This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the actuator were designed and developed. A neural network based adaptive controller was implemented to control the non-linear nature of the SMA actuator. The actuator control modules were integrated into the solar sail attitude model with a quaternion PD controller that formed a cascade control. The feasibility and performance of the proposed actuator for attitude control were investigated and evaluated, showing that the actuator could generate 1.5 × 10⁻³ Nm torque which maneuvered a 1600 m² CubeSat based solar sail by 45° in 14 h. The results demonstrate that the proposed SMA bending actuator can be effectively integrated in attitude control for solar sailing under moderate external disturbances using an appropriate controller design, indicating the potential of a lighter solar sail for future missions.

The application of deep learning to the detection of gravitational wave (GW) signals has attracted much attention in recent years, and nearly all of the existing deep learning-based GW detection algorithms are based on the convolutional neural network (CNN), which can be used to identify the GW merger moment and produce responding triggers. However, current CNN-based models are not best optimized tools in modeling the causal logic of sequential data in the time dimension to incorporate the information of the inspiral, merger and ringdown phases of the GW waveform. To address this issue we propose a deep learning model designed based on the bidirectional gated recurrent unit (BiGRU), a variant of the recurrent neural network (RNN) that is sensitive to the sequential logic of GW data. We train the neural network and implement the GW detection task by using the data obtained during the first, second and third observation runs (O1/O2/O3) of the LIGO-VIGRO scientific collaboration. We find that our BiGRU model achieves a false alarm probability (FAP) of $4.88\times {10}^{-4}$ (corresponding false alarm rate is about 1 per 18.2 hours) and maintains a detection ratio of about 89.6%. We also construct a model ensemble containing two independently trained BiGRU models and find it does not trigger any false alarm events on our testing dataset. Meanwhile, our BiGRU model not only recovers all the reported GW events of O1-O2 runs, including the unique (as of today) confident (with observed electromagnetic counterparts) GW event originating from a binary neutron star (BNS) merger (GW170817), but also achieves a detection ratio of about 80% of the O3 run. By calculating and comparing the receiver operator characteristic (ROC) curves we demonstrate the superiority of the BiGRU model over traditional CNN-based models, and show that the BiGRU model, being optimized to deal with GW waveform that sequentially contains the inspiral, merger and ringdown phases, is a potentially powerful tool to study the GW data in the future.

In this paper, we present a four-layer tunable wideband electromagnetic shield based on cold plasma. It consists of two dielectric matched layers, one backside housing dielectric layer and one vacuum chamber in between where cold plasma can be produced through a glow discharge. No plasma is produced within the vacuum chamber when low-power communication signal impinges on the shield and it can easily propagate through with very low return and absorption loss. When a high-power microwave pulse impinges on the shield, cold plasma can be timely produced within the chamber to reflect and absorb the high-intensity microwave energy. Input impedance, standing wave ratio, transmission and reflection coefficients and shielding effectiveness for proposed four-layer structure electromagnetic shield are deduced and formulated. Transparent mode of the electromagnetic shield for communication frequency at 3.5GHz is developed based on transmission line and plane wave theory. Simulation results show that the transmission coefficient is close to unity, and the reflection coefficient is near to zero. The half-power transmission bandwidth is 16% for proposed structure with central communication frequency of 3.5GHz. The impact of plasma frequency and plasma collision frequency on the absorption, reflection, shielding effectiveness for interference frequency between 0-6GHz are investigated. Simulation results show that higher the plasma frequency, better the shielding effectiveness is in the investigated frequency band when plasma collision frequency is fixed. It was also demonstrated that the shielding effectiveness of proposed structure can be tuned by changing plasma collision frequency.

We investigate the dynamical stability of circular orbits around a Kerr black hole embedded in a Dehnen-type dark matter halo. The effective spacetime metric of the combined system is constructed using the Newman–Janis algorithm, and the effective potential for test-particle motion in the equatorial plane is derived. The stability of circular orbits is analyzed through the Hessian matrix of the effective potential, while the stability strength and restoring-force distribution are employed to quantify the orbital response to small perturbations. Our results show that the presence of the dark matter halo significantly alters the spatial structure of stable circular orbits, leading to non-continuous stable regions whose location and extent depend sensitively on the halo’s characteristic density, scale radius, and the black hole spin. The innermost stable circular orbit (ISCO) is shifted relative to the vacuum Kerr case, with its position determined by the combined effects of the spin and halo parameters. Two-dimensional heatmaps, parameter scans, and three-dimensional visualizations systematically illustrate how the black hole spin and dark matter halo properties influence the ISCO and the distribution of stable orbits. Finally, we analyze the influence of the dark matter halo on the structure of the black hole event horizon. These results provide a detailed theoretical investigation of orbital dynamics around rotating black holes in dark-matter-rich environments.

The popularity of smart phones has brought significant convenience to people’s lives, but also there are many security problems. In recent years, malicious applications are increasingly rampant, which threaten users and society as security challenges to network reliability and management. However, due to neglecting the sequential features between network flows, existing malicious application recognition methods based on network traffic analysis have low recognition accuracy. Based on the network traffic characteristics of Android applications, this paper firstly applies Long Short-Term Memory network-based variational Auto-Encoder to extract the sequential feature of the application running time. Then, we design the BP neural network for initial classification and connect the class vector output of the BP neural network with the original data. The output is fed into the cascade forest for further feature learning and classification. The integrated methods are easy to implement with data independency and efficiency. We conduct experiments to evaluate the proposed with Android malware dataset CICAndMal2017, with a 97.29% high accuracy, comparatively significant precision and recall rates when benchmarked against other methods.