–Ji Area) field campaign. The campaign covered the period November 2014 to January 2015 during the winter coal heating season. The average values and standard deviations of the extinction, scattering, absorption coefficients, and the aerosol single scattering albedo (SSA) at λ = 470 nm during the measurement period were 201 ± 240, 164 ± 202, 37 ± 43 Mm−1, and 0.80 ± 0.08, respectively. The average values for the real and imaginary components of the effective complex refractive index (CRI) over the campaign were 1.40 ± 0.06 and 0.03 ± 0.02, while the average mass scattering and absorption efficiencies (MSEs and MAEs) of PM1.0 were 3.6 and 0.7 m2 g−1, respectively. Highly time-resolved air pollution episodes clearly show the dramatic evolution of the PM1.0 size distribution, extensive optical properties (extinction, scattering, and absorption coefficients), and intensive optical properties (SSA and CRI) during haze formation, development, and decline. Time periods were classified into three different pollution levels (clear, slightly polluted, and polluted) for further analysis. It was found that (1) the relative contributions of organic and inorganic species to observed aerosol composition changed significantly from clear to polluted days: the organic mass fraction decreased from 50 to 43 % while the proportion of sulfates, nitrates, and ammonium increased strongly from 34 to 44 %. (2) Chemical apportionment of extinction, calculated using the IMPROVE algorithm, tended to underestimate the extinction compared to measurements. Agreement with measurements was improved by modifying the parameters to account for enhanced absorption by elemental carbon (EC). Organic mass was the largest contributor (52 %) to the total extinction of PM1.0, while EC, despite its low mass concentration of ∼ 4 %, contributed about 17 % to extinction. When the air quality deteriorated, the contribution of nitrate aerosol increased significantly (from 15 % on clear days to 22 % on polluted days). (3) Under polluted conditions, the average MAEs of EC were up to 4 times as large as the reference MAE value for freshly generated black carbon (BC). The temporal pattern of MAE values was similar to that of the OC / EC ratio, suggesting that non-BC absorption from secondary organic aerosol also contributes to particle absorption