Aerospace Defense

Abstract Pitting corrosion is one of the most destructive forms of localized corrosion in aerospace passive alloys such as steel and aluminum, and the presence of chloride ions accelerates the initiation and growth of this phenomenon.. The aim of this study is to synthesize a hybrid nanocomposite of reduced graphene oxide and a stable metal–organic framework with an eight-membered structure and to evaluate its performance in inhibiting pitting corrosion of steel through barrier and active inhibition mechanisms. The hybrid nanocomposite of reduced graphene oxide and the stable metal–organic framework with an eight-membered structure was synthesized via in situ growth on reduced graphene oxide sheets and characterized by Raman spectroscopy, X-ray diffraction, and field emission scanning electron microscopy. The corrosion resistance performance of reduced graphene oxide, the metal–organic framework, and the hybrid nanocomposite was evaluated in 3.5wt.% saline solution using electrochemical impedance spectroscopy and polarization measurements. Microstructural results indicated that the metal–organic framework nanoparticles with uniform polyhedral morphology grew on the reduced graphene oxide surface, forming a stable hybrid structure.Impedance tests showed that the hybrid nanocomposite provided the highest total resistance, reaching 4038 ohm.cm² after 35 hours of immersion. Moreover, polarization measurements demonstrated a significant reduction in corrosion current density and an increase in protection efficiency up to 83%. The superior performance of this system was attributed to the synergistic effect of the physical barrier of reduced graphene oxide and the controlled release of zinc ions and 2-methylimidazole from the metal–organic framework, simultaneously inhibiting anodic and cathodic reactions.

Abstract Load forecasting and energy management in self-sufficient buildings have become increasingly important due to the growing demand for sustainable, reliable, and resilient energy systems. This paper presents a comprehensive framework for forecasting and optimal energy scheduling of a building equipped with distributed generation resources capable of supplying its energy demand independently from the utility grid while maintaining the capability of power exchange with the upstream network when required. To improve system reliability and operational flexibility, a combination of renewable and non-renewable energy resources, including photovoltaic systems, wind turbines, microturbines, combined heat and power (CHP) units, boilers, and electrical and thermal energy storage systems, is considered.To achieve more accurate energy planning, uncertainties associated with electrical load demand, wind speed, and solar irradiance are modeled using Monte Carlo simulation, Weibull probability distribution, and Beta probability distribution, respectively. The energy management problem is formulated as a multi-objective optimization model with the simultaneous objectives of minimizing operating costs and environmental emissions while satisfying technical and operational constraints.The main contribution of this study is the development of an integrated framework that simultaneously considers renewable generation uncertainties, grid-connected and islanded operating modes, and the implementation of a high step-up switching power converter based on an enhanced SEPIC topology. The proposed converter offers high voltage gain, reduced component count, lower current stress on switches and diodes, improved efficiency, and enhanced reliability, thereby supporting passive defense requirements and increasing the resilience of the building energy system.

Abstract This paper examines an audio signal encoding method based on the Grachberg-Saxton phase recovery algorithm in the fractional Fourier transform domain and presents a hybrid model for providing security and data hiding in information channels. This algorithm first encodes the original audio signal into an image format and encrypts it.
This algorithm first encodes the original audio signal into an image format and encrypts it. Subsequently, the G-S phase recovery algorithm in the fractional Fourier domain is used to extract the phase information of the audio signal. Accordingly, the original audio phase is replaced with a reference phase representing the hidden information to achieve information embedding.
This technology includes the ability to generate two different types of keys according to the encryption requirements, which increases the difficulty of decryption and ensures information security.
Finally, the encrypted image can be decrypted and restored to the original audio signal. This scheme realizes the effective embedding and extraction of hidden information in audio signals through the replacement and recovery of phase information. In this scheme, information security has been significantly increased by about 98%

Abstract Accurate ultraviolet (UV) photodetection is a critical issue in the development of optoelectronic technologies. Conventional semiconductor materials commonly used in UV photodetectors suffer from limitations in terms of response speed, cost, and fabrication complexity. In contrast, UV photodetectors based on quantum dots (QDs), owing to their unique optical and electronic properties, can potentially overcome these limitations. Nevertheless, the synthesis of these nanoparticles is typically based on trial-and-error approaches. In this study, using machine learning models and information extracted from the scientific literature, we investigate and predict the most influential variables governing the synthesis of gallium nitride (GaN) QDs applicable to UV photodetectors. The results obtained from the AdaBoost algorithm indicate that the Ga/N molar ratio, the aluminum-to-metal flux ratio, time, and growth temperature are the most important variables affecting the bandgap of GaN QDs. Moreover, the AdaBoost algorithm is employed to predict the optimal values of the key reaction variables within desired GaN QD bandgap ranges.

Abstract Silicene is a two-dimensional form of silicon with a hexagonal honeycomb structure similar to graphene (a two-dimensional form of carbon). The widespread applications of graphene in the manufacture of lightweight and durable composites, protective coatings, and advanced electronic systems, due to its outstanding electronic and structural behavior in the aerospace industry, have paved the way for scientific research to investigate the behavior of materials with a two-dimensional structure similar to graphene, such as silicene. With the increasing importance of two-dimensional structured materials in advanced technologies, understanding their electronic behavior through quantum calculations such as the Tight-Binding (TB) method is essential. This research focuses on refining the TB method parameters and calculating the band structure of single-layer silicene along the high-symmetry paths K-Γ-M-K. A TB-based Hamiltonian for single-layer silicene was developed using MATLAB coding. Our refined calculations reveal a key finding: at the Γ point, the fifth band energy 1.705 eV lies below the fourth band energy 2.013 eV. This band ordering presents a significant deviation from ab-initio method results, which report an inverse trend, highlighting the sensitivity of TB method predictions to parameterization. After resolving this mismatch, the π and π* bands linearly intersect at the Fermi level, a characteristic crucial for Dirac cone formation and high-frequency electronic applications. These findings provide a more accurate computational model for silicene, essential for its effective integration into advanced military and aerospace electronic systems.