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Forest fires are significant disturbances to ecosystems, necessitating accurate mapping of burned areas and assessment of burn severity. First, we reconstruct a dataset whose label uses a more flexible classification method from Landsat imagery and establish auxiliary environmental datasets for fire-affected regions. Leveraging vegetation change prefire and postfire, we propose a transformer-based change detection model that integrates remote sensing and environmental information effectively. We introduce a multilevel feature fusion mechanism to address spatial resolution degradation in burn severity estimation. Experimental results show our model closely approximates evaluation dataset labels. For burned area segmentation, our method achieves the highest F1 (0.897) and mIoU of 0.781. For burn severity estimation, our method also achieves the highest mIoU (0.851). Incorporating auxiliary features improves performance by nearly 30%, while the multilevel feature fusion mechanism reduces resolution degradation by 9.6%.

As a noninvasive, nonradiative and high-speed imaging modality, fluorescence imaging in the second near-infrared window (NIR-II, 1,000-1,700 nm) has demonstrated great potential for biomedical research and clinical study. The NIR-II window can be further divided into two spectral regions: NIR-IIa (1,000-1,300 nm) and NIR-IIb (1,500-1,700 nm). Compared to NIR-IIa, imaging in NIR-IIb region affords high-resolution imaging at subcentimeter tissue depths due to suppressed photon scattering and diminished tissue autofluorescence at long wavelengths, but relies on probes with high toxicity. To address the problem, researchers employ deep learning networks to attain NIR-IIb images from NIR-IIa images. However, current methods require numerous paired or unpaired images (more than 2800 images) as training sets, which can hardly acquire. In this work, an innovative convolutional neural network (BRCycle-GAN) is trained based on a small training set (merely 63 images) to transform NIR-IIa images into images with NIR-IIb imaging qualities. The NIR-IIb images generated by BRCycle-GAN outperform previous network models in terms of peak signal-to-noise ratio, cosine similarity and other image evaluation indices.

Fire radiative power (FRP) is a key indicator for evaluating the intensity of wildfires, unlike traditional real-time fire lines or combustion areas that only provide binary information, and its accurate prediction is more important for firefighting actions and environmental pollution assessment. To this end, we used a combination of data from geostationary satellites and polar orbit satellites to correct the FRP data. Incorporating various factors that affect wildfire spread, such as meteorological conditions, topography, vegetation indexes, and population density, we constructed a comprehensive California wildfire spread dataset, covering information since 2017. Then, we established a deep learning framework that integrates various modules to analyze multimodal data for the accurate prediction of FRP imagery. We investigated the impact of input sequence length and loss function design on model predictive performance, leading to subsequent model optimization. Furthermore, our model has demonstrated acceptable performance in transfer learning and multistep prediction, emphasizing its application value in wildfire prediction and management. It can provide more detailed information about wildfire spread, showcasing the powerful capability of deep learning to process multimodal data and its potential in the emerging field of real-time FRP prediction.

In recent years, change detection (CD) of optical remote sensing images has made remarkable progress through using deep learning. However, current CD deep learning methods are usually improved from the semantic segmentation models, and focus on enhancing the separability of changed and unchanged features. They ignore the essential characteristics of CD, i.e., different land cover changes exhibit different change magnitudes, resulting in limited accuracy and serious false alarms. To address this limitation, in this article, a category context learning-based difference refinement network (CLDRNet) based on our previous work is proposed. Considering the semantic content differences of heterogeneous land covers, a category context learning module is designed, which introduces a clustering learning procedure to generate an overall representation for each category, guiding the category context modeling. The clustering learning process is differentiable and can be integrated into the end-to-end trainable CD network, so it considers the semantic content differences from the CD perspective, thereby improving the CD performance. In addition, to address the magnitude differences of different land cover changes, a two-stage CD strategy is introduced. The two stages correspond to difference map learning and difference map refinement, aiming at ensuring high detection rates and revising false alarms, respectively. Finally, experimental results on three CD datasets verify the effectiveness of our CLDRNet in both visual and quantitative analysis.

In recent years, the introduction of multiobjective evolutionary algorithms (MOEAs) into the field of endmember extraction (EE) in hyperspectral unmixing has demonstrated a breadth of results that surpass those derived from single-objective-based methodologies. Despite these advancements, the adaptation of MOEAs to EE and the attainment of globally optimal solutions represent unresolved challenges meriting continued exploration. This study addresses two principal obstacles in MOEA-based EE: the notorious “curse of dimensionality” in high-dimensional optimization, and the difficulty in striking a balance between convergence and population diversity. We propose a two-stage, evolutionary-based EE algorithm, referred to as TSEA, designed to confront these issues. A novel solution space splitting strategy is incorporated into TSEA that efficiently mitigates the curse of dimensionality by strategically contracting the search space. This advantage is largely attributed to the significant reduction of invalid solutions achieved through the simple application of a clustering procedure. Furthermore, a two-stage optimization approach is employed to meticulously uphold the convergence and diversity of the population, aiming to attain the optimal solution within the realm of high-dimensional optimization. Empirical evidence from four real hyperspectral images demonstrates that the proposed TSEA outperforms other comparison multiobjective optimization algorithms. Thus, this study contributes to the ongoing discourse on the optimization and applicability of MOEAs in the context of EE.

The widely use of covered conductor increases the reliability of distribution network. However, a new problem of partial discharge caused by covered conductor at the position of tower head is generated at the same time. This kind of partial discharge will damage the insulation layer or accelerate the deterioration of insulation, which may cause the single-phase grounding fault and affect the power supply reliability. At present, there is few systematic studies on the characteristics of this partial discharge. This paper mainly analyzes the electric field distribution near the covered conductor around tower head, builds the partial discharge experiment platform, and obtains the pulse current characteristics of partial discharge under different working conditions. The results show that the maximum electric field intensity is located at the interface between covered conductor, binding wire and air. The main causes of partial discharge are the wet and dirty of the surface of porcelain insulator and conductor insulation. The minor cause is the breakage of covered conductor insulation. Under different conditions, the initial partial discharge pulse current wave front time is 8ns ~ 45ns, the tail time is 30ns ~ 150ns. The discharge time interval can be as low as $30~\mu \text{s}$ . In addition, it is found that there are two types of partial discharge through spectral analysis. One occurs when the covered conductor insulation is damaged under binding wire; the other is the covered conductor insulation is intact.

Experimental results indicate that the conventional program suspend scheme in 3D NAND flash memory chip can generate unexpected additional read fail bits and reduce the reliability of 3D NAND flash memory. These extra read fail bits are observed when the program suspend command is issued during the program stage, and particularly, they become more obvious as the delay time between program suspend operation and other following operations exceeds tens of milliseconds. By analyzing the waveform of conventional program suspend scheme, it is suggested that the unexpected extra read fail bits are caused by the different occupancy of grain boundary traps (GBTs) in the polycrystalline silicon (poly-Si) channel during the idle time after the program suspend operation. Accordingly, a novel program suspend scheme is proposed by adding a “stabilizing” pulse immediately after the program stage. Silicon experimental data show that the proposed scheme can effectively limit the read fail bit count (FBC) to a normal range, thus improving the reliability of 3D NAND flash memory significantly.

Software-defined radio dongles are small and inexpensive receivers well known to amateur radio enthusiasts. When connected to an antenna, they enable monitoring of a wide range of the radio spectrum by conditioning the input signal and transferring a downconverted version of it to a personal computer for software processing. Here, we employ a composite of two such dongles, interfaced with codes written in MATLAB and GNU Radio, as a measuring instrument to study the flexural vibrations of a few-layer graphene nanomechanical resonator. Instead of an antenna, we connect the dongles to the split output of a photodetector used to detect vibrations optically. We first perform a quantitative analysis of the dynamics of the first vibrational mode. We then measure the response of the first two vibrational modes in parallel. To illustrate our technique, we detect changes in the vibrational amplitude of both modes induced by periodic strain modulation with a delay of $\approx 1$ ms between measurements. Last, we show that our software-based instrument can be employed to demodulate human voice encoded in the vibrations of our resonator. For parallel measurements of several frequency channels, and provided that the input signal is not too weak, our composite system may offer an alternative to the use of multiple lock-in amplifiers or multiple spectrum analyzers, with the distinct advantage of being cost-effective per frequency channel.

Two-dimensional direction of arrival (2D-DOA) estimation, or estimating the azimuth and elevation angles of sources simultaneously, is an increasingly important area in array signal processing. This paper presents a 2D-DOA algorithm together with a novel structure of array named Arc-Array (ArcA). The ArcA is enlarged to a virtual Uniform Circular Array (UCA) through a Deep-Neural-Network (DNN) based covariance matrix completion strategy; afterward, the MUSIC algorithm is performed with the completed covariance matrix. The proposed method is named as ArcA-DNN, and the performance of ArcA-DNN is evaluated by computer simulations. The simulation results indicate that the performance of 2D-DOA estimation in ArcA is able to approach that of a complete UCA; meanwhile, the number of physical elements is substantially reduced compared to the UCA. Moreover, the proposed ArcA-DNN algorithm gives access to implementing underdetermined 2D-DOA estimation with reasonable results.

Terahertz radiation can provide unprecedented vistas in physical studies such as transient matter state control and advanced free-electron manipulation. Here, we present one feasible path towards high-repetition-rate, intense THz sources by combining the wire-based THz source on a consecutively running, wire-conveying tape design. The proof-of-principle experimental results show an upper 30 mW average THz power output under 1 kHz laser excitation, which presents the first high-repetition-rate, and most importantly, portable and application in handy THz source that is generated by solid plasmas. The generated THz pulses are characterized with a 0.3 THz center frequency and ∼0.3 THz bandwidth. We believe this tabletop laser-driven high-repetition-rate THz source will open a new door for THz and related interdisciplinary sciences.

A near-infrared (NIR) supercontinuum and ultrashort pulses are generated via phase-mismatched cascaded frequency conversion in DSTMS (4-N, N-dimethylamino-4'-N'-methyl-stilbazolium 2, 4, 6-trimethylbenzenesulfonate) crystal. NIR pulses with a central wavelength of 1.5 μm and 63 fs pulse duration are incident into the crystal, and the spectrum is broadened to over 1200-2100 nm. Compensating the dispersion by fused silica, ultrashort NIR pulses with 16.6 fs pulse duration are obtained. Meanwhile, intense single-cycle terahertz pulses are generated via optical rectification in DSTMS crystal.

This paper proposes a nonsingular terminal sliding mode control scheme with fast fixed-time convergence for a class of second-order nonlinear systems in the presence of matched uncertainties and perturbations. First, based on fixed-time stability theory, a novel stable system is proposed. Then, using the fixed-time stable system, a fast fixed-time nonsingular terminal sliding surface is derived. The settling time is independent of the initial system state and can be set in advance with the design parameters; the upper-bound of convergence time is derived from the Lyapunov theory. Moreover, the proposed control scheme has an advantage in convergence rate over existing results and achieves better control performance with low control energy cost. The simulation results for a tracking system with a single inverted pendulum are presented to validate the effectiveness and superiority of the proposed control method.

It is extremely difficult to collect deep lunar soil samples during lunar exploration, as the average thickness of lunar soils is very large, and the power of lunar soil sampling systems is inherently limited. In this paper, we propose the use of serpentine robots to explore lunar craters. Our approach is to first design a kinematic wave model that allows the serpentine robot to satisfy all the requirements of lunar exploration. The resistance encountered by each part of the robot during its motions was measured to identify the parts with the greatest influence on each mode of locomotion to maximize its locomotive efficiency. Accordingly, we have designed a serpentine robot system based on the characteristics of snakes and constructed a kinematic model for serpentine robots in lunar crater environments. In addition, we constructed a lunar soil model using a discrete element method-multibody dynamics co-simulation platform, thus optimizing the locomotion strategy of the robot. The EDEM-ADAMS co-simulation platform was used to simulate the motions of the serpentine robot in a lunar crater, which yielded the resistance, speed of motion, and displacement of the robot's modules during its motions. The optimization analysis of the motion and environmental parameters of the serpentine robot included three variables: the inclination of the lunar crater's walls, the crawling angle of the robot, and the amplitude of the robot's kinematic wave. The dependence of the motions of the serpentine robot on these variables was analyzed using maximum and averaged force values. Finally, validation experiments were performed using a serpentine robot model. Thus, we have optimized the locomotion strategy of the serpentine robot for lunar craters.

Dempster-Shafer evidence theory has been widely applied to solving data fusion problems. However, it is still an open issue about how to combine the evidences effectively when the high conflict evidences are collected. Many scholars have made improvements to solve this problem, but there are new problems such as violation of the theoretical attributes of D-S combination rules and limitations of application scope of improvement methods. Considering these shortcomings, a new evidence synthesis formula based on correlation coefficient of belief functions is proposed in this paper. Our contribution is that the proposed formula can solve the highly conflict issues mentioned above effectively. Moreover, the various types of evidences collected can be well combined. One of the advantages of the proposed model is that conflict coefficient Kr is the coefficient of the fusion formula which represents the degree of conflict about evidences. So the fusion process is more flexible and useful. Several examples and comparative experimental simulation are used to illustrate the effectiveness of the proposed methodology.

The performance of a core drill is the key to an entire sample task, and various working conditions are currently being studied. Using a grain size distribution method, three simulated lunar soils can be produced. Based on the positions of the critical particle and bit, new consumption and coring rate models are proposed. Four parameters of a rig were obtained in a lunar soil drilling simulation, including core rate, rotational power consumption, penetration, and drilling time. To conduct a rigorous evaluation of the performance of drilling under different working conditions, a criterion is proposed based on the drilling parameters. The result shows that the coring drill has a better performance at drilling rock in a soil working condition; however, it exhibits a weaker performance at drilling critical particle soil.

Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm - extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to low-frequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic.

Our ``You Only Move Once”(YOMO) detector based on depthwise separable convolutions is a single stage face detector that balances accuracy and latency. YOMO performs scale-invariantly by utilizing top-down architecture with feature agglomeration, and multiple detection modules instead of in an image pyramid approach. At the same time, we propose a semi-soft random cropping algorithm that enables different detection module adequately trained by different scales of samples. Several experiments are conducted on the FDDB dataset with discrete and continuous measures, to demonstrate that the methods have strongly competitiveness results. After using an ellipses regressor, the recall rates reached a satisfactory 97.59% and 83.66%, respectively. Surprisingly, YOMO has only 21 million parameters and achieves superior performance with 51 frames per second (FPS) for a 544 × 544 input image on a GPU.

In this paper, a three-dimensional (3D) impact-angle-control guidance law is proposed based on the approaches of nonsingular terminal sliding mode control and fixed-time convergence theory to intercept a maneuvering target. The guidance law is derived based on the coupled 3D engagement dynamics. To intercept the target with an expected impact angle, the nonsingular terminal sliding mode control is designed to ensure that the line-of-sight (LOS) angles and rates rapidly reach the desired values. Furthermore, the fixed-time convergence algorithm is proposed to guarantee that the LOS angles and rates are convergent in fixed time. The upper bound of the settling time can be obtained by presetting the parameters of the proposed guidance law, and it is independent of the initial conditions. The stability of the guidance system is proven via Lyapunov stability theory. The simulation results are presented to validate the efficiency and superiority of the proposed guidance law.

Packaging that is currently used for aero-engine precision bearings faces several issues, such as insufficient cushioning performance, insufficient sealing performance, and poor visibility. Herein, we propose a new method in which the bearings are fully covered with polydimethylsiloxane (PDMS). Samples were prepared for tensile and moisture-permeability tests according to the optimum ratio 10:1 (PDMS: Curing agent) of PDMS provided in the literature. The stress and strain curves of PDMS were then obtained via the tensile test. Additionally, the permeability of a polyethylene/polyamide composite film (the packaging material currently used) and PDMS were compared via a moisture-permeability test. According to the test results, the cushioning coefficient of silicone was 1.9, and the minimum thickness of the pad was 12 mm. Using a finite-element software, the bearing stress of the buffer pad was compared for thicknesses of 7, 12, 17, and 22 mm. The simulation results indicated that the liner with a pad thickness of 12 mm provided the optimal protection and economy. According to the simulation results, the precision bearing is packed in such a manner that it is fully enclosed, and the suitability of the proposed method wherein the PDMS material is used as a cushion pad was verified by simulating a drop test of the product during transportation. When the thickness of the PDMS cushion was 12 mm, the shock acceleration of the bearing was 2. Thus, the proposed method can successfully protect aero-engine precision bearings.

In this paper, we propose an effective THresholding method based on the Order Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure uses the order statistic to find an optimal threshold for classification, requiring almost no knowledge of the classifier itself. Unlike common data-driven methods, we analytically show that the THORS has theoretical guaranteed performance, theoretical bounds for the costs, and low-time complexity. Coupled with empirical results on several real-world data sets, we argue that the THORS is the preferred cost-sensitive learning technique.

In this paper, we propose and fabricated a novel scheme of SOI-based 1 × 3 coupler with variable splitting ratio. The coupler consists of two cascaded MMI with different sizes, and a wide range of splitting ratios from 1:0.1:1 to 1:18:1 can be achieved by modifying the size of the first-step MMI. Using the structure of cascaded step-size MMI, this kind of device has a compact footprint of below 15 μm × 15 μm and simple fabrication process. Meanwhile, the simulation analyses prove that the proposed couplers have a large operation bandwidth above 60 nm and be robust to the fabrication errors. Couplers with several splitting ratios were fabricated and well measured. Based on the measurement results, the splitting ratios of fabricated couplers from 1:0.67:1 to 1:18:1 can be achieved, and stable performance with the transmission efficiencies above 80% can be maintained over 50 nm wavelength range, which is the largest operation bandwidth to the best of our knowledge.

We propose and experimentally demonstrate a novel in-band optical signal-tonoise ratio (OSNR) measurement method based on normalized autocorrelation function. Experimental results indicate that OSNR of four-channel 32-Gbaud pulse-duration modulation-QPSK wavelength-division-multiplexing signals is precisely measured with applicability to flexible channel spacing. The measurement range is 37 dB from -15 to 22 dB with error less than ±0.5 dB. The proposed method is also robust to bit rate, modulation format, chromatic dispersion, and input optical power. Besides, the choice of delay in calculation of normalized autocorrelation function is flexible from 1.6 to 30 ps.

To address multiple data types and high speeds in a business satellite network based on the virtual channel multiplexing technology of advanced orbiting systems (AOSs), an AOS virtual channel multiplexing system model is established. AOS queue management problems and the constraint problems in virtual channel scheduling are analyzed. On this basis, a priority adaptive random early detection and vaccine updating immune genetic algorithm is proposed. In queue management, based on the scheme of a multi-queue sharing cache with a minimum allocation, the algorithm adaptively adjusts packet dropping probability according to the packet priority. Based on an immune genetic algorithm, virtual channel scheduling automatically updates the vaccine database according to the matching degree of the vaccines and population. Meanwhile, the algorithm efficiently combines queue management and virtual channel scheduling. Experimental results show that the algorithm has excellent evolution performance and can maintain smaller queue lengths, smaller packet loss rates, less delay for high-priority virtual channels, and also has preferable fairness.

A novel mode converter with broad bandwidth, high mode conversion efficiency, and large fabrication tolerance is proposed and fabricated on a silicon-on-insulator (SOI) platform. By using a modified 3 × 3 multimode interference coupler and an enlarged butterfly-shaped phase shifter, mode conversion from the fundamental mode (TE0) to second-order mode (TE2) is achieved numerically and experimentally. Performance analysis of the mode converter is carried out numerically, and the results show high performance of the mode converter with mode conversion efficiency larger than 91% (i.e., insertion loss less than 0.40 dB) in the whole C-band. In addition, the mode converter has large fabrication tolerance. For example, the width and the length deviations of the multimode interference coupler can be as large as 50 and 250 nm, respectively, whereas the width and the length deviations of the phase shifter can be up to 25 and 400 nm, respectively.