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We report on the study of electron transport and band offset across β -Ga 2 O 3 /4H-SiC N–n isotype heterojunction. N-type β -Ga 2 O 3 of thickness 2.7 μ m was grown using low-pressure chemical vapor deposition using germanium (Ge) as the dopant on an n-type 4H-SiC substrate. The grown epilayer having (− 201) orientation was verified through XRD. Temperature-dependent I–V and C–V measurements were performed (50–300 K) to investigate the transport properties across the heterojunction. First, lateral diodes were fabricated on β -Ga 2 O 3 , and from C–V, n-doping was estimated to be 2.3 × 10 17 cm − 3 in the epilayer while the Schottky barrier height was estimated to be 1.75 eV. In top-down I–V sweeps, the reverse current across the heterojunction exhibited marginal dependence on temperature, indicating a possible tunnelling-based transport mechanism, while the forward current exhibited an exponential dependence on both temperature and the applied bias. The band diagram indicated the formation of a two-dimensional electron gas (2DEG) at the hetero-interface, which was indirectly confirmed using C–V measurement and TCAD simulation at low temperatures. From the position of the Fermi level in SiC and band diagram, a conduction band offset of 0.4–0.5 eV was estimated between β -Ga 2 O 3 and 4H-SiC. [ABSTRACT FROM AUTHOR]

The detection of harbors presents difficulties related to their diverse sizes, varying morphology and scattering, and complex backgrounds. To avoid the extraction of unstable geometric features, in this paper, we propose an unsupervised harbor detection method for polarimetric SAR images using context features and polarimetric reflection symmetry. First, the image is segmented into three region types, i.e., water low-scattering regions, strong-scattering urban regions, and other regions, based on a multi-region Markov random field (MRF) segmentation method. Second, by leveraging the fact that harbors are surrounded by water on one side and a large number of buildings on the other, the coastal narrow-band area is extracted from the low-scattering regions, and the harbor regions of interest (ROIs) are determined by extracting the strong-scattering regions from the narrow-band area. Finally, by using the scattering reflection asymmetry of harbor buildings, harbors are identified based on the global threshold segmentation of the horizontal, vertical, and circular co- and cross-polarization correlation powers of the extracted ROIs. The effectiveness of the proposed method was validated with experiments on RADARSAT-2 quad-polarization images of Zhanjiang, Fuzhou, Lingshui, and Dalian, China; San Francisco, USA; and Singapore. The proposed method had high detection rates and low false detection rates in the complex coastal environment scenarios studied, far outperforming the traditional spatial harbor detection method considered for comparison. [ABSTRACT FROM AUTHOR]

Proton exchange membrane electrolytic cell (PEMEC) is considered a promising way to produce green hydrogen due to its advantages of high current density, rapid start and stop, and high purity of hydrogen production. However, the existing macroscopic modeling of the PEMEC lacks the consideration of MPLs and the comprehensive study of heat and mass transport. In this paper, a one-dimensional two-phase non-isothermal PEMEC model with microporous layers is established, which accounts for a complete description of mass, charge, and heat transport. The polarization curves of numerical simulation are in good agreement with the experimental data. Then, the model is used to parametric analyze the effects of operating conditions and component parameters on heat and mass transfer, voltage loss, and efficiency for the PEMEC. The results show that operating pressure has a significant influence on the electrolytic performance. Voltage loss reduced of 30.75 mV and maximum temperature of 1.74 K occur when operating pressure increases from 1atm to 3atm, respectively. The characteristics of PTL and MPL mainly affect mass transport, and when PTL thickness and MPL thickness reduce by 100 μm and 20 μm respectively, the oxygen molar fraction reduces by 4.96% and 0.94%, respectively. The application of higher porosity, permeability, and thinner PTL with a thinner microporous layer (MPL) can effectually improve the operation efficiency of PEMEC. The model results presented in this study provide a theoretical basis for the optimization and design of the PEMEC. • A 1D two-phase, non-isothermal macroscopic model of a PEMEC with MPLs is presented. • The model is validated against experimental data from literature. • A parametric analysis of operating condition and component parameter are performed. • Operating condition of High voltage, high temperature and high operating pressure are desirable. • Requiring thin PTL with high porosity and low permeability is coated with thin MPL. [ABSTRACT FROM AUTHOR]

In recent years, the integration of polarimetric imaging into robotic perception systems has increased significantly, driven by the accessibility of affordable polarimetric sensors. This technology complements traditional color imaging by capturing and analyzing the polarization characteristics of light. This additional information provides robots with valuable insights into object shape, material composition, and other properties, ultimately enabling more robust manipulation tasks. This review aims to provide a comprehensive analysis of the principles behind polarimetric imaging and its diverse applications within the field of robotic perception. By exploiting the polarization state of light, polarimetric imaging offers promising solutions to three key challenges in robot vision: Surface segmentation; depth estimation through polarization patterns; and 3D reconstruction using polarimetric data. This review emphasizes the practical value of polarimetric imaging in robotics by demonstrating its effectiveness in addressing real-world challenges. We then explore potential applications of this technology not only within the core robotics field but also in related areas. Through a comparative analysis, our goal is to elucidate the strengths and limitations of polarimetric imaging techniques. This analysis will contribute to a deeper understanding of its broad applicability across various domains within and beyond robotics. [ABSTRACT FROM AUTHOR]

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms. [ABSTRACT FROM AUTHOR]

Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods—for example, clipping, pasting, and mixing—are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods. [ABSTRACT FROM AUTHOR]

An embedded series resonator for compact co‐polarized circular patch antenna pairs is presented in this paper. With a compact center spacing (CS) of 0.32λ0 and edge spacing (ES) of 0.03λ0 (λ0 is the free‐space wavelength at the center frequency), strong mutual coupling inevitably arises. However, by introducing a cross‐shaped fence and two quarter‐wavelength open‐ended slots onto a circular patch, port isolation of the first proposed antenna pair is significantly enhanced by 25.2 dB within a bandwidth of 6.2%. Moreover, the effectiveness of the embedded series resonator is demonstrated by achieving a wider 20‐dB decoupling bandwidth of 10.4% for the second proposed compact bandwidth‐enhanced antenna pair. For validation, both proposed prototypes are fabricated and measured, showing good agreement between measurements and simulations. To demonstrate the universality, a 1 × 8 patch array with an embedded series resonator can achieve a beam scanning angle of ±70◦ with a gain attenuation of 2.6 dB. Benefiting from compact size, high isolation, and enhanced decoupling bandwidth, the proposed designs hold promising potential for application in multiple‐input multiple‐output system and antenna array. [ABSTRACT FROM AUTHOR]

Nowadays, pervasive vision through synthetic aperture radar (SAR) imaging sensors is a crucial part of air-borne and space-borne platforms to reach the concept of internet of multimedia things over satellites and visual flying ad-hoc networks. SAR sensors always need high-performance data reconstruction techniques to provide quality of experience for end-users of the distributed surveillance nodes and ensuring reliable decision-making in the autonomous vehicles. Using prior information in SAR image reconstruction, improves the quality of reconstructed image. Many sparse regularization methods use pre information terms in which the image is sparsely presented based on a predefined dictionary. However, if the desired features of the real image have not a sparse representation based on the predefined dictionary, image reconstruction with enhanced interested features will be failed. Dictionary learning to better adapt with the underlying scene can lead to better image reconstruction. On the other hand, using the idea of dictionary learning in the image reconstruction problem, and considering that the data used to train the dictionary may be incomplete and noisy, will create limitations for this method. In this paper, a new idea based on the use of an overcomplete dictionary consisting of a learned dictionary and a predefined dictionary (pre-learned dictionary) for SAR image reconstruction problem is presented. Developing the necessary mathematical relations and providing the framework for SAR image reconstruction based on the use of the overcomplete pre-learned dictionary, to simultaneously enhance the features considered in advance and the features associated with the image, from incomplete and noisy SAR raw data is presented in this article. We also present an iterative algorithm for solving the corresponding optimization problem. Simulation results based on the real data of TerraSAR-X and the Lynx airborne system show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The present world pattern has undergone major changes, the trend of world multi-polarization is constantly emerging, and peace and development remain the main themes of today's society. The formal signing and implementation of the Regional Comprehensive Economic Partnership (RCEP) is important for meeting the needs of peace and development of many countries and regions. China is in a new era of reform and opening-up, and the RCEP content is in line with China's concept of pursuing a path of rejuvenation. Based on the locations of important borders, Yunnan Province has become one of the key places for China's opening-up and development regarding Southeast Asia and South Asia, and the high-quality development of its tourism service trade is of great significance. This study first used the grounded theory analysis method to organize, analyze and process the records of international conferences and government press conferences held in Yunnan Province, as well as important related issues since the signing of RCEP. Then, the social network analysis method was used to analyze the social recognition degree of RCEP in promoting the high-quality development of the tourism service trade in Yunnan Province from the perspectives of different levels and group consensus. The results show that cooperation is the key macro driving force for RCEP in promoting the high-quality development of the tourism service trade in Yunnan Province. Regarding the opportunities and environment of the transportation dividend, policy dividend and investment dividend, port construction, border township construction and exhibition-related construction are important cooperation contents, so they are conducive to the recovery of the tourism industry in a broader development space. Competition can improve the driving force of RCEP in promoting the high-quality development of the tourism service trade in Yunnan Province at the micro level, and has more practical significance. For example, RCEP can better rely on its own geographical advantages and international market competitiveness, optimize the use of resources inside and outside ports, and use high-tech products to form a more green, ecological, organic and sustainable development. In different levels of social identity, RCEP's power of cooperation is more strongly recognized, while the power of competition is less recognized. However, from the perspective of grouping the various groups, RCEP's powers of cooperation and competition are jointly recognized by the other groups, indicating that cooperation and competition are necessary for the realization of RCEP's power. The results of this study provide a certain reference for understanding the dynamics of RCEP in more detail from more angles and for the local implementation of RCEP rules and measures. [ABSTRACT FROM AUTHOR]

Modern small satellite development represents a new trend, a new design idea, and it can be used as a transmitter to assist helicopter monitoring. The imaging model of the arc array bistatic SAR with a small satellite transmitter is studied. Due to the long resident time of small satellite platform and the wide-area observation capability of arc antenna, it has a wide application prospect in the field of earth detection and remote sensing. However, the motion state of the small satellite and the special scanning mode of the arc antenna have some effects on the SAR imaging results. Therefore, the imaging geometry of the arc array bistatic SAR with a small satellite transmitter is established, and an improved Chirp Scaling imaging algorithm is proposed. Firstly, the motion compensation function is used to compensate the migration caused by the high-speed motion of the small satellite. Then, the two-dimensional spectrum is derived by using standing phase principle and scaling function. Next, the coupling between range and azimuth is compensated by consistent range migration correction and secondary range compression, and residual phase is compensated in azimuth frequency domain. Finally, simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camerabased victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm--Stacked Convolutional Neural Network--Bi--Long Short Term Memory--Gated Recurrent Unit (MOASConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purposebuilt robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster. [ABSTRACT FROM AUTHOR]

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input. [ABSTRACT FROM AUTHOR]

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones. [ABSTRACT FROM AUTHOR]

In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices. [ABSTRACT FROM AUTHOR]

Ship detection in synthetic aperture radar (SAR) images is widely applied in marine monitoring. In recent years, convolutional neural networks (CNNs) have made significant advances in SAR ship detection. However, the scene imbalance problem, i.e. much more offshore scenes than inshore scenes in SAR images, severely hampers the improvement of detection accuracy. In this article, we propose scene-aware data augmentation to address the problem. First, a novel concept, inshore rate, is introduced to estimate the land area percentage of various scenes in SAR images. We can divide image samples into inshore and offshore ones based on the inshore rate without manual category annotations. Second, to augment inshore samples, we synthesize dummy inshore images by cropping the ground truth bounding boxes (GT bboxes) from offshore images and pasting them onto inshore images. This offline data augmentation strategy can address the scene imbalance problem and improve detection accuracy. The experiments conducted on SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset (LS-SSDD) demonstrate the effectiveness of the proposed method in improving detection performance. [ABSTRACT FROM AUTHOR]

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas. [ABSTRACT FROM AUTHOR]

Accurate estimates of tropical cyclone (TC) intensity are important for improving forecasts as well as studying ocean dynamics during such extreme events. Since most cyclone life occurs over the open ocean, remote sensing techniques play an important role in obtaining the necessary data. The possibility of using the configuration of spiral signatures of mature tropical cyclones (TCs) observed in synthetic aperture radar (SAR) images to estimate the maximum wind speed of a TC is considered. This study assessed the intensity of 14 TCs in the Atlantic and Pacific Oceans using radar images obtained by the Radarsat Hurricane Application Project. TC intensity was estimated using the hyperbolic-logarithmic approximation of TC spiral signatures (HLS approximation). Additionally, the edges of the spiral signatures were partially fitted using a logarithmic spiral to improve the reliability of the HLS approximation. For the first time, a physical model of changing the crossing angle of the logarithmic portion of the edges was proposed and tested on SAR images of the TC. HLS maximum wind speed estimates were compared with Best Track estimates. The results showed the closeness of both estimates with a correlation of 0.95 and a standard deviation of 2.9 m s−1. The results indicate the possibility of using the HLS approximation to estimate the intensity of mature TCs from SAR data. [ABSTRACT FROM AUTHOR]

In this paper, a 3-bit circularly polarized reconfigurable reflectarray is proposed. The array consists of 64 units in an 8 × 8 configuration, with each unit containing a circular metal patch loaded with phase-delay lines and eight PIN diodes. To independently control each unit, a corresponding DC control circuit was designed and tested with the array. In the bandwidth of 3.43–3.71 GHz, the circularly polarized reconfigurable reflectarray achieved a gain of 16 dB, an aperture efficiency of 27%, an axial ratio of ≤3 dB, an operating bandwidth of 8%, and a beam scanning range of ±60°. The circularly polarized reconfigurable reflectarray can also achieve a good dual-beam radiation performance after testing. The 3-bit circularly polarized reconfigurable reflectarray proposed in this paper offers several advantages, including low loss, high aperture efficiency, a wide beam scanning range, and excellent stability in wide-angle oblique incidence. It has potential applications in low-cost phased array, satellite communications, and deep space exploration. [ABSTRACT FROM AUTHOR]

This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

The Canadian Arctic Archipelago (CAA) serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. We use observations from Sentinel-1, RADARSAT-2, the RADARSAT Constellation Mission (RCM), and CryoSat-2, together with the Canadian Ice Service ice charts, to quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage shipping route. Results indicate that the CAA primarily exports ice to the Arctic Ocean and Baffin Bay, with an average annual (October to September) ice area flux of 137 ± 72 × 10 3 km 2 and a volume flux of 58 ± 68 km 3. The CAA contributes a larger area but smaller volume of ice downstream to the North Atlantic than what is delivered via Nares Strait. The average annual ice area flux from the Queen Elizabeth Islands to Parry Channel was 27 ± 10 × 10 3 km 2 and the volume flux was 34 ± 12 km 3 , with a majority occurring through Byam Martin Channel, which is directly above the central region of Northwest Passage. Over our study period, annual multi-year ice (MYI) replenishment within the CAA was resilient, with an average of 14 ± 38 × 10 3 km 2 imported from the Arctic Ocean and an average of 56 ± 36 × 10 3 km 2 of first-year ice (FYI) retained following the melt season. The considerable ice flux to Parry Channel, together with sustained MYI replenishment, emphasizes the continued risk that sea ice poses to practical utilization of key shipping routes in the CAA, including the Northwest Passage. [ABSTRACT FROM AUTHOR]

Objection detection of vehicles and pedestrians in fog is of great significance for intelligent transportation and autonomous driving. Polarization image is beneficial to improve the object detection under adverse weather conditions. This study proposed a polarization image fusion method based on grouped convolutional attention network (GCAnet) to improve the object detection for cars and persons in foggy street scenes. Based on the international available Polar LITIS image dataset, a multi-channel grouped convolution matrix was first constructed to input different types of polarization images. Then, a grouped attention module was added to enhance the features in each type of polarization image, and finally each convolutional matrix was further connected to the detection network in series to perform objection detection. The experimental results prove that three types of polarization image fusion are obviously better than those of any two types of polarization image fusion and one single polarization image; and adding ECA attention module after multi-channel convolution can further enhance the accuracy of I04590 + Pauli + Stokes fused image to the highest value of 76.46%. The improvement of network lightweight shows that the Mobilenet-ECA has increased the speed by 26% with a slightly reduced accuracy. The proposed GCAnet method has significantly surpassed traditional objection detection networks of SSD300, SSD512, Faster R-CNN600, Yolov3, and Yolov4, which has increased the mAP@0.5 by 28.90%, 27.60%, 15.01%, 24.98%, and 16.45%, respectively; and has increased the mAP@0.5 by 9.36% and 6.20% compared to foggy image detection methods of AOD-Net SSD and DeRF-Yolov3-X, respectively. This work demonstrates the potential of GCAnet enabled polarization image fusion technology to be used as an effective foggy objection detection method in the field of intelligent transportation and autonomous driving. [ABSTRACT FROM AUTHOR]

Ship detection is a critical and challenging task in aerial images. Due to the special generation method of SAR images, they have unique characteristics. However, different from objects in natural images, ships in SAR images are often distributed with arbitrary orientations and dense distributions. Recently, key points-based anchor-free object detection algorithms have attracted the attention of quite a few researchers. To solve the task of oriented ship detection based on key points, in this paper, we propose an oriented object detection method based on coordinate system projection (CSProjection). In this work, we first detect the key point of the ship, namely, the center point, then establish a coordinate system with the object center point as the base point, and obtain a bounding box of the oriented object through the projection information of the object. Our method can effectively reduce the number of parameters applied to determine the oriented bounding box during training and decrease the network complexity. Experimental results on several SAR ship detection datasets, including SSDD, SRSDD-v1.0 and the optical remote sensing dataset HRSC2016, indicate that our method can compete with state-of-the-art algorithms for oriented ship detection, even those with more complex backbones. [ABSTRACT FROM AUTHOR]

This article aims to exploit Ricci tensor with certain geometric properties which are used for feature representation and ship detection in synthetic aperture radar (SAR) image. The proposed method is composed of the following key points. Firstly, Riemannian metrics on the Gamma manifold are constructed based on the family of Gamma density functions. Secondly, direct calculation gives the Ricci tensor of Gamma manifold, where the curvature tensor resorts to the torsion-free affine connection. Thirdly, a general scheme for Zermelo navigation problem on the Riemannian manifold is addressed, and the solution of the navigation problem is proposed. Fourthly, feature representation problems are formulated as certain forms of Finsler metric of Randers type, indicating a joint framework for extracting low-dimensional features with closed-form solutions. Comprehensive experiments on real SAR image data sets demonstrate the effectiveness of the proposed method against compared state-of-the-art detection approaches. [ABSTRACT FROM AUTHOR]

In this paper, a compact dual-polarized antenna with wide band and high isolation is proposed, which can be applied to the 5G WiFi frequency band. The antenna is composed of 2×2 arrayed patches and two orthogonal L-shaped probe structures with reduced middle patch width and loaded U-shaped slots. The proposed antenna achieves a compact size by eliminating the need for a complex feeding network, instead utilizing only two closely spaced L-shaped probes for feeding. The antenna's radiation modes excited by two ports are orthogonal in polarization direction, and each port can excite two linearly polarized radiation modes respectively within the operating frequency band, thereby achieving dual-linear polarization and wideband performance. The antenna is analyzed and designed using characteristic mode analysis (CMA). By reducing the patch middle width and loading U-shaped slots on the L-shaped probe of the antenna, the suppression of high-order modes, improvement of isolation, and reduction of cross-polarization levels are achieved. The size of the antenna is 0.54λ0 × 0.54λ0 × 0.068λ0 (λ0 is the free-space wavelength of central frequency). The measured bandwidth is 23.5% (4.73 GHz-5.99 GHz) with |S11| < -10 dB, |S21| < -27.9 dB, boresight gain of 4.8 dBi-6.2 dBi, and cross-polarization levels better than -23 dB. [ABSTRACT FROM AUTHOR]

A general strategy for designing bi‐layer reflection‐transmission integrated dual‐mode metasurfaces is presented with flexible bandwidth control. The design of such metasurfaces is enlightened by introducing a cutting hole in the ground plane, which not only acts as a filter for reflection and transmission but also has an eloquent role for bandwidth control. For proof of concept, a bi‐layer metasurface consisting of split ring resonators (SRRs) is designed on the top side and I‐shaped resonators on both sides. The proper adjustment of ground cut/hole shifts the two co‐polarized resonances (initial resonances of the SRRs) close to each other, and in addition, converts wideband cross‐polarized reflection (in between the two initial co‐polarized resonances of SRR) to co‐polarized reflection. This results in a broadband response by shifting two co‐polarized resonances close to each other and converting cross‐polarized reflection into co‐polarized reflection. It is demonstrated that a simple SRR can be responsible for broadband (7.9–12 GHz) reflection while an I‐shaped structure is used for transmission with central frequency at 17.58 GHz with independent phase controls. To endorse the proposed strategy, a metasurface prototype is fabricated and tested for the generation of vortex beams and focusing. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) interferometry (InSAR) is a well-established remote sensing technique capable of providing accurate topographic information of an area. Multiple scattering occurring at different heights within a single resolution cell, however, cannot be resolved using a single baseline and results in a degradation of the height accuracy. Techniques such as polarimetric InSAR and SAR tomography tackle this problem using additional measurements to obtain the three-dimensional structure of the volume. However, polarimetry-based methods assume orthogonal and deterministic scattering mechanisms and tomography requires numerous observations. This paper presents a novel approach to retrieving the three-dimensional structure of semitransparent media using wide fractional bandwidth InSAR. The frequency dependency of the InSAR coherence is exploited to retrieve the three-dimensional structure, assuming that signals with wide fractional bandwidth are available, as is the case for unmanned aerial vehicles (UAVs) or drones. As the extinction is, in general, frequency-dependent, a third observation may improve the inversion results. Simulations of different scenarios show that the frequency profile can be inverted to obtain information about the three-dimensional scattering structure. The acquired data also enable retrieval of the phase center depth as a function of frequency. This method allows for frequent and accurate monitoring of semitransparent targets, e.g., forests and vegetation, using a swarm of two or three drones. [ABSTRACT FROM AUTHOR]

In this paper, the mechanism of proton exchange membrane fuel cell (PEMFC) cathode cycling is discussed and the high potential suppression control problem for fuel cell vehicles is investigated by using cathode cycling. Firstly, a rigorous single-cell experimental investigation is performed to reveal the impact mechanism of cathode recirculation on the internal polarization processes of PEMFC. Results show that an increase in the recirculation ratio leads to an increase in the polarization impedance in the low and medium frequency regions but decreases the ohmic impedance. Consequently, when the recirculation ratio increases to 1, the voltage drop rates are 2.2%, 3%, 4.6%, 5.8%, and 7% as the current density varies from 0 to 0.2 A/cm2, respectively, and the ohmic impedance decreases by 28.1%. On the basis, the optimal control scheme for high potential suppression based on cathode circulation in vehicular PEMFC system is proposed. The performance comparison of different control method is carried out on a software-in-the-loop testing platform. The results show that, for the same control objective, the model predictive control (T-S MPC) method is 55.5% and 16.7% lower than the proportional integral (PI) controller and the Takagi-Sugeno linear quadratic Gaussian (T-S LQG) in terms of controller input cost. On the contrary, the cumulative error of voltage tracking is reduced by 62% and 30.4% and the voltage response against disturbances is improved by 9.7% and 42.8%. Overall, the T-S MPC achieves the optimal control effect with less control effort. • An insight into the mechanism of cathode recirculation affecting PEMFC performance. • Cathode recirculation reduces ORR reaction rate and oxygen diffusion rate. • The T-S MPC provides optimal closed-loop control of high potential suppression. [ABSTRACT FROM AUTHOR]

Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships. [ABSTRACT FROM AUTHOR]

We implement a plastic damage parametrization, distinct from the elastic damage in the elasto-brittle framework, in the standard viscous–plastic (VP) sea ice model to disentangle its effect from resolved model physics (visco-plastic with and without damage) on its ability to reproduce observed scaling laws of deformation. To this end, we compare scaling properties and multifractality of simulated divergence and shear strain rate, as proposed in the Sea Ice Rheology Experiment (SIREx) studies, with those derived from the RADARSAT Geophysical Processor System (RGPS). Results show that including a plastic damage parametrization in the standard viscous–plastic model increases the spatial but decreases the temporal localization of simulated linear kinematic features (LKFs) and brings all spatial deformation rate statistics in line with observations from RGPS without the need to increase the mechanical shear strength of sea ice as recently proposed for lower-resolution viscous–plastic sea ice models. In fact, including damage with a healing timescale of th=30 d and an increased mechanical strength unveils multifractal behavior that does not fit the theory. Therefore, a plastic damage parametrization is a powerful tuning knob affecting the deformation statistics of viscous–plastic sea ice. [ABSTRACT FROM AUTHOR]

Reflective polarization conversions with a simplistic design of an ultrathin, single-layered, and multi-functional anisotropic metasurface as a polarization converter is utilized for Ku- and Kband applications. The designs with two substrate thicknesses (0.095 λ0 and 0.069 λ0, respectively) are capable of a cross-polarization converter (CPC) and a linear-to-circular (LTC) polarization conversion. The design with 0.095 λ0 thickness achieves a CPC between 17.96 and 26.90GHz with the efficiency of more than 90% and a relative bandwidth of 40% under normal incidence. It maintains angular stability by altering the oblique incidence angles up to 300 with greater than 80% of the PCR in the K-band. Meanwhile, an LTC in two frequency bands, 10.30-10.53GHz and 28.65-29.70GHz, is also numerically demonstrated. The second design with 0.069 λ0 thickness provides a CPC above the PCR value of 87% in the frequency range from 10.46-23.05GHz (covering the entire Ku- and part of the K-band) with angular stability of 40 above the PCR value of 80%. In the meantime, an LTC with relative bandwidth of 75% in the frequency range from 9.53-9.79&24.74-25.27GHz is numerically revealed. These polarization converters exhibit relatively good performances of facile structure and multi-functional properties, which can be useful in Ku- and K-band applications. [ABSTRACT FROM AUTHOR]

This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. [ABSTRACT FROM AUTHOR]

Deep learning methods have greatly promoted the ship detection performance in synthetic aperture radar (SAR) images. However, due to the different imaging mechanisms and target characteristics, detecting ships in SAR imagery precisely and efficiently is still challenging. To address this issue, a streamlined rotational ship detection network (SRSD-Net) is proposed in this letter. First, we construct a lightweight network using one-level feature and a receptive field block (RFB) to extract ship features with rich context. Second, a simple and effective representation for rotated ship bounding boxes using the minimum external horizontal box and two phase factors is proposed. Finally, to reduce the missed detections of small ships, k-nearest label assignment (KNLA) is designed to make all targets equally contribute to the network training. Extensive experiments are conducted on high-resolution SAR images dataset (HRSID) and the results show that the proposed method can achieve high detection accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

Among the existing circularly polarized multibeam transmitarray antennas, circularly or linearly-tocircularly polarized transmitarray units are always required. Naturally, designing circularly polarized units is more challenging than linear polarization. To simplify design and increase operation bandwidth, a novel method using linearly polarized transmitarray units and feeding sources is proposed for designing the circularly polarized multi-beam antenna. Broadband circular polarization is realized by utilizing sequential rotation technology and 1-bit phase compensation of linearly polarized transmitarray units. Meanwhile, beam scanning is achieved by using linearly polarized feeding sources offset. To validate the design, two multi-beam antenna samples are demonstrated. Simulated and measured results show that the designed multi-beam transmitarray antennas can realize beam scanning to 0°, ±10°, and ±20° in E-plane and H-plane. Moreover, two antennas maintain –10-dB impedance bandwidth and 3-dB axial ratio (AR) bandwidth at 8.5–10.4 GHz and 8.5–10.5 GHz, respectively. The proposed circularly polarized multi-beam transmitarray antennas have the advantages of broadband operation, simple design, and low cost. [ABSTRACT FROM AUTHOR]

Urban floods can affect people's lives and properties, and therefore, urban flood mapping is crucial for reliable risk assessment and the development of effective mitigation strategies. With the advent of high spatial and temporal resolution satellite images, remote sensing has become popular for urban flood mapping. Synthetic aperture RADAR (SAR) sensors can capture image data during a flood event because their emitted signal can penetrate through the clouds. However, they have some limitations, such as layover, shadowing, and speckle noise, that might challenge their usage, especially for urban flood mapping. Deep learning (DL) algorithms have been widely used for automatic urban flood mapping using remote sensing data, but the flood mapping accuracy achieved using SAR and DL algorithms is still uncertain. This paper proposes a DL-based change detection framework, convolutional Siamese network (CSN), for flood mapping in three urban areas: parts of Ottawa, ON and Gatineau, QC, Abbotsford, BC, and Leverkusen, Germany. The datasets applied were Sentinel-1 and dual-polarized RADARSAT Constellation Mission (RCM) data. The applied data were captured in C-band, and their resolutions were 10 m and 5 m for Sentinel-1 and RCM, respectively. Comparison with other DL-based segmentation algorithms, including Unet, Unet++, DeepLabV3+, and Siamese-Unet, confirmed the reliability of the proposed CSN. Although a promising flood recall rate of about 0.7 was achieved, it was inferred from the flood precision and F1 score that Sentinel-1 data medium resolution might hinder its application for urban flood mapping. Further, RCM data were also tested in both urban and non-urban areas, and a precision of 0.79 was achieved for the non-urban case. Experiments on two existing datasets, SEN12FLOOD and SEN1FLOOD11, showed that the proposed CSN achieved a higher precision index of 0.75 on SEN12-FLOOD than SEN1FLOOD11 dataset, with a precision of 0.2, because of different labeling formats between the two datasets. [ABSTRACT FROM AUTHOR]

There are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7–4.9% based on different backbone networks, and the detection performance is better than other advanced algorithms. [ABSTRACT FROM AUTHOR]

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users' behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption. [ABSTRACT FROM AUTHOR]

Aircraft detection in SAR images of airports remains crucial for continuous ground observation and aviation transportation scheduling in all weather conditions, but low resolution and complex scenes pose unique challenges. Existing methods struggle with accuracy, overlapping detections, and missed targets. We propose You Only Look Once-SAR Aircraft Detector (YOLO-SAD), a novel detector that tackles these issues. YOLO-SAD leverages the Attention-Efficient Layer Aggregation Network-Head (A-ELAN-H) module to prioritize essential features for improved accuracy. Additionally, the SAR Aircraft Detection-Feature Pyramid Network (SAD-FPN) optimizes multi-scale feature fusion, boosting detection speed. Finally, Enhanced Non-Maximum Suppression (EH-NMS) eliminates overlapping detections. On the SAR Aircraft Detection Dataset (SADD), YOLO-SAD achieved 91.9% AP(0.5) and 57.1% AP(0.5:0.95), surpassing the baseline by 2.1% and 1.9%, respectively. Extensive comparisons on SADD further demonstrate YOLO-SAD's superiority over five state-of-the-art methods in both AP(0.5) and AP(0.5:0.95). The outcomes of further comparative experiments on the SAR-AIRcraft-1.0 dataset confirm the robust generalization capability of YOLO-SAD, demonstrating its potential use in aircraft detection with SAR. [ABSTRACT FROM AUTHOR]

A dual-band, dual-polarized frequency selective surface (FSS)-backed multilayer reflectarray antenna is designed for 5G high-speed satellites operating at Ka-band uplink and downlink frequencies (20/30 GHz). A reflectarray antenna system consists of two reflectarrays that are separated from each other by an FSS layer that behaves as a planar bandpass filter at Ka-band satellite uplink frequencies. Each reflectarray antenna is designed with dual-polarized unit cells. In order to achieve a uniform phase distribution across the reflectarray surface, physical dimensions and positions of the unit cells with a fixed periodicity are carefully chosen. The FSS conductor is etched to the bottom layer of the 30 GHz reflectarray substrate to save cost and weight. The reflectarray performance is analyzed by using CST Microwave Studio and array theory. A prototype is fabricated, and the results are experimentally verified. The gain of the reflectarray is measured as 21.13 dBi and 26.94 dBi at 20 and 30 GHz, respectively. A crosspol level of more than 35 dB is observed at both frequencies. The simulated and measured results show that the proposed reflectarray is suitable for high-speed Ka-band satellites. [ABSTRACT FROM AUTHOR]

The development of metamaterial-based photonic components has acquired a significant interest in technological developments at terahertz frequencies. The manipulation of the state of polarization is an important parameter in optical devices. In this study, we have investigated, both numerically and experimentally, a toroidal excitation-based metamaterial that is capable of converting terahertz from its linearly polarized state to an orthogonally polarized state over a broad spectrum. The meta-molecule unit of the proposed geometry is comprised of a pair of resonators connected to each other having a split gap in each arm. We have studied both the horizontal and vertical components of transmission for numerous in-plane rotations of the proposed geometry. A multipolar analysis confirms a significant contribution of the toroidal component. Polarization conversion of nearly 40% is observed over a broad spectrum of 1.19–2.5 THz. Such a broadband cross-polarization converter could have remarkable implications for the development of terahertz toroidal metamaterial devices. [ABSTRACT FROM AUTHOR]

Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clutter interference, and densely arranged ships in complex inshore, have not been well solved so far. Therefore, this article puts forward a novel SAR ship-detection method called multi-level feature-refinement anchor-free framework with a consistent label-assignment mechanism, which is capable of boosting ship-detection performance in complex scenes. First, considering that SAR ship detection is susceptible to complex background interference, we develop a stepwise feature-refinement backbone network to refine the position and contour of the ship object. Next, we devise an adjacent feature-refined pyramid network following the backbone network. The adjacent feature-refined pyramid network consists of the sub-pixel sampling-based adjacent feature-fusion sub-module and adjacent feature-localization enhancement sub-module, which can improve the detection capability of multi-scale objects by mitigating multi-scale high-level semantic loss and enhancing low-level localization features. Finally, to solve the problems of unbalanced positive and negative samples and densely arranged ship detection, we propose a consistent label-assignment mechanism based on consistent feature scale constraints to assign more appropriate and consistent labels to samples. Extensive qualitative and quantitative experiments on three public datasets, i.e., SAR Ship-Detection Dataset (SSDD), High-Resolution SAR Image Dataset (HRSID), and SAR-Ship-Dataset illustrate that the proposed method is superior to many state-of-the-art SAR ship-detection methods. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The proposed MA mechanism fully considers the comprehensive calculation of spatial attention (SPA), channel attention (CHA), and coordinated attention (CA). It can input feature maps for comprehensive weighting to enhance the features of the regions of interest, in order to improve the recognition rate of vehicle targets in SAR images.The superiority of our algorithm was verified through experiments on the MSTAR dataset. [ABSTRACT FROM AUTHOR]

Severe hailstorms result in substantial damage to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250 000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false-alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of damaged building in 91 %, their total cost in 77 %, the number of damaged vehicles in 74 %, and their total cost in 60 % of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland. [ABSTRACT FROM AUTHOR]

InSAR technology is currently a crucial tool for large-scale surface deformation monitoring, particularly excelling in regional subsidence monitoring. In this study, focusing on the Jinan section of the Shandong railway, newly launched LUTAN-1 satellite SAR data was employed. DInSAR and Stacking techniques were applied to analyze the subsidence in response to the long-term operation load of high-speed trains and surrounding human activities. Through the analysis of data from June 2023 to December 2023, regional subsidence was identified in this section, with a subsidence rate reaching 8 cm/year. Comparative analysis between DInSAR technology monitoring results and precise leveling monitoring results showed consistency in the subsidence deformation trend in the region, achieving centimeter-level deformation monitoring in the study area.The LuTan-1 satellite provided robust SAR data support for railway subsidence monitoring, offering substantial reliability and accuracy to deepen the understanding of railway subsidence issues. These research findings hold significant practical implications for adopting timely maintenance and repair measures, ensuring the safety and reliable operation of the railway system. [ABSTRACT FROM AUTHOR]

The objective of this article is to describe a compact 4-port MIMO planar antenna with a high axial ratio bandwidth (ARBW) that is suitable for usage in 5G applications that operate at frequencies lower than 6 GHz. The optimized dimension of the proposed antenna element is 37 × 30 × 0.8 mm3 and FR-4 substrate has been used as a dielectric material with relative permittivity of 4.4, and a height of 1.6 mm. The proposed antenna has an impedance bandwidth (− 10 dB) in the range of 3.3–4.3 GHz (Simulated)/3.4–4.7 GHz (measured), with a 3-dB ARBW of 3.0–4.4 GHz (simulated)/3.0–4.0 GHz (measured) respectively. The configuration that has been suggested for a MIMO antenna consists of a single entity that has a radiating element in the shape of a T and a ground plane in the shape of a C, both of which are co-planar. The findings of the simulation are then validated by the measurement results which are carried out by using the VNA55071C Vector network analyzer. The suggested module has the potential to serve as a MIMO antenna for impending fifth-generation wireless applications as it achieves an isolation level of better than 15 dB, has an envelope correlation coefficient (ECC) of less than 0.005, and has a diversity gain (dB) > 9.95 dB. [ABSTRACT FROM AUTHOR]

The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model's significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset's SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research. [ABSTRACT FROM AUTHOR]

Sea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. Plain Language Summary: Sea ice topography estimates are important for improving ocean‐climate models, understanding sea ice evolution, and planning ice travel. Estimates can be obtained from altimetry missions, but these data are spatially and/or temporally limited compared to recent synthetic aperture radar missions such as the C‐band RADARSAT Constellation Mission (RCM). By comparing RCM backscatter with measurements of height and roughness obtained using Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 in the Canadian Arctic, we demonstrate the potential for using RCM to estimate sea ice topography. We show there is a notable relationship between sea ice roughness and backscatter on first‐year ice in late winter, particularly in the horizontal‐transmit/vertical‐receive channel, and a notable relationship between sea ice height and backscatter on multi‐year ice. We also observe that the relationship between the RCM signal and roughness varies with incidence angle. Based on this analysis we develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with ICESat‐2 data, we find the model performs effectively at estimating key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. These findings will facilitate the use of RCM to validate geophysical models, map rough areas of ice, assess ice evolution and develop new topographic models. Key Points: Sea ice roughness measured by Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 and horizontal‐transmit/vertical‐receive RADARSAT Constellation Mission (RCM) backscatter exhibit a notable relationship on late‐winter first‐year iceSea ice height, as measured by ICESat‐2, and RCM backscatter exhibit a notable relationship on late‐winter multi‐year iceWe develop/validate a model, based on comparison with ICESat‐2 data, for estimating sea ice roughness using RCM backscatter in the Arctic [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) and transformers have boosted the rapid growth of object detection in synthetic aperture radar (SAR) images. However, it is still a challenging task because SAR images usually have the characteristics of unclear contour, sidelobe interference, speckle noise, multiple scales, complex inshore background, etc. More effective feature extraction by the backbone and augmentation in the neck will bring a promising performance increment. In response, we make full use of the advantage of CNNs in extracting local features and the advantage of transformers in capturing long-range dependencies to propose a Swin Transformer-based detector for arbitrary-oriented SAR ship detection. Firstly, we incorporate a convolution-based local perception unit (CLPU) into the transformer structure to establish a powerful backbone. The local-perception-enhanced Swin Transformer (LP-Swin) backbone combines the local information perception ability of CNNs and the global feature extraction ability of transformers to enhance representation learning, which can extract object features more effectively and boost the detection performance. Then, we devise a cross-scale bidirectional feature pyramid network (CS-BiFPN) by strengthening the propagation and integration of both location and semantic information. It allows for more effective utilization of the feature extracted by the backbone and mitigates the problem of multi-scale ships. Moreover, we design a one-stage framework integrated with LP-Swin, CS-BiFPN, and the detection head of R3Det for arbitrary-oriented object detection, which can provide more precise locations for inclined objects and introduce less background information. On the SAR Ship Detection Dataset (SSDD), ablation studies are implemented to verify the effectiveness of each component, and competing experiments illustrate that our detector attains 93.31% in mean average precision (mAP), which is a comparable detection performance with other advanced detectors. [ABSTRACT FROM AUTHOR]

The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments. [ABSTRACT FROM AUTHOR]