This letter proposes a dual-stopband frequency selective surface (FSS) with tightly spaced frequency response characteristics. The metal layer of this FSS structure is composed of a metal strip of a bow-type ring and a metal strip of a square ring type, which forms the center symmetric ring structure. This dual-band FSS can achieve reflection characteristics at 6.29 and 8.35 GHz, which belong to C-band and X-band, respectively. In addition, two resonant frequencies can be independently controlled for good flexibility. This structure has excellent polarization stability and angular stability. Furthermore, a prototype sample of the proposed structure in this letter is processed and produced, and the test results are highly consistent with the simulation structure after being measured in a microwave anechoic chamber.

In this letter, a miniaturized frequency selective rasorber (FSR) based on complementary bandpass frequency selective surface (FSS) for fifth generation applications is designed. It is formed by the superposition of tortuous annular resistive layer and complementary bandpass FSS layer. The tortuous annular resistive layer consists of a regular octagonal patch and two bent metal rings. Four lumped capacitors and eight chip resistors are loaded to achieve impedance matching and wave absorption. In the bandpass layer, a complementary Jerusalem cross structure is adopted to realize electromagnetic wave transmission and structural miniaturization. The proposed FSR exhibits A-T-A filtering characteristics. The center frequency of passband is located at 2.6 GHz and transmission rate reaches 87.5%. Under 80% absorption rate, low-frequency absorption band ranges from 1.64 to 2.36 GHz, and high-frequency absorption band ranges from 1.64 to 2.36 GHz. Finally, a 500 mm × 500 mm prototype is fabricated, and the measured results are highly consistent with the simulated results.

This letter studies the impact of network-coded multiple access (NCMA) on the outage performance of full-duplex cooperative non-orthogonal multiple access (NOMA). In particular, we propose a network-coded cooperative NOMA (NC-CNOMA) strategy, under which user devices utilize physical-layer network coding (PNC) to demodulate signals with greater probability. The exact analytical expression of the outage probability is derived to evaluate the performance of the proposed scheme. The provided simulation results validate our analysis and the proposed NC-CNOMA scheme outperforms the conventional cooperative NOMA with regard to outage performance.

This letter proposed a miniaturized frequency selective rasorber (FSR). This FSR consists of a conducting sheet layer with embedded dual-bow resonators and a bandpass frequency selective surface (FSS) layer with cross-ring slot. The structure exhibits absorption-transmission-absorption (A-T-A) characteristics which shows excellent transmission performance in 2.4GHz band. The unit size of structure is 0.113 $\lambda _{L}$ and the thickness is 0.071 $\lambda _{L}$ , where $\lambda _{L}$ is the lowest frequency of the absorption band satisfying 80% absorption rate. The simulation results show that the centre frequency of the transmission band is located at 2.42GHz with 0.24dB insertion loss. Under the condition of 80% absorption rate, the absorption bandwidth is 58.82% for low frequency absorption band, and 58.6% for high frequency absorption band. The concept of slope in mathematics is introduced into FSR for the first time to define steep drop characteristics. Finally, in order to verify the correctness of the analysis results, a prototype of FSR with 20×20 units is processed and measured, and the measured results are in good agreement with the simulation results.

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models.