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Real Aperture Radar (RAR) observes wide-scope target information by scanning its antenna. However, because of the limited antenna size, the angular resolution of RAR is much lower than the range resolution. Angular super-resolution methods can be applied to enhance the angular resolution of RAR by inverting the low-rank steering matrix based on the convolution relationship between the antenna pattern and target scatterings. Because of the low-rank characteristics of the antenna steering matrix, traditional angular super-resolution methods suffer from manual parameter selection and high computational complexity. In particular, these methods exhibit poor super-resolution angular resolution at low signal-to-noise ratios. To address these problems, an iterative adaptive approach for angular super-resolution imaging of scanning RAR is proposed by combining the traditional Iterative Adaptive Approach (IAA) with a deep network framework, namely IAA-Net. First, the angular super-resolution problem for RAR is transformed into an echo autocorrelation matrix inversion problem to mitigate the ill-posed condition of the inverse matrix. Second, a learnable repairing matrix is introduced into the IAA procedure to combine the IAA algorithm with the deep network framework. Finally, the echo autocorrelation matrix is updated via iterative learning to improve the angular resolution. Simulation and experimental results demonstrate that the proposed method avoids manual parameter selection and reduces computational complexity. The proposed method provides high angular resolution under a low signal-to-noise ratio because of the learning ability of the deep network.

Abstract Chemotherapy resistance has been a great challenge in pancreatic ductal adenocarcinoma(PDAC) treatments. Current first-line chemotherapy regimens for PDAC include gemcitabine-based regimens such as AG regimen (albumin paclitaxel and gemcitabine), fluorouracil-based regiments such as FOLFIRINOX regimen ((5-fluorouracil5-FU), oxaliplatin, Irinotecan) and platinum-based regimens for patients with BRCA mutations. large amounts of work have been done on exploring the mechanism underlying resistance of gemcitabine-based and platinum-based regimens, while little research has been achieved on the mechanism of FOLFIRINOX regimens resistance. Hence, we identified Polypeptide N-Acetylgalactosaminyltransferase 5, (GALNT5) as a vital regulator and a potential therapeutic target in FOLFIRINOX regimens resistance. Colony formation assays and flow cytometry assays were performed to explore the roles of GALNT5 in cell proliferation and apoptosis in PDAC treated with FOLFIRINOX. IC50 alterations were calculated in GALNT5 knockdown and overexpressed cell lines. RNA-seq followed by GSEA (gene set enrichment analysis) was displayed to explore the potential mechanism. WB (western blotting), real-time PCR, and IF (immunofluorescence) were performed to validate relative pathways. The mouse orthotopic xenograft PDAC model was established to examine GALNT5 functions in vivo. GALNT5 was highly expressed in PDAC tissues and predicted poor prognosis in PDAC. Upregulation of GALNT5 in PDAC cells conferred FOLFIRINOX resistance on PDAC by inhibiting DNA damage. Moreover, GALNT5 interacted with MYH9, thus participating in the activation of the NOTCH pathways, resulting in hampering FOI-induced DNA damage. Functions of GALNT5 promoting FOLFIRINOX resistance were validated in vivo. In this study, we found that aberrantly overexpressed GALNT5 in PDAC took part in the activation of the NOTCH pathway by interacting with MYH9, thus inhibiting the DDR to achieve FOLFIRINOX resistance and causing poor prognosis. We identified GALNT5 as a potential therapeutic target for PDAC patients resistant to FOLFIRINOX chemotherapy.

Abstract In this work, AlGaN‐based deep ultraviolet Fabry–Perot (FP) laser diodes (DUV LDs) are designed by using Technology Computer Aided Design (TCAD) simulations, and the physical models for DUV LDs are also developed. It is found that the optical absorption in the p‐region significantly increases the optical loss and reduces the laser power. Hence, properly increasing the Al composition for the p‐waveguide (p‐WG) and the p‐type cladding layer (CL) helps shift the optical field to the n‐region, which is effective in decreasing the free‐carrier absorption in the p‐region. However, if not properly optimized, this will decrease the optical confinement factor, which will decrease the stimulated recombination rate between electrons and holes. Then, the electron leakage becomes significant. The studies show that the p‐electron blocking layer (p‐EBL) will not strongly affect the optical field profiles. Hence, the Al composition in the p‐EBL has more freedom for electrical optimization. Therefore, a compromised design is required so that both the optical and the electrical properties can be improved.

The imaging of aerial targets using Inverse Synthetic Aperture Radar (ISAR) is affected by micro-Doppler effects resulting from localized micromotions, such as rotation and vibration. These effects introduce additional Doppler frequency modulation into the echo, leading to spectral broadening. Under ultrahigh-resolution conditions, these micromotions interfere with the focusing process of subject scatterers, resulting in images with poor focus showing significantly reduced quality. Furthermore, micro-Doppler signals exhibit temporal variability and nonstationary characteristics, posing difficulties in their estimation and differentiation from the echo. To address these challenges, this paper proposes a nonparametric method based on Variational Mode Decomposition (VMD) and mode optimization to separate the echo of the subject from micro-Doppler components. This separation is achieved by utilizing differences in their respective time-frequency distributions. This methodology mitigates the effect of micro-Doppler signals on the echo and obtains imaging results of a drone with ultrahigh-resolution. The VMD algorithm is introduced and subsequently extended to the complex domain. The method entails the decomposition of the ISAR echo along the azimuth direction into several mode functions distributed uniformly across the Doppler sampling bandwidth. Subsequently, image entropy indices are employed to optimize the decomposition parameters and select the imaging modes. This ensures the effective suppression of micro-Doppler signals and preservation of the subject echo. Compared to existing methods based on Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD), the proposed method exhibits superior performance in suppressing image blurring caused by micro-Doppler effects while ensuring complete retention of fuselage details. Furthermore, the effectiveness and advantages of the proposed method are validated through simulations and processing of ultrawideband microwave photonic data obtained from drone measurements.

Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.

Microwave Photonic (MWP) radars have remarkably improved traditional microwave radar hardware architectures using photonics devices. With the exceptional physical properties of photonic devices, MWP radars can emit ultra-wideband, high-linearity, high-quality linear frequency modulation signals, allowing ultra-high-resolution target imaging and detection. Different target regions exhibit distinct responses to different frequency signals during target imaging and detection due to their diverse structures and characteristics. Therefore, MWP radars have the potential to generate pseudo-color images based on scattering differences, further enhancing the information retrieval capability of MWP Synthetic Aperture Radar (MWP-SAR). Pseudo-color images generated using traditional remote sensing techniques cannot achieve centimeter-level resolution. Therefore, we propose a method for generating pseudo-color images while maintaining the resolution of MWP-SAR. The algorithm first determines an optimal sub-band echo search model and subsequently employs the optimal sub-band search algorithm to process the ultra-wideband echoes to obtain sub-band echo channels with the largest scattering characteristic differences. The multi-sub-band images are then color-composited to generate pseudo-color images that best describe the target scattering characteristics. However, to ensure the high resolution of MWP-SAR, a fusion model is established to combine the full-resolution SAR image with the multi-sub-band image. Finally, full-resolution pseudo-color images are successfully synthesized using the measured airborne MWP-SAR data, validating the effectiveness of the algorithm. This algorithm enables MWP-SAR to obtain more target information during imaging, offering assistance in implementing imaging radar and microwave vision.

With the widespread application of deep learning methods in Synthetic Aperture Radar (SAR) image interpretation, the explainability of SAR target recognition deep networks has gradually attracted the attention of scholars. Class Activation Mapping (CAM), a commonly used explainability algorithm, can visually display the salient regions influencing the recognition task through heatmaps. However, as a post hoc explanation method, CAM can only statically display the salient regions during the current recognition process and cannot dynamically show the variation patterns of the salient regions upon changing the input. This study introduces the concept of perturbation into CAM, proposing an algorithm called SAR Clutter Characteristics CAM (SCC-CAM). By introducing globally distributed perturbations to the input image, interference is gradually applied to deep SAR recognition networks, causing decision flips. The degree of change in the activation values of network neurons is also calculated. This method addresses the issue of perturbation propagation and allows for dynamic observation and measurement of variation patterns of salient regions during the recognition process. Thus, SCC-CAM enhances the explainability of deep networks. Experiments on the MSTAR and OpenSARShip-1.0 datasets demonstrate that the proposed algorithm can more accurately locate salient regions. Compared with traditional methods, the algorithm in this study shows stronger explainability in terms of average confidence degradation rates, confidence ascent ratios, information content, and other evaluation metrics. This algorithm can serve as a universal method for enhancing the explainability of networks.

Background: Cognitive impairment is a common manifestation in patients with T2DM mellitus (T2DM). Ginsenoside Rg1 (GRg1) is the main active substance extracted from ginseng or Panax notoginseng. Methods: T2DM was induced by feeding rats with a high-sugar and high-fat diet combined with a low-dose intraperitoneal injection of streptozotocin (STZ, 35 mg/kg) on an empty stomach. Subsequently, different concentrations of GRg1 (25, 50, 100 mg/kg/d) were used to intervene for 8 weeks and explore its therapeutic effects and potential mechanisms on cognitive impairment in T2DM rats. Results: Our data suggested that administration of GRg1 improved insulin resistance, specifically manifesting in a reduction of insulin resistance index by approximately 57.1 % with high doses of GRg1 (100 mg/kg/d). Besides, it has been observed to lower cholesterol, triglycerides, and low-density lipoprotein by approximately 20–50 % in T2DM rats. In addition, GRg1 treatment dramatically improved the spatial memory and learning ability in T2DM rats. Furthermore, administration of GRg1 to the T2DM rats dose-dependently up-regulated ERKs phosphorylation and blunted phosphorylation of JNKs and p38. Furthermore, GRg1 treatment also dose-dependently increased the expression of Bcl-2 but inhibited the expression of Bax and Caspase 3 expression in T2DM rats brain cortex and hippocampus neurons. Conclusion: GRg1 effectively improves mild cognitive impairment in T2DM rats by inhibiting oxidative stress and reducing the apoptosis of neurons in the cerebral cortex and hippocampus.

Automatic crop mapping is essential for various agricultural applications. Although fully convolutional networks (FCNs) have shown effectiveness in crop mapping, they rely on labor-intensive pixel-level annotations. Weakly supervised semantic segmentation (WSSS) methods offer a solution by enabling pixel-level segmentation with less costly annotations, such as point, bounding box (bbox), and image-level annotations. However, these weak annotations often lack complete object information, leading to reduced accuracy. Moreover, there is limited exploration of WSSS methods in medium-resolution satellite imagery, such as Sentinel-2. Therefore, this study proposes SAMWS, a WSSS method based on the Segment Anything Model (SAM) for crop mapping using Sentinel-2 time series images. Leveraging SAM, our method can generate high-quality pseudo labels and accommodate various weak annotations without extensive adjustments. SAMWS comprises three stages: 1) finetuning SAM with adapters; 2) generating pseudo labels using weak annotations and finetuned SAM; and 3) training a segmentation network using pseudo labels for crop mapping. Experiments conducted on PASTIS and Munich datasets demonstrate the superiority of our approach. After SAM was finetuned with adapters, the F1-scores for parcel segmentation using point and bbox prompts increased by 75.0 % and 14.4 %, respectively, reaching 0.880 and 0.931. Additionally, our method achieves classification results closest to fully supervised learning when using point, bbox, and image-level annotations, with F1-scores of 0.810, 0.817, and 0.779, respectively. Our approach offers valuable insights into leveraging foundation models in the remote sensing domain and contains significant potential for crop monitoring. The relevant code will be made publicly available at https://github.com/Nick0317Sun/SAMWS.

BackgroundRecent studies have shown that gut microbiota may be related to the occurrence of brain tumors, but direct evidence is lacking. This study used the Mendelian randomization study (MR) method to explore the potential causal link between gut microbiota and brain tumors.MethodWe analyzed the genome-wide association data between 211 gut microbiota taxa and brain tumors, using the largest existing gut microbiota Genome-Wide Association Studies meta-analysis data (n=13266) and combining it with brain tumor data in the IEU OpenGWAS database. We use inverse-variance weighted analysis, supplemented by methods such as Mendelian randomization-Egger regression, weighted median estimator, simple mode, and weighted mode, to assess causality. In addition, we also conducted the Mendelian randomization-Egger intercept test, Cochran’s Q test, and Mendelian randomization Steiger directionality test to ensure the accuracy of the analysis. Quality control includes sensitivity analysis, horizontal gene pleiotropy test, heterogeneity test, and MR Steiger directionality test.ResultOur study found that specific gut microbial taxa, such as order Lactobacillales and family Clostridiaceae1, were positively correlated with the occurrence of brain tumors, while genus Defluviitaleaceae UCG011 and genus Flavonifractor were negatively correlated with the occurrence of brain tumors. The Mendelian randomization-Egger intercept test showed that our analysis was not affected by pleiotropy (P>0.05).ConclusionThis study reveals for the first time the potential causal relationship between gut microbiota and brain tumors, providing a new perspective for the prevention and treatment of early brain tumors. These findings may help develop new clinical intervention strategies and point the way for future research.

Abstract We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. First, low-rank and sparse constraints as regularization norms are incorporated to establish objective function, and the alternating direction multiplier method is used to solve simultaneous super-resolution and target detection problems. Then, the solving process is expanded into a neural network, where the weight parameters of each level are obtained through adaptive learning. At last, experiments are conducted to verify the effectiveness of the proposed method.

Parameter extraction of radar signals is an important but challenging task in electronic warfare. In the modern electromagnetic environment, the radiation sources greatly increase, causing different radar signals to overlap, making the parameter extraction of radar signals difficult. Meanwhile, using radar signal parameter extraction methods that are not suitable for dealing with overlapping signals can lead to serious errors in this case. To address this, we propose a parameter extraction network for overlapping radar signals using modulation recognition-guided semantic segmentation. Specifically, we first design an encoder–decoder to segment overlapping radar signals, which uses channel rearrangement and modulation type filtering to increase the accuracy of segmentation. In this encoder–decoder, channel rearrangement is an optimization of convolution operation, aiming to increase the perceptual field while reducing feature information loss. And modulation type filtering can convert the results of semantic segmentation into masks corresponding to each radar signal, increasing the accuracy of segmentation. After the encoder–decoder, signal segmentation masks are obtained. Then, we compress these segmentation masks in the time and frequency domains, and extract the span of them to achieve accurate extraction of the pulsewidth and bandwidth of each radar signal. The experiments validate the feasibility of the proposed method.

Raw data simulation is very important for system parameter design, performance evaluation, and route planning of bistatic synthetic aperture radar (SAR). However, most conventional simulation methods focus on improving the efficiency of echo simulation, without considering the impact of terrain fluctuations, resulting in insufficient fidelity of simulated echoes. In this article, a raw data simulation framework under undulating terrain for bistatic SAR with arbitrary configuration is proposed. In the framework, based on the prior information of digital elevation model of undulating terrain and the small facet theory, the time-variant scattering coefficient is computed first with the empirical formula related with the angle variation of transmitter and receiver for bistatic SAR. Then the shadow is judged from the perspectives of transmitter and receiver, respectively, and the formation of double shadow is discussed. At last, the above process was embedded into the time domain pulse by the pulse echo simulation algorithm, forming a framework for simulating bistatic SAR raw data under undulating terrain with arbitrary configuration, and the simulated and measured data experiments are illustrated to verify the effectiveness of the proposed framework.

To overcome the effect of grid mismatch on the superresolution performance, a sparse Bayesian learning-based multichannel radar forward-looking superresolution imaging scheme is proposed in this article. In the scheme, a coarse imaging grid is initialized first, and local grid refinement is performed based on the preliminary estimation results of the target information. Then, based on the refined grid, an off-grid superresolution model considering grid mismatch is established, and the total least squares method is used to estimate the mismatch error for modifying the steering matrix in superresolution processing. At last, based on the modified steering matrix, sparse Bayesian learning algorithm is iteratively executed to achieve multichannel radar forward-looking superresolution imaging. Simulated and measured data processing results are illustrated to verify the effectiveness of the proposed scheme.

Recently, a sparse super-resolution method based on $L_{1}$ iterative reweighted norm (IRN) has been proposed to improve the azimuth resolution of forward-looking radar. However, this method suffers from poor adaptability and high computational complexity due to its noise-sensitive user-parameter and the necessity for high-dimensional matrix inversion. To this end, a fast adaptive $L_{1}$-IRN sparse super-resolution method is derived in this article, allowing for the user-parameter-free and efficient sparse imaging of forward-looking radar. First, we establish the super-resolution model of forward-looking radar and analyze the user parameter selection problem in the conventional $L_{1}$-IRN method. Second, based on Bayesian theory, adaptive iterative weights of different azimuths are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Finally, by using QR decomposition and Sherman–Morrison formula, the dimensionality of the echo and antenna pattern involved in the iteration is reduced to further diminish the computational complexity. Compared to the existing $L_{1}$-IRN method, the proposed method eliminates the need for any user parameters, and the computational complexity has been reduced from ${O}({JN}^{3})$ to ${O}({JN}^{2}{a})$. Simulation and measured data demonstrate the superiority of the proposed method.

Remote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.

ABSTRACTDue to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model’s spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance.

Abstract In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

In recent years, millimeter-wave radar has been widely used in safety detection, nondestructive detection of parts, and medical diagnosis because of its strong penetration ability, small size, and high detection accuracy. However, due to the limitation of hardware transmission bandwidth, achieving ultra-high two-dimensional resolution using millimeter-wave radar is challenging. Two-dimensional high-resolution imaging of altitude and azimuth can be realized using radar platform scanning to form a two-dimensional aperture. However, during the scanning process, the millimeter-wave radar produces sparse tracks in the height dimension, resulting in a sparse sampling of the altitude echo, thus reducing the imaging quality. In this paper, a high-resolution three-dimensional imaging algorithm for millimeter-wave radar based on Hankel transformation matrix filling is proposed to solve this problem. The matrix filling algorithm restores the sparse sampling echo, which guarantees the imaging accuracy of the millimeter-wave radar in the scanning plane. First, the low-rank prior characteristics of the millimeter-wave radar's elevation-range section were analyzed. To solve the problem of missing whole rows and columns of data during sparse trajectory sampling, the echo data matrix was reconstructed using the Hankel transform, and the sparse low-rank prior characteristics of the constructed matrix were analyzed. Furthermore, a matrix filling algorithm based on truncated Schatten-p norm combining low-rank and sparse priors was proposed to fill and reconstruct the echoes to ensure the three-dimensional resolution of the sparse trajectory millimeter-wave radar. Finally, using simulation and several sets of measured data, the proposed method was proved to achieve high-resolution three-dimensional imaging even when only 20%–30% of the height echo was used.

Abstract In recent years, levitated particles of optical traps in vacuum have shown the enormous potential for precision sensor development and new physics exploration. However, the accuracy of the sensor is still hampered by the uncertainty of the calibration factor relating the detected signal to the absolute displacement of the trapped particle. In this paper, we suggest and experimentally demonstrate a novel calibration method for optical tweezers based on free-falling particles in vacuum, where the gravitational acceleration is introduced as an absolute reference. Our work provides a calibration protocol with a great certainty and traceability, which is significant in improving the accuracy of precision sensing based on levitated optomechanical systems.

Abstract Collective cells, a typical active matter system, exhibit complex coordinated behaviors fundamental for various developmental and physiological processes. The present work discovers a collective radial ordered migration behavior of NIH3T3 fibroblasts that depends on persistent top‐down regulation with 2D spatial confinement. Remarkably, individual cells move in a weak‐oriented, diffusive‐like rather than strong‐oriented ballistic manner. Despite this, the collective movement is spatiotemporal heterogeneous and radial ordering at supracellular scale, manifesting as a radial ordered wavefront originated from the boundary and propagated toward the center of pattern. Combining bottom‐up cell‐to‐extracellular matrix (ECM) interaction strategy, numerical simulations based on a developed mechanical model well reproduce and explain above observations. The model further predicts the independence of geometric features on this ordering behavior, which is validated by experiments. These results together indicate such radial ordered collective migration is ascribed to the couple of top‐down regulation with spatial restriction and bottom‐up cellular endogenous nature.

Abstract Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.

Against the backdrop of rapid urbanization and coordinated development in the Beijing–Tianjin–Hebei region of China, urban residential land prices in Hebei Province have experienced significant increases, exacerbating housing pressures on residents. This study aims to elucidate the spatio-temporal evolution characteristics of urban residential land prices, identify the key influencing factors in Hebei Province of China, and offer insights on macro-control of the land market, optimization of the land supply structure, and guidance on the sustainable development of land and real estate markets in the region. Utilizing land price monitoring data from 11 prefecture-level cities in Hebei Province spanning the past five years, this research employs quantitative methods, such as the Theil index, the standard deviation ellipse, and the geographic detector model, to analyze the spatio-temporal dynamics and factors shaping urban residential land prices. The results show that: (1) Urban residential land prices in Hebei Province exhibited an overall upward trend from 2018 to 2022, characterized by pronounced spatial variations, with higher prices predominantly concentrated in the cities along the Shijiazhuang–Baoding–Langfang–Tangshan corridor; (2) The distribution pattern of urban residential land prices generally mirrors that of GDP, indicating a consistent movement of price centers with urban land price escalation; (3) Urban land prices are influenced by multiple factors in combination, where the interactions among these factors outweigh the impact of any single factor. Specifically, the proportion of GDP attributed to the tertiary industry and location conditions emerge as pivotal factors affecting urban residential land prices in Hebei Province. Given these significant spatial disparities, addressing the industrial structure and optimizing urban land resource allocation are critically imperative.

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.

This research provides a comparative analysis of the optimization of ultra-high-performance concrete (UHPC) using artificial neural network (ANN) and response surface methodology (RSM). By using ANN and RSM, the yield of UHPC was modeled and optimized as a function of 22 independent variables, including cement content, cement compressive strength, cement type, cement strength class, fly-ash, slag, silica-fume, nano-silica, limestone powder, sand, coarse aggregates, maximum aggregate size, quartz powder, water, super-plasticizers, polystyrene fiber, polystyrene fiber diameter, polystyrene fiber length, steel fiber content, steel fiber diameter, steel fiber length, and curing time. Two statistical parameters were examined based on their modeling, i.e., determination coefficient (R2) and mean square error (MSE). ANN and RSM were evaluated for their predictive and generalization capabilities using a different dataset from previously published research. Results show that RSM is computationally efficient and easy to interpret, whereas ANN is more accurate at predicting UHPC characteristics due to its nonlinear interactions. Results show that the ANN model (R = 0.95 and R2 = 0.91) and RSM model (R = 0.94, and R2 = 0.90) can predict UHPC compressive strength. The prediction error for optimal yield using an ANN and RSM was 3.5% and 7%, respectively. According to the ANN model’s sensitivity analysis, cement and water have a significant impact on compressive strength.

Collective cell migration is a coordinated movement of multi-cell systems essential for various processes throughout life. The collective motions often occur under spatial restrictions, hallmarked by the collective rotation of epithelial cells confined in circular substrates. Here, we aim to explore how geometric shapes of confinement regulate this collective cell movement. We develop quantitative methods for cell velocity orientation analysis, and find that boundary cells exhibit stronger tangential ordering migration than inner cells in circular pattern. Furthermore, decreased tangential ordering movement capability of collective cells in triangular and square patterns are observed, due to the disturbance of cell motion at unsmooth corners of these patterns. On the other hand, the collective cell rotation is slightly affected by a convex defect of the circular pattern, while almost hindered with a concave defect, also resulting from different smoothness features of their boundaries. Numerical simulations employing cell Potts model well reproduce and extend experimental observations. Together, our results highlight the importance of boundary smoothness in the regulation of collective cell tangential ordering migration.

Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.

The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.

Bi/multistatic Synthetic Aperture Radar (SAR) using spaceborne illuminator utilizes spaceborne platforms as transmitters and satellites, near-space vehicles, aircraft, and ground platforms as receivers, which allows high-resolution imaging of ground and marine scenes and targets. The system’s benefits include a broad imaging field of view, high concealment, and potent antijamming abilities. By using beam steering methods, a variety of imaging modes can be achieved, such as staring spotlight and sliding spotlight modes, which obtain abundant information about the imaging scene and offer broad application prospects both in civil and military fields. Thus far, the bi/multistatic SAR using spaceborne illuminator has been intensively investigated, and several research findings have been reported. This paper examines the technology from the aspects of system components, configuration design, echo model, imaging techniques, bistatic synchronization, and experimental verification and systematically reviews the state-of-the-art research progress. Finally, it is anticipated that spaceborne illuminator technology will be used to develop bi/multistatic SAR in the future.

The world's forests are experiencing rapid changes due to land use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally using Landsat time series, the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm, and the Google Earth engine platform. First, stable forest samples collected based on the first all-season sample set and an automated sample migrate method were used to determine annual normalized burn ratio (NBR) thresholds for forest gain detection. Second, with the NBR time series from 1982 to 2021 and LandTrendr algorithm, we produced a dataset of global forest gain year from 1983 to 2021 based on a set of decision rules. Our results reveal that over 60% gains occurred in Russia, Canada, the United States, Indonesia, and China, and approximately half of global forest gain occurred between 2001 and 2010. The forest gain map developed in this study exhibited good consistency with statistical inventories and independent regional and global products. Our dataset can be useful for policy-relevant research on the global carbon cycle, and our method provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Regularization methods, including single constraint regularization and joint constraints regularization, have been applied to radar forward-looking super-resolution imaging. However, the ill-posedness of the antenna measurement matrix is serious, which degrades the imaging performance in low signal-to-noise ratio (SNR) conditions. In our work, the following two-step regularization strategy is proposed to achieve super-resolution imaging in low SNR conditions: 1) in the first step, a projection regularization method is designed to repair the ill-posed antenna measurement matrix by truncating and modifying singular values, which mitigates the ill-posedness of the deconvolution process and suppresses noise amplification and 2) in the second step, based on the repaired convolution model, the $L_{1}$ norm is introduced for sparse targets to improve the radar azimuth resolution. The iteratively reweighted norm solver is employed to solve the optimization problem. The superiority of the proposed two-step strategy is analyzed from the perspectives of singular value decomposition. The effectiveness of the proposed strategy is verified by simulated and experimental data.

Consumers need external information to support their product evaluation, especially with regard to experiencing the product during online shopping. Review keywords provide aggregated information of online reviews for consumers. However, whether and how review keywords affect consumers’ product consideration is still unelucidated. Considering clothing as the research target, we built a model to depict the impacts of website-generated quality- and fit-related review keywords on consumers’ consideration of clothing by bridging cue diagnosticity frameworks and product uncertainty theory. The hypotheses were verified by analyzing the objective data collected from e-commerce platforms and experiments. Results indicate that quality-related review keywords have a more salient positive impact on clothing consideration compared with fit-related keywords. Meanwhile, further complementary analysis based on self-generated review keywords suggests that presenting consumers with social-related keywords and consumer buyback keywords can improve clothing consideration significantly. The research contributes to the literature of product consideration and online review keywords, and related findings can help the platforms and e-merchants to better leverage the advantages of online review keywords.

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multiscale features and the deployment of the prior knowledge of the ship shape. In this article, a novel ship augmented attention network (SA$^{2}$Net) for ship recognition is proposed, which comprehensively utilizes the multiscale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA$^{2}$Net demonstrates superior performance when compared to the current state-of-the-art methods.

Radar operation mode recognition holds an increasingly critical place in electronic countermeasure as well as in remote sensing. However, the overlapped waveform parameters pose huge challenges to performing the radar operation mode recognition task in severe electromagnetic environments, particularly with large measurement errors or small sample lengths. By analyzing the timing patterns of a single radar pulse parameter and the correlation characteristics of multiple radar pulse parameters, this article first provides a revolutionary representation of the radar operating state by integrating interpulse parameter characteristics. Subsequently, a multifeature fused stream-level recognition framework with an attention mechanism, named residual-and-shrinkage ConvNet, is proposed to identify typical radar operating states. This tailored-made deep learning framework can effectively extract the timing and correlative features, which are conducive to pattern classification. The results of numerical experiments suggest that the proposed approach affords superior performance for the operation mode recognition task, even when the measurement error is large and the sample length is small, signifying the proposed method is strongly robust and time-efficient.

The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification.

To repair reinforced concrete beams efficiently in a limited building space, the four-sided application of a reinforcing thin layer of reactive powder concrete (“RPCTL”) was proposed to improve the bending capacity of the members. Static flexural tests of one comparison beam and five reinforced beams were completed on a four-point centralized loading device. Changes in deflection, cracks, stresses, and damage characteristics of the specimens were measured under various levels of loading. The test results showed that the damage patterns of the reinforced specimens were dominated by the yielding of longitudinal tensile reinforcement at the bottoms of the beams and the crushing of the cementitious material in the top compression zones of the beams. The cracking load greatly increased by 1.42 to 7.12 times, and the ultimate bearing capacity increased by 0.29 to 1.41 times. The distribution characteristics and dynamic changes in the displacement, stress, and damage of the specimens were dynamically simulated by finite element software. The effects of reinforcement and initial load-holding level on the reinforcement effect were investigated. A bending capacity calculation formula for RPCTL reinforcement technology is proposed that aligns with the test results and can provide a reference for the design of RPCTL reinforcement.

Shotcrete is widely used in civil engineering as a supporting structure. In this paper, the compressive behavior of ultra-high-performance shotcrete (UHPSC) with different steel fiber content by volume (0, 0.5%, 0.75%, 1%, 1.25%, 1.5%) was investigated. The results showed that the failure pattern of UHPSC was changed from brittle failure to ductile failure with the increase in steel fiber content. The compressive strength, peak strain and compressive toughness of UHPSC increased with the increase in steel fiber content, but the elastic modulus and Poisson’s ratio did not change significantly. With content of 1.5% steel fibers, its axial compressive strength, peak strain and compressive strain energy were 122.7 MPa, 3749 με and 0.269 MPa, respectively, increased by 14%, 23.5% and 55.5% compared with those without steel fiber. The peak strain and compressive toughness were higher than that of ultra-high-performance concrete (UHPC), while the elastic modulus of UHPSC was lower than that of UHPC. Based on the experimental data, the relationship between compressive strength, peak strain, compressive toughness and the change in the characteristic value of steel fiber content (λf) were revealed. The uniaxial compressive constitutive model of UHPSC with different steel fiber content was established and reflected the change rule of the shape parameter of α (constitutive model ascending section) and β (constitutive model descending section) with λf. The experimental results were in good agreement with the model calculation results, which can provide theoretical support for the structural design of UHPSC.

Immobilized metal ion affinity chromatography (IMAC) adsorbents generally have excellent affinity for histidine-rich proteins. However, the leaching of metal ions from the adsorbent usually affects its adsorption performance, which greatly affects the reusable performance of the adsorbent, resulting in many limitations in practical applications. Herein, a novel IMAC adsorbent, i.e., Cu(II)-loaded polydopamine-coated urchin-like titanate microspheres (Cu-PDA-UTMS), was prepared via metal coordination to make Cu ions uniformly decorate polydopamine-coated titanate microspheres. The as-synthesized microspheres exhibit an urchin-like structure, providing more binding sites for hemoglobin. Cu-PDA-UTMS exhibit favorable selectivity for hemoglobin adsorption and have a desirable adsorption capacity towards hemoglobin up to 2704.6 mg g−1. Using 0.1% CTAB as eluent, the adsorbed hemoglobin was easily eluted with a recovery rate of 86.8%. In addition, Cu-PDA-UTMS shows good reusability up to six cycles. In the end, the adsorption properties by Cu-PDA-UTMS towards hemoglobin from human blood samples were analyzed by SDS-PAGE. The results showed that Cu-PDA-UTMS are a high-performance IMAC adsorbent for hemoglobin separation, which provides a new method for the effective separation and purification of hemoglobin from complex biological samples.

Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM.

Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there are limitations in the classification detail, spatial resolution, and rapid change monitoring capability of global land cover change data. Building on the earlier Global Land Cover Mapping (Finer Resolution Observation and Monitoring – Global Land Cover, FROM-GLC), we developed the improved Global Land Cover Change Monitoring Platform (FROM-GLC Plus) using methods such as multi-season sample space-time migration, multi-source data time series reconstruction, and machine learning. The FROM-GLC Plus system provides a capacity for producing global land cover change data set from the 1980s with flexibility in spatio–temporal details. The preliminary results show that FROM-GLC Plus provides a framework for near real-time land cover mapping at multi-temporal (annual to daily) and multi-resolution (30 m to sub-meter) levels.

Abstract Mesenchymal migration usually happens on adhesive substrates, while cells adopt amoeboid migration on low/nonadhesive surfaces. Protein‐repelling reagents, e.g., poly(ethylene) glycol (PEG), are routinely employed to resist cell adhering and migrating. Contrary to these perceptions, this work discovers a unique locomotion of macrophages on adhesive–nonadhesive alternate substrates in vitro that they can overcome nonadhesive PEG gaps to reach adhesive regions in the mesenchymal mode. Adhering to extracellular matrix regions is a prerequisite for macrophages to perform further locomotion on the PEG regions. Podosomes are found highly enriched on the PEG region in macrophages and support their migration across the nonadhesive regions. Increasing podosome density through myosin IIA inhibition facilitates cell motility on adhesive–nonadhesive alternate substrates. Moreover, a developed cellular Potts model reproduces this mesenchymal migration. These findings together uncover a new migratory behavior on adhesive–nonadhesive alternate substrates in macrophages.

A geosynchronous (GEO) satellite can provide continuous illumination with broad beam coverage for a Low Earth Orbit (LEO) receiver, used as the transmitting station of bistatic Synthetic Aperture Radar (SAR). Meanwhile, because the bistatic SAR system comprises a separate transmitter and receiver, the LEO receiver can realize multiview imaging such as downward-, forward-, and backward-looking. Therefore, GEO-LEO bistatic SAR is widely used in earth surveying and mapping to reconnaissance and surveillance application. To realize large-scene imaging, the pulse repetition rate of the GEO SAR transmitter should be low. Meanwhile, the LEO SAR receiver introduces a wide Doppler bandwidth, resulting in the azimuth undersampling of the GEO-LEO bistatic SAR. Although the multichannel technology in the receiver can suppress the ambiguity, the multichannel unambiguous recovery method requires numerous channels, resulting in the undersampling of the GEO-LEO bistatic SAR, and hindering the miniaturization of the receiving system. To address the problem of ambiguous imaging of complex observation scenes under the condition of severe azimuth subsampling condition, a sequential joint multiframe and multireceiving channel recovery unambiguous imaging method is proposed. The unambiguous imaging is recovered jointly from the correlation between sequential multiframe observation scenes and multireceiving channel sampling information. First, the unambiguous imaging problem of the GEO-LEO bistatic SAR is modeled as a joint low rank and sparse tensor optimization problem. Second, in the iterative solution of the alternating direction multiplier method, the multireceiving channel information is used to realize the unambiguous imaging of the GEO-LEO bistatic SAR for complex observation scenes. The proposed method can significantly reduce the number of receiving channels required for unambiguous imaging compared with the imaging method based on traditional multichannel The results obtained by the proposed method are validated by simulations and experiments.

Measurement(s) planting years of plantations Technology Type(s) remote sensing and LandTrendr algorithm Sample Characteristic - Organism forest

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

Marine surveillance radar is widely used in marine monitoring for its ability of observing sea surface all-time and all-weather. However, the radar target detection performance is seriously affected by the existence of sea clutter. In this article, we propose a new sea clutter suppression method based on machine learning approach. We first employ a cyclic structure network with a pair of generative adversarial networks to sufficiently learn the characteristics of sea clutter, which converts the problem of sea clutter suppression as a transformation from the clutter radar data domain to the clutter-free radar data domain. In addition, we propose a target-consistency loss for the cost function of the network to effectively preserve the target information while suppressing the sea clutter. Therefore, the proposed method can not only effectively remove the sea clutter from the radar data but also protect the target information from being damaged during sea clutter suppression, thereby achieving excellent sea clutter suppression performance. Experimental results have shown the superiorities of the proposed sea clutter suppression method on both simulated and measured marine surveillance radar data.

Sufficient synthetic aperture radar (SAR) target images are very important for the development of research works. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this article, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: 1) generator, 2) discriminator, and 3) predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate an SAR target image. Then, a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the research works of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation.

The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this article, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for a fast and high-probability searches for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the low-altitude targets can be improved. Second, the search problem is transformed into a constrained multiobjective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

In terms of the global coverage provided by global navigation satellite system (GNSS) signals, radar images of a large field-of-view can be obtained via passive synthetic aperture radar (SAR) using these signals of opportunity. Usually, large field-of-view imaging is challenging using conventional SAR methods owing to the space-variant resolution. In addition, only a moderate range resolution can be provided by the GNSS transmitters in the passive SAR system. In this article, an optimal imaging method for a large field-of-view using GNSS-based passive SAR is proposed, which exploits the inherent multistatic nature of GNSS. At first, the spatial resolution of each scatterer in the observation area relative to each GNSS transmitter is calculated according to the geometry. Then, the selection of the transmitter set is modeled as an optimization problem, where the minimal resolution cell is used as the optimal criterion. After that, an evolutionary algorithm is applied to solve the optimization problem. Finally, the space-time combination of the echoes over a long dwell time concerning selected GNSS transmitters is performed to form the radar image with a minimal spatial resolution. The proposed method is extensively validated using two experimental datasets, where the passive radar consists of BeiDou satellites and a fixed receiver. The spatial resolution improvement and the large field-of-view imaging capability of the proposed method are forcefully demonstrated according to the experimental results.

Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy making. Faced with the rich natural ecological environment and variable agricultural production conditions of Mid-Spine Belt of Beautiful China (MSBBC), this study developed a novel operational assessment framework that combined the near real-time land cover mapping platform (i.e., FROM-GLC Plus), the FAO Agricultural Stress Index System, and the land degradation monitoring method suggested by United Nations Convention to Combat Desertification for the timely monitoring of cropland extent change, cropland conditions, and cropland degradation. With integrated monitoring system, this framework can provide convenient access to high-spatiotemporal- resolution cropland maps (30 m, dekadal) and instant (near real time) cropland dynamics. According to the monitoring results, we found that the abnormally high temperatures of summer 2022 adversely affected crop health in the southwest of MSBBC. Besides, our results suggested that China’s ecological restoration projects made remarkable achievement in MSBBC. The productivity of more than 70% of cropland in MSBBC has improved, and only ~6% cropland (~3.69 × 104 km2) has degraded since 2000, mainly distributed in cropland with steep slope, insufficient precipitation, and intensive use. Site-specific measures, such as conservation tillage, improved tillage systems, and cropland ecological projects, should be adopted for sustainable cropland use and further increase in land carrying capacity of MSBBC to achieve balanced east-west development in China.