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The emergence of TKX-50, an energetic ionic salt with a high enthalpy of formation and low sensitivity, has opened a new path for the development of high-energetic, insensitive composite explosives. However, due to the poor interfacial binding properties of TKX-50 with conventional binders, there is a lack of effective guidance for the design of TKX-50 based composite explosives. To address the above issues, the interactions between carboxymethyl cellulose acetate butyrate (CMCAB) and other binders with explosives TKX-50/HMX were compared using the molecular dynamics method. Based on the simulations, TKX-50/HMX/CMCAB-based polymer-bonded explosives (PBXs) were prepared with CMCAB as binder, which displays a high binding energy (Ebind) with TKX-50 and high cohesive energy density (CED), and the effect of TKX-50 content on the performance of PBXs was investigated. The physical properties of PBXs, specifically the morphology, mechanical sensitivity, and thermal conductivity, were analyzed by SEM, sensitivity apparatus, and thermal conductivity meter, respectively. The specific heat capacity (Cp) and non-isothermal decomposition temperature of PBXs were tested by DSC, and then the corresponding thermal kinetic parameters were analyzed to evaluate their thermal safety. The adiabatic thermal decomposition processes of PBXs were tested using an ARC instrument. The decomposition mechanism and kinetics were also explored to further analyze their thermal stability and thermal safety under adiabatic conditions. The computer code EXPLO5 was used to predict the detonation parameters of PBXs. The results showed that CMCAB and TKX-50 displayed favorable interfacial bonding properties, and TKX-50 can be bonded with HMX to form a molding powder with a desirable morphology and safety profile. The TKX-50 in PBXs effectively improves the mechanical sensitivity and thermal safety of PBX and has a significant effect on the detonation performance of PBX. This research demonstrates a novel method suitable for screening and investigating high-energetic insensitive explosive systems compatible with TKX-50.

Low sensitivity octahydro-1,3,4,7-tetranitro-1,3,5,7-tetrazocine (HMX) has garnered significant attention from researchers due to its reduced shock sensitivity. However, the crystallization process poses challenges due to the high solidity and viscosity of the metastable α phase. Despite efforts to address this with additional energy sources like ultrasonic irradiation, prolonged exposure duration often results in small particle sizes, hindering the production of HMX with a consistent particle size distribution, thus limiting its applicability. To overcome these challenges, a method combining ultrasonic irradiation and trace H+ additive was proposed and investigated for their impact on the polymorphic transformation of HMX. The H+ additive was found to modify barriers, thus there was a lack of competitive driving force for the nucleation or growth of the metastable α form, thereby shortening the transformation pathway and duration. Moreover, the H+ additive significantly accelerated the nucleation rate of the β form (67.7 orders of magnitude faster with 0.10 wt ‰ H+) and the growth rate of β form HMX (5.8 orders of magnitude faster with 0.10 wt ‰ H+). While H+ additive alone was insufficient to induce spontaneous nucleation of the β form, combining it with short-duration ultrasonic irradiation further promoted β nucleation and shortened the polymorphic transformation duration (almost 20 orders of magnitude shorter). This rational approach led to effective control of the transformation process. The resulting low sensitivity HMX crystals exhibited varying mean sizes ranging from 20 to 340 μm, with purity exceeding 99.6 %, an apparent density greater than 1.8994 g/cm3, and few internal defects, fully meeting the requirements of low-sensitivity HMX, thus significantly expanding its potential applications. Our study sheds light on the mechanisms governing HMX polymorphic transformation in the presence of additives and ultrasonic irradiation, offering guidance for the rational control of this complex transformation.

Aiming to address the problem that autonomous underwater vehicle (AUV) with multibeam echo sounder system (MBES) for underwater topographic survey cannot autonomously obtain the sound velocity of the acoustic path to correct bathymetric data, this article proposes a framework to correct sound velocity error (SVE) for AUV with MBES, which include sound velocity profile (SSP) extension and SVE correction using genetic algorithm (GA) and support vector regression (SVR). The SSP is first calculated by extending the temperature and conductivity profiles to the seafloor based on the trend of temperature and conductivity with depth, which is obtained by using the least squares method to fit the data of conductivity temperature depth sensor carried by AUV. Then, the SSP is calculated according to the empirical sound speed formula. To eliminate the SVE in the extended SSP, the function of water depth, lateral distance, and SVE was first derived. Then, the SVE of the main survey line was corrected, including calculating the discrepancy values between the matching point pairs of the main and auxiliary survey lines, and establishing a regression model about depth error using the SVR algorithm. Finally, the GA algorithm was used to optimize the hyperparameters of the SVR algorithm. The test results demonstrate that the proposed method effectively reduces the impact of SVE on multibeam bathymetric data in both middle–deep and shallow water areas. The algorithm is not limited by terrain complexity, providing a solution for the lack of SSP in AUV autonomous topographic survey.

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Abstract To evaluate the possible thermal risks associated with the storage of octogen (HMX), non-isothermal differential scanning calorimetry (DSC) experiments were conducted in order to ascertain the kinetic model and parameters governing its thermal decomposition. DSC measurements indicate that HMX underwent a crystal transformation prior to thermal decomposition. A kinetic model for the autocatalytic thermal decomposition process was developed through the analysis of its primary exothermic peaks. Subsequently, numerical simulations were performed using the aforementioned kinetic model to assess the potential thermal explosion hazard of HMX under two distinct storage conditions. The comparison was made between the models of HMX autocatalytic decomposition temperature and thermal explosion critical temperature under two distinct storage conditions. The prediction of the influence of ambient temperature on the critical temperature of thermal explosion is conducted simultaneously. Finally, the thermal hazard parameters of HMX under different package quality are given.

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data.

The cocrystallization of 1,3,5,7-tetranitro-1,3,5,7-tetrazolidine (HMX) with cyclopentanone was achieved via a controlled cooling method, followed by comprehensive characterization that confirmed the α-configuration of HMX within the cocrystal. The enthalpy of dissolution of HMX in cyclopentanone was assessed across a range of temperatures using a C-80 Calvert microcalorimeter, revealing an endothermic dissolution process. Subsequently, the molar enthalpy of dissolution was determined, and kinetic equations describing the dissolution rate were derived for temperatures of 303.15, 308.15, 313.15, 318.15, and 323.15 K as follows: dα⁄dt = 10−2.46(1 − α)0.35, dα⁄dt = 10−2.19(1 − α)0.79, dα⁄dt = 10−1.76(1 − α)1.32, dα⁄dt = 10−1.86(1 − α)0.46, and dα⁄dt = 10−2.02(1 − α)0.70, respectively. Additionally, molecular dynamics (MD) simulations investigated the intermolecular interactions of the HMX/cyclopentanone cocrystallization process, demonstrating a transformation of HMX from β- to α-conformation within the cyclopentanone environment. Theoretical calculations performed at the ωB97XD/6-311G(d,p) level affirmed that α-HMX exhibited stronger binding affinity toward cyclopentanone compared to β-HMX, corroborating experimental findings. A comprehensive understanding of the dissolution behavior of HMX in cyclopentanone holds significant implications for crystal growth methodologies and cocrystallization processes. Such insights are pivotal for optimizing HMX dissolution processes and offer valuable perspectives for developing and designing advanced energetic materials.

Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar target image samples, a novel augmentation method employing self-training with a Disrupted Student model is designed (DS-SIAUG). The process begins by inputting a dataset of side-scan sonar target images, followed by augmenting the samples through an adversarial network consisting of the DDPM (Denoising Diffusion Probabilistic Model) and the YOLO (You Only Look Once) detection model. Subsequently, the Disrupted Student model is used to filter out representative target images. These selected images are then reused as a new dataset to repeat the adversarial filtering process. Experimental results indicate that using the Disrupted Student model for selection achieves a target recognition accuracy comparable to manual selection, improving the accuracy of intelligent target recognition by approximately 5% over direct adversarial network augmentation.

Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets.

Super-resolution (SR) is a technique that restores image details based on existing information, enhancing the resolution of images to prevent quality degradation. Despite significant achievements in deep-learning-based SR models, their application in underwater sonar scenarios is limited due to the lack of underwater sonar datasets and the difficulty in recovering texture details. To address these challenges, we propose a multi-scale generative adversarial network (SIGAN) for super-resolution reconstruction of underwater sonar images. The generator is built on a residual dense network (RDN), which extracts rich local features through densely connected convolutional layers. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to capture detailed texture information by focusing on different scales and channels. The discriminator employs a multi-scale discriminative structure, enhancing the detail perception of both generated and high-resolution (HR) images. Considering the increased noise in super-resolved sonar images, our loss function emphasizes the PSNR metric and incorporates the L2 loss function to improve the quality of the output images. Meanwhile, we constructed a dataset for side-scan sonar experiments (DNASI-I). We compared our method with the current state-of-the-art super-resolution image reconstruction methods on the public dataset KLSG-II and our self-built dataset DNASI-I. The experimental results show that at a scale factor of 4, the average PSNR value of our method was 3.5 higher than that of other methods, and the accuracy of target detection using the super-resolution reconstructed images can be improved to 91.4%. Through subjective qualitative comparison and objective quantitative analysis, we demonstrated the effectiveness and superiority of the proposed SIGAN in the super-resolution reconstruction of side-scan sonar images.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes—the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

The speed of sound in seawater plays a crucial role in determining the accuracy of multibeam bathymetric measurements. In deep-sea multibeam measurements, the challenge of inadequate longitudinal coverage of sound speed profiles arises from variations in seafloor topography, meteorological conditions, measurement equipment, and operational efficiency, resulting in diminished measurement precision. Building upon the EOF (Empirical Orthogonal Function), a method employed to analyze spatiotemporal data such as sound speeds, this paper addresses the limitations of the EOF method caused by the shallowest sampling depth of the sound speed profile samples. It proposes two methods for EOF reconstruction of measured sound speed profiles extended to full water depth by splicing measured sound speed profiles at non-full water depths with historical average sound speed profiles of the surveyed sea area. Specially, Method 2 introduces the latest metaheuristic optimization algorithm, CPO (Crested Porcupine Optimizer), which exhibited superior performance on multiple standard test functions in 2024. The study reconstructs randomly sampled measured sound speed profiles using the two proposed methods and commonly employed substitution and splicing methods, followed by a comparative analysis of the experimental outcomes. At a sampling depth of 200 m, Method 2 demonstrates performance superior to other methods, with RMSE, MAE, MAPE, and R2 values of 0.9511 m/s, 0.8492 m/s, 0.0566%, and 0.9963, respectively. Method 1 yields corresponding values of 0.9594 m/s, 0.8492 m/s, 0.0568%, and 0.9962, respectively. Despite its slightly inferior performance compared with Method 2, it offers substantial advantages over the substitution and splicing methods. Varying the sampling depth of measured sound speed profiles reveals that Methods 1 and 2 exhibit inferior reconstruction performance in shallow water compared with the substitution and splicing methods. Nevertheless, when the sampling depth surpasses the depth range of initial spatial modes with abrupt variations, both methods achieve notably higher reconstruction accuracy compared with the substitution and splicing methods, reaching a stabilized state. Sound ray tracing reveals that the reconstructed sound speed profiles from both methods meet the stringent accuracy standards for bathymetric measurements, achieving an effective beam ratio of 100%. The proposed methods not only provide rapid reconstruction of sound speed profiles, thereby improving the efficiency of multibeam bathymetric surveys, but also provide references for the reasonable determination of sampling depths of sound speed profiles to ensure reconstruction accuracy, demonstrating practical application value.

Geomagnetic diurnal variation significantly influences the precision of marine magnetic measurements. Precise estimation of this variation is crucial for enhancing the accuracy of offshore magnetic surveys. To address the challenges in achieving the desired accuracy with current estimation methods for geomagnetic diurnal variation, this study introduces a high-precision estimation model that integrates support vector machine (SVM) and random forest (RF) techniques. Initially, the data preprocessing phase includes an innovative extreme value adjustment method to rectify the temporal discrepancies across different stations, alongside employing the base period technique for daily baseline correction. Subsequently, we construct models to capture the daily variation trends at various times, facilitating an in-depth analysis of the diurnal variation patterns. The culmination of this process involves employing a fusion model algorithm to compute the diurnal variations across all stations comprehensively. Comparative analyses with conventional methods, such as distance weighting, bifactor weighting, and latitude weighting, reveal that our proposed model achieves a significant reduction in the root mean square error (RMSE) by an average of 31%, decreases the mean absolute error (MAE) by 35%, and enhances the Pearson correlation coefficient by 20% on average. These improvements underscore the superior accuracy of our geomagnetic diurnal variation estimation model.

Side-scan sonar (SSS) image sample augmentation plays an important role in improving the effect of deep-learning-based underwater target detection. However, the existing sample augmentation methods for cross-domain conversion always result in weak representativeness of the augmented samples since the targets in the nondomain images are similar but not exactly the same as the actual underwater target to be detected. In this article, an augmentation method for SSS image samples of underwater targets based on the cross-domain mapping relationship of images of the same object is proposed. A physical model of the actual underwater target was first constructed using three-dimensional printing. A series of optical images and SSS images of underwater targets can be obtained by using the actual measurement of underwater targets under different conditions. To achieve the augmentation of SSS target samples, a single-cycle-consistency network structure with a channel and spatial attention and generative adversarial networks with least squares loss was designed for efficient and robust conversion of information between optical and SSS acoustic samples. To verify the effectiveness of the proposed method in generating high-quality samples, underwater targets were detected using the detection model trained by the generated samples. The experimental results revealed that the proposed method achieved impressive performance with a more than 5.8% improvement in average precision value for zero-sample underwater mine target detection and 4.3% for few-sample shipwreck target detection, compared with using only real SSS data.

The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models.

In response to the absence of standardized work practices, work safety measures, efficient work procedures, and suitable line planning methods for exploring seabed topography using autonomous underwater vehicles (AUVs) equipped with side-scan sonar systems, this paper proposes a design framework for side-scan sonar measurement technology based on AUVs. Detailed research has been conducted on line planning methods that differ significantly from those used in shipborne operations. The new line planning method was developed based on the performance characteristics of AUV equipment, the principles of motion modes, and shipborne side-scan sonar operations. The validity of the line planning method was verified through actual sea experiments using different types of AUVs in various terrains. The experimental results demonstrate that the new survey line planning method is distinct from existing AUV path planning methods and is highly applicable for conducting seabed topography exploration operations. Furthermore, the technical design scheme studied in this article is applicable in different sea areas and ensures the safety, operational efficiency, and data quality of AUVs. It holds significant importance for marine science engineering and practical applications. The article also features an analysis of successful data collected from AUVs equipped with side-scan sonar for conducting seabed topography exploration operations.

Abstract During application, energetic materials may suffer different temperature environmental stimulation. In order to study the influence of temperature environmental on performance of HNIW/FOX-7 based PBXs, HNIW/FOX-7 based PBX modeling powders and PBX columns were treated by LT (low temperature), HT (high temperature), HLC (high-low temperature cycle) and HLS (high-low temperature shock). Then scanning electron microscope (SEM), infrared spectra (IR), X-ray diffraction (XRD) and differential scanning calorimetry (DSC) were used to study the variation of PBX modeling powders after LT, HT, HLC and HLS treatments; in addition, the mass, size and mechanical properties of PBX columns were characterized after different temperature adaptability treatments as well. The results indicate that the change ratios of mass and size of HNIW/FOX-7 based PBX columns are less than 1%, illustrating that mass and size of PBX columns are at acceptable level after different temperature adaptability treatments. The unevenness degree of the surface of PBX modeling powders followed the order of HLC > HT > LT > HLS, which agrees well with mass loss order. Moreover, IR and XRD results indicated that the molecular structure and crystal form of HNIW and FOX-7 did not change after different temperature adaptability treatments. Additionally, thermal stabilities of PBX modeling powders are decreased after different temperature adaptability treatments, among which HLS has the largest influence on HNIW/FOX-based PBX modeling powders. The compression strengths and elastic moduli of HNIW/FOX-based PBX columns are enhanced after different temperature adaptability treatments, among which the strength of PBX columns after HLC has the maximum increase, indicating that HLC has more significant effect on mechanical property.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Given the lack of systematic research on bathymetric surveys with multi-beam sonar carried by autonomous underwater vehicles (AUVs) in unfamiliar waters, this paper proposes a method for multi-beam bathymetric surveys based on the constant-depth mode of AUVs, considering equipment safety, operational efficiency, and data quality. Firstly, basic principles for multi-beam bathymetric surveys under the constant-depth mode are proposed based on multi-beam operational standards and AUV constant-depth mode characteristics. Secondly, a vertical effective height model for the vehicle is established, providing vertical constraints and a basis for determining fixed depth in constant-depth missions. Subsequently, according to these basic principles and the vertical effective height model, the operational process for multi-beam bathymetric surveys in unfamiliar waters under the AUV constant-depth mode is outlined. Finally, we validate the proposed method through sea trials in the Xisha Sea of the South China Sea. The test results show that the method proposed in this paper not only ensures the vehicle safety operation and multi-beam data quality, but also improves the operation efficiency by about 68%, demonstrating the reliability of the proposed method and its significant engineering value and guidance implications.

Current methods of fusing side-scan sonar images fail to tackle the issues of shadow removal, preservation of information from adjacent strip images, and maintenance of image clarity and contrast. To address these deficiencies, a novel curvelet-transform-based approach that integrates the complementary attribute of details from side-scan sonar strip images is proposed. By capitalizing on the multiple scales and orientations of the curvelet transform and its intricate hierarchical nature, myriad fusion rules were applied at the corresponding frequency levels, enabling a more-tailored image fusion technique for side-scan sonar imagery. The experimental results validated the effectiveness of this method in preserving valuable information from side-scan sonar images, reducing the presence of shadows and ensuring both clarity and contrast in the fused images. By meeting the aforementioned challenges encountered in existing methodologies, this approach demonstrated great practical significance.

When used to recognize side-scan sonar images of shipwreck targets, the Faster R-CNN model is time-consuming and has low efficiency and a high missed detection rate for small targets. Considering that existing datasets of side-scan sonar images of shipwreck targets are small, we propose a YOLOv3 model that can automatically recognize side-scan sonar images of shipwreck targets based on transfer learning. Based on the Darknet-53 network, we froze part of the convolutional layer of the YOLOv3 model trained on COCO dataset images, and conducted transfer learning. Multi-scale training of shallow feature fusion was done based on multi-scale feature fusion with Feature Pyramid Networks (FPN) support, and the proportion of recognized shallow features of shipwreck targets increased. Meanwhile, the parameters and sizes of target anchor boxes were reset using K-means clustering, which allowed us to improve the speed of target recognition and the precision of smaller target recognition and positioning. Lastly, the binary classification cross entropy function was used to improve the loss function of the YOLOv3 algorithm. Experimental results show that under the same recognition target, the average precision (AP) value of the YOLOv3 model based on transfer learning reached 89.49%, which is an improvement of 0.31% and 1.77%, respectively, compared with the Faster R-CNN model and the traditional YOLOv3 model. Moreover, the YOLOv3 model based on transfer learning had the highest harmonic mean (F1), reaching 90.71%, which is 3.96% and 1.63% higher, respectively, than the harmonic means of the Faster R-CNN and the traditional YOLOv3 model. Lastly, the traditional YOLOv3 model takes an average 0.17s to identify a target. In contrast, the Faster R-CNN model takes an average of 2.8s to identify a target. Hence, our transfer learning YOLOv3 model greatly improves detection efficiency, meets the needs of real-time target recognition, and ultimately has better overall performance than existing methods.

The limitations of underwater acoustic communications mean that the side-scan sonar data of an autonomous underwater vehicle (AUV) cannot be transmitted back and processed in real time, which means that targets cannot be detected in real time. To address the problem, this paper proposes an autonomous underwater vehicle-based side-scan sonar real-time detection method for underwater targets. First, the paper describes the system and operation of real-time underwater-target detection by the side-scan sonar mounted on the autonomous underwater vehicle. Next, it proposes a real-time processing method for side-scan sonar data, method for constructing a deep-learning-based underwater-target detection model, and real-time method for underwater-target detection based on navigation strip images, which, together, solve the three key technical problems of real-time data processing, deep-learning-based detection model construction, and real-time target detection based on the autonomous underwater vehicle. Finally, through sea-based experiments, the effectiveness of the proposed methods is evaluated, providing a new solution for the autonomous underwater vehicle-based side-scan sonar real-time detection of underwater targets.

Non-ignition impact and heat stimuli are the most common external stimuli loaded on energetic materials. Nevertheless, there is thereby an urgent need, but it is still a significant challenge to comprehend their coupling effects on the decay and safety mechanisms of energetic materials. Then, reactive molecular dynamics simulation was employed to mimic practical situations and reveal the impact heat coupling effect on the decay mechanism of FOX-7. The temperature and the degree of compression of the crystal caused by the impact are considered variables in the simulation. Both increasing the degree of compression and elevating the temperature promotes the decay of FOX-7. However, their underlying response mechanism is not the same. The acceleration of decomposition is due to the elevated potential energy of the FOX-7 molecules because of elevating the temperature. In addition to the elevated potential energy of the molecule, the main contribution to the decomposition from the compression is to change the decomposition path. The results of the analysis show that compression reduces the stability of the C=C bond, so that chemical reactions related to the double bond occur. In addition, interestingly, the compression along the c direction has an almost equal effect on the final product as the compression along the b direction. Finally, the decay reaction networks are proposed to provide insights into the decomposition mechanism on atomic level. All these findings are expected to pave a way to understand the underlying response mechanism for the FOX-7 against external stimuli.

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.

Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.

We report a reactive molecular dynamic (ReaxFF-MD) study using the newly parameterized ReaxFF-lg reactive force field to explore the initial decomposition mechanism of 3-Nitro-1,2,4-triazol-5-one (NTO) under shock loading (shock velocity >6 km/s). The new ReaxFF-lg parameters were trained from massive quantum mechanics data and experimental values, especially including the bond dissociation curves, valence angle bending curves, dihedral angle torsion curves, and unimolecular decomposition paths of 3-Nitro-1,2,4-triazol-5-one (NTO), 1,3,5-Trinitro-1,3,5-triazine (RDX), and 1,1-Diamino-2,2-dinitroethylene (FOX-7). The simulation results were obtained by analyzing the ReaxFF dynamic trajectories, which predicted the most frequent chain reactions that occurred before NTO decomposition was the unimolecular NTO merged into clusters ((C2H2O3N4)n). Then, the NTO dissociated from (C2H2O3N4)n and started to decompose. In addition, the paths of NO2 elimination and skeleton heterocycle cleavage were considered as the dominant initial decomposition mechanisms of NTO. A small amount of NTO dissociation was triggered by the intermolecular hydrogen transfer, instead of the intramolecular one. For α-NTO, the calculated equation of state was in excellent agreement with the experimental data. Moreover, the discontinuity slope of the shock-particle velocity equation was presented at a shock velocity of 4 km/s. However, the slope of the shock-particle velocity equation for β-NTO showed no discontinuity in the shock wave velocity range of 3–11 km/s. These studies showed that MD by using a suitable ReaxFF-lg parameter set, could provided detailed atomistic information to explain the shock-induced complex reaction mechanisms of energetic materials. With the ReaxFF-MD coupling MSST method and a cheap computational cost, one could also obtain the deformation behaviors and equation of states for energetic materials under conditions of extreme pressure.

Bladder cancer is a significant public health concern and social burden due to its high recurrence risk. Intravesical drug instillation is the primary therapy for bladder cancer to prevent recurrence. However, the intravesical drug therapeutic effect is limited by bladder penetrating barriers. The inadequate intravesical treatment might cause the low drug concentration in lesions, resulting in a high recurrence/progression rate of bladder cancer. Many strategies to get drugs across bladder penetrating barriers have been developed to improve intravesical treatment, including physical and chemical methods. This review summarizes the classical and updated literature and presents insights into great therapeutic potential strategies to overcome bladder penetrating barriers for improving the intravesical treatment of bladder cancer.

Titanate-based bonding agents are a class of efficient bonding agents for improving the mechanical properties of composite solid propellants, a kind of special composite material. However, high solid contents often deteriorate the rheological properties of propellant slurry, which limits the application of bonding agents. To solve this problem, a series of long-chain alkyl chelated titanate binders, N-n-octyl-N, N-dihydroxyethyl-lactic acid-titanate (DLT-8), N-n-dodecyl-N, N-dihydroxyethyl-lactic acid-titanate (DLT-12), N-n-hexadecyl-N, N-Dihydroxyethyl-lactic acid-titanate (DLT-16), were designed and synthesized in the present work. The infrared absorption spectral changes of solid propellants caused by binder coating and adhesion degrees of the bonding agents on the oxidant surface were determined by micro-infrared microscopy (MIR) and X-ray photoelectron spectroscopy (XPS), respectively, to characterize the interaction properties of the bonding agents with oxidants, ammonium perchlorate (AP) and hexogen (RDX), in solid propellants. The further application tests suggest that the bonding agents can effectively interact with the oxidants and effectively improve the mechanical and rheological properties of the four-component hydroxyl-terminated polybutadiene (HTPB) composite solid propellants containing AP and RDX. The agent with longer bond chain length can improve the rheological properties of the propellant slurry more significantly, and the propellant of the best mechanical properties was obtained with DLT-12, consistent with the conclusion obtained in the interfacial interaction study. Our work has provided a new method for simultaneously improving the processing performance and rheological properties of propellants and offered an important guidance for the bonding agent design.

In this study, sulfur-free expanded graphite (EG) was obtained by using flake graphite as the raw material, and EG/Fe3O4 composites with excellent microwave absorption properties were prepared by a facile one-pot co-precipitation method. The structure and properties of as-prepared EG/Fe3O4 were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR), X-ray diffraction (XRD), Raman, X-ray photoelectron spectrometry (XPS), thermogravimetric (TG), and vibrating sample magnetometry (VSM) characterizations. The Fe3O4 intercalated between the layers of expanded graphite forms a sandwich-like structure which is superparamagnetic and porous. When applied as a microwave absorber, the reflection loss (RL) of EG/Fe3O4 reaches −40.39 dB with a thickness of 3.0 mm (10 wt% loading), and the effective absorption bandwidth (EAB < −10 dB) with RL exceeding −10 dB is 4.76–17.66 GHz with the absorber thickness of 1.5–4.0 mm. Considering its non-toxicity, easy operation, low cost, suitability for large-scale industrial production, and excellent microwave absorbing performance, EG/Fe3O4 is expected to be a promising candidate for industrialized electromagnetic absorbing materials.

In this article, organic–inorganic hybrid materials with different functional groups were used to form organic–inorganic hybrid dense membranes for selective separation of mono/divalent ions by blending these materials and polyvinylidene fluoride (PVDF) in dimethylacetamide with HCl as the catalyst. The membranes prepared by 3-(ureido benzene) propyltriethoxysilane (H1), 3-(ureido-4-methoxyphenyl) propyltriethoxysilane (H2), 3-(ureido-3-chloro-4-methoxyphenyl) propyltriethoxysilane (H3), 3-(ureidoindazolyl) propyltrieth-oxysilane (H4), or 3-(ureidopentanol) propyltriethoxysilane (H5) were labeled as HM1–HM5, respectively. The transport properties of different chlorides were tested. The effects of different anions on sodium cation transport were also tested. The results showed that HM1–HM4 could transport monovalent Li+, Na+, and K+ except Ca2+ and Mg2+, and the permeability of Li+, Na+, and K+ through the hybrid membranes followed the order of PNa+ > PK+ > PLi+. Moreover, membranes with different H2 content were also prepared due to HM2 having the best ion transport performance. The ion transport performance increased accordingly with the mass ratio of H2 to PVDF, and the permeability of Na+ was twice that of Li+ and K+ when the mass ratio was 15/10. Under this condition, it was also proved that NH4+ could not transport through the hybrid membrane with various selectivity for different anions as Cl− > NO3− > HCO3− > SO42−.

The traditional method for processing multi-beam real-time attitude compensation data did not consider the influence of attitude compensation error, which left attitude residual in data. So a new method for processing multi-beam data of real-time attitude compensation was proposed. By studying the nature of attitude compensation error, the calculation method of systematic attitude compensation error was put forward. The concept of correction threshold was introduced and the specific determination method was given. Finally, combining with the principle of real-time attitude compensation, a systematic attitude compensation error correction method, which makes use of a tracking algorithm considering attitude to make secondary attitude correction, was proposed, and the specific processing flow was given. By comparing the data processing accuracy of the tradition method and the new method, the results showed that the new method can effectively reduce the influence of systematic attitude compensation error, and significantly improve the data processing accuracy.

The backscattering intensity collected by multi beam sonar system and scanning sonar system can be used to classify seabed bottom characteristics. However, there are many problems that have not been solved in the practical application. This paper attempts to sort out and analyze the problems encountered in the classification process and put forward some solutions. The relationship among acoustic image, seabed topography and sediment is analyzed

Renewable polymers have attracted considerable attentions in the last two decades, predominantly due to their environmentally friendly properties, renewability, good biocompatibility, biodegradability, bioactivity, and modifiability. The nanofibers prepared from the renewable polymers can combine the excellent properties of the renewable polymer and nanofiber, such as high specific surface area, high porosity, excellent performances in cell adhesion, migration, proliferation, differentiation, and the analogous physical properties of extracellular matrix. They have been widely used in the fields of wound dressing to promote the wound healing, hemostasis, skin regeneration, and treatment of diabetic ulcers. In the present review, the different methods to prepare the nanofibers from the renewable polymers were introduced. Then the recent progress on preparation and properties of the nanofibers from different renewable polymers or their composites were reviewed; the application of them in the fields of wound dressing was emphasized.

A polyampholytic superabsorbent polymer (PASAP), sodium alginate-g-(polyacrylic acid-co-allyltrimethylammonium chloride) (SA-g-(PAA-co-PTM)), was prepared by free-radical graft copolymerization and characterized. The polymer exhibited pH-dependent swelling behaviors with extremely high swelling ratios, and was saline tolerant. The dye adsorption properties of SA-g-(PAA-co-PTM) were investigated using methylene blue (MB) as a cationic dye model. It was found that its dye adsorption capacity was significantly affected by the TM content in PASAP and pH of dye solution. The dye adsorption kinetics and isotherm obey the pseudo-second-order kinetic model and the Langmuir isotherm model, respectively, and the adsorption process is chemisorption in nature. In addition, SA-g-(PAA-co-PTM) exhibited high MB adsorption capacities in a wide pH range and reusability in at least five adsorption-desorption cycles, indicating its great application potentials as the adsorbent for dye removals from effluents.

In the present work, a fluorescent H2O2 biosensor was constructed by encapsulating fluorescent probe Rhodamine B (RhmB) in the hydrophobic cavity of the cyclodextrin (β-CD) and immobilizing catalase (CAT) on the 2-NH2 of chitosan (CTS) in a chitosan 6-OH immobilized β-cyclodextrin derivative (CTS-6-CD). The inclusion complex of CTS-6-CD to RhmB (CTS-6-CD-RhmB) was prepared by a solution method. Its structure and inclusion efficiency were determined by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and fluorescence spectroscopy (FL). CAT was immobilized on CTS-6-CD-RhmB to eventually form the functional membrane, CTS-6-CD-RhmB-CAT, via glutaraldehyde crosslinking, which was further characterized by FTIR and FL, and used as a H2O2 biosensor. The functional membrane was used to simultaneously oxidize and detect H2O2. The detection condition was optimized as pH 8, a reaction temperature of 25 °C, and an immobilized enzyme concentration of 2 × 10−4 mol/L. The fluorescence response of the biosensor exhibited a good linear relationship with the concentration of H2O2 in the range of 20 mΜ–300 μM and the detection limit of 10−8 mol/L.

The title compound TNDFIW, C8H8N10O10, is a caged heterocycle substituted with four nitro and two formyl groups. It is related to the hexaazaisowurtzitane family of high-density high-energy polycyclic cage compounds. Four nitro groups are appended to the four N atoms of the two five-membered rings, while the other two formyl groups are attached to the two N atoms of the six-membered ring, which adopts a boat conformation. The compound has a cage structure which is constructed from one six-membered and two five-membered rings which are closed by a C—C bond, thus creating two seven-membered rings. There are a number of close intermolecular contacts [O...O = 2.827 (5), 2.853 (4) and 2.891 (5) Å; O...N = 2.746 (2) and 2.895 (2) Å] The calculated density of TNDFIW is 1.891 Mg m−3.

The title compound, C7H7N11O11·C3H6O, consisting of one molecule of 10-formyl-2,4,6,8,12-pentanitro-2,4,6,8,10,12-hexaazatetracyclo[5.5.0.05,9.03,11]dodecane (pentanitromonoformylhexaazaisowurtzitane, PNMFIW) and one acetone solvent molecule, is a member of the caged hexaazaisowurtzitane family. PNMFIW has a cage structure which is constructed from one six-membered and two five-membered rings which are linked by a C—C bond, thus creating two seven-membered rings. In the PNMFIW molecule, one formyl group is bonded to the N heteroatom of the six-membered cycle, and five nitro groups are appended to other five N heteroatom of the caged structure. The acetone solvent molecule is arranged beside a five-membered plane of PNMFIW with an O atom and an H atom close (with respect to the sum of the van der Waals radii) to the neighbouring nitro O atom [O...O = 2.957 (3) and 2.852 (3) Å; O... H = 2.692 (2), 2.526 (3) and 2.432 (3) Å].

The title compound, C7H7N11O11 (PNMFIW), is a caged heterocycle substituted with five nitro and one formyl groups. It is related to the hexaazaisowurtzitane family of high-density high-energy polycyclic cage compounds. Four nitro groups are appended to the four N atoms of the two five-membered rings, while a nitro group and a formyl are attached to the two N atoms of the six-membered ring.

In response to the current challenges in efficiently acquiring sound speed profiles and ensuring their representativeness, considering the need to fully leverage historical sound speed profiles while accounting for their spatiotemporal variability, we introduce a model for sound speed profile prediction based on a CNN-BiLSTM-Attention network, which integrates a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism (AM). The synergy of these components enables the model to extract the spatiotemporal features of sound speed profiles more comprehensively. Utilizing the global ocean Argo grid dataset, the model predicted the sound speed profiles of an experimental zone in the Western Pacific Ocean. In predicting sound speed profiles of a single point, the model achieved a root mean square error (RMSE), relative error (RE), and accuracy (ACC) of 0.72 m/s, 0.029%, and 0.99971, respectively, surpassing comparative models. For regional sound speed profile prediction, the mean RMSE, RE, and ACC of different water layers were 0.919 m/s, −0.016%, and 0.9995, respectively. The experimental outcomes not only confirm the high accuracy of the model, but also highlight its superiority in sound speed profile prediction, particularly as an effective compensatory approach when profile measurements are untimely or contain representational errors. [ABSTRACT FROM AUTHOR]

A novel ternary energetic cocrystal composed of 3,4-diamino-furazan (DAF), 3,4-dinitropyrazole (DNP), and H2O in a 1:1:1 stoichiometry with low impact sensitivity is reported. This cocrystal has a predicted detonation velocity higher than DAF and TNT. Preliminary attempts are reported to obtain the even more energetic cocrystals DAF/DNP and DAF/DNP/H2O22 For the latter, an original approach based on the urea/H2O2 cocrystal as a source of H2O2 is described. [ABSTRACT FROM AUTHOR]