In this letter, a flexible 3-D phase adjusting unit (PAU) lens is designed and loaded in front of a conventional Vivaldi antenna which is able to reconfigure with three typical status called planar, inward and outward by physically bending. The distribution of energy and phase in the aperture of the antenna is therefore altered on demand for the enhancement of antenna gain and the elimination of radiation pattern distortion. A Vivaldi antenna samples loaded with reconfigurable 3-D PAU in the aperture are designed for demonstration, and their performance are compared in the interested frequency range from 6 - 18 GHz. The measured data shows that the antenna gain of the sample with 3-D PAU lens is increased by ~ 1 - 2.5 dBi within the range of 11-18 GHz. In the higher frequency band from 13 - 18 GHz, its main lobe is not split or distorted due to the effective suppression of side lobe by ~ 1 - 5.5 dBi. So the practical working bandwidth is significantly extended by 71%, from approximately 6-13 GHz to 6-18 GHz, demonstrating the proposed method improve the antenna gain and stabilize the radiation pattern of Vivaldi antenna concurrently over a wide frequency band.

A quad-band circularly-polarized antenna, for applications of a global navigation satellite system (GNSS), 5G, and WIFI-6E, is designed, fabricated, and measured. The proposed antenna is formed by an L-shaped radiator, a rectangular frame ground with an L-shaped stub, and a rectangular strip at the opposite corner. The microstrip antenna can generate four frequency bands, covering WIFI-6E (5925–7125 MHz), 5G n77 (3300–4200 MHz), n78 (3300–3800 MHz), and the GNSS bands. This antenna generates right hand circular polarization (RHCP) waves in the low frequency band (0.95–2.11 GHz), covering GPS, BDS, GLONASS, and GALILEO applications. Moreover, an L-shaped aperture and three rectangular slits are cut on the ground to broaden the axial-ratio bandwidth at the upper band. A prototype is fabricated and measured to verify the performance of this design. It is shown that the agreement between the simulation and measurement is satisfactorily good. The measured −10 dB bandwidths for each band are 75.8% (0.95–2.11 GHz), 55.8% (3.05–5.39 GHz), 39.9% (5.84–8.19 GHz) and 10.3% (9.14–10.68 GHz), respectively. While the measured 3 dB axial-ratio bandwidths are 59.4% (1.16–2.14 GHz), 35.8% (3.23–4.64 GHz), 8.4% (5.70–6.20 GHz), and 2.6% (7.51–7.71 GHz), the measured gains are 4.56, 2.28, 4.26, and 4.30 dBi at 1.5 GHz, 3.8 GHz, 6 GHz, and 7.6 GHz, respectively.

Great attention has been paid to the propagation of electromagnetic (EM) waves across the sea surface due to its important applications. Most of the previous research, however, focus on the half-space model illustrating the deep sea environment. In this communication, by taking into account the presence of a seafloor, the EM fields generated above shallow sea by a submerged horizontal electric dipole have been investigated theoretically and experimentally with a three-layer model. A set of formulas for EM fields in air expressed by Sommerfeld integrals are derived with recursive propagation approach, and the fields are computed using a complementary numerical integration technique. The effects of frequency, sea depth, seafloor conductivity, and receiver height on the fields are discussed. An experiment was conducted on shallow sea, and the test results agree well with theoretical predication in the quasi-near range. A transmission distance over 3 km above the sea surface was realized with reasonable transmitting power, which shows the potential of our theoretical model for applications in shallow sea environment.

In this article, we propose a novel detection method for underwater moving targets by detecting their extremely low frequency (ELF) emissions with inductive sensors. The ELF field source of the targets is modeled by a horizontal electric dipole at distances more than several times of the targets’ length. The formulas for the fields produced in air are derived with a three-layer model (air, seawater and seafloor) and are evaluated with a complementary numerical integration technique. A proof of concept measurement is presented. The ELF emissions from a surface ship were detected by inductive electronic and magnetic sensors as the ship was leaving a harbor. ELF signals are of substantial strength and have typical characteristic of harmonic line spectrum, and the fundamental frequency has a direct relationship with the ship’s speed. Due to the high sensitivity and low noise level of our sensors, it is capable of resolving weak ELF signals at long distance. In our experiment, a detection distance of 1300 m from the surface ship above the sea surface was realized, which shows that this method would be an appealing complement to the usual acoustic detection and magnetic anomaly detection capability.

The magnetic induction (MI) communication is an emerging undersea communication method, but its practical application is restricted by its limited communication distance. In this paper, an air-to-undersea magnetic induction communication scheme based on relay transmission is proposed, which can not only increase the penetrating depth but also expand the radial distance, so as to obtain a large submerged coverage of magnetic induction communication. What's more, the undersea magnetic field distribution is used to define the submerged communication range which is used as a measurement for evaluating the performance of the proposed scheme. The numerical results demonstrate that the relay transmission can multiply the submerged depth and radial distance of MI communication by deploying multiple relay nodes in predetermined positions, thus significantly increase underwater coverage of the air-to-undersea MI communication.

In this paper, the Efficient Channel Attention (ECA) mechanism is incorporated at the terminal layer of the YOLOv10 backbone network to enhance the feature expression capability. In addition, Transformer is introduced into the C3 module in the feature extraction process to construct the C3TR module to replace the original C2F module as the deepening network extraction module. In this study, both the ECA mechanism and the self-attention mechanism of Transformer are thoroughly analyzed and integrated into YOLOv10. The C3TR module is used as an important part to deepen the effect of network extraction in backbone network feature extraction. The self-attention mechanism is used to model the long-distance dependency relationship, capture the global contextual information, make up for the limitation of the local sensory field, and enhance the feature expression capability. The ECA module is added to the end of the backbone to globally model the channels of the feature map, distribute channel weights more equitably, and enhance feature expression capability. Extensive experiments on the electrical equipment dataset have demonstrated the high accuracy of the method, with a mAP of 89.4% compared to the original model, representing an improvement of 3.2%. Additionally, the mAP@[0.5, 0.95] reaches 61.8%, which is 5.2% higher than that of the original model.