Browse Topic: Telecommunications
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
Graphene materials have recently been shown to produce measurable propulsion when exposed to light, raising the possibility of using them to enhance solar sail performance for satellite deorbiting. The pendulum-based experiments on graphene samples under controlled laser illumination inside a vacuum chamber and a quantitative model that describes the propulsion force as a function of laser power, illuminated area, incident angle, and ambient pressure were carried out. The measured pressure dependence and its shift with sample characteristic size point to Knudsen forces as the dominant mechanism. Orbital dynamics simulations incorporating this model show that a graphene-based sail can deorbit a 100 kg satellite from 660 km altitude roughly 21% faster than a conventional membrane sail of the same area, because the graphene configuration combines atmospheric drag with light-induced propulsion in a hybrid decay process.
To satisfy the real-time beam tracking requirement of low earth orbit (LEO) satellite ground terminals under highly dynamic pass conditions, this paper proposes a three-state hierarchical state-machine beam-control algorithm for liquid crystal phased array antennas (LCPAs). The tracking process is divided into three stages. S1 coarse nine-point acquisition is used to rapidly cover a wide pointing-uncertainty region. S2 fine nine-point refinement introduces quadratic-surface fitting to obtain local peak estimation at sub-step resolution. S3 predictive tracking adopts an interacting multiple model Kalman filter (IMM-KF) based on the constant-acceleration (CA) model and the Singer model, and designs an adaptive covariance regulation mechanism for both measurement and process noise. Combined with explicit delay compensation for liquid-crystal response time, the method suppresses pointing lag and error accumulation in high-dynamic intervals. A dual-threshold hysteretic loss-of-lock criterion and a filter-reset mechanism are also integrated to guarantee autonomous link recovery. Simulation results show that, in an LEO pass scenario with a maximum angular rate of about 2 °/s, the proposed method reduces the median 2D RMSE by about 52%, reduces the 90% quantile error by about 54%, and lowers the median average power loss from 0.14 dB to 0.03 dB compared with the traditional scheme.
To address the low computational efficiency problem by numerical calculation and large cumulative error by long-term sequential iterative prediction during the process of predicting the satellite’s transient temperature fields, this paper proposes a Physics-Informed U-Net (PI-Unet) model. Specifically, this PI-Unet model uses multi-branch spatiotemporal features as its inputs and combines hard constraints with a physics-informed loss to ensure the physical consistency of the predicted temperature field. Furthermore, for the error accumulation problem in long-sequence autoregressive prediction models, this paper presents a model training method based on the concept of filtering. Under the guidance of this method, the PI-Unet model can develop a long-term sequence prediction capability while keeping the accuracy of spatial temperature field prediction, achieving high-precision spatiotemporal prediction. By actively injecting prediction errors through a scheduled sampling strategy, along with a multi-step rolling training strategy, the PI-Unet model not only learns the governing physical laws of the spatial distribution of the transient temperature field, but also acquires the filtering capability to mitigate the accumulation of the long-term sequence rolling prediction errors. The experimental results indicate that the PI-Unet model maintains the maximum absolute error of predicting spatial transient temperature field stably below 5.5K and the error of the rolling-predicted transient temperature field in one orbit period less than 6K. In addition, the time for a single-step prediction is reduced to 0.005s, and the time for a total-period prediction is less than 0.5s. Therefore, this research provides a strong and high-efficiency technical support for the real-time monitoring of the satellite’s on-orbit temperature fields.
The rapid evolution of satellite communication systems enables direct network access for terrestrial users via portable devices, but also introduces critical security challenges in access control. In particular, identity hijacking attacks allow malicious users to exploit legitimate access channels, leading to inefficient occupation of scarce satellite resources. Existing approaches mainly depend on trust evaluation or heuristic-based decision strategies, which are inadequate for adapting to dynamic environments. To address this issue, this paper proposes a trust-aware deep reinforcement learning (DRL) framework for satellite access control, which jointly considers user behavior evaluation and decision optimization. Specifically, a Bayesian trust evaluation model is first developed to quantify the credibility of user behavior by incorporating historical interaction records as prior information. Unlike conventional static trust schemes, an adaptive mechanism is introduced to enhance sensitivity to behavioral dynamics. Building upon the trust assessment, the satellite access process is formulated as a Markov Decision Process (MDP), where system states integrate channel conditions, user trust levels, and service demands. A Deep Q-Network (DQN) is then employed to learn optimal admission and scheduling policies that maximize long-term system utility under resource constraints. Simulation results demonstrate that the proposed method significantly outperforms conventional greedy and threshold-based strategies. In particular, it effectively mitigates the impact of malicious behaviors and improves system utility by over 80%, while achieving more efficient resource utilization in dynamic environments.
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Modern space exploration relies more and more on vehicles that carry expansive antenna arrays, a trend driven by steady progress in the field of space engineering. The study centers on constructing a coupled model that accounts for structural rigidity, flexibility, and thermal effects in a sizable orbital unfurling parabolic antenna, along with designing its orientation regulation system. A blended coordinate formulation and a heat-based discretization approach are employed to derive the motion and temperature-governing equations of the assembly. A unified pointing control method is then developed by merging classical feedback control principles with torque compensation control. Computational experiments involving a spacecraft fitted with such a dish antenna are executed. The findings reveal that sudden heat fluctuations, exemplified by the transition out of Earth’s umbra, trigger oscillatory behavior in the antenna framework and disrupt the satellite’s angular position. The designed orientation regulator successfully counteracts these thermally driven perturbations, even when the system representation contains inaccuracies.
This SAE Aerospace Standard (AS) establishes guidelines for enhancement to IEEE 1394-2008 Beta (formerly IEEE 1394b) PHYsical (PHY) layer. It encompasses enhancements to the IEEE 1394-2008 Beta PHY to reduce port connection times and increase port connection reliability. Therefore, this document contains extensions/restrictions to “off-the-shelf” IEEE 1394 standards and assumes that the reader already has a working knowledge of IEEE 1394. The enhancements covered in this document include: Detect loss of descrambler synchronization Fast-ReTrain (FRT) Fast Power-on Re-connect (FPR) Fast Connection Tone Debounce (FTD) Programmable invalidCount Bus Reset Cause This document does not identify specific environmental requirements (electromagnetic compatibility, temperature, vibration, etc.); such requirements will be vehicle-specific and even LRU-specific. One should refer to the appropriate sections of MIL-STD-461E for their particular LRU and utilize handbooks such as MIL-HDBK-454A and MIL-HDBK-5400 for guidance. This document is referred to as a “slash sheet” and accompanies the AS5643B base standard.
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel ends. Traditional brake-force distribution strategies do not consider these pad-life differences, which can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO) in mixed-trailer operations [7, 9]. While modern electronically controlled braking systems (EBS) already incorporate pad wear based braking for the tractor itself [5], these capabilities do not extend across the entire vehicle combination because trailer-side communication is typically limited to standardized CAN protocols such as ISO 11992 and J1939 [1, 2, 3]. As braking systems become more software defined and rely heavily on distributed electronic communication, ensuring the authenticity and integrity of trailer originated brake information becomes essential for both functional safety and cybersecurity [6]. In the proposed architecture, trailers and dollies communicate brake related data to the tractor over the ISO 11992 Tractor-Trailer CAN (TT-CAN) network [1, 2], allowing the tractor Brake Control ECU to securely validate the source of the information and register each towed unit for health aware braking. Once authenticated pad life data is available, the tractor constructs a combination level brake health map covering every wheel end in the configuration. During normal braking, a supervisory allocator computes wheel end specific brake pressure targets that bias braking toward wheel ends with greater remaining pad life while ensuring full compliance with stopping distance regulations and stability requirements [4, 7]. By integrating authenticated pad wear information with tractor hosted supervisory control, the system improves braking consistency across mixed combinations, harmonizes pad utilization, enhances maintenance predictability, and reduces TCO while meeting the safety and cybersecurity expectations of modern commercial vehicle fleets.
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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