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In-Orbit Demonstration (IOD) missions represent flight opportunities to acquire data on spacecraft and space environment and to demonstrate new techniques at system, subsystem, and payload level. Realizing this type of mission means accepting a greater level of risk. However, when very compact and relatively simple platforms like CubeSats are adopted, this becomes admissible since costs can be kept low. Distributed Synthetic Aperture Radar (SAR) is a novel Earth Observation (EO) technique for microwave imaging. It uses multiple cooperating receivers and suitable processing to enable performance and products that exceed those of the single components of the system. RODiO (Radar for earth Observation by synthetic aperture Distributed on a cluster of CubeSats equipped with high-technology micro-propellers for new Operative services) is an IOD of the distributed SAR concept. The mission is based on four, 16 U, CubeSats, that embark a receiving-only X-band SAR instrument able to collect bistatic echoes exploiting a monostatic SAR as an opportunity illuminator, i.e PLATiNO-1 mission. Distributed SAR demonstration is thus pursued in RODiO combining the principles of both multistatic and bistatic SAR. The paper, moving from mission goals, individuates different families of EO products that RODiO can demonstrate, e.g. in the contest of ground motion, digital elevation models, and ship detection. The main result is the definition of the RODiO system requirements for product generation that drive satellites and payload design. • RODiO is an IOD mission of DSAR principle based on four 16U CubeSat receivers. • The combination of mono and bistatic images enables the generation of new products. • These products provide advantages with respect to the monostatic-only counterparts. • Preliminary performance assessment of multi-baseline products is shown in the paper. • Requirements for product generation have been derived to drive RODiO design. [ABSTRACT FROM AUTHOR]

In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction models are deteriorated. Due to narrow rotated and translated blades acquired in PROPELLER, the technique of data augmentation that is used for deep learning-based Cartesian MRI reconstruction cannot be directly applied. To address the issue, this paper introduces a novel approach for the generation of synthetic PROPELLER blades, and it is subsequently employed in data augmentation for undersampled blades reconstruction. The principal aim of this study is to address the challenges of reconstructing undersampled blades to enhance both image quality and computational efficiency. Evaluation metrics including PSNR, NMSE, and SSIM indicate superior performance of the model trained with augmented data compared to non-augmented counterparts. The synthetic blade augmentation significantly enhances the model's generalization capability and enables robust performance across varying imaging conditions. Furthermore, the study demonstrates the feasibility of utilizing synthetic blades exclusively in the training phase, suggesting a reduced dependency on real PROPELLER blades. This innovation in synthetic blade generation and data augmentation technique contributes to enhanced image quality and improved generalization capability of the associated deep learning model for PROPELLER MRI reconstruction. [ABSTRACT FROM AUTHOR]

The utilization of Synthetic Aperture Radar (SAR) images for ship recognition holds significant importance within the realm of maritime surveillance and security. SAR images are useful for ship detection and recognition because they can penetrate through clouds and capture detailed information about ships, such as their size, shape, and orientation. In the context of ship recognition using SAR images, the primary objective is to employ automated methods for the identification and categorization of ships present in SAR imagery. The detection of ships in SAR images is a significant research area, but it remains challenging due to speckle noise, land clutters, and low signal-to-noise ratio. Researchers have developed various approaches to overcome this challenge, such as adaptive filtering, speckle reduction, and segmentation techniques. Hence, a ship detection method is devised that combines the active contour method and the YOLO-v8 model using deep learning techniques. In the first step, the SAR images undergo pre-processing and normalization, and the model is trained with the backbone network. The YOLO-v8 model, renowned for its proficiency in object detection, is applied to delineate precise bounding boxes around ships within the images. The results obtained from experiments conducted on a variety of SAR images convincingly demonstrate that the suggested approach attains proficient ship target detection, striking a balance between accuracy and comprehensiveness. This approach represents a promising solution to enhance ship detection in challenging SAR scenarios. [ABSTRACT FROM AUTHOR]

The natural terrain (e.g., farm and forest) in temperate zones changes dramatically between seasons due to distinct temperatures and precipitation variations from summer to winter. Moreover, built-up areas vary little in this short period. Therefore, extracting built-up areas via change detection on polarimetric synthetic aperture radar (PolSAR) images is feasible. A common type of PolSAR change detection method is based on hypothesis testing theory. However, in these methods, pixels are selected as the processing units; as a result, these models are computationally complex and poorly maintain the boundaries of built-up areas. In this paper, we innovatively introduce superpixels into the hypothesis test theory and propose a superpixelwise PolSAR change detection method for built-up area extraction. First, we oversegment the PolSAR images into a set of superpixels and derive the probability density function (PDF) of a superpixel's reflectivity on a PolSAR image. Based on this distribution, we present a superpixelwise likelihood-ratio test (LRT) statistic to measure the similarity of two superpixelwise covariance matrices for unsupervised change detection. When actually computing the superpixelwise LRT, the large variation in the areas of the superpixels makes the likelihood functions very complex and estimating the parameters difficult. We further simplify the calculation of the LRT statistic and apply it to built-up area extraction. Compared to the state-of-the-art built-up area extraction methods, our approach provides the best results with overall accuracy values of 91.41%, 92.71%, 93.67% and 93.91% for the four studied areas respectively. In addition, the computational complexity of our method is assessed, and the run time (seconds) of the proposed method in the four study cases is 11.14, 6.32, 5.31, and 4.88, respectively, superior to the values of 66.12, 36.26, 33.65, and 29.67 for DRT, respectively. Code and datasets: https://github.com/SunXJ7/Sentinel-1-Datasets-for-Built-up-Area-Extraction. [ABSTRACT FROM AUTHOR]

Due to the high mobility and strong concealment characteristics of synthetic aperture radar (SAR) targets, the corresponding SAR datasets exhibit few-shot data properties, and there is a significant lack of research on few-shot target detection methods in the SAR domain. Furthermore, this study is subject to the following limitations: the scarcity of SAR data and significant sample variations make it difficult to control class centers using existing methods, and the learned models tend to be biased towards base classes while easily confusing novel classes with base classes. These limitations hinder the generalization of knowledge from base classes when detecting novel class targets. In this work, we propose a novel few-shot SAR target detection method based on Gaussian meta-feature balanced aggregation (GMFBA), which is based on meta-learning. Specifically, we first propose two novel feature aggregation methods with Gaussian metrics, namely Gaussian projection distribution metric (GPDM) and Gaussian kernel mean embedding metric (GKMEM). By estimating class distribution with variational autoencoders to replace traditional class prototypes, we sample from robust distributions and measure projection Wasserstein distance and Gaussian kernel mean embedding distance with prior distributions, obtaining the best robust support features under the optimal measurement results. Then, based on GPDM and GKMEM, we propose a novel balanced inter-class uncorrelated aggregation (BICUA) method, which extracts support features of each class according to the proportion of samples and aggregates them with query features in a balanced manner, promoting feature representation between different classes and ensuring no interference between features to significantly reduce confusion between base classes and novel classes. Specifically, GMFBA outperforms the state-of-the-art method G-FSOD significantly in all settings, achieving state-of-the-art performance. In contrast, the novel class detection performance of GMFBA has shown an average improvement of 8.56% on split1 and split2 of the SRSDD-v1.0 dataset, and an average improvement of 1.41% on split1 and split2 of the MSAR-1.0 dataset. The code is available at https://github.com/Caltech-Z/GMFBA. [ABSTRACT FROM AUTHOR]

Internal solitary waves (ISWs) induce convergence and divergence of sea surface currents, manifesting as alternating bright and dark stripes in optical and synthetic aperture radar (SAR) remote sensing images. Although the relationship between ISW surface features in SAR images and ISW-induced surface currents has been extensively explored, it has not been clearly quantified in optical images. This study contrasts the surface features of the same ISWs using optical and SAR images with short time intervals and statistically analyses the 450 ISW stripe widths in the northern South China Sea and the Andaman Sea. The results demonstrate that the ISW surface features in optical and SAR images differ, with the ISW stripe widths of SAR images being 0.83 times those of optical images. Laboratory experiments are conducted to simulate the ISW surface features of optical and SAR images, which exhibit a consistent quantitative relationship with remote sensing results. Further experiments reveal that the ISW-induced free surface displacement is the cause of the differences in the ISW surface features between optical and SAR images. This research provides support for broader quantitative applications of optical remote sensing images. • The stripe widths of SAR images are 0.83 times smaller than those of optical images. • Laboratory experiments exhibit a consistent relationship with remote sensing results. • The free surface displacement is the reason for large stripe widths of optical image. [ABSTRACT FROM AUTHOR]

• A high-speed, high-efficiency solution utilizing phase-change materials (PCM) within a metal-dielectric-metal (MDM) nano-cavity configuration method. • Our 4-layer nano cavity, ∼600 nm thin metal-dielectric cavity offers dynamic switching between bright-field (low pass filtering) and dark-field (high pass filtering) modes. Enhancing optical imaging, it provides essential morphological information. • Pioneering the application of nano-photonics devices in reflection mode for optical analog computing, demonstrated through experimental schemes involving interferometry setups, offering potential applications in biomedical imaging, computer vision, and artificial intelligence. A combination of optical analog computing and nanophotonic design is attractive due to its fast-processing times, large data processing rates, and high integration with modern imaging systems. However, most of the proposed nano-photonics designs for this purpose have static functionality, which limits their applicability. By modulating the Green's function response of the thin film nanocavity using phase change properties of S b 2 S 3 , we propose tunable bright and dark-field imaging. We optimize the nano-cavity for the bright field in a crystalline and dark field in the amorphous phase of S b 2 S 3. The proposed design shows a near-unity numerical aperture which enables the resolution of the system to ∼500 nm. Lastly, we examine the potential integration of this cavity into a standard interferometric setup and scattering microscopy, leveraging the tunable modulation capability of the proposed cavity as a substrate in reflection mode scheme. This ultrathin, on-chip, and real-time tunable lithographic-free imaging system can play a crucial role in a host of applications such as machine vision, medical imaging, and sensing. [ABSTRACT FROM AUTHOR]

In this paper, we introduce pySimFrac , an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. pySimFrac allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, pySimFrac includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present pySimFrac along with integration examples and discuss the ability to extend pySimFrac from a single complex fracture to complex fracture networks. • A python library to develop synthetic complex fractures. • Fractures can be passed to single- and multiphase simulations for analysis. • Examples of fracture generation and analysis is provided in Jupyter notebooks. • The code-base and user-documentation are made available on GitHub and dfnWorks websites, respectively. [ABSTRACT FROM AUTHOR]

In this study, a refined and improved theoretical model was proposed for calculating the synthetic aperture radar (SAR) images of internal wave wakes. This model was based on an equivalent source model of internal wave wakes and the velocity-bunching principle. Wake field, scattering field, and SAR image characteristics were systematically calculated for multiple scenarios. The model described the transfer and solution processes by inputting the background flow field temperature and salinity profiles to map the radar image intensity distribution. The physical mechanisms of the internal wave wake generation and its capture by SAR were comprehensively explained. The effects of various parameters on the internal wave wake SAR images were analyzed comprehensively. The SAR image features containing internal wave wakes were determined based on the gray-level co-occurrence matrix and image power spectrum, which revealed that the maximum surface current induced by the internal wave modes of the same amplitude was considerably larger than that induced by surface-wave mode. When traveling far from the water surface, the rate of change in the total scattering coefficient because of the Bragg effect was approximately 20 times that of the slope effect. In SAR images, the recognizability of wakes depends primarily on the maximum intensity rather than the average disturbance on the sea surface. Therefore, SAR images exhibit considerable differences during downwind radar observations, with the most prominent contrast differences occurring at the maximum spreading angle of wake scattering and 1500 m behind underwater objects. In the image power spectrum, the frequency range of the first modal internal waves was approximately 10–30 Hz, whereas the higher-order modal internal waves had frequencies lower than 10 Hz. Furthermore, the frequency range of the surface-wave mode was larger than that of internal-wave modes, approximately 50–60 Hz. • A comprehensive theoretical and simulation model are developed to accurately depict the synthetic aperture radar (SAR) imaging of internal wave wakes generated by underwater moving objects. • The physical process of exciting internal wave wakes by moving objects in real sea environments and their significant influence on SAR image modulation is thoroughly elucidated. The modulation effects of factors such as wake magnitude and flow velocity on SAR images are thoroughly analyzed, leading to the determination of optimal navigation and radar parameters for observation. • Leveraging the flow field characteristics of internal wave wakes, the contrast features and power spectrum information of wake signals are successfully extracted from SAR images. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to determining the disappearance time of deformation increase after the conclusion of mining activity using Knothe's time function and persistent scatterer interferometric synthetic aperture radar (PSInSAR). As a result, a method was proposed for determining a parameter of the function, the time coefficient c , using SAR data. The area of study was a strongly urbanised (city centre) post-mining region within the Upper Silesian Coal Basin (USCB), located in central-eastern Europe (Poland), which was characterised by intense hard coal mining that was concluded in 2015. The study analysed ground displacements generated directly after the conclusion of extraction as well as the time after which the rock mass assumes a relatively calm state. The obtained results were validated based on the results of classic geodesic measurements. The average time coefficient c determined by the developed method was 0.84, while the average time coefficient based on classic geodesic measurements was 1.01. So the theoretical disappearance time of deformation increase was, respectively, 5 years and 6 months and 4 years and 7 months. The research revealed that the developed method is helpful in determining the parameter in Knothe's function and enables a more accurate definition of the disappearance time of deformation increase after the conclusion of extraction. This has great significance in the context of safety and the potential limitation of damage generated as a result of the negative influence of underground mining on the ground surface as well as for the spatial planning of post-mining areas. • Developed method to determine coefficient c Knothe's time function using SAR data. • PSI-based approach enhances theoretical disappearance time accuracy. • PSI yields lower deviation (1.7 %) vs. geodesic measurements (34 %). • Methodology shows disappearance time: 5 years, 6 months, matching ground subsidence. [ABSTRACT FROM AUTHOR]

Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system's rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve the image quality to meet interpretation needs, we first delineate the imaging process of the rotating primary mirror and analyze the characteristics of image degradation based on the system's imaging mechanism. Then, we propose a dual super-resolution (SR) framework based on Swin Transformer and introduce a self-supervised learning method for jointly training the unified SR network using wavelet fusion. The self-supervised learning method effectively utilizes the spatiotemporal correlation of the information contained in images captured at different rotation directions of the rectangular pupil. Moreover, the attention mechanism in Transformer can adopt a global perspective and utilize content-based interactions between image content and attention weights to model strong long-range dependencies in remote sensing images. This approach significantly enhances image quality along the pupil's shorter side, consequently yielding superior results. Extensive digital and semi-physical imaging experiments, involving six aspect ratios of the primary mirror, demonstrate that our SR method surpasses state-of-the-art methods. The work in this paper can serve as a valuable reference for future space applications of the RSA technology. • Analyzing the imaging mechanisms of the rotating synthetic aperture system. • Self-supervised learning enhances the utilization of image internal information. • Designing a dual framework that can be trained jointly for image super resolution. • Interactions between image content and attention weights improve performance. • Performance is evaluated through digital and semi-physical experiments. [ABSTRACT FROM AUTHOR]

Using satellite-based SAR (Synthetic Aperture Radar) imagery to detect and track ships is a formidable challenge. However, accurate analysis is hampered by inherent difficulties such as obscured edges, multiple targets, varying dimensions and complex backgrounds. These factors contribute to a poor signal-to-noise ratio. Various artificial intelligence models have been developed to improve these problems, especially with YOLO-based models. To overcome the above challenges and achieve robust performance, this study presents a model called SwinYOLOv7, a novel fusion of the YOLOv7 framework with the Features Pyramid Network and the Swin Transformer using ground-breaking anchor-free detection algorithms. This innovative method aims to increase the accuracy of vessel detection while reducing the impact of background clutter. The proposed model improved by YOLOv7 examined three different datasets in detail: SRSDD-v1.0, HRSID, and SSDD to optimize the performance of the model. The training process was consistently verified using superior recall, precision, and F1-score values, which can be easily compared with previous studies. The results show that the model using the Swin Transformer attention mechanism and using an image size of 640×640 achieves the highest accuracy of 96.59%. Alternative attention mechanisms, including the Squeeze-and-Excitation Network (SEnet), the Convolutional Block Attention Module (CBAM), Channel Attention (CA) and Efficient Channel Attention (ECA), deliver poorer accuracy rates. The combination of YOLOv7 and Swin Transformer yielded encouraging results that enabled the proposed model to outperform the current benchmarking models. Therefore, the proposed model provides a compelling solution to ensure accurate vessel identification in complex search and rescue scenarios. [Display omitted] • A new version of YOLOv7 model was developed based on Swin Transformer. • Different data sources were used for training and testing new model for ship detection. • The performance of new model was compared with other 18 older models. • The new YOLOv7 model boosts the detection performance better than previous ones. • Model using 640×640 image size achieved the highest accuracy of 96.59%. [ABSTRACT FROM AUTHOR]

• The multi-mode laser ultrasonic F-SAFT and F-TFM imaging have realized. • The frequency-domain imaging results were weighted by phase circular statistical vector factor. • Achieved blind spot free images of the minimum 0.3 mm transverse hole defect. • Continuous scanning frequency-domain imaging of multiple defects verified the detection ability. Far-field laser technology has greatly promoted the progress of nondestructive ultrasonic imaging of bulk structures. However, under thermoelastic excitation, the body waves exhibit a relatively low signal-to-noise ratio, resulting in images with low resolution and contrast. Based on the motivation, this paper developed a frequency-domain phase weighted imaging method to improve the quality of laser ultrasonic defect imaging. Firstly, laser ultrasonic scanning was performed on the sample with artificial transverse hole defects. The cylindrical lens focused line source was used to improve the intensity of the body wave signals, and ensure that there was no damage on the material surface under high laser energies. Then, the frequency-domain phase shift migration (PSM) algorithm was used to perform multimode imaging of defects, achieving frequency-domain synthetic aperture focusing technique (F-SAFT) and total focused method (F-TFM) imaging based on full matrix capture. Furthermore, the phase circular statistical vector (PCSV) was proposed for weighted optimization, which improved the image quality, suppressed the background noise and multimode artifacts. Finally, the imaging quality of several algorithms were discussed. The results indicate that frequency-domain images were superior to time-domain results. After phase weighting, the imaging quality can be further improved, and the detection blind zone was significantly reduced. This work will contribute to the rapid and high-quality defect imaging of laser ultrasonic. [ABSTRACT FROM AUTHOR]

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems. • We present a novel, multisource method to monitor deforestation in near real-time. • Uses a simple approach for data harmonization based on time series models. • Demonstrated for two regions in northern Myanmar with contrasting forest types. • Tradeoffs between detection accuracy and latency are quantified. [ABSTRACT FROM AUTHOR]

The silicon vertex detector (SVD) is a four-layer double-sided strip detector installed at the heart of the Belle II experiment, taking data at the high-luminosity B -Factory SuperKEKB since 2019. SVD has been operating smoothly and reliably, showing a stable and above-99% hit efficiency, and a large signal-to-noise ratio in all sensors. In June 2022 the data-taking of the Belle II experiment was stopped for the Long Shutdown 1, primarily required to complete the vertex detector (VXD) with the inner two-layer DEPFET detector and to upgrade several components of the accelerator. This article reports on the excellent performance of SVD in terms of the signal-to-noise ratio, the hit position resolution, as well as the hit-time resolution. We briefly describe the challenges and delicate phases of the VXD re-installation and the SVD status for operation starting in early 2024. In SVD layer 3, which is closest to the interaction point, the average occupancy has been less 0.5%, well below the estimated limit for acceptable tracking performance. However, higher machine backgrounds are expected at increased luminosity, and so also increased hit occupancy. To enhance the robustness of offline software in a high-background environment, new algorithms of background suppression using the excellent SVD hit-time information have been developed, which allows a significant reduction of the fake rate, while preserving the tracking efficiency. With the increasing luminosity also the radiation levels are expected to increase, with possible deterioration of the sensor performance. The SVD integrated dose is estimated by the correlation of the SVD occupancy with the dose rate measured by the diamonds of the radiation monitor and beam-abort system. The effects of radiation damage are starting and in good agreement with our expectations. So far, no harmful impact due to the radiation damage on the detector performance has been observed. [ABSTRACT FROM AUTHOR]

Maritime surveillance using remote-sensing images is one of the emerging technologies. Because of the inaccessibility of real-time remote-sensing images, it is imperative to model them. The main intention of this study is to investigate the factors influencing the detection of submarines, irrespective of the imaging sensors. In the present work, submarine wakes (Bernoulli hump and Kelvin wake) are modeled and integrated with the sea surface to examine the key features of the submarine. Here, the sea surface is simulated by linear Airy theory using the Elfouhaily spectrum, and novel submarine position projection in imaging coordinate (SPPIC) technique is introduced to generate synthetic free surface wakes in different orientations. Besides, Computational Fluid Dynamics (CFD) technology is utilized to assess and endorse the SPPIC-based simulated wake models. The Beaufort scale is used to validate the modeled sea surface, and the root mean square error of the model is 0.314 m. Then, the simulated submarine wakes are verified by analyzing its essential features and extracting the significant parameters of submarine, such as moving velocity, heading angle, and diving depth. Further, the current research explores these significant characteristics in the context of ocean waves. The Fourier transform technique is employed to retrieve the heading angle and velocity of the submarine. The introduction of variance distribution in the Radon transform method resolves the 180⁰ ambiguity problem in heading angle calculation. Additionally, negative exponential relation is established between the diving depth of the submarine and the height of the Bernoulli hump. From this empirical equation, one can fetch the diving depth of the submarine. The discrete wavelet transform approach enhances the edges of the wedge Kelvin wake in the presence of sea waves and is demonstrated with real-time synthetic aperture radar images. Moreover, primary wake features are investigated and evaluated for different classes of submarines. • Modeling of free surface submarine wakes at various orientations, velocities, and depths using the SPPIC method. • Computational Fluid Dynamics (CFD) technology is utilized to assess and endorse the SPPIC-based simulated wake models. • The exploration of submarine's wake characteristics in the context of ocean waves using wavelet filter. • Retrieval of the submarine's primary parameters such as velocity, heading angle, and depth using Fourier and Radon techniques. • A new empirical relation is derived to inverse the depth of the submarine from its Bernoulli hump height measurements. [ABSTRACT FROM AUTHOR]

The primary objective of prescribed burning (PB) treatment is to promote increased ecosystem resistance to high fire severity and ecosystem adaptive resilience after unplanned wildfires under worsening climates. Yet, empirical evidence involving regional-scale assessments with enough spatial representativeness on how vegetation will respond to PB-wildfire interactions in Mediterranean shrublands is absent. This is particularly relevant to provide comprehensive insights on the effectiveness of PB policies and programs in southern European countries, where shrublands are the main PB target. Here, we investigated the PB effect on vegetation responses in the short-term (1–3 years) after subsequent wildfire encounters in Mediterranean shrublands at the regional scale across mainland Portugal. We leveraged geospatial cloud-computing platforms and remote sensing data (optical and synthetic aperture radar -SAR-) and techniques with physical basis to procure reliable proxies for vegetation fractional cover (FCOVER), vigor (normalized difference vegetation index; NDVI) and structural complexity (cross-polarization ratio of C-band SAR backscatter data; SAR-CR) at the landscape scale throughout the time series. The role of site productivity and topographic configuration variables as putative drivers of vegetation responses was also analyzed. We found that the mitigating effect of PB on wildfire severity translated into stronger vegetation responses in treated areas than in untreated counterparts after the first PB-wildfire encounter. Although FCOVER, NDVI and SAR-CR recovered gradually in both areas, the recovery rate was markedly higher in PB-treated areas 3-years after the PB-wildfire encounter than at earlier times in the time series. The effect of PB treatment was stronger for the FCOVER recovery response than for NDVI and SAR-CR. The effect of the PB treatment on FCOVER and NDVI responses was modulated by site environmental characteristics, such that their differences between PB-treated and untreated counterparts were maximal for both higher altitudes and warmer, drier locations in the landscape. • First regional-scale assessment of vegetation responses in PB-wildfire intersections. • PB mitigates fire severity in reburns and enhances short-time vegetation responses. • The strongest vegetation response was found 3-years after the PB-wildfire encounter. • PB effect was higher for vegetation cover than for vigor or structural complexity. • PB effect on vegetation responses was modulated by site environmental characteristics. [ABSTRACT FROM AUTHOR]

Label-free microscopy directly images live samples without fluorescent markers, providing a panoramic view of biological structures and functions. However, pursuing high-throughput and high-content live cell imaging requires both higher spatial resolution and larger field of view, which are limited by the optical system. In this study, we custom-built a 5▪/0.35 objective with a field number of 28 mm, providing a space-bandwidth product of 34 megapixels, which is four times greater than that of commercial high-throughput objectives such as the Nikon Lambda series (4▪/0.2, 10▪/0.45, 20▪/0.75). Furthermore, we incorporated Fourier ptychography microscopy (FPM) into the system, achieving a synthetic numerical aperture of 0.72, suitable for digital pathology and long-term live cell observation. To improve the FPM performance, we proposed a phantom-based calibration method for quantitative correction of illumination angle errors. Additionally, this method can also serve as an initial step for the on-line calibration. The remarkable capabilities of our 5▪/0.35 objective have been demonstrated, as well as the effectiveness of computational label-free microscopy. Furthermore, by combining our original FPM with more advanced hardware and algorithms, our FPM can achieve higher performance. • Custom-built a high-throughput objective lens. • Developed a Fourier ptychography microscopy system based on the high-throughput objective lens. • Introduced a phantom-based calibration method for illumination angle correction. • Integrated traditional optical design techniques with computational imaging methods. [ABSTRACT FROM AUTHOR]

The novel rotating synthetic aperture imaging system has the advantages of short processing cycle, light weight, low cost, and convenient transport, which is a potential development direction for future optical satellite payloads with ultra-large aperture. However, influenced by the coupling effects of the rectangular primary mirror, rotating imaging, and link perturbation, images from this system may have unique spatial and temporal variability with resolution anisotropy, resulting in the dynamics and weak features of targets. The insufficient recognition of system characteristics seriously restricts the application ability of corresponding target interpretation methods. To address this problem, we start with the analysis of system imaging characteristics and target interpretation mechanisms. Considering calculating texture, grayscale, and edge feature parameters in evaluation regions of different sizes, we propose a degradation assessment method based on multi-scale feature characterization. Then, combined with the theoretical calculation and practical target detection experiments, the influence of crucial parameters such as the aspect ratio and rotation angle of the primary mirror on the target interpretation capability is analyzed. The results will provide guidance for the integrated design and optimization of optical imaging systems and intelligent image processing methods. • A degradation assessment method based on multi-scale feature characterization is proposed. • The effect of rotating synthetic aperture system on image target characteristic degradation is analyzed. • The impacts of key system parameters on target detection performance are analyzed. [ABSTRACT FROM AUTHOR]

• From a traditional perspective, an unsupervised spatial self-similarity difference method is proposed. • The detection results are proved to be feasible using multi-source datasets. • It effectively avoids the effect of noise and fully considers the image spatial information. Multi-source heterogeneous change detection has been widely used in dynamic disaster monitoring, land cover updating, etc. Various methods have been proposed to make heterogeneous data comparable. However, heterogeneous images are difficult to compare directly and may be affected by noise. Most existing methods obtain change information through mapping and regression, lacking the utilisation of image spatial information and a comprehensive portrayal of the changes, which may affect change detection results. To address these challenges, we propose an unsupervised spatial self-similarity difference-based change detection (USSD) method for multi-source heterogeneous images to evaluate the similarity of spatial relationships in heterogeneous images. First, the images are divided into image blocks to construct spatial self-difference images between individual image blocks aiming to make the data comparable. Second, the change information is portrayed in terms of both the magnitude differences and similarity differences to obtain a more comprehensive spatial self-difference change magnitude map. Then, the spatial neighbourhood information of the spatial self-difference change magnitude map is considered to avoid noise. Experimental results on six open datasets indicate that the overall accuracy of the USSD method was approximately 85%–95%. This method improves the change magnitude map discrimination, better detects the change region, and avoids noise in synthetic aperture radar images. [ABSTRACT FROM AUTHOR]

Laser ultrasound is a widely used tool for industrial quality assurance when a contactless and fast method is required. In this work, we used a laboratory setup based on a confocal Fabry–Perot interferometer to examine weld seam models. The focus was placed on small samples with curved surfaces (small in the sense that the radius of curvature is comparable to the largest ultrasonic wavelength) and on efficient ways to detect the presence and volume of process pores, with the goal to transfer this method to industrial applications. In addition to this experimental method for investigating welds, a numerical method that models the experimental setup was implemented in MATLAB. For this purpose, first the thermal effects of the excitation process were taken into account by solving the thermal diffusion equation with an explicit scheme. Then, the elastodynamic equations were solved using the Elastodynamic Finite Integration Technique, taking into account the stresses induced by the excitation process. The B-Scans generated with this numerical model were compared with experimental B-Scans for simple test cases and good agreement was found. In a next step, the additional structures in the B-Scans resulting from air inclusions were identified and investigated with both methods using flat test specimens at first. Besides the direct echoes, structures from skimming surface waves and multiple reflections were visible. These additional structures are unwanted in defect reconstruction methods like the Synthetic Aperture Focusing Technique (SAFT) as they would lead to artifacts. In samples much larger than the largest ultrasound wavelength, however, these unwanted structures are still negligible in amplitude or can be well separated temporally, but for small samples this is no longer the case. As a result, reconstruction methods based on direct echoes like SAFT are difficult to apply. For many industrial applications, the reconstruction is not decisive at all, but only the knowledge of the total volume of process pores (TVPP). It is shown with both experimental and numerical methods, that this TVPP can be estimated from the variation in the B-Scans from various small weld seam models. • Defect detection for curved surface samples aiming for industrial applicability. • Numerical model based on EFIT (Elastodynamic Finite Integration Technique). • Estimation of process pore volume in small welds from laser ultrasound B-Scans. • Applicability of SAFT (Synthetic Aperture Focusing Technique) investigated. [ABSTRACT FROM AUTHOR]

• Analyzing the imaging characteristics of the rotating synthetic aperture system. • Intra-frame self-attention enhances the utilization of spatial information. • Inter-frame mutual attention enhances the utilization of temporal information. • Interactions between image content and attention weights improve performance. • Performance is evaluated through digital and semi-physical experiments. Rotating synthetic aperture (RSA) technology offers a promising solution for achieving large-aperture and lightweight designs in optical remote-sensing systems. It employs a rectangular primary mirror, resulting in noncircular spatial symmetry in the point-spread function, which changes over time as the mirror rotates. Consequently, it is crucial to employ an appropriate image-fusion method to merge high-resolution information intermittently captured from different directions in the image sequence owing to the rotation of the mirror. However, existing image-fusion methods have struggled to address the unique imaging mechanism of this system and the characteristics of the geostationary orbit in which the system operates. To address this challenge, we model the imaging process of a noncircular rotating pupil and analyse its on-orbit imaging characteristics. Based on this analysis, we propose an image-fusion network based on a vision transformer. This network incorporates inter-frame mutual attention and intra-frame self-attention mechanisms, facilitating more effective extraction of temporal and spatial information from the image sequence. Specifically, mutual attention was used to model the correlation between pixels that were close to each other in the spatial and temporal dimensions, whereas long-range spatial dependencies were captured using intra-frame self-attention in the rotated variable-size attention block. We subsequently enhanced the fusion of spatiotemporal information using video swin transformer blocks. Extensive digital simulations and semi-physical imaging experiments on remote-sensing images obtained from the WorldView-3 satellite demonstrated that our method outperformed both image-fusion methods designed for the RSA system and state-of-the-art general deep learning-based methods. 1 1 Dataset is available at https://github.com/sherlockyusun/RSA-Fuse. [ABSTRACT FROM AUTHOR]

This work presents an autonomous remote sensor measurement system to monitor structural change detection. We utilized a portable network analyzer as a core component of a ground-based synthetic aperture radar to achieve structural monitoring. First, we realized a 2-meter rail apparatus, where antennas and radio frequency components of the radar can move, to obtain a synthetic radar aperture. Then, we develop signal processing algorithms to obtain radar images and to perform change detection. Although the range resolution of our system is 15 cm due to the frequency bandwidth of the radar, we have successfully developed an interferometric change detection technique to achieve up to 440 μ m change detection of the monitored structure. • Developed a remote sensor system for sub-cm structural changes using GB-SAR. • Implemented interferometric SAR algorithm for precise structural change detection. • Installed an automated radar system for continuous monitoring of building exteriors. • Achieved mm-level detection for early deformations and movements in structures. • Demonstrated the system's capability to track and measure lateral structural changes. [ABSTRACT FROM AUTHOR]

• Develop a cascaded beam scanning device with an effective aperture of 20 mm based on microlens array scanner and adaptive fiber collimator. • The scanning beam quality is close to the diffraction limit. • The device achieves a scan FOV of 20° and a scan angular velocity of 10,000°/s. • The device is equipped with precise electronically controlled alignment and 2D scanning capabilities. • The experiment shows the ability of wide-angle, high-resolution, high-speed continuously addressed beam scanning. Microlens array scanner (MLAS), as a rapidly growing branch of the semisolid micro-mechanical beam scanning system, can achieve large-scale beam scanning through small-scale (μm) physical motion, which is an effective beam scanning scheme that balances aperture, field of view (FOV), inertia, and other vital indicators. However, the discrete addressing caused by the periodic structure of microlens array (MLA) will affect the scanning accuracy in applications. We design and develop a continuous laser beam scanning device with an effective aperture of 20 mm based on MLAS and adaptive fiber collimator (AFOC), and the scanning beam quality is close to the diffraction limit. In addition, the electrical control mechanism of the device is explained, which achieves a scan FOV of 20°, a scan angular velocity of 10,000°/s, and two-dimensional (2D) scan. This approach effectively realizes a beam scanning system with excellent comprehensive performance, including large aperture, large scan angle, high precision, high speed, tunable wavelength, and high power. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a metalens with a hybrid unit-cell arrangement that operates at a wavelength of 0.94 μm. The metalens consists of 0.8 μm unit-cells on the inner and 0.4 μm unit-cells on the outer. The transmittance of 0.8 μm and 0.4 μm unit-cells are about 0.5 and 1, respectively. Therefore, the proposed metalens with hybrid unit-cells, behaving like a synthetic aperture lens, suppresses the low spatial frequency signal and enhances the high spatial frequency one. As a result, the proposed metalens with hybrid unit-cells presents a higher spatial resolution than that with an identical unit-cell size and holds higher transmittance than that of a conventional synthetic aperture lens. Moreover, we comprehensively discuss the optical performance and limitations of pixelate metalens and expand the application prospects of synthetic aperture metalenses in optical imaging. [ABSTRACT FROM AUTHOR]

In this article, we propose a recursive orbital elements filter for autonomous control of Distributed Satellite Systems (DSS) that significantly reduces the variance of relative orbital elements between the observed and the desired satellite orbits. Leveraging satellite kinematics and control inputs data, combined with a model of relative dynamics, the filter provides smooth and continuous orbital control, while minimizing propellant consumption. In conjunction with Precise Point Positioning (PPP) navigation, the proposed filter enables onboard continuous low-thrust control compatible with high-performance electric propulsion. We also propose a restricted transverse/normal control law that simplifies the thruster's configurations and/or attitude manoeuvres required for propulsion pointing. The applicability and validity of our proposed techniques are verified by numerical simulations with two case studies: a constellation for Differential Interferometric Synthetic Aperture Radar (DInSAR) for global infrastructure monitoring; and a maritime domain awareness mission based on along-track interferometric synthetic aperture radar which requires single-pass interferometry for responsive ship traffic surveillance, and the coverage of a very large maritime zone with high revisit rates. [ABSTRACT FROM AUTHOR]

Optical sparse aperture imaging technology has promising applications in the next generation of high-resolution imaging systems, but the phasing errors correction of sparse aperture is an urgent problem to be addressed. In this paper, we propose a blind structured-illumination super-resolution imaging system using a sparse aperture as the transmitter to overcome phasing errors and obtain higher resolution. In our scheme, an unknown pattern is projected onto the object by a sparse aperture system. By translating the unknown pattern, a sequence of modulated raw images is obtained. Based on these low-resolution raw images, the super-resolution object and the unknown illumination pattern are jointly estimated by the Adam algorithm. Here we show, both in the simulation and experiment, the resolution of reconstructions using sparse aperture as transmitter are similar to those using monolithic lens. Furthermore, we validate that the proposed method can protect the reconstruction effect from piston error and widen the tolerance range of tip/tilt error (peak-valley values) up to 0.5λ. At the same time, our proposed system can also alleviate to some extent the problem of mid-frequency modulation transfer function (MTF) attenuation that exists in sparse aperture imaging. Our approach not only simplifies the process of constructing synthetic aperture system, but also provides a novel application for synthetic aperture systems. • The introduction of structured illumination improves the resolution of synthetic aperture imaging. • Protect the reconstruction effect from piston error in the sparse aperture transmitter. • Widen the tolerance range for imaging system of tip/tilt error in sparse aperture transmitter. • Alleviate to some extent the problem of mid-frequency attenuation that exists in sparse aperture imaging. [ABSTRACT FROM AUTHOR]

Periodic inspection of concrete structures for detecting internal defects through nondestructive imaging is recommended for ensuring structural safety and integrity. Ultrasonic imaging is one of the emerging and widely accepted techniques in nondestructive diagnostics of concrete structures. However, manually screening the ultrasonic images to detect and localize anomalies over large areas especially those from public infrastructure is tedious and prone to misjudgments. This work proposes a Region-based CNN to automatically detect, localize, and segment those signatures corresponding to defects from the noisy ultrasonic images with multiple features. This network replaces the conventional RoI pooling adopted in RCNNs with RoIAlign. To optimize the model for real-life complexity and noise levels, the network is trained on real experimental data. The ultrasonic image dataset has been generated by applying the imaging technique based on the Synthetic Aperture Focusing Technique on reflected waveform data obtained on various specimens with installed defects. The proposed network has been trained on the labelled image dataset containing signatures corresponding to multiple flaws like debonded rebars, delaminations, and planar crack-like defects in concrete members. The predictions of the trained model over the test datasets detect and masks the pixels corresponding to defects in ultrasonic images with a best mAP of 0.98. Segmentation results of the developed model upon testing are found superior to other state-of-the-art defect detection networks. • Presents a two-stage Convolutional Network for automatic detection and segmentation of defects in ultrasonic images. • First CNN-based model to segment defects in ultrasonic images of Reinforced Concrete. • Model trained on images with intact rebars in concrete, debonded concrete-rebar interfaces, delaminations, and inclined defects. • The model during testing recorded accuracies of 98% & 85% on clean and artifact-filled test sets, respectively. • Predictions from the proposed masking model are superior to those from the one-stage detector model on the same datasets. [ABSTRACT FROM AUTHOR]

Ultrasonic testing is widely used in the industrial field because of its high sensitivity, safety and low-cost, which plays an increasingly important role in detecting defects on industrial. However, at present, defect detection only stays in scanning surface defects and classifying the internal defects of the specimen. The accuracy of quantitative imaging of the morphology of the internal defects of the specimen is still an issue to be improved. In this paper, the simple defects in several specimens are detected by ultrasound based on finite element simulation software and the accuracy of the signal can be verified by ultrasonic experiment. Then, the features can be extracted by signal processing method. The neural network imagines the inversion results with the Gaussian weight function fusion model. Finally, this paper uses the signals of simple shape defects as the training set to detect the shapes of complex defects and obtain the corresponding imaging results. • The defect detection using moving ultrasonic A-scan testing and signal processing. • Defect image inversion by neural network and Gaussian weight data fusion algorithm. • Complex defect inversion by learning the features of simple defects. [ABSTRACT FROM AUTHOR]

• DAS and DMAS responded differently to changes in f-number. • DAS showed a tradeoff in spatial resolution when optimizing contrast. • DMAS did not show the same tradeoff as DAS when optimizing contrast. • DAS typically outperformed DMAS for image contrast. • DMAS typically outperformed DAS for spatial resolution. The choices of transmit and receive f-numbers impact both ultrasound image contrast and spatial resolution. Although previous studies have evaluated the impact of receive f-number in delay-and-sum (DAS) plane wave imaging, there has not been a systematic study of f-numbers in DAS or delay-multiply-and-sum (DMAS) synthetic aperture (SA) imaging. In this study, we measured the impact on main lobe to side lobe energy ratio (MSER), generalized contrast-to-noise ratio (gCNR), and spatial resolution when varying receive and transmit f-numbers from 1 to 5 in 0.2 increments in DAS and DMAS reconstructed SA images. A wire target in a water tank and a standard imaging phantom were used to measure these metrics. From the water tank wire target images, higher MSER values were achieved with middle-range transmit f-numbers (2–4) and high receive f-numbers (>4) for both DAS and DMAS. From the phantom contrast target images, DAS produced images with high gCNR when using high transmit f-numbers (>4) and high receive f-numbers (>4). This came at the cost of reduced spatial resolution. DMAS produced images with high gCNR when using low transmit f-numbers (<3) and high receive f-numbers (>4). DMAS was not found to have as severe of a tradeoff in spatial resolution when seeking maximum gCNR. However, gCNR was typically lower for DMAS than DAS. For both DAS and DMAS, point target images had high spatial resolution when using low receive f-numbers (<2). Spatial resolution was typically higher for DMAS than DAS. Hanning apodization was found to produce similar trends as those found with rectangular apodization. These findings give insight on the behaviors of DAS and DMAS SA reconstruction algorithms and could be used to guide f-number selection. [ABSTRACT FROM AUTHOR]

• Holographic images can be combined coherently to increase image resolution and field of view. • The choice of illumination and observation geometries affect the point-spread and transfer characteristics of the imaging system. • Phase contrast imaging in transmission and reflection geometries is possible. This paper discusses a modular implementation of synthetic aperture interferometry (SAI) using inexpensive telecommunications technologies that can be expanded for use in optical instruments with arbitrarily large fields of view. Exploiting multiple image sensors and multiple illumination sources, transmission and reflection SAI geometries, capable of emulating the phase contrast and interference modes of conventional interference microscopes, are considered. The point-spread and transfer characteristics of these geometries are illustrated for the case of a transmission mode instrument. It is shown that although imaging is strictly field dependent (i.e. shift-variant), the response of modular instruments is periodic and with careful choice of illumination and sensor positioning can be made quasi shift-invariant. We demonstrate SAI in a transmission configuration comprising of a single coherent imager that holographically records the field scattered from a test target sequentially illuminated by 16 different plane waves. This set-up is equivalent to a single module of a larger SAI system and since brightfield regions in the individual reconstructed images overlap, provides an ideal platform to examine the effect of tilt and phase offset errors in the illumination. By applying a first order correction and assuming phase continuity over the overlapping regions, we demonstrate a significant improvement in the SAI reconstruction. [ABSTRACT FROM AUTHOR]

(a) Illustration of the principle to change the focal length when the rectangular OSA is closer and farther. Schematics of (b) SSM, (c) SDM and MSDM, (d) MSQM and SQM with rectangular OSAs. (e-i) E-field distributions in x-y planes when m = 0 for (b-d), respectively. [Display omitted] • Five designs of metalenses with synthetic rectangular aperture are proposed. • By reconfiguring the synthetic aperture, focusing properties can be tuned with great flexibility. • By utilizing spatial multiplexing, THz variable foci metalenses are investigated. • Stretchable axis synthetic rectangular optical sparse aperture (OSA) is designed to achieve tunable multifocal performances. As ultrathin optical elements with subwavelength structures densely arranged, planar lens or called metalens can effectively focus incident wave. Due to the compact design and wide operating frequency of metalens, it becomes promising in applications like information encryption, imaging, and wireless communication fields. However, the fabrication of large-aperture metalens is still time-consuming and costly while the focusing performances are commonly tuned by introducing active materials requiring extra stimuli, such as laser and electrical pulses. In this study, five designs of metalenses with synthetic rectangular aperture are proposed and demonstrated with excellent focusing performances. By reconfiguring the synthetic aperture, focusing properties like focusing lengths, full width at half maximum values, and modulation transfer function can be tuned with great flexibility. With a polarization-dependent phase-shifting structure, the proposed metalens shows different focusing performances with incident wave under transverse magnetic field and transverse electric field modes. By utilizing spatial multiplexing, terahertz variable foci metalenses based on a stretchable single-, dual- and quad-axis synthetic rectangular optical sparse aperture are investigated to achieve tunable multifocal performances. These designs provide a novel strategy that can reconfigure the synthetic aperture with stretchable substrate to make metalens with multifunction and active tunability, which show promising applications in imaging, information encryption, and space telescope areas. [ABSTRACT FROM AUTHOR]

A laser ultrasonics (LU) based frequency domain synthetic aperture focusing technology (LUB-F-SAFT), which uses 2D equivalent velocity (SEV) and fuses multi-mode ultrasound, is developed to image the internal horizontal hole defects with a diameter of ∼ 1. 0 mm in bimetallic composites. Bulk waves non-destructively excited by a line-shaped laser are detected by a vibrometer away from the excitation. The results indicate that the LUB-F-SAFT with SEV can improve the imaging quality and locating capability, and the testing error is about 0. 02 mm. The improvement in the imaging quality using SEV for different ratios of the first layer thickness to defect-to-interface distances is discussed. In addition, a theoretical approach based on the directionality of the detected ultrasound is applied to determine a reasonable excitation-detection distance for better defect imaging. A LUB-F-SAFT imaging combining multi-mode waves is employed to improve the range of defect profiles. The results show that the range of the imaged defect profile becomes larger using the multi-mode fused LUB-F-SAFT imaging, which is an improvement of about 49.2% compared to the shear wave imaging result. • LUB-F-SAFT imaging with SEV is developed for bilayer media. • The SEV corrects distortion and position bias of defect. • A optimized excitation-detection distance for better defect imaging. • A multi-mode fused LUB-F-SAFT improve the profile of the defect. [ABSTRACT FROM AUTHOR]