Component analysis and source identification of atmospheric aerosols at the neighborhood scale in a coastal industrial city in China.

A multiple-site filter-sampling observation study was conducted in a coastal industrial city (Rizhao, 35°10'59″N, 119°23'57″E) to understand the main components, formation mechanisms, and potential sources of particulate matter. The average (±σ) mass concentration of PM across all the sites was 42 (±27) μg/m, with high variability (6-202 μg/m). Water-soluble inorganic ions (WSIIs) were the major contributors (54%-60%) to PM with mean values for sulfate (13 μg/m), nitrate (6 μg/m), and ammonium (7 μg/m) (SNA).

Research on the Laser Scattering Characteristics of Three-Dimensional Imaging Based on Electro-Optical Crystal Modulation.

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro-optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO-Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles.

Theoretical study of solvent effects on the white light emission mechanism of single molecule 4-OH-naphthalimide.

Organic white light materials fabricated on the basis of single molecules have applied to manufacture the white light-emitting diodes due to their good photostability and relatively simple material preparation. In this work, the different fluorescence emission mechanisms of the NapH1 in n,n-dimethylformamide (DMF) and toluene are investigated to elucidate the process for generating single-molecule near-white-light. From the analysis of bond lengths and electrostatic potential analysis, the intermolecular hydrogen bond can form in DMF instead of in toluene.

An unpaired SAR-to-optical image translation method based on Schrödinger bridge network and multi-scale feature fusion.

SAR-to-optical (S2O) translation is able to covert SAR into optical images, which help the interpreter to extract information efficiently. In the absence of strictly matched datasets, it is difficult for existing methods to complete training on unpaired data with a minimum amount of data. By employing the recent Schrödinger bridge-based transformation framework, a multiscale axial residual module (MARM) based on the concept of multi-scale feature fusion has been proposed in this paper.

A new method for determining coordinates of the space TT&C radar rotation center based on GNSS.

The coordinate determination of the rotation center of a space TT&C (Telemetry, Tracking and Command) radar is an important task of radar-calibration. The conventional method mostly using total stations is expensive, inefficient, and faces technological limitations. This paper presents a new method of measuring the rotation center coordinates of TT&C radar equipment using GNSS (Global Navigation Satellite System) high precision positioning technology, providing the principle, mathematical model, and data processing algorithm of the new method.

Vehicle Localization Method in Complex SAR Images Based on Feature Reconstruction and Aggregation.

Due to the small size of vehicle targets, complex background environments, and the discrete scattering characteristics of high-resolution synthetic aperture radar (SAR) images, existing deep learning networks face challenges in extracting high-quality vehicle features from SAR images, which impacts vehicle localization accuracy. To address this issue, this paper proposes a vehicle localization method for SAR images based on feature reconstruction and aggregation with rotating boxes. Specifically, our method first employs a backbone network that integrates the space-channel reconfiguration module (SCRM), which contains spatial and channel attention mechanisms specifically designed for SAR images to extract features.

High-Precision Multi-Object Tracking in Satellite Videos via Pixel-Wise Adaptive Feature Enhancement.

In this paper, we focus on the multi-target tracking (MOT) task in satellite videos. To achieve efficient and accurate tracking, we propose a transformer-distillation-based end-to-end joint detection and tracking (JDT) method. Specifically, (1) considering that targets in satellite videos usually have small scales and are shot from a bird's-eye view, we propose a pixel-wise transformer-based feature distillation module through which useful object representations are learned via pixel-wise distillation using a strong teacher detection network; (2) targets in satellite videos, such as airplanes, ships, and vehicles, usually have similar appearances, so we propose a temperature-controllable key feature learning objective function, and by highlighting the learning of similar features during distilling, the tracking accuracy for such objects can be further improved; (3) we propose a method that is based on an end-to-end network but simultaneously learns from a highly precise teacher network and tracking head during training so that the tracking accuracy of the end-to-end network can be improved via distillation without compromising efficiency.

Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem.

This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism.

A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification.

Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition.

An ultra lightweight neural network for automatic modulation classification in drone communications.

Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios.

Theoretical study on multi-perspective interaction analysis of ADN and ADN-HO-CHOH solutions.

Context: Revealing the mechanism of intermolecular interactions in dinitroamine ammonium (ADN)-based liquid propellants and exploring the reasons for their performance changes, multi-perspective interaction analyses of ADN and ADN-water (HO)-methanol (CHOH) solutions have been conducted via theoretical methods. The band structure, density of states (DOS), surface electrostatic potential (ESP), Hirshfeld surface, reduced density gradient (RDG), AIM topological analysis, and detonation performance were studied and the results showed that both the ADN and ADN-HO-CHOH solutions had hydrogen bonds and van der Waals interactions. By introducing the small molecules HO and CHOH, the detonation performance of the ADN-HO-CHOH solution slightly decreased, but its sensitivity also decreased.

Research on Polarization Modulation of Electro-Optical Crystals for 3D Imaging Reconstruction.

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm.

Response Characteristics of the Community Structure and Metabolic Genes of Oil-Recovery Bacteria after Targeted Activation of Petroleum Hydrocarbon-Degrading Bacteria in Low-Permeability Oil Reservoirs.

The microbial enhanced oil recovery (MEOR) process has been identified as a promising alternative to conventional enhanced oil recovery methods because it is eco-friendly and economically advantageous. However, the knowledge about the composition and diversity of microbial communities in artificially regulated reservoirs, especially after activating petroleum hydrocarbon-degrading bacteria (PHDB) by injecting exogenous nutrients, is still insufficient. This study utilized a combination of high-throughput sequencing and metagenomics technology to reveal the structural evolution characteristics of the indigenous microbial community in the reservoir during the PHDB activated for enhanced oil recovery, as well as the response relationship between the expression of its oil production functional genes and crude oil biodegradation.

Research on Student's T-Distribution Point Cloud Registration Algorithm Based on Local Features.

LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student's t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme.

Laser-Induced Interference to Infrared Detector Using Continuous Wave and Short-Pulse Lasers.

The response of a DPbS3200 infrared detector irradiated by a nanosecond pulsed laser and CW laser has been investigated to study laser-induced interference. A laser interference experiment system was constructed to measure the time-varying response signal. A nanosecond pulsed laser and a CW laser of 10 Hz were used, with a 1064 nm wavelength and a millimeter-scale irradiation spot diameter.

Damage Characteristics Analysis of Laser Ablation Triple-Junction Solar Cells Based on Electroluminescence Characteristics.

To study the physical property effects of the laser on GaInP/GaAs/Ge solar cells and their sub-cell layers, a pulsed laser with a wavelength of 532 nm was used to irradiate the solar cells under various energy conditions. The working performance of the cell was measured with a source meter. The electroluminescence (EL) characteristics were assessed using an ordinary and an infrared camera.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer.

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information.

Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT).

Mode division multiplexing reconstructive spectrometer with an all-fiber photonics lantern.

This study presents a high-accuracy, all-fiber mode division multiplexing (MDM) reconstructive spectrometer (RS). The MDM was achieved by utilizing a custom-designed 3 × 1 mode-selective photonics lantern to launch distinct spatial modes into the multimode fiber (MMF). This facilitated the information transmission by increasing light scattering processes, thereby encoding the optical spectra more comprehensively into speckle patterns.

Tackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy.

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging.

Temporal and Spatial Distribution Characteristics of Crosstalk Lines Generated by Irradiating Progressive Scan Charge-Coupled Device Camera with Continuous Laser.

To study the interference effect of the laser in motion mode on a CCD, the continuous laser with the wavelength of 532 nm at different motion speeds was used to scan the CCD. The experimental results show that the crosstalk phenomenon produced by static and dynamic irradiation is significantly different. When the continuous laser statically radiates the CCD, the vertical crosstalk line is observed in the output image.

Study on the Fast Search Planning Problem of Lost Targets for Maritime Emergency Response Based on an Improved Adaptive Immunogenetic Algorithm.

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region.