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Model predictive control-based satellite docking control for on-orbit refueling mission
No full textpeer reviewedOn-orbit satellite refueling is an essential aspect of satellite operations, as it enables satellites to prolong their lifetime and improves their overall performance. One of the critical challenges in the docking phase of such a mission is the fuel sloshing disturbance, which can affect the accuracy and safety of the docking process. In this study, we propose a control strategy for the docking phase of a refueling mission, where the objective is to safely and efficiently refuel a stationary target satellite. We use a combination of model predictive control and linear quadratic gaussian control to address the fuel sloshing disturbance, which is modeled using a spherical pendulum. The effectiveness and feasibility of the proposed approach are evaluated through numerical simulations using the Zero-G Lab facilities of the University of Luxembourg. The results demonstrate that the proposed strategy is capable of achieving a safe and fuel-efficient docking trajectory in the presence of fuel sloshing disturbance
2.5D Inductive Intertwined Frequency Selective Surface for Band-Pass and High Miniaturization Applications
No full textpeer reviewedThis paper presents a detailed design, simulation, measurement, and validation of an Inductive Intertwined Frequency Selective Surface (IIFSS) employing a 2.5D configuration (2.5DIIFSS). The proposed unit cell achieves high miniaturization, with dimensions as small as 0.0096 λ 0 × 0.0096 λ 0 at 0.268 GHz, using 4 vias per unit cell. The compact design delivers a fractional bandwidth of 87% for the passband associated with the fundamental harmonic. Additionally, the unique 2.5D configuration introduces extra inductance and capacitance, effectively shifting higher harmonics to higher frequencies, thus maintaining a stable stop band beyond the fundamental harmonic. The angular stability analysis reveals minimal variation up to an incidence angle of 60° for both TE and TM modes across the fundamental harmonic. To elucidate the underlying physics, an equivalent circuit model was developed, accurately capturing the fundamental harmonic behavior of the structure. To further validate the design and demonstrate its scalability, a prototype was designed and fabricated for operation at 2.4 GHz, addressing the measurement challenges associated with the original 0.268 GHz design. This prototype was rigorously tested in a transmission regime, with measurement results showing good agreement with simulation data, thereby confirming the efficacy and practicality of the proposed design
Synergistic Design of Practical High-Capacity Satellite Communication: Leveraging FFT-Based Digital Beamforming
No full textpeer reviewedThe growing demand for high-capacity satellite communications, particularly in Medium Earth Orbit (MEO) and Low Earth Orbit (LEO) constellations, has made digital beamforming essential to enhance system performance by producing simultaneous beams. Among various techniques, Fast Fourier Transform (FFT)-based beamforming is favored for its power efficiency and effectiveness in terms of Signal-to-Interference Ratio (SIR) when the number of antennas matches the number of beams. However, to reduce costs and complexity in the RF-chain, the number of antennas is often reduced relative to the number of beams, compromising beam pattern orthogonality and degrading the SIR. This article investigates the combination of techniques to mitigate this degradation, including regular spaced triangular-lattice beam pattern and antennas, hexagonal subarray lattices, 4-color scheme, and tapering, all working synergistically to enhance the overall SIR. The proposed method employs regular hexagonal sampling grids, enabling the generation of triangular-lattice beam pattern using standard rectangular FFT-routines. By avoiding interpolation, this approach minimizes artifacts in beam pattern generation over wide fields of view and preserves the SIR, making it particularly suitable for satellite applications. The simulation results demonstrate that the proposed strategy, using a 16-point FFT-beamforming approach with only 100 antenna elements, significantly enhances the SIR by nearly 20 dB compared to the regular scenario
Partial semiorthogonal decompositions for quiver moduli
No full textpeer reviewedWe embed several copies of the derived category of a quiver and certain line bundles in the derived category of an associated moduli space of representations, giving the start of a semiorthogonal decomposition. This mirrors the semiorthogonal decompositions of moduli of vector bundles on curves. Our results are obtained with QUIVERTOOLS, an open-source package of tools for quiver representations, their moduli spaces and their geometrical properties
PocketQube picosatellites: survey of missions and technologies
No full textpeer reviewedThe proliferation of miniaturized spacecraft and reusable launchers have boosted access to space, enabling the development of satellite platforms even by small companies and universities. Among miniaturized spacecraft, PocketQubes have gained significant prominence since their inception at Morehead State University in 2009. PocketQubes are modular picosatellites comprising 5 cm³ cubic units, weighing up to 250 g per unit. This paper offers a comprehensive review of both past and current PocketQube missions and associated technologies, with a detailed analysis of the key aspects related to the utilization of PocketQubes in space missions. Furthermore, the paper investigates current limitations and explores the enabling technologies for future PocketQube missions, providing insights into this novel and emerging field. Key findings of this work evidence that 1) no PocketQubes have ever been deployed beyond low Earth orbit, 2) the current mass range of PocketQubes lies within 125 to 850 g, 3) the most adopted form factors are the 2P and 3P, and 4) the main enabling technology for future PocketQubes is a propulsion system
Improving Adversarial Training for Two-player Competitive Games via Episodic Reward Engineering
No full textpeer reviewedIn recent years, training adversarial agents has become an effective and practical approach for attacking neural network policies. However, we observe that existing methods can be further enhanced by distinguishing between states leading to win or lose and encouraging the policy training by reward engineering to prioritize winning states. In this paper, we introduce a novel adversarial training method with reward engineering for two-player competitive games. Our method extracts the historical evaluations for states from historical experiences with an episodic memory, and then incorporating these evaluations into the rewards with our proposed reward revision method to improve the adversarial policy optimization. We evaluate our approach using two-player competitive games in MuJoCo simulation environments, demonstrating that our method establishes the most promising attack performance and defense difficulty against the victims among the existing adversarial policy training techniques. The source code is available at https://github.com/alsachai/episodic_reward_engineering
Multi-Objective Decomposition Evolutionary DRL for UAV-Assisted MEC in Internet of Vehicles
No full textpeer reviewedDynamic multi-objective optimization in Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) for Internet of Vehicles (IoV) faces significant challenges, due to complex operational environments and conflicting objectives. While Deep Reinforcement Learning (DRL) enables real-time optimization, conventional weighted-sum approaches fail to balance these objectives effectively. To address this, we propose a Multi-Objective Decomposition Evolutionary DRL (MODE-DRL) framework, which include the following three innovative aspects. Firstly, a multi-objective optimization model is developed, aiming to minimize delay and energy consumption while maximizing the number of completed tasks, thus ensuring overall network performance. Secondly, a novel MODE strategy that dynamically associates weight vectors with learning agents to optimize policy distribution and enhance population diversity. Lastly, two integrated algorithms, called MODE with Proximal Policy Optimization (MODE-PPO) and MODE with Deep Deterministic Policy Gradient (MODE-DDPG), are developed to combine DRL's dynamic decision-making with MODE's global optimization capabilities, enabling agents to rapidly adapt strategies based on different weights. Experimental results demonstrate that the MODE-DRL achieves a 33.2% improvement in hypervolume, along with a 16.3% reduction in average delay, a 15.5% decrease in average energy consumption, and a 34.4% increase in average number of completed tasks. These results confirm that MODE-DRL exhibits significant advantages in both convergence and diversity, while enhancing overall network performance. This work provides a scalable paradigm for real-time multi-objective decision-making in UAV-assisted MEC for IoV systems
Hybrid Optimization for NOMA-Based Transmissive-RIS Mounted UAV Networks
No full textpeer reviewedIn this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted uncrewed aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system’s sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV’s location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV’s location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks
Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization
No full textpeer reviewedThis letter studies the problem of hybrid holographic beamforming for sum-rate maximization in a reconfigurable holographic surface (RHS) assisted system. We establish the mathematical relationship between the mean squared error (MSE) and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE) criterion. Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights, resulting in linear complexity in terms of the RHS size, thereby ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights