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ABSTRACTVarious applications have been achieved by Gaofen-3 images, while the time series interferometric synthetic aperture radar (TS-InSAR) analysis was rare due to the issues with baseline instability. In this study, we revealed that the primary artefacts degrading the quality of Gaofen-3 interferograms are the long spatial baselines and inaccuracy of orbit information induced by the lack of consideration of satellite orbital tube manoeuvres. To address these challenges, we have proposed an enhanced interferometry processing method aimed at isolating the main artefacts from wrapped interferograms and enhancing the interferometry capabilities of Gaofen-3 images. It mainly includes tips on estimating precise baselines based on orbital information and fringe frequency in the wrapped interferogram and refining the residual phase further by a quadratic polynomial regression model. Subsequently, the TS-InSAR analysis is successfully implemented to acquire high-accuracy land subsidence in Beijing during 2020–2021 based on these refined interferograms. This case study demonstrates the achievement of the land subsidence analysis through TS-InSAR with Gaofen-3 images, highlighting that our proposed method could enhance the quality of Gaofen-3 interferograms.

Water injection and oil production often lead to significant surface deformations in oilfields. Such geological hazards are substantial potential threats to both the oilfield and its surrounding infrastructure. Liaohe oilfield is China’s largest production base for heavy and high-viscosity oil. It has a long history of surface deformation in its core production area near the Bohai Bay. Multitemporal synthetic aperture radar interferometry (MT-InSAR) offers long-term monitoring capabilities and has previously been used to monitor deformations in Liaohe oilfield. However, quantitative analysis of deformation-driving factors is less common in previous studies, and investigations are limited to data up until 2021. Continuous monitoring and analysis of deformation patterns and influencing factors in this region are of crucial practical importance. In this study, we acquired the latest long-term deformation data and the driving factors in Liaohe oilfield via MT-InSAR. For the first time, a quantitative analysis of deformation-driving factors was performed, and the ultra-long-term (from 1958 to 2023) deformation data with respect to Liaohe oilfield were collected and analyzed to interpret the deformation characteristics, variation tendency, and influencing factors. We employed 217 Sentinel-1A/B SAR images from 2019 to 2023 to calculate the deformation rates and cumulative deformations in this area. The results revealed three subsidence funnels and one uplift area, with deformation rates ranging from [−152, 41] mm/year and [−182, 51] mm/year (negative values indicating subsidence and positive values indicating uplift) for the ascending and descending datasets, respectively. Principal component analysis (PCA) was applied to the cumulative deformation sequence for extracting the primary components. The PCA results, groundwater data, and precipitation data were utilized for a quantitative analysis of primary deformation-driving factors. The analysis results indicate that the surface deformations exhibit a strongly linear trend mainly due to oilfield exploitation, coupled with slight variations related to precipitation and groundwater extraction. By integrating historical deformation information with the monitored results, the development process of the surface deformation was investigated and divided into six stages. This process was related to the oil energy demand, extraction techniques, and reservoir compaction phases. This study contributes to understanding the spatiotemporal evolution of surface deformation in the Liaohe oilfield and the driving factors, and it provides valuable insights for similar research in other oilfields.

Multiple dynamic impact signals are widely used in many engineering scenarios. In the process of penetrating multilayer hard targets, the overload signals from multiple dynamic impacts are difficult to identify due to “signal adhesion” problems caused by nonlinear strong disturbances. For this problem, a customized lightweight neural network framework is proposed here. First, the characteristics of multiple impact signals and their disturbances are analyzed by dynamics modeling and continuous wavelet transform. Second, considering these characteristics with SqueezeNet, a simplification rule for a network redundancy structure is proposed. A customized residual feedback structure and attention mechanism are nested within the structure, thus improving feature recognition performance with less computational complexity. Third, experimental results show that the proposed method maintains comparable excellent feature recognition results to the complex GoogleNet under all test conditions with varying impact speeds, while its metrics for computational complexity are even lower than those of SqueezeNet and other classical lightweight networks by 23%–61.6%. Therefore, the proposed method is of great practical value for penetrating ammunition and other weak hardware application platforms that require accurate identification of multiple dynamic impact signals.

Efficient water disinfection is vitally needed in rural and disaster-stricken areas lacking power supplies. However, conventional water disinfection methods strongly rely on external chemical input and reliable electricity. Herein, we present a self-powered water disinfection system using synergistic hydrogen peroxide (H2O2) assisted electroporation mechanisms driven by triboelectric nanogenerators (TENGs) that harvest electricity from the flow of water. The flow-driven TENG, assisted by power management systems, generates a controlled output with aimed voltages to drive a conductive metal–organic framework nanowire array for effective H2O2generation and electroporation. The injured bacteria caused by electroporation can be further damaged by facile diffused H2O2molecules at high throughput. A self-powered disinfection prototype enables complete disinfection (>99.9999% removal) over a wide range of flows up to 3.0 × 104L/(m2h) with low water flow thresholds (200 mL/min; ∼20 rpm). This rapid, self-powered water disinfection method is promising for pathogen control.

The maximum detectable deformation gradients (MDDG) for interferometric synthetic aperture radar (InSAR) technology is important for the selection of SAR images and processing algorithms to perform accurate slope displacement monitoring, which is strongly influenced by terrain factors in mountainous areas. In this article, a functional model is proposed to determine the MDDG of InSAR with respect to arbitrary slope gradients/aspects and wavelengths. Based on this model, regional MDDG characteristics are explored and compared in Mao County, Sichuan Province, China. The MDDG distribution regarding Sentinel-1, ALOS-2/PALSAR-2, and TerraSAR-X SAR satellite data using arbitrary slope gradient/aspect are derived. Furthermore, the MDDG from variable satellites for three different bands (X/C/L-band) are compared and the influence factors with respect to the wavelength and resolution on MDDG are discussed. The proposed model is helpful in selecting SAR data or processing algorithms based on calculated MDDG, in the meanwhile, it has significant implications for understanding and analyzing real slope displacement monitored by InSAR regarding different SAR images in mountainous areas.

Synthetic aperture radar interferometry (InSAR) technology has been widely used for landslide monitoring in mountainous areas. The troposphere in steep mountainous areas is affected by the variable topography, temperature, and humidity, which differs from that in plain areas and thus exhibits large spatial heterogeneity. Traditional InSAR troposphere correction methods are limited in this area, and the accuracy of InSAR measurements will be significantly affected. In this article, we proposed a tropospheric delay correction method based on deep learning (AtmNet) without external data considering the spatial heterogeneity in each individual interferogram. The tropospheric correction and landslide monitoring based on Sentinel-1 SAR data was carried out in Mao County, a high landslide-prone area in southwest Sichuan Province (China). A simulation experiment was conducted to analyze the adaptability of the model and evaluate the effectiveness of the AtmNet method. Furthermore, we demonstrated the good performance of the AtmNet method through a comparison with the linear model (LM) and generic atmospheric correction model (GACOS), revealing that the proposed method could effectively model the spatial heterogeneity of tropospheric delay in steep mountains. The slope displacements that cannot be seen in the interferogram were very clear after the tropospheric delay correction. This method provides important technical support for the accurate DInSAR and time-series InSAR for landslide monitoring in steep mountainous areas in the future.

ABSTRACTLandslides are major geological hazards and frequently occur in mountainous areas with steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar (InSAR) has been widely used in landslide measurement over the last three decades. However, InSAR only can measure one-dimensional displacements (i.e. those in the radar’s line of sight (LOS) direction), resulting in the uncertainty between LOS displacement and the real slope displacement. In this paper, based on ascending and descending data from Sentinel-1 satellite, a wide-area potential landslide early identification was carried out using SBAS-InSAR in the whole of Mao County, a mountainous area in western Sichuan (China), with a total of 41 potential landslides successfully detected. Based on the quantitative analysis, the results show that the InSAR LOS measurement values are slope aspect and gradient-dependent. Finally, we innovatively derived a LOS displacement sensitivity map of InSAR in landslide measurement, revealing the relationship between LOS displacement, real displacements on slopes with arbitrary aspects and gradients, and SAR geometric distortion. This is a generalized finding useful for any slopes. It provides theoretical support to acquire and understand the real slope displacement from InSAR landslide measurement, which is vital to assist in correctly interpreting LOS displacement and carrying out subsequent quantitative geological engineering analysis.

Soft actuators can realize delicate adaptive grasping of fragile and irregularly shaped objects, which are essential in biological and engineering systems. Yet, critical concerns in the frontier soft graspers are insufficient grasping ability and functional limitations. Here, we propose a Bobbit worm‐inspired multimodal‐sensing adaptive soft grasper (MSASG) enabled by harnessing the Miura‐origami skeleton integrated with self‐generated triboelectric force sensors (TFSs). The Miura‐origami skeleton endows the soft grasper a high grasping force while provides an energy‐efficient way to passively embrace an object without extra energy input. A multimodal TFSs with tactile sensing (TFS‐1) and pressure‐feedback (TFS‐2) functions is constructed by directly packaging a pyramid‐pattern elastic film on the Miura‐origami skeleton. The MSASG implement fast grasping or releasing action by evaluating pressures of various approaching prey, including hermit crabs, crickets, and beetle, etc. And its sensitivity greater than 0.18 V mN−1. In particular, the grasping performances and triboelectric sensing capacity can be optimized by modulating topological parameters (crease length, and surface film thickness, etc.), using a combination of theoretical modeling, finite element simulations, and experiments. The bionic design of soft graspers broadens the future applications for versatile human‐robot‐environment interaction scenarios, such as adaptive robots, reconfigurable architectures, medical devices, and deep‐sea explorations. A Bobbit worm‐inspired multimodal‐sensing adaptive soft grasper (MSASG) is proposed by employing Miura‐ori skeleton and self‐generated triboelectric force sensors (TFSs). Such a MSASG can successfully identify and swiftly capture diverse living organisms, including hermit crabs (24 mN), and crickets (10 mN), with an extremely low energy consumption of 0.514 mJ of the MCU circuit.

ABSTRACTReservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.

Vibration monitoring and buffering is an important technical task in many industrial fields, such as bridges, machine tools and other specific applications. Recently, a magneto-rheological elastomer (MRE) has been utilized in an intelligent vibration absorber system with tunable stiffness, which can reversibly regulate its resonant frequency in real time to effectively prevent the resonance of the mechanical system. However, this MRE must be integrated with a vibrational sensor and equipped with an external magnetic field (e.g., electromagnetic coils or permanent magnets). Thus, the overall system is usually bulky and energy-intensive, which severely limits its broad application potential. In this work, we present an MRE-based tunable vibration absorber (MRE-TVA) with an elaborate 3-layered architecture integrated with a self-sensing triboelectric nanogenerator (TENG) sensor. The inner two-layer MRE structure exhibits variable stiffness, while the outer layer of the negative Poisson's ratio structure can confine the deformation of the inner MRE layers and form a contact-separation TENG sensor with the MRE, where the sensor can recognize external vibration information in a timely manner. In particular, the TENG sensor enables sensitive identification of the abnormal state of resonant excitation and other mechanical disturbances through dual logic judgment control based on vibration frequency and amplitude. For energy-space efficiency, a bistable permanent magnet controlled by a pulse current is integrated to generate a desirable magnetic field for modulation of the MRE stiffness. Compared with traditional electromagnetic coils, the energy and space utilization rates are increased by 94.25% and 75%, respectively. This combination of self-sensing and vibration damping systems achieves high energy-space efficiency, which will enable the intelligent health monitoring of bridges and other autonomous, smart, and long-term engineering tasks.

Joint radar-communication systems based on integrated waveforms can simultaneously realize high-speed data transmission and target detection in one device, providing information sharing networks and information sources for decision-making of intelligent transportation systems. In this letter, based on a combination of linear frequency modulation (LFM) signals and continuous phase modulation (CPM) signals with an adjustment of communication symbols of length ${k}$ , an integrated waveform ${k}$ -LFM-CPM and a complete system framework cascaded with low-density parity-check (LDPC) codes is carried out. With a limited bandwidth, this system achieves a better bit-error rate than original LFM-CPM-LDPC systems, benefiting from an almost non-broadening power spectrum and additional information used in the cascaded decoding process. Finally, through the optimization of the mismatched filter, it is verified that the detection performance of the integrated waveform with accumulation is almost the same as that of the LFM signal.

Since each individual has distinct gait characteristics, monitoring human motion can enable identity recognition. Here, we report a self-powered band that can recognize human identity through gait pattern which is achieved by detecting muscle activity. The self-powered band is a soft and stretchable triboelectric nanogenerator (TENG) that is biocompatible and low-cost, which is looped around human body parts and generates electrical outputs in response to body motions involving muscle activities. The band can quantitatively detect walking step, speed and distance. Furthermore, the detected unique motion pattern of each individual allows the band to be used for identity recognition such as personal computer login and employee clock in through gait monitoring and analysis. This work opens new frontiers for the development of self-powered electronics and inspires new thoughts in human-machine interface.

Supercapacitors exhibit a self-sensing phenomenon with voltage sensitivity under mechanical effects. This paper proposes a dynamic model that reveals the mechanism of this sensitivity phenomenon. During a mechanical process such as an extreme impact or a finger press, transient changes of the coupled micro-deformation, porosity and output voltage of the supercapacitors are simulated, and the relationship between change in voltage and strength of the mechanical impacts is obtained. In particular, the experimental phenomenon that the output signal of the supercapacitors under extreme effects exhibits few clutters is theoretically explained and simulated. Finally, for the application of electronic skin and wearable electronics, the self-sensing characteristics of the supercapacitors under finger pressing are experimentally studied, and the ability to perceive the magnitude and duration of pressing is verified. This work provides a strong theoretical foundation for designing self-sensing supercapacitors with superior performance and expands their applications under various mechanical effects, from electronic skin to car crash protection.

Working in the ambient environment, the behavior of triboelectric nanogenerator (TENG) is inevitably limited by the air breakdown phenomenon. In this paper, the load characteristics of TENG considering the air breakdown effect are systematically studied. The maximum retainable surface charge density varies with resistive loads, and a saturation state is seen during the capacitive charging process in the TENG due to the air breakdown effect. The optimization criterion to maximize the TENG output was also investigated with the consideration of air breakdown effect. Through theoretical analysis and experimental verification, this work provides a solid criterion to guide the TENG design and operational process.

In the field of automobiles and many other industries, there is an urgent demand for the sensing of high-gacceleration. In this paper, a self-powered high-gacceleration sensor based on a triboelectric nanogenerator is proposed for the first time. It is micro-fabricated with a total volume of 14×14×8mm3, and its sensing ability is confirmed via a Machete hammer experiment, with a measurement range of up to 1.8×104g, a sensitivity of 1.8mV/g, and a Pearson correlation coefficient of 0.99959. In addition, the output signal of this novel acceleration sensor has few clutters, which is beneficial for recognition and subsequent signal processing. The effects of the aluminum-electrode thickness on the sensitivity and linearity of the sensor are investigated via modeling, theoretical analysis, and experiment, providing a reliable basis for the parameter optimization of the structural design. Experiment results indicate that this novel acceleration sensor covers a wide measurement range and meets the urgent needs of monitoring various high-gimpacts for military equipment and automobiles.

Calreticulin (CRT) is an endoplasmic reticulum luminal calcium-binding protein with multiple cellular functions, including intracellular Ca2+homeostasis, oxidative stress responses, and lectin binding. CRT can also modulate cell adhesion, cell-cell interactions, migration, phagocytosis, integrin-dependent Ca2+signaling, and immune responses, and plays an important role in cellular proliferation, differentiation, and apoptosis. Given these roles, it is not surprising that CRT function has important implications in health and disease. Considerable evidence in recent years suggests that CRT dysfunction is associated with cancer and that CRT could be a diagnostic marker and a target for cancer therapy. These topics are discussed in depth in this review.

A bioinspired soft and stretchable triboelectric nanogenerator (TENG) is developed as energy-harvesting skin to drive personal electronics by scavenging biomechanical energy. Drawn inspiration from biological cells, the TENG consists of patterned interconnected cellular structures, with physiological saline as the electrode and silicone rubber as the encapsulation and triboelectric layer. The TENG can withstand a strain of 600% and has a transmittance of as high as 62.5%. The TENG can keep its high performance under various strain. The TENG also has the desirable features of biocompatibility, simple fabrication, light weight and environmental protection. The maximum instantaneous power density (2.3Hz) and direct current power density of the TENG are ~ 11.6W/m2and ~ 2.65mW/m2respectively. Mounted on the skin, the TENG integrating with a power management unit can sustainably drive an electronic watch sorely by harvesting energy from hand motion. A stretchable self-charging power unit with a TENG and a micro supercapacitor sharing the same solution is created, with the solution as both the electrode of the TENG and the electrolyte of the supercapacitor. This work opens up new insights for clean power sources of skin-mounted electronics and promotes the development of sustainable energy supply for wearable and portable electronics.

Vibration is one of the most common energy sources in ambient environment. Harvesting vibration energy is a promising route to sustainably drive small electronics. This work introduces an approach to scavenge vibrational energy over a wide frequency range as an exclusive power source for continuous operation of electronics. An elastic multiunit triboelectric nanogenerator (TENG) is rationally designed to efficiently harvest low-frequency vibration energy, which can provide a maximum instantaneous output power density of 102 W·m–3at as low as 7 Hz and maintain its stable current outputs from 5 to 25 Hz. A self-charging power unit (SCPU) combining the TENG and a 10 mF supercapacitor gives a continuous direct current (DC) power delivery of 1.14 mW at a power management efficiency of 45.6% at 20 Hz. The performance of the SCPU can be further enhanced by a specially designed power management circuit, with a continuous DC power of 2 mW and power management efficiency of 60% at 7 Hz. Electronics such as a thermometer, hygrometer, and speedometer can be sustainably powered solely by the harvested vibration energy from a machine or riding bicycle. This approach has potential applications in self-powered systems for environment monitoring, machine safety, and transportation.

Vibration energy harvesting is promising to supply power autonomy for many electronic devices. This paper proposes a novel nonlinear piezoelectric energy harvester with a two-degree-of-freedom self-coupled structure. The operating frequency range and the power output of the new harvester is enhanced simultaneously compared with the conventional stopper-based nonlinear energy harvester. The dynamic model of the self-coupled structure is established, and then the bucket hat-like elastic potential energy function of the structure is obtained, which accounts for the superior performance of the harvester. Moreover, an optimization methodology for developing the nonlinear piezoelectric energy harvester was proposed. The conventional and novel energy harvesters were designed, optimized, and fabricated. Experimental results indicate that the maximum output power of the novel energy harvester can be enhanced to 3.54 times of that of the conventional energy harvester with a stopper structure. Besides, the working bandwidth is broadened from 4.7 Hz to 5.2 Hz. It is expected that this work is devoted to the future improvement of vibration energy harvesters in practical applications.

Ground subsidence is one of the key factors damaging transportation facilities, e.g., road networks consisting of highways and railways. In this paper, we propose to apply the persistent scatterer synthetic aperture radar interferometry (PS-InSAR) approach that uses high-resolution TerraSAR-X (TSX) imagery to extract the regional scale subsidence rates (i.e., average annual subsidence in mm/year) along road networks. The primary procedures involve interferometric pair selection, interferogram generation, persistent scatterer (PS) detection, PS networking, phase parameterization, and subsidence rate estimation. The Xiqing District in southwest Tianjin (China) is selected as the study area. This district contains one railway line and several highway lines. A total of 15 TSX images covering this area between April 2009 and June 2010 are utilized to obtain the subsidence rates by using the PS-InSAR (PSI) approach. The subsidence rates derived from PSI range from −68.7 to −1.3 mm/year. These findings show a significantly uneven subsidence pattern along the road network. Comparison between the PSI-derived subsidence rates and the leveling data obtained along the highways shows that the mean and standard deviation (SD) of the discrepancies between the two types of subsidence rates are 0.1 and ±3.2 mm/year, respectively. The results indicate that the high-resolution TSX PSI is capable of providing comprehensive and detailed subsidence information regarding road networks with millimeter-level accuracy. Further inspections under geological conditions and land-use categories in the study area indicate that the observed subsidence is highly related to aquifer compression due to groundwater pumping. Therefore, measures should be taken to mitigate groundwater extraction for the study area.

Direct-Current Triboelectric Nanogenerators (DC-TENGs) achieves an direct-current (DC) output instead of traditional alternating-current (AC) output of TENG. Among the various structure designs of DC-TENGs, the air-breakdown DC-TENG has created a record of energy density and an almost constant-current output, which has great potential in practical applications. This paper proposes a transient physical-field model and carries out comprehensive optimal design for air-breakdown DC-TENG. The beneficial breakdown domain (BBD) and harmful breakdown domain (HBD) are introduced to reveal the dynamic mechanism that distinguishes the DC-TENGs from AC-TENGs. Indexes of the breakdown charge ratio (BCR) and leading charge density (LCD) are defined to evaluate the extent to which the high-density surface charge is effectively exploited in electricity generation based on the beneficial breakdown effect. The output characteristics of air-breakdown DC-TENGs are theoretically analyzed, revealing the influence of the load resistance, device structure parameters, and initial charge density. Therefore, the optimal design of air-breakdown DC-TENGs is carried out with a high output power and low matching resistance. Finally, these superior performances are verified by experiments, so that the proposed transient physical model and comprehensive optimization method can be reliably extended to the design of all types of air-breakdown DC-TENG, and promote its application in a wide range of fields.

•The voltage of lithium-ion batteries under high impact is measured, with a sharp drop and slow rise.•The partial short circuit of the separator and the relaxation effect contribute to the impact failure.•MI-PNGV model is proposed to simulate the failures under different extreme mechanical conditions.•The design guideline is proposed to avoid the mechanic impact failure of lithium-ion batteries.

For wireless sensor network (WSN) nodes with an extremely tight energy supply, herein, an adaptive energy management strategy based on a hybrid energy system composed of a photovoltaic device, Li battery, and supercapacitor is proposed. According to information fusion of the irradiation intensity, load consumption and the state of charge of the battery and supercapacitor, the estimation of energy shortage caused by future wake‐up pulses is carried out. Based on this, the charge/predischarge power of the Li battery is adaptively adjusted. Simulations validate that much lower Li battery capacity loss than the previous strategies is achieved without sacrificing the wake‐up success rate, thereby extending the life of the hybrid energy module in a WSN node by an order of magnitude. In addition, the proposed strategy has excellent robustness in the double‐random environmental and working conditions including the irradiation intensity, driving event frequency, etc., and can always maintain excellent performance while these parameters are changing within a wide range. Therefore, an effective energy guarantee for long life and high wake‐up success rate of WSN nodes under double‐random environmental and working conditions is provided and the practical application of WSN nodes in such scenarios is promoted. A synergic management strategy for a hybrid energy system in double‐random environmental and working conditions of the irradiation intensity and driving event frequency is presented, with a parameter optimization scheme avoiding large computation via typical training data, resulting in a robust long battery life and high wake‐up success rate in variable harsh conditions.