Exact finite element formulation of quasi-3D beam based on analytical internal force fields for accurate static analysis of FG sandwich beams

The burgeoning interest within the space community in digital beamforming is largely attributable to the superior flexibility that satellites with active antenna systems offer for a wide range of applications, notably in communication services. This paper delves into the analysis and practical implementation of a Digital Beamforming and Digital Down Conversion (DDC) chain, leveraging a high-speed Analog-to-Digital Converter (ADC) certified for space applications alongside a high-performance Field-Programmable Gate Array (FPGA). The proposed design strategy focuses on optimizing resource efficiency and minimizing power consumption by strategically sequencing the beamformer processor ahead of the complex down-conversion operation. This innovative approach entails the application of demodulation and low-pass filtering exclusively to the aggregated beam channel, culminating in a marked reduction in the requisite digital signal processing resources relative to traditional, more resource-intensive digital beamforming and DDC architectures. In the experimental validation, an evaluation board integrating a high-speed ADC and a FPGA was utilized. This setup facilitated the empirical validation of the design’s efficacy by applying various RF input signals to the digital beamforming receiver system. The ADC employed is capable of high-resolution signal processing, while the FPGA provides the necessary computational flexibility and speed for real-time digital signal processing tasks. The findings underscore the potential of this design to significantly enhance the efficiency and performance of digital beamforming systems in space applications.

This paper introduces an enhanced beam formulation for predicting the natural frequencies of thin-walled composite beam-type structures under initial loading. Each wall of the cross-section is idealized as a thin, symmetric, and balanced angle-ply laminate. The formulation is based on Hooke’s law and a geometrically nonlinear framework, taking into account restrained warping and large-rotation effects, respectively. Shear deformation effects are incorporated by applying the Timoshenko–Ehrenfest beam theory for bending and a modified Vlasov theory for nonuniform torsion. Coupling between transverse shear forces and warping-induced torsional moments arising from cross-sectional asymmetry is explicitly included. A consistent mass matrix, accounting for coupling between translational, rotational, and warping degrees of freedom, is derived using a kinetic-energy-based approach for the thin-walled beam element. Within the framework of Hamilton’s variational principle, the governing equations of the structure in global coordinates are formulated, and the associated eigenvalue problem is derived. The proposed formulation is validated through selected benchmark examples, demonstrating its effectiveness in predicting the natural frequencies of geometrically nonlinear, shear-deformable thin-walled beam and frame structures under initial loading.

Abstract This study presents a mixed finite element (MFE) formulation designed to efficiently determine the geometrically nonlinear behavior of laminated composite beams, ensuring rapid convergence and reduced computational cost. This is achieved by incorporating all 3D strain components into the constitutive equations while satisfying the beam theory stress-free surface conditions. Von Kármán nonlinear strains are derived from a displacement field including three displacements and three rotations per node. The governing equations, obtained from the first variation of the Hellinger–Reissner functional, are linearized via an incremental formulation and solved iteratively using the Newton–Raphson algorithm. The proposed MFE is based on Timoshenko beam theory and enhanced by the integration of the cross-sectional warping deformations over a displacement-based FE formulation. The two-noded MFE involves twelve degrees of freedom per node and achieves rapid convergence with substantially reduced computational cost. Its performance is assessed through comparison with advanced beam formulations featuring refined kinematics, as well as 3D solid element simulations for asymmetric [0°/90°] cross-ply laminated beams. It provides satisfactory convergent results via very few degrees of freedom compared to the 20-node brick finite elements and 4-node shell finite elements of ANSYS. Parametric studies discuss the influence of cross-ply lamination, material anisotropy, and geometric design on the ratio of geometrically nonlinear to linear displacements, highlighting the significance of design-induced nonlinearities in high-performance structural applications.

The mesh reference computational phantoms from International Commission on Radiological Protection (ICRP) Publication 145 have been implemented for the first time in the FLUKA Monte Carlo code. Conversion coefficients from particle fluence to effective doseEhave been calculated with these mesh phantoms for an extended set of particles and energies, covering all relevant exposure scenarios, including those at high-energy accelerator facilities. The conversion coefficients were calculated for theoretical exposure conditions in the form of broad monoenergetic particle beams for the six irradiation geometries anterior-posterior, posterior-anterior, right-lateral, left-lateral, rotational, and isotropic. The results are compared to those calculated with voxel phantoms from ICRP Publication 116 and with results recently computed with the Geant4 Monte Carlo code and the mesh phantoms for a smaller set of particles and energies. This work shows that the FLUKA code is capable of reliably calculating effective dose for a wide range of particles and energies with the most recent mesh phantoms of the ICRP. FLUKA is well suited to be used for effective dose calculations for the next general recommendations of the ICRP, expected for the early 2030s, in which mesh phantoms will supersede voxel phantoms.

ABSTRACT The evolution of 5G mmWave technology has significantly advanced wireless communication by enabling ultrafast data transmission and reduced latency. The integration of large‐scale multiple‐input multiple‐output (MIMO) systems has improved spectral efficiency and supported high user density for more robust and scalable network infrastructures. The interference and dynamic channel fluctuations present substantial obstacles in multicellular systems. User mobility complicates effective beam forming. In this manuscript, Performance Enhancement of 5G MIMO Antennas utilizing Verifiable Convolutional Neural Network optimized with Human Evolutionary Optimization Algorithm (5G‐MIMO‐VCNN‐HEOA) is proposed. The different antenna characteristics for the proposed antenna are analyzed by using optimization and parametric analysis through high frequency electromagnetic solver tool (Ansys HFSS). Then, the suppression of mutual coupling among MIMO antenna elements and enhancement of the isolation are achieved by using different techniques and the fabrication and verification of the proposed model by designing the prototype. Then, the Verifiable Convolutional Neural Network (VCNN) is used for design and development of MIMO Antenna in 5G applications. Human Evolutionary Optimization Algorithm (HEOA) is implemented for optimizing the VCNN hyper parameters. The proposed 5G‐MIMO‐VCNN‐HEOA accurately displays the outcomes of the design. The proposed technique is simulated, and efficiency is examined under several performance metrics like variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The proposed 5G‐MIMO‐VCNN‐HEOA approach attains 15.21%, 18.11%, and 16.22% lower MAE; 17.13%, 14.18%, and 14.25% lower MAPE; and 15.16%, 18.12%, and 21.23% lower MAE when compared with existing methods like broadband high gain performance MIMO antenna array in 5Gmm‐wave applications (BHG‐MIMO‐5G), compact and highly effective four‐port MIMO antenna directivity prediction for 5G mmwave applications (DP‐MIMO‐5G), and MIMO rectangular dielectric resonator antenna in 5G NR mmwave (MIMO‐RDRA‐5G), respectively.

By means of the variational method and numerical simulations, we demonstrate the existence of stable 3D nonlinear modes, viz. vortex "bullets," in the form of pulsed beams carrying orbital angular momentum, that can self-trap in a 2D waveguiding structure. Despite the attractive self-interaction, which is necessary for producing the bullets (bright solitons), and which readily leads to the collapse in the 3D setting as well as to spontaneous splitting of vortex modes, we find a critical value of the trapping depth securing the stabilization of the vortex bullets. We identify experimental conditions for the creation of these topological modes in the context of coherent optical and matter waves. Collisions between the bullets moving in the unconfined direction are found to be elastic. These findings contribute to the understanding of self-trapping in nonlinear multidimensional systems and suggest new possibilities for the stabilization and control of 3Dtopological solitons.

This paper describes recent engineering designs that allow full-duplex SerDes connectivity between a number of cascaded Xilinx radio frequency system-on-chip (RF-SoC) and VCU FPGA systems. The design allows for unlimited scalability with all-to-all connectivity across FPGA systems and RF-SoCs that allow for bidirectional data transport in streaming mode at a capacity of 50 Gbps per ADC-DAC channel. A custom massively parallel systolic-array architecture supporting 8 parallel data streams from time-interleaved ADC/DACs allow real-time matrix–vector-multiplication (MVM). The MVM can be 8 × 8, 8 × 16, …, 8 × 1024 in supported matrix size, and is demonstrated in real time sustained throughput of 1 TeraMAC/second, for matrix size 8 × 512. The MVM is the building block supporting machine learning and filtering, with the computational graph split across FPGA systems using the SerDes connections. The RF data processed by the FPGA chain can be further utilized for higher-level AI workloads on an NVIDIA DGX Spark platform connected to the system. We demonstrate two platforms in which ZCU111 and ZCU1285 RF-SoC boards perform direct-RF data acquisition, while compute engines operating in real time on VCU128 and VCU129 FPGA boards showcase both digital beamforming and polyphase FIR filterbanking in a real-time bandwidth of 1.0 GHz.

Abstract Porous orthotropic thin-walled I-beams (TWI-Bs) are commonly used in aerospace, civil, and mechanical applications due to their high stiffness-to-weight ratio. However, accurately predicting their lateral-torsional buckling (LTB) behavior remains challenging, especially under non-uniform loading and in the presence of material porosity and orthotropy. Classical beam theories often fail to capture essential deformation mechanisms, particularly shear deformation and warping effects, which become significant in porous and thin-walled configurations. This study develops a novel analytical model for evaluating the LTB response of porous orthotropic doubly symmetric I-beams subjected to non-uniformly distributed transverse loadings. To the author’s knowledge, this study is among the first to integrate an HSDT-based thin-walled beam formulation with porosity-dependent orthotropic constitutive modeling to the elastic lateral–torsional buckling of thin-walled I-beams under various non-uniform transverse loadings. Three trigonometric-based non-uniform porosity distribution patterns are considered. The solution is obtained using Galerkin’s method, and the model is validated against available benchmark solutions. The results reveal that neglecting higher-order shear deformation leads to a significant overestimation of critical buckling loads, especially for beams with high porosity or under non-uniform loading conditions. Among porosity patterns, NUDP1 yields the highest buckling resistance, whereas NUDP2 results in the lowest. The position of the applied load and its distribution type (e.g., TRL, TGL) substantially influence the LTB behavior, particularly in shear-sensitive configurations. Geometric parameters, such as flange and web thickness ratios and the orthotropy ratio, further interact with porosity and loading to affect buckling performance. These findings underscore the importance of incorporating advanced shear deformation models and realistic porosity distributions to ensure accurate LTB predictions and a robust structural design.

This paper develops a comprehensive nonlinear model for a smart composite string integrating ultra-thin piezoelectric sensors and actuators. The modeling framework is based on the geometrically nonlinear Timoshenko beam theory, incorporating von Karman strain terms to account for large-amplitude vibrations. The formulation couples mechanical and electro-mechanical fields to describe active control of the string’s vibrational response, enabling tuning of sustain and suppression of undesired overtones. Governing equations are derived through Hamilton’s principle, leading to coupled partial differential equations for transverse and axial motions. The proposed model provides a theoretical foundation for developing next-generation actively tunable string instruments. The model is implemented to evaluate the effects of pluck amplitude, pluck position, piezoelectric patch geometry, and electrical control parameters on tonal and dynamic performance. The tonal performance index (TPI), piezoelectric performance index (PPI), and acoustic output (SPL) are computed to quantify tonal richness, energy conversion efficiency, and control responsiveness. The results reveal that geometric nonlinearity leads to a measurable increase in both TPI (up to 4.5%) and PPI (up to 1.2%), while simultaneously reducing higher-harmonic energy by approximately 7%, indicating a non-trivial trade-off between tonal richness and harmonic complexity. Under active control, optimal performance is achieved for intermediate pluck positions (≈40% of string length) and moderate pluck amplitudes (≈10 mm), producing the highest TPI (~0.9) and PPI (~0.79) with sub-millisecond (ms) settling times.

The 5th and 6th (5G and 6G) generation wireless communications exploit large antenna arrays to serve a large number of users over large distances. In 6G sky communication, large antenna arrays will be used for communications with unmanned aerial vehicles (UAV), satellites and high altitude platforms (HAP) along with terrestrial infrastructures. The manuscript at hand dispenses an organized technical survey of the effects of mutual coupling in massive MIMO (mMIMO) (multiple input multiple output) systems, subsuming the effects on te direction-of-arrival (DoA) of the signals and digital beamforming, which substantiate the performance of the design of smart antenna (SA). The mutual coupling distorts the wave front of the incoming signal, resulting in an erroneous DoA estimation and majorly degrading other performances of the antenna array in an mMIMO system. An assortment of compensation techniques is elucidated since it is unfeasible to completely eliminate the mutual coupling. Further, some investigated results of isotropic antennas and dipole arrays are explicated, screening the mutual coupling effects. For practical antenna array design, compensation for the effect of mutual coupling is necessary, especially for densely populated arrays in an mMIMO system. Investigations on various methods of compensation of mutual coupling in antenna array design are surveyed.

Efficient Hardware and Software Co-Design of Adaptive Digital Beamforming With DoA Estimation in RFSoC for 5G Communication Systems

Digital and programmable metasurfaces provide novel approaches to connect electromagnetic (EM) physics with information science for flexible EM wave manipulation and information modulation. However, most recent studies on programmable metasurfaces have been focused on reflective phase coding and only provide limited regulation capability. To overcome these limitations, we propose a transmissive programmable metasurface with both amplitude and phase coding. The function of the metasurface can vary according to different control voltages. Typically, the metasurface achieves multiple-bit phase control and phase-amplitude joint control. To prove the unique features, we design and fabricate a prototype of the metasurface to verify the functions of vortex beam generation, hologram imaging, and beam forming. The measured results show good performance of the proposed metasurface, implying powerful EM manipulation capability and indicating a wide range of application potentials in imaging, data storage, wireless communications, and so on.

The aim of this study is to develop an accurate size-dependent model for the free vibration analysis of functionally graded (FG) porous microbeams. To achieve this, a quasi-3D shear beam formulation incorporating Chebyshev polynomial shape functions is proposed and coupled with the modified strain gradient theory. The third-order Chebyshev function ( p = 3 ) is adopted as it naturally satisfies zero transverse shear-stress conditions on the both beam surfaces. Material properties, including variable material length-scale parameters (MLSPs), are defined using the mixing rule and expressed as functions of the thickness, porosity coefficient and gradient index, enabling precise representation of material heterogeneity and porosity. Size-dependent governing equations are derived from Hamilton’s principle, and closed-form solutions for simply supported beams are obtained. Validation against existing results confirms the reliability of the proposed approach. A detailed parametric study demonstrates that the gradient index, porosity coefficient and especially the variation of the MLSP significantly influence the vibration response, highlighting the critical importance of accounting for MLSP variability in FG porous microbeam analysis.

The current toast packaging has not met customer satisfaction due to the use of inappropriate packaging materials and unattractive packaging design. Therefore, it is necessary to plan and develop packaging with Kansei Engineering (KE) that is able to translate consumer emotions. It is important to apply the use of elements that are in accordance with the concept. The method of determining the elements used is Rough Set Theory (RST). The result of the concept with the K-Means Genetic Algorithm method is “practical” while the elements produced by the RST method get a horizontal block shape with paper & polymer materials, window features, telescope locks, minimalist design, surface direct and images consisting of vector illustrations. The use of RST in this study proved to be optimal in realizing practical concepts, with supporting elements in the form of horizontal beams that have a support size value of 3 and a Laplace value of 0.887.

ABSTRACT Low Earth Orbit (LEO) satellite constellations must be able to communicate reliably, strongly, and with little energy use in order to meet 5G/6G performance goals. This paper introduces Self‐Healing Swarm Beamforming (SHSB), a new way to do distributed beamforming. SHSB lets adaptive digital beamforming happen across the whole constellation by using federated deep reinforcement learning (FDRL) and topology‐aware dynamic antenna arrays. SHSB creates a virtual massive multiple‐input multiple‐output (MIMO) array by treating satellites as a cooperative swarm. This increases signal‐to‐interference‐plus‐noise ratio (SINR), lowers energy use, and improves spatial coverage. Reconfigurable intelligent surfaces (RIS) make beam directivity even better at 28 GHz. In case of a satellite failure, a predictive self‐healing mechanism reallocates beams within five seconds. It uses graph neural networks (GNNs) to predict topology, which fills in gaps in the previous FDRL‐RIS methods that focused on energy but did not have resilient beamforming. SHSB reduces energy consumption by 25%, delivers SINR 18 dB at 16‐dB SNR, and achieves a spectral efficiency of 120 bits/s/Hz at 20‐dB SNR, according to MATLAB simulations. The feasibility is confirmed by theoretical limits on FDRL convergence and system stability. For next‐generation satellite networks, SHSB provides a scalable, autonomous, and high‐capacity solution with direct applications in disaster recovery, Internet of Things (IoT) connectivity, and international broadband services. This study proposes Self‐Healing Swarm Beamforming (SHSB), a distributed framework integrating federated deep reinforcement learning (FDRL), reconfigurable intelligent surfaces (RIS), and graph neural network (GNNs) for resilient Low Earth Orbit (LEO) satellite communications. This work enables predictive self‐healing with beam reallocation in less than 5 s upon satellite failure, ensuring constellation‐wide topology adaptation. This research forms virtual massive multiple‐input multiple‐output (MIMO) arrays via swarm coordination, achieving SINR >18 dB at 16‐dB SNR and spectral efficiency of 120 bits/s/Hz at 20‐dB SNR. This work demonstrates 25% energy reduction compared to centralized DBF baselines through MATLAB simulations with 100 Monte Carlo runs under Ricean fading. This study provides theoretical convergence bounds for FDRL and stability proofs, affirming scalability for 5G/6G applications in disaster recovery and Internet of Things (IoT).

Satellite communication has become a fundamental component of global information exchange. Advances in high-capacity geostationary satellites (HTS systems), clusters of low-orbitingsatellites (LEOconstellations), adaptive coding, digital beamforming, and AI-driven network management have created new possibilities for high-speed, low-latency communication systems. Misra et al. (2013) and Fourati and Alouini (2021) highlight how innovations in satellite architecture and modulation have significantly improved bandwidth efficiency. Meanwhile, integration with 5G and future 6G technology is reshaping global communication infrastructure, though challenges such as cybersecurity, regulation, and space debris remain serious concerns (Tarek et al., 2024; Abdelsalam et al., 2023). India’s experience demonstrates how satellite communication supports rural development, education, telemedicine, and national security (Manjunath et al., 2007; Annadurai, 2018). This report synthesizes findings from existing literature—including contributions by Ibim (2025), Saeed et al. (2021), Makam (2023), and Hashima et al. (2025)—to provide a comprehensive overview of current advancements, persistent challenges, and future prospects in satellite communication

Elevation Digital Beamforming (DBF) technology is key to achieving high-resolution wide-swath (HRWS) imaging in spaceborne Synthetic Aperture Radar (SAR) systems. However, multi-channel DBF-SAR systems face a prominent conflict between the need for real-time channel error calibration and the constraints of limited on-board hardware resources. To address this bottleneck, this paper proposes a real-time channel error calibration method based on Fast Fourier Transform (FFT) pulse compression and introduces a “calibration-operation” dual-mode control with a parameter-persistence architecture. This scheme decouples high-complexity computations by confining them to the system initialization phase, enabling on-board, real-time, closed-loop compensation for multi-channel signals with low resource overhead. Test results from a high-performance Field-Programmable Gate Array (FPGA) platform demonstrate that the system achieves high-precision compensation for inter-channel amplitude, phase, and time-delay errors. In the 4-channel system validation, the DBF synthesized signal-to-noise ratio (SNR) improved by 5.93 dB, reaching a final SNR of 44.26 dB. This performance approaches the theoretical ideal gain and significantly enhances the coherent integration gain of multi-channel signals. This research fully validates the feasibility of on-board, real-time calibration with low resource consumption, providing key technical support for the engineering robustness and efficient data processing of new-generation SAR systems.

The paper presents a systematic analysis of modern methods and algorithms for controlling the directivity pattern of Smart antennas used on moving objects in dynamic conditions. The theoretical foundations of adaptive directivity pattern formation, the principles of digital beamforming (Digital Beamforming), as well as the features of the functioning of the LMS, NLMS, RLS, MUSIC, ESPRIT, PSO and GA algorithms are considered. Their advantages, disadvantages and conditions for effective application are analyzed, taking into account the speed of adaptation, computational complexity, accuracy of estimating the direction of arrival of signals (DOA) and resistance to interference. The study found that LMS/NLMS class algorithms provide simple implementation and high speed, but are limited in accuracy under conditions of intense interference. RLS methods are characterized by improved convergence, but require significant computational resources. The MUSIC algorithm demonstrates high resolution in determining the direction of arrival of signals, but is not suitable for real-time use due to the complexity of spectral analysis. Stochastic and evolutionary algorithms (PSO, GA) have shown potential for optimizing the phase and amplitude parameters of antenna elements, but have a low convergence rate under variable conditions. The results of the analytical review indicate the relevance of the task of creating combined and adaptive approaches that combine high accuracy of assessment with operational response to environmental changes. The conclusions obtained can be used as a theoretical basis for further research in the direction of developing new methods for controlling the directivity pattern of Smart antennas on moving objects, in particular using machine learning and intelligent optimization.

The paper presents a systematic analysis of modern methods and algorithms for controlling the directivity pattern of Smart antennas used on moving objects in dynamic conditions. The theoretical foundations of adaptive directivity pattern formation, the principles of digital beamforming (Digital Beamforming), as well as the features of the functioning of the LMS, NLMS, RLS, MUSIC, ESPRIT, PSO and GA algorithms are considered. Their advantages, disadvantages and conditions for effective application are analyzed, taking into account the speed of adaptation, computational complexity, accuracy of estimating the direction of arrival of signals (DOA) and resistance to interference. The study found that LMS/NLMS class algorithms provide simple implementation and high speed, but are limited in accuracy under conditions of intense interference. RLS methods are characterized by improved convergence, but require significant computational resources. The MUSIC algorithm demonstrates high resolution in determining the direction of arrival of signals, but is not suitable for real-time use due to the complexity of spectral analysis. Stochastic and evolutionary algorithms (PSO, GA) have shown potential for optimizing the phase and amplitude parameters of antenna elements, but have a low convergence rate under variable conditions. The results of the analytical review indicate the relevance of the task of creating combined and adaptive approaches that combine high accuracy of assessment with operational response to environmental changes. The conclusions obtained can be used as a theoretical basis for further research in the direction of developing new methods for controlling the directivity pattern of Smart antennas on moving objects, in particular using machine learning and intelligent optimization.