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Solar sailing is a promising propellant-free approach to propelling spacecraft in space. However, the propelling efficiency of conventional solar sail spacecraft is limited by their area-to-mass ratios. This paper proposes a novel design of micro solar sails with area-to-mass ratios above 100 m2/kg for next-generation chip-scale spacecraft. Bilayer thin films developed by Microelectromechanical Systems (MEMS) technologies were patterned into grid microstructures, and theoretical analysis of a sail prototype was conducted. The electro-thermal and thermo-mechanical models of the solar sail in geospace were established by taking effects of Joule heating, solar radiation, and thermal re-emission into consideration, enabling rapid prediction of its three-dimensional (3-D) reconfiguration from the as-released two-dimensional (2-D) microstructure. Adjustment of the ChipSail’s acceleration arising from the sail’s morphing was also analytically modeled. Fabrication and characterization of the sail prototype made of multiple Al/Ni50Ti50bilayer beams were accomplished. In-situ SEM imaging of the sail prototype in vacuum chamber witnessed an active and continuous 3-D reconfiguration under Joule heating, and over 90° deformation was detected by applying a DC voltage of 0.078 V. Theoretical and experimental work on the solar sail with at least 10 times higher area-to-mass ratios than conventional ones will lay a solid foundation for efficient solar sailing.

In the impending 6G era, the integration of sensing and communication (ISAC) systems in the millimeter-wave (MMW) band will assume a pivotal role across a myriad of applications, thus enhancing everyday convenience. The photonic-based MMW ISAC system, renowned for mitigating electromagnetic interference and integrating with the fiber-wireless network, exhibits tremendous potential across diverse application domains. In intricate application contexts, the ISAC system should possess the capability to execute multitarget detection to preclude false target generation, simultaneously maintaining a balance between radar sensing and communication performance. This article presents a flexible photonic-based ${W}$ -band ISAC system, designed to facilitate centralized, seamless fiber-wireless networks. It boasts the ability to detect multiple targets within the same angular sector. We scrutinize the potential causes for false target generation and the corresponding solutions in detail, validating the correctness of this theory through simulations and experiments. In addition, we incorporate subcarrier modulation (SCM) and linear frequency modulation (LFM) signals using frequency-division multiplexing (FDM). This allows us to allocate the bandwidth and power of these two signals, thus achieving a balance between sensing and communication. To demonstrate this concept, we designed a photonic-based ISAC system for the fiber-wireless network operating at 97.5 GHz over 10-km fiber transmission, which yielded access rates ranging from 15 to 60 Gbit/s after 1-m free-space transmission and range resolutions varying from 1.76 to 3.15 cm. The distance error subsequent to external calibration is under 3 cm.

In the upcoming 6G, the integrated sensing and communication (ISAC) system in the millimeter wave (MMW) band will have a vital role in numerous application scenarios, enhancing the convenience of our lives. The photonic-based MMW ISAC system can exploit the broad bandwidth of photonic devices, significantly improving system performance. Furthermore, the use of photonic devices enables the seamless integration of the ISAC system with the fiber-wireless network. In this paper, we proposed a simple-structured photonic-based W-band ISAC system, incorporating optical fiber transmission into the system. Additionally, we designed integrated waveforms for time-frequency-division multiplexing (TFDM), allowing for flexible tradeoffs between data rate, range resolution, and detection distance according to the requirements of application scenarios. As a proof-of-concept, a photonic-based ISAC system for the fiber-wireless network with flexible TFDM waveforms at 96.5 GHz over 10-km fiber transmission was demonstrated, achieving adaptive access rates from 15 to 60 Gbit/s after transmission over 1-m free space, and adaptive range resolutions from 1.53 to 4.39 cm. Moreover, this paper provides a detailed analysis of the causes of system distance error and corresponding solutions. In the experiment, the distance error of the proposed system can be reduced to less than 3 cm after external calibration.

Low-resolution characteristics of multispectral images restrict their usability. Various approaches (e.g., single-image super-resolution reconstruction (SISR) and pansharpening method) have been proposed to enrich the spatial details of low-resolution multispectral images (LRMSs) to obtain high-resolution ones. While the pansharpening method inevitably depends on panchromatic (PAN) images, which limits its application scenarios, SISR does not need auxiliary images, yet the blurred edge details within the reconstructed super-resolution multispectral (SRMS) images still remain a big challenge. In this work, we propose a novel high-frequency feature transfer (HFFT-PAN) method for multispectral image super-resolution to tackle the above drawbacks. Specifically, we first exploit the inherent low-frequency features among LRMS images to facilitate the extraction of high-frequency features from PAN images. After that, the high-frequency features from PAN images are transferred to the reconstruction procedure of multispectral images so that the SRMS images can not only benefit from the edge detail information from PAN images but also avoid the utilization of any PAN images during inference. Moreover, we employ an additional contrastive loss during training to ensure the fidelity of the generated SRMS images. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods. The model and code are available at https://github.com/wx0110wx/HFFT.

Synthetic aperture radar (SAR) image intelligence interpretation is always a challenging task due to its special imaging mechanism. This article focuses on the aircraft detection and recognition in large-scale SAR images. The objects in large-size SAR images are usually sparsely distributed and tend to be highly concentrated in certain areas. Therefore, the detection and classification in SAR images are affected easily by the imaging environment, leading to ineffective and instability. It suffers from time-consuming, high false-alarm rate, and low recognition rate. To alleviate these problems, a joint probability detector integrated with scattering attention (SA) is designed for aircraft detection and classification. Based on the fact that all aircraft are parked in the airport area, an airport detection method with mixed strategy is proposed to obtain the valid region (airport), narrowing down the scope of detection area, increasing the detection efficiency, as well as suppressing a lot of false alarms outside the airport. Considering that it is easy to generate false alarms within the airport on account of clutter scattering interference, a two-stage detector is introduced to improve the accuracy by replacing a stronger region proposal network and optimizing a lower bound to a joint probabilistic objective over two stages. Furthermore, the classification encoder module incorporates SA into a convolution neural network to leverage the distribution relation among strong scattered points to enhance the classification performance. Finally, extensive experiments on the Zhong Ke Xing Tu (ZKXT) and GaoFen-3 datasets verify the effectiveness and superiority of the proposed approach, and surpass runner up by 5.3% with the mF1 indicator. On a large GaoFen3 image (16 550 × 11 945 pixels), the proposed method improves the detection speed by 187.52%, without sacrificing the accuracy.

With the rapid development and widespread deployment of radar technology, the interpretation of the vast amount of synthetic aperture radar (SAR) imagery obtained daily has emerged as a hot topic. The complexity of the electromagnetic scattering mechanisms contained within radar images makes SAR image interpretation a challenging task. Current methodologies for SAR image interpretation primarily involve feature extraction-based techniques, categorized into image-domain and frequency-domain algorithms. However, these methods are faced with issues, such as rough segmentation in images, high-computational complexity, and poor robustness, presenting significant challenges in the field. In this article, a novel framework for SAR image interpretation is proposed, leveraging reinforcement learning (RL) for the extraction of electromagnetic scattering features and the inversion of parameters. Within this framework, a nonsparse reward function, combined with curriculum learning, is introduced as the supervisory information. It enables more efficient policy updates through a structured two-stage training approach. In addition, an algorithm that integrates a four-neighbor breadth-first search (BFS) with the watershed segmentation process is proposed, aiming to enhance the accuracy of scattering center analysis in SAR imagery. The attribute scattering center model (ASCM) is utilized as a prototype for conducting algorithmic research and experimentation. Experimental results on both simulation data and measured data have indicated that the proposed method significantly improves efficiency while ensuring accuracy, demonstrating its capability to extract parameters from measured data in most scenarios.

Amidst the landscape of contemporary remote-sensing (RS) technology, the endeavor to detect and recognize aircraft within RS images (RSIs) assumes pivotal strategic and practical significance. The complex nature of fine-grained aircraft recognition is a result of the intricate interplay between aircraft and their background environments, alongside category imbalance, which collectively leads to the emergence of a long-tail distribution within the dataset. However, experts proficient in RSI interpretation can effectively address these challenges through the application of prior knowledge. This article introduces the aircraft reasoning network (ARNet), a framework tailored for aircraft detection and fine-grained recognition in RSIs, building upon prior knowledge employed in expert interpretation. Specifically, the knowledge reasoning module (KRM) introduces a knowledge graph (KG) that incorporates both common and expert knowledge into the end-to-end network. Additionally, the network encompasses a spatial context module (SCM) and an airport facility relationship module (AFRM). These components facilitate highly accurate detection and recognition of fine-grained aircraft in diverse environmental contexts by employing adaptive prior knowledge reasoning and optimizing target spatial location. Furthermore, an independent aircraft component discrimination module (ACDM) distinguishes aircraft based on their predominant component features, contributing to improved classification performance in both the few-shot and easily confused categories. Moreover, this article introduces the AR-RSI dataset, a compilation of RSIs capturing fine-grained aircraft targets from diverse locations. The effectiveness and superiority of ARNet are exemplified by AR-RSI, achieving a minimum of 3.7% higher mean average precision (mAP) than the mainstream aircraft detection framework.

Target recognition for synthetic aperture radar (SAR) images has been a longstanding hotspot. However, using polarimetric information to enhance recognition performance is under-researched. In this article, we develop a visualization approach to analyze and highlight the contributions of polarimetric elements and propose a simple polarimetric correlation feature for target recognition. In the visualization method, a channel-wise convolutional structure is well developed, which serves as a proxy of the polarimetric elements. By using the gradients of the target class flowing into each developed convolutional channel with normalization, we obtain activation maps indicating the contribution of each polarimetric element. Then, a comprehensive quantitative evaluation of polarimetric element contribution is also performed. We demonstrate again that polarimetric information maintains significant advantages over single-polarization intensity, and the correlations between cross- and co-polarization emerge as key components for target recognition. Exactly inspired by these insights, the straightforward feature is defined as the correlation between the target and typical scatterers, seamlessly fusing intensities and correlations. This polarimetric correlation feature aptly encapsulates the physical scattering of the target, shows a clear mapping relationship with the geometric structure, and captures the differences between categories, achieving intracategory compactness and intercategory separation. Experimental results on both synthetic and real datasets underscore the effectiveness and superiority of this proposed feature.

Superpixel analysis is showing great potential for high-resolution synthetic aperture radar (SAR) image change detection (CD), as it uses a larger detection granularity and enhances computational efficiency. However, some deficiencies still exist. It is difficult for previous methods to extract the complete change regions from cluttered backgrounds. Meanwhile, the local spatial correlation and structure consistency are not well represented. To address the above problems and achieve better separation of changed and unchanged superpixels in complicated scenarios, we design a novel unsupervised CD framework from the perspective of low-rank matrix decomposition (LRMD) theory. The entire framework is carried out in two stages. First, the $\ell _{1}$ -norm sparsity constraint LRMD model is constructed to decompose change features into a low-rank component associated with background and a sparse component representing changed regions. Then, the local spatial correlation and structure consistency constraint are explicitly modeled by introducing a Laplacian regularization term. The unified model smooths the local similarity superpixels and enlarges the distance between changed regions and the background in the feature subspace. In this stage, the saliency difference image (DI) is generated to indicate the change probabilities of each superpixel. Furthermore, a classification refining (CR) module is designed to learn the projection from the change feature matrix to the saliency DI, which can further fine-tune such obscure regions and boost the binary classification. Extensive experiments on five challenging datasets from the TerraSAR-X sensor demonstrate the effectiveness and superiority of the proposed method.

This paper describes our solution for the protein homology prediction task in KDD Cup 2004 competition. This task is modeled as a supervised learning problem with multiple performance metrics. Several key characteristics make the problem both novel and challenging, including the concept of data blocks and the presence of large-scale and imbalanced training data. These features make a naive application of the traditional classification algorithms infeasible. Our approach focuses on making full use of the abundant information within the blocks, and developing a new technique for reducing and balancing training data to make the support vector machine applicable to this kind of large-scale and imbalanced learning tasks.

Multimodal image fusion has recently garnered increasing interest in the field of remote sensing. By leveraging the complementary information in different modalities, the fused results may be more favorable in characterizing objects of interest, thereby increasing the chance of a more comprehensive and accurate perception of the scene. Unfortunately, most existing fusion methods tend to extract modality-specific features independently without considering intermodal alignment and complementarity, leading to a suboptimal fusion process. To address this issue, we propose a novel interactive generative adversarial network (IG-GAN), for the task of multimodal image fusion. IG-GAN comprises guided dual streams tailored for enhanced learning of details and content, as well as cross-modal consistency. Specifically, a details-guided interactive running-in module (GIR1) and a content-guided interactive running-in module (GIR2) are developed, with the stronger modality serving as guidance for detail richness or content integrity, and the weaker one assisting. To fully integrate multigranularity features from dual-modality, a hierarchical fusion and reconstruction branch is established. Specifically, a shallow interactive fusion (SIF) module followed by a multilevel interactive fusion (MIF) module is designed to aggregate multilevel local and long-range features. Concerning feature decoding and fused image generation, a high-level interactive fusion and reconstruction module (HRM) is further developed. In addition, to empower the fusion network to generate fused images with complete content, sharp edges, and high fidelity without supervision, a loss function facilitating the mutual game between the generator and two discriminators is also formulated. Comparative experiments with 14 state-of-the-art methods are conducted on three datasets. Qualitative and quantitative results indicate that IG-GAN exhibits obvious superiority in terms of both visual effect and quantitative metrics. Moreover, experiments on two RGB-IR object detection datasets are also conducted, which demonstrate that IG-GAN can enhance the accuracy of object detection by integrating complementary information from different modalities. The code will be available at https://github.com/flower6top.

The heat transfer characteristics of feedthrough flange under high alternating current excitation were investigated by three-dimensional finite element model. The correlations between the heat transfer behaviors and system parameters including applied current, ceramic thickness and working time were discussed comprehensively. A remarkable absorbed power beyond 100 W in flange would be produced by the electromagnetic field from wires. Under natural cooling condition, there is an obvious temperature rise about 40°C in the flange center after 10 min of current excitation. Three types of water cooling solutions acting on flange surface, e.g. line type, circle type and S type solutions, were proposed to reduce the flange temperature under high alternating current and long working time condition. By the S type water cooling mode, the best cooling effect can be achieved that the maximum temperature rise is about 20°C and the average temperature rise is less than 5°C.

Synthetic aperture radar (SAR) has emerged as a vital tool for ship monitoring due to its all-weather, all-day, high-resolution imaging capabilities. In practical operations, the wide coverage and sparse ship distribution in very large full-scene SAR images pose challenges in terms of low efficiency and high false alarm rates. Traditional methods perform poorly in complex scenarios, while deep learning (DL) methods have high computation costs. This study proposes a fast progressive detection algorithm for ship targets in large SAR images, combining the advantages of traditional Non-DL methods and DL approaches. First, at a global scale, image preprocessing operations based on traditional methods are designed to quickly extract candidate regions. Then, at the regional scale, an oriented ship detector (OSD) is designed for refined ship detection within candidate regions. Finally, at an individual-target scale, a false alarm discrimination network is constructed to further remove false alarms. Experimental results on GF-3 full-scene SAR images demonstrate that the proposed method can achieve minute-level detection efficiency in images of billion-pixel-level size while achieving high detection accuracy.

This study introduces an innovative approach to hyperspectral imaging (HSI) classification by integrating convolution, recurrence, and self-attention mechanisms in a 3-D configuration. We address several challenges such as the 1) disruption of spectral continuity by traditional dimensionality reduction methods like PCA, 2) the overlooking of band-to-band continuous features in existing spatial-only 2-D multiscanning strategy, and 3) the limitations in model design by simply cascading recurrent neural networks (RNNs) with transformers for HSI analysis. Our solution involves three core components: 1) subband grouping with group-wise convolution for refined dimension reduction; 2) a novel cubed 3-D-multiscanning technique enabling thorough multidirectional analysis in both spectral and spatial domains; and 3) the development of a Cubic-Net framework with a specially designed segmented recurrent transformer (SRT). This SRT is tailored to effectively utilize spectral continuity along with spatial contextual features, overcoming common sequential data analysis challenges seen in RNNs and transformers. Furthermore, our feature fusion strategy successively integrates “short-term” and “long-term” SRT features, thereby enhancing the model’s ability to process both spectral and spatial features effectively. Experimental results from three public HSI datasets indicate our method’s improved performance over existing baselines and state-of-the-art methods. This research offers a new perspective on 3-D sequential HSI classification.

A supervised image decomposition algorithm is proposed in this study for human heart rate measurement under low-light environments. First, the comparative human face image pairs under different light intensities are captured, and special image capturing and matching algorithms are designed in this procedure. Then, a multilayer image decomposition network is constructed and trained to obtain the correlation of the images under different lighting intensities. Specifically, a loss function, including illumination loss, illumination smoothness loss, and interframe loss, is designed for network optimization. A heart rate measurement algorithm runs at last for the calculation of heart rate. Verification experiments under different human-camera distances and light intensities confirm the effectiveness of the proposed method. Specifically, the proposed method can be used for stale and effective human heart rate measurement when the human–camera distance is within 0.8 m and the light intensity is higher than 400 lux.

Benefiting from deep learning, defocus blur detection (DBD) has made prominent progress. Existing DBD methods generally study multiscale and multilevel features to improve performance. In this article, from a different perspective, we explore to generate confrontational images to attack DBD network. Based on the observation that defocus area and focus region in an image can provide mutual feature reference to help improve the quality of the confrontational image, we propose a novel mutual-referenced attack framework. Firstly, we design a divide-and-conquer perturbation image generation model, where the focus region attack image and defocus area attack image are generated respectively. Then, we integrate mutual-referenced feature transfer (MRFT) models to improve attack performance. Comprehensive experiments are provided to verify the effectiveness of our method. Moreover, related applications of our study are presented, e.g., sample augmentation to improve DBD and paired sample generation to boost defocus deblurring.

Remote sensing datasets pose long-tailed data distribution, and such unbalanced datasets will reduce the performance of existing remote sensing object classification models. Existing methods mainly rely on resampling datasets, modifying loss functions, data augmentation, and transfer learning to cope with such challenges. Unlike these, our study takes a novel perspective and focuses on mitigating the long-tailed distribution problem by generating a large number of tail-class images with consistency and diversity. Specifically, this article introduces a novel text-guided tail-class generation network (TGN). TGN comprises two main components: knowledge mutual distillation network (KMDN) and class-consistent diverse tail-class generation network (CDTG). KMDN resolves the isolation issue of the head and tail knowledge by facilitating mutual learning of feature representations between the head and tail data, thereby improving the feature extraction capability of the tail model. CDTG focuses on generating class-consistency diverse tail-class images that uses tail-class features extracted by KMDN. Especially, the class consistency is guaranteed by contrastive language-image pre-trainings (CLIP’s) powerful text-image alignment capability. These generated images are then added back into the original dataset to alleviate the long-tailed distribution, thereby improving the tail-class accuracy. Extensive experiments on the widely used DIOR, FGSC-23 and DOTA datasets demonstrate that the proposed method outperforms state-of-the-art methods. Dataset and code are publicly available at https://github.com/XinR-Tang/TGN.

In this work, a novel semi-supervised twin neural network (semiTNN)-based post-equalizer (PE) is proposed and developed for high-speed 4-level pulse amplitude modulation (PAM4) underwater wireless optical communication (UWOC). Furthermore, semiTNN is enabled by a dual-drop strategy and interleaved consistency regularization (ICR), thereby only requiring a small amount of labeled PAM4 symbols as references to be well-trained. The experimental results over a 14-m/4-Gbps underwater link reveal that the proposed semiTNN PE paired with ICR, with only 5% labeled data, can achieve nearly the same bit error rate (BER) performance as the one trained by a fully-supervised learning process, resulting in 0.8-dB and 4.9-dB sensitivity improvements comparing with the traditional Volterra nonlinear equalizer (VNE) and linear feedforward equalizer (FFE) at a specific BER of 1 × 10−3, respectively. When the transmission distance is further extended to 56 m, the semiTNN PE with 10% labeled data can reach a data rate up to 4.26 Gbps under the hard-decision forward error correction (HD-FEC) threshold, which is 90 Mbps and 150 Mbps larger than that of VNE and FFE, respectively. Moreover, even facing bias-current fluctuations, turbulence, and bubbles, the UWOC systems supported by the semiTNN can still maintain a satisfying BER performance to some extent. This is the first time to utilize the proposed semiTNN PE combined with an ICR-based loss function for high-speed UWOC, and it can be highly beneficial to the practical deployment of NN-based PEs in the future.

Service robots are often required to determine the liquid level inside a container. However, traditional approaches using camera-based or ultrasonic wave sensors are unreliable and can introduce challenges in movement trajectory planning. In this study, we developed a novel gripper system that is equipped with a capacitive proximity sensor, enabling the robot to sense the liquid through the container. In addition, we developed a control module that ensures easy integration with a robot operating system (ROS)-based control systems commonly used in service robots. Our gripper represents a significant improvement over traditional camera-based or ultrasonic wave sensor solutions. Laboratory experiments revealed that our gripper can perform data-lossless detection in just 0.085 s (11.8 Hz) and dynamic liquid-level measurement in 0.31 s. Furthermore, the liquid-level measurement resolution of the gripper is sufficient for functional applications of service robots in a domestic environment. The present work demonstrates that a gripper with capacitive proximity sensors is a practical and cost-effective solution for service robots to manipulate opaque cups filled with liquid, which are frequently encountered in domestic environments.

In this paper, we first utilize the interleaved single-carrier frequency division multiplexing (I-SC-FDM) scheme with a novel sparse weight-initiated deep neural network (SWI-DNN) equalizer for underwater optical communication (UWOC) systems. In addition to the superior characteristics of I-SC-FDM over the original OFDM and localized SC-FDM (L-SC-FDM), such as lower peak-to-average power ratio (PAPR) and lower computational complexity, the implementation of a special sparse weight-initiated (SWI) structure can significantly reduce the necessary training epochs up to 10.3%, which enables the proposed SWI-DNN equalizer to outperform the traditional random weight-initiated DNN equalizer. Besides, further pruning operation can dramatically enhance the final sparsity of the SWI-DNN equalizer to 96.88% at the expense of an inappreciable performance penalty, thereby effectively saving occupied computing resources. Following this, a data rate of up to 660 Mbps over a 90-m underwater transmission can be achieved in a standard 50-m swimming pool, with a relatively low received optical power of −31.12 dBm (at the BER of 3.8 × 10−3). Such a data rate is 17.9% higher than that obtained with a common hybrid time-frequency domain (TFD) equalizer. Moreover, the computational complexity of the SWI-DNN equalizer after pruning operation is only 11.83% of that of the hybrid TFD equalizer and can still support a 625-Mbps transmission. This is the first time to employ I-SC-FDM combined with an SWI-DNN equalizer for long-distance high-speed UWOC transmission, and it can be highly beneficial to the cost-sensitive and power-sensitive systems in future deployment.

Lacking timely access to rescue resources is one of the main causes of casualties in tunnel collapse. To provide timely rescue, this study proposed a multi-objective preallocation model of special emergency resources for tunnel collapse based on demand time. Efficiency, multiple coverage, and cost-effectiveness are taken as the key objectives of the model; the demand time service range is used as a coverage decision factor considering the unique nature of tunnel collapse. The weight of potential disaster-affected points and other general factors are also considered in this model in order to thoroughly combine the distribution of disaster points and service areas. Further, we take 15 main tunnel projects under construction in China as an example. When the relative proximity to the ideal point of the selected optimal scheme εais smaller than 0.5, we will adjust the weight of three objectives and reselect the optimal scheme until εa> 0.5. Compared with the not preallocated case, the number of rescue rigs needed is reduced by 8.3%, the number of covered potential disaster-affected points is increased by 36.36%, the weighted coverage times are increased from 0.853 to 1.383, and the weighted distance is significantly reduced by 99% when the rescue rigs are preallocated, verifying the feasibility and superiority of the proposed model.

To achieve high performance heterojunctions between continuous carbon fiber reinforced polyetheretherketone (CCF30/PEEK) and titanium alloy, a novel method of constructing strong micro-mechanical interlocks between two dissimilar materials was proposed and demonstrated through laser joining process. The influence of the laser joining conditions and the geometric characteristics of microcones on the joint performance was investigated. The interface morphologies were characterized to evaluate the quality of the formed mechanical interlocks at the joining interface. Shear tensile tests were conducted to evaluate the joint strength in different variables. Results revealed that wonderful micro-mechanical interlocks were successfully constructed between CCF30/PEEK and titanium alloy under the clamping pressure by using the optimal laser joining condition. Structure density of the fabricated microcones on titanium alloy surface is recognized as a crucial factor influencing the joint strength. And a maximum joint strength of over 52 MPa was achieved when the microcone density was 8.2 cones/mm2. The proposed novel approach offers new insights for the construction of strong micro-mechanical interlocks between metal and plastic and the preparation of high performance heterojunctions.

Reconstructing temperature changes along the Silk Road (SR) over the last two millennia can provide insights into past global changes and their impact on the rise and fall of ancient civilizations in this region. Numerous high-quality single-site paleotemperature records have been produced for the eastern part of the SR (mainly for the Xinjiang region and its surrounding areas), which provide the data basis for a comprehensive synthesis. In this study, we used objective criteria to select 10 high-quality ones from 30 temperature reconstructions derived from various geological archives including lacustrine sediments, ice cores, and tree rings in this region. Our aims are to summarize the pattern of temperature change over the past 2000 years, to provide a long-term viewpoint on the present warming, and to evaluate the impact of climate change on civilizations along the SR. The principal results are as follows: (1) The temperature variations over the last millennium are mutually consistent within these records. The study area experienced typical climate anomalies during the Medieval Warm Period (MWP, AD 1000–1250), the Little Ice Age (LIA, AD 1450–1850), and the Current Warm Period (CWP, AD 1850 to present); however, contrary to previous knowledge, the amplitude of climatic warming during the CWP did not exceed that during the MWP. (2) Fewer temperature records were available for the interval AD 1–1000, and there were large differences between them. For example, the reconstructed climate during both the Han Dynasty and the Sui-Tang Dynasties was either warm or cold, without prevailing consensus. (3) The warming during the MWP favored the rapid development of the SR route along the northern slopes of the Tianshan Mountains, and the cooling during the LIA contributed to the decline of the SR marked by the closure of the Jiayuguan Pass. Notably, the scarcity of temperature records and the discrepancies between them during AD 1–1000 in the eastern part of the SR have hindered our understanding of the hydroclimatic changes and their influence on the development of civilizations along the SR. Thus, it is important to obtain an increased number of high-quality reliable records spanning the past 2000 year, and to examine the occurrence of local signals of temperature changes during the period of AD 1–1000. On the other hand, the paleotemperature investigation on the western part of the SR over the last two millennia is wholly insufficient, and thus more high-quality single-site and integrated studies are needed, to facilitate more comprehensive insights into the coupled relationship between climate change and the rise and fall of civilizations along the entire length of the Silk Road.

The emerging research line of cross-modal learning focuses on the issue of transferring feature representation manner learned from limited multimodal data with labelings to the testing phase with partial modalities. This is essentially common and practical in the remote sensing (RS) community when only modal-incomplete data are in users’ hands due to inevitable imaging or access restrictions under large-scale observation scenarios. However, most of the existing cross-modal learning methods have been designed with exclusive reliance on labeling, which can be either limited or noisy due to their costly production. To address this issue, we explore, in this article, the possibility to learn cross-modal feature representation in an unsupervised fashion. By integrating the multimodal data into a fully recombined matrix form, we propose the following: 1) the use of common subspace representation as the regression target instead of conventionally adopted binary labels and 2) the orthogonality and manifold alignment regularization terms to shrink the solution space while preserving the pairwise manifold correlations. Through this manner, the modality-specific and mutual latent representations in this common subspace as well as their corresponding projections can be learned simultaneously, and their optimums can be efficiently reached through a nearly one-step computation with the help of eigendecomposition. Finally, we show the superiority of our method through extensive image classification experiments on three multimodal datasets with four remotely sensed modalities involved (i.e., hyperspectral (HS), multispectral, synthetic aperture radar, and light detection and ranging (LiDAR) data). The code and dataset will be made freely available at https://github.com/jingyao16/UCSL to encourage the reproduction of our method and further use.

The goal of hyperspectral image (HSI) classification is to assign land-cover labels to each HSI pixel in a patchwise manner. Recently, sequential models, such as recurrent neural networks (RNNs), have been developed as HSI classifiers, which need to scan the HSI patch into a pixel sequence with the scanning order first. However, RNNs have a biased ordering that cannot effectively allocate attention to each pixel in the sequence, and previous methods that use multiple scanning orders to average the features of RNNs are limited by the validity of these orders. To solve this issue, it is naturally inspired by Transformer and its self-attention to discriminatively distribute proper attention for each pixel of the pixel sequence and each scanning order. Hence, in this study, we further develop the sequential HSI classifiers by a specially designed RNN–Transformer (RT) model to feature the multiple sequential characters of the HSI pixels in the HSI patch. Specifically, we introduce a multiscanning-controlled positional embedding strategy for the RT model to complement multiple feature fusion. Furthermore, the RT encoder is proposed for integrating ordering bias and attention reallocation for feature generation at the sequence level. In addition, the spectral–spatial-based soft masked self-attention (SMSA) is proposed for suitable feature enhancement. Finally, an additional fusion Transformer (FT) is deployed for scanning order-level attention allocation. As a result, the whole network can achieve competitive classification performance on four accessible datasets than other state-of-the-art methods. Our study further extends the research on sequential HSI classifiers.

Remote sensing object labels require high specialization, resulting in a limited number of labeled samples. Without large labeled samples to support training, general remote sensing object recognition models have limited accuracy. Addressing this issue, this article proposes a weakly correlated distillation learning framework for remote sensing object recognition with a small number of samples. Benefitting from large-scale natural image datasets, many recognition models achieve superior feature extraction capabilities. Thus, we use them as backbones to build teacher models, and then fine-tune the teacher models with a small-scale remote sensing dataset. However, due to the limited number of remote sensing samples, the teacher models may produce noisy features that reduce the performance of the student model. Therefore, we propose a weakly correlated distillation method that selects the weakly correlated features from teacher models to distill the student. Since the weakly correlated features contain different noise distributions that can be mutually suppressed, thereby improving the performance of the student. Extensive experiments on three widely-used datasets of DOTA, HRRSD, and NWPU VHR-10 demonstrate the superior performance of our method compared with the state of the arts (SOTA). Code is available at: https://github.com/wdzhao123/WCD.

Nowadays, the proliferation of small satellites brings the skyrocketing rise in space data, especially the shift to on-orbit computing needs. On one hand, with the increasing volume of data generation, like high-resolution remote sensing images, on-orbit computing produces near real-time onboard solutions and quick responses. However, constrained by the limited size and energy supply of satellites, achieving energy-efficient on-orbit computing remains a crucial challenge. In this article, an on-orbit remote sensing image processing complex task scheduling model facing a heterogeneous multiprocessor system (HMPS) is proposed. First, aiming at accelerating image processing, we establish a novel parallel task execution model using a directed acyclic graph (DAG) to universally describe typical missions, i.e., cloud detection, geometric correction, and image classification. Subsequently, a mathematical task scheduling formulation is defined to calculate the makespan, and total energy consumption (TEC) required when executing DAG on HMPS. Second, a new Pareto-based iterated greedy optimizer (PIGO) is devised to complete the energy- and time-efficient task execution and resource allocation on HMPS through confined inserting mutation, destruction-reconstruction, and local search. Finally, we build an emulated on-orbit HMPS to conduct experiments. The results show that, in comparison with the scheme without model scheduling, the cost savings of around 51% and 54% in makespan and TEC, respectively, are achieved by the proposed model. Moreover, the HMPS configured with our methodology can obtain $2.2{\times }$ improvement in energy efficiency and process up to $2.56\times 10^{5}$ pixels per unit of power (W) and time (s).

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as $n$n-Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.

Change detection has played an increasingly important role in multitemporal remote sensing applications recently. Long time series analysis is providing new information of land cover changes and improving the quality and accuracy of the change information being derived from remote sensing. The purpose of this study is to dig for more change temporal information and change pattern information from synthetic aperture radar (SAR) image time series (ITS), which is of great significance for monitoring urban area changes, conducting land use surveys, and renovating illegal constructions. In the study, a novel unified framework for long time series SAR image change detection and change pattern analysis (SAR-TSCC) was proposed for land cover change mapping. To obtain the most notable change time rapidly, a fast SAR ITS change point search method based on pruned exact linear time (SAR-PELT) algorithm was adopted. Meanwhile, the deep time series classification network, named SAR time series transformer (SAR-TST), was implemented to recognize the change patterns, which is based on time series transformer (TST) architecture. Considering the lack of real training data, a novel synthetic data generation method is developed. The combination of the synthetic and real data enhanced the generalization of the classifiers. The proposed framework was used for monitoring a large urbanization area in the northwest of Hong Kong, China. The Cosmo Skymed (CSK) time series data acquired from 2013 to 2020 were exploited for land cover change analysis. Experiment results showed that our approach achieved the state-of-the-art performance, as the time accuracy reached 86% and the classification accuracy on the four main change patterns (impulse, step, cycle, and complex) is over 99%. In particular, the proposed SAR-TST model showed remarkable advantages in the presence of insufficient real data.

This prospective study investigated the relationship between the lipoprotein-associated phospholipase A2(Lp-PLA2) level and cognitive impairment (CI) in type II diabetes mellitus (TIIDM) patients with white matter hyperintensity (WMH). A total of 87 TIIDM patients diagnosed with WMH were included in this study. They were grouped into CI group and noncognitive impairment (NCI) group based on the MoCA scales. The serum Lp⁃PLA2 levels and MoCA scores of the patients with WMH were compared with those of control (Ctl). Logistic regression was used to analyze the risk factors affecting CI and diagnostic value of serum Lp⁃PLA2 for CI. The WMH group had significantly higher serum level of Lp⁃PLA2 and significantly lower MoCA score than Ctl group. There were significant differences in serum levels of homocysteine, high-density lipoprotein cholesterol (HDL-C) and Lp⁃PLA2 (P < 0.05) between the groups. Logistic regression showed that HDL-C, homocysteine and Lp⁃PLA2 were the risk factors for CI in the patients (P < 0.05); receiver operating characteristic curve analysis showed that HDL-C and Lp⁃PLA2 had significant diagnostic value for CI in WMH patients. Therefore, Lp⁃PLA2 can be assessed in the elderly as a screening to identify putative CI patents for preventive treatment and management.

Phase selection and microstructure evolution of the undercooled eutectic Ti-Si alloy were systematically investigated by the electromagnetic levitation method, and the maximum undercooling achieved was 318 K (0.2TE). The migration of the liquid-solid interface was in-situdetected by a high-speed camera system. When the undercooling is smaller than 140 K, the liquid-solid interface is smooth. Once the undercooling arrives at 230 K, the liquid-solid interface is irregular, which reflects the growth transition from the solute control to the combined controls of solute and thermal. The eutectic growth velocity increases as an exponential function of undercooling. The electromagnetic stirring effect makes it difficult to increase undercooling, but plays an important role in accelerating the eutectic reaction velocity at low and moderate undercoolings. Primary dendritic β-Ti phase appears in the solidified alloy from 63 to 176 K undercoolings, and the microstructure is completely composed of eutectic once the undercoolings increase up to 230 K. When the undercoolings exceed 273 K, the microstructure consists of uniformly distributed irregular eutectic. For the drop tube experiments, the microstructures composed of a large amount of dendritic α-Ti phase and eutectic phase are found in a wide range of diameters from 69 to 725 µm. As the decrease of diameter, the solubility of Si in the dendritic α-Ti phase dramatically increases from 6.80% to 10.73%, and the ratio of the area occupied by the dendritic α-Ti on a cross-section of solidified alloy obviously increases from 23.52% to 41.02%, which result from the combined effects of high undercooling and large cooling rate.

In this study, the effects of processing parameters on the geometry of femtosecond laser fabricated microgrooves on 4H–SiC wafer were investigated. To achieve a fast, accurate and intelligent manufacturing process, response surface methodology (RSM) and artificial neural network (ANN) method were applied for modeling and predicting the geometric features of microgrooves. In addition, analysis of variance (ANOVA) was employed to determine the most significant input variables on the responses. The RSM result showed that the third-order polynomial model equation was in high accordance with the experiments. During the analysis, laser power, scanning speed, and scanning times were set as input variables, and the depth, width and surface roughness (Ra) were set as response/output variables. Finally, the predictive capabilities of the RSM and ANN models were compared with each other in terms of coefficient of determine (R2), root mean squared error (RMSE), and relatively error (RE). The results indicated that the ANN method have a higher accuracy compared to those of the RSM model.

In wet friction clutches, the friction pair is separated by adding separating springs to reduce the drag torque, but the increase of the separating spring will inevitably have a certain impact on the engagement characteristics of the friction clutch. In view of this characteristic, considering the structural parameters of the separating spring, the inter-disc bearing capacity and transmission torque model of the wet multi-plate friction clutch during the engagement process are established, and the variable-order numerical differentiation method is used to solve the problem to explore the influence of the wave number, thickness and width of the separating spring on the characteristics of the rotational speed, inter-sheet bearing capacity, transmission torque and oil film thickness of the wet multi-plate friction clutch engagement process. The results show that the wave number of the separating spring has a significant influence on the mating characteristics of other parameters, so the wave number should be appropriately reduced to reduce the clutch engagement time under the condition of ensuring that the separating spring has sufficient strength.

A coupled prediction model for temperature and pressure is developed for the annulus of multiple casings to prevent damaging the integrity of oil and gas wells due to annular pressures. The complex structure of the oil and gas well requires dividing the calculation of the annular pressure into three stages based on the annulus level. The annular temperature and pressure gradients of the wellbore are then studied. The hydrostatic pressure changes of the annular fluid at different temperatures, pressures, and well depths are analyzed considering the influence of the isobaric expansion coefficient and isothermal compression coefficient in annular fluids. The pressure gradients of annular fluids A, B, and C with production time and well depth are discussed along with the deformation of production casings. Results show that the annular fluid density and hydrostatic pressure decrease with the increase of temperature, while they will increase with the increase of annular pressure. The annular pressure increases first and then decreases from the bottom of the well to the wellhead. The annular pressure increases the fastest in the early production stages.

Tumor cells present anti-apoptosis and abnormal proliferation during development. Senescence and stemness of tumor cells play key roles in tumor development and malignancy. In this study, we show the transient stimulation by a single-time scanning of tightly focused femtosecond laser to tumor cells can modulate the stemness and senescence in vitro and in vivo. The laser-induced cellular senescence and stemness present distinct transitions in vitro and in vivo. The cells 1.2 mm deep in tumor tissue are found with significant senescence induced by the transient photostimulations in 100-200 µm shallow layer in vivo, which suppresses the growth of whole tumor in living mice.

Three Longmaxi Formation shale samples from different well depths were analyzed for their total organic carbon (TOC) content and mineral phases. Synchrotron-based multi-energy X-ray computed tomography (CT) slices have been acquired for these samples. Deviations of the sample center away from the harmonic trajectory during X-ray CT imaging were corrected to improve the accuracy of CT reconstructed tomographic slices. The three-dimensional (3D) distribution of porosity and mineral in the samples was derived using the data-constrained modeling (DCM) method. The equivalent spherical diameters of connected pore-organics clusters and the connection probabilities were calculated to evaluate the spatial agglomeration and the spatial correlation of pore-organics in the samples. Numerical results showed that the distribution of the connected regions size and the connection probabilities of pore-organics for three samples have similar characteristics. The connection probabilities versus the distance follow exponential law. The connection probability appeared to be positively correlated to the number of pore-organics connected clusters when the distance between the voxels is shorter than 10 μm. Comparing samples from the three well depths, both the numbers of connected regions and connection probabilities of the samples from a deeper well are higher. The approach would be applicable for structural characterization of other similar materials.

This study reports the vibrational turbine piezoelectric nanogenerators (VT-PENG) that use magnetic coupling to trigger a set of silicon rubber strips embedded with piezoelectric films for energy harvesting in amphibious environments. The piezo-rubber strips are deflected by the tip-driven magnets in the strip-anchor systems, such that the VT-PENG can be efficiently triggered to generate electrical energy from mechanical vibration. The VT-PENG prototypes are fabricated, and the experiments are conducted to investigate the energy harvesting performance (i.e., voltage and electrical power) of single and multiple strips of the VT-PENG. Numerical simulations are carried out to validate the experimental results, and satisfactory agreements are observed. The maximum electrical power is obtained as approximately 9 uW for each curved piezoelectric strip and 4 uW for each planar strip. In the end, the field experiments are conducted to test the energy harvesting in the lake. The reported VT-PENG can be applied in low-medium energy harvesting needs under various flow fields.

In this paper, the numerical simulation was used to investigate the effects of the leading-edge slat installation angles (βfor airfoils from 0° to 40° and β1for blades from −20° to 40°) on the aerodynamic characteristics of the airfoil and the wind turbine blade. The chord length of the leading-edge slat is 0.1c (the chord length of the clean airfoil). The horizontal and vertical distances from its center to the leading edge of the clean airfoil are 0.005c and 0.009c, respectively. The results indicated that the lift coefficient could be significantly improved by the leading-edge slat (except β= 40°) when the attack angle exceeded 10.2°. For β= 0°, the lift coefficient increased the most. The trailing vortex of the leading-edge slat played an important role at the process of flow control. It could transfer kinetic energy from the bounder layer to its out-flow region. Furthermore, the vorticities of trailing vortex generated by the leading-edge slat with different installation angles were different, promoting several effects on the airfoil at the different cases. The torque of the blade with leading-edge slat (except β1= −20°) was improved significantly as the leading-edge slat trailing-vortices became stronger with the higher wind-speeds.

The red phosphorus (RP) anode has attracted great attention due to its high theoretical specific capacity (2596 mAh/g) and suitable lithiation potential. To solve the inherent poor electrical conductivity and the large volume expansion due to the lithiation process, a vaporization–condensation strategy is considered as a promising method. However, there are two important issues that deserve attention in the vaporization–condensation process. First, the low P mass loading in the carbon-based frameworks (∼30 wt %) limits the energy density. Second, a residual white phosphorus (WP) leads to the safety problems of flammability and high toxicity. Herein, we found that the edge structure of carbon framework can offer the strong adsorption for P4and form a P–C bond, which accelerate the adsorption and polymerization of P4leading to high P mass loading and safety. When the porous carbon (PC) with plenty of edge carbons was used as the matrix to load P by vaporization–condensation, the RP loading is close to the highest theoretical mass loading of ∼50 wt % calculated based on the feeding ratio of RP/PC = 1/1. Therefore, the RP-PC anode provides a high specific capacity of 965.2 mAh/g even after 1100 cycles at 1000 mA/g (equivalent to 1 C) and a high-rate capacity of 496.8 mAh/g at 8320 mA/g (equivalent to 16.7 C) after 1000 cycles (the specific capacity and current density are calculated based on the total weight of RP and PC).

Stainless steel (SS) has unsatisfied corrosion resistance in many aggressive environments, particularly under a low pH condition in the bioleaching industry. In this study, through surface analyses and electrochemical measurements, the corrosion resistance of a novel Cu-bearing 316L SS was evaluated in the presence of an acid-producing bacterium, Acidithiobacillus caldus SM-1 that was able to create an extremely acidic corrosive environment. The significantly enhanced anti-microbiologically-induced-corrosion performance could be explained by the intracellular reactive oxygen species (ROS) and Fenton reaction on the Cu-bearing 316L SS. [ABSTRACT FROM AUTHOR]

Ca2+channels are essential to cell birth, life, and death. They can be externally activated by optogenetic tools, but this requires robust introduction of exogenous optogenetic genes for expression of photosensitive proteins in biological systems. Here we present femtoSOC, a method for direct control of Ca2+channels solely by ultrafast laser without the need for optogenetic tools or any other exogenous reagents. Specifically, by focusing and scanning wavelength-tuned low-power femtosecond laser pulses on the plasma membrane for multiphoton excitation, we directly induced Ca2+influx in cultured cells. Mechanistic study reveals that photoexcited flavins covalently bind cysteine residues in Orai1 via thioether bonds, which facilitates Orai1 polymerization to form store-operated calcium channels (SOCs) independently of STIM1, a protein generally participating in SOC formation, enabling all-optical activation of Ca2+influx and downstream signaling pathways. Moreover, we used femtoSOC to demonstrate direct neural activation both in brain slices in vitro and in intact brains of living mice in vivo in a spatiotemporal-specific manner, indicating potential utility of femtoSOC.

Context:Pseudomeningocele is a relatively uncommon postoperative complication of spine surgery. Although the condition tends to be asymptomatic and self-limiting, it may cause radicular pain and neurological defect due to herniation of the nerve root or the spinal cord. Its pathophysiology remains unclear. Only few cases with intraoperative photos have been reported.Finding:We present a case of pseudomeningocele with nerve root entrapment after percutaneous endoscopic lumbar discectomy (PELD). A 52-year-old man had undergone PELD for sciatic pain and showed good postoperative recovery. Unfortunately, he was readmitted for progressive right leg pain at six weeks after the surgery. After the failure of conservative therapy, he received PELD again to explore the surgical site. Intraoperatively, a pseudomeningocele-containing nerve root, herniating through a small defect in the dural sac, was identified. During the dissection process, the pseudomeningocele was broken, which led to entrapment of the nerve root. Thereafter, the microsurgical technique was adopted to relocate the nerve root into the thecae sac and to repair the dural tear by non-resorbable suture.Conclusion:To our knowledge, this case report is the first documented instance of identification of a pseudomeningocele under an endoscope, and provides insights into the transformation of a pseudomeningocele into a cerebrospinal fluid fistula with nerve root entrapment. For neurological deficit caused by pseudomeningocele following PELD, operative revision by the microsurgery technique is the appropriate strategy.

A high resolution range profile (HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar. Generally, HRRPs obtained in a non-cooperative complex electromagnetic environment are contaminated by strong noise. Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition. In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model. To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP. The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition. The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.

In this roadmap, we address the development and perspectives of hydrogen evolution reaction, oxygen reduction reaction, oxygen evolution reaction, carbon dioxide reduction reaction and nitrogen reduction reaction through solar-, photoelectro- and electro-assisted ways.