With advancements in aerospace technology, remote sensing satellite imagery applications are evolving toward high precision, refinement, and commercialization. The geometric positioning accuracy of satellites has reached the meter level, imposing new requirements on satellite development and attitude determination. In remote sensing applications, acquiring accurate geometric positioning information is critical. Satellite positioning accuracy is closely tied to attitude determination precision, where a 1″ error in attitude determination can cause a 3?5 m deviation in positioning. Consequently, attitude determination accuracy has become the most critical factor limiting improvements in geometric positioning. The star camera is the most commonly used sensor in satellite attitude determination, and there are two primary methods based on star camera measurements. The first constructs an observation model using star vectors captured by the star camera and determines orientation relative to the inertial coordinate system by comparing observed and reference vectors. The second builds upon this method to obtain absolute attitude by fusing data from multiple sensors, such as gyroscopes, using filtering algorithms for high-precision measurements. Although these filtering algorithms enhance accuracy, they require additional sensors, increasing power consumption and costs, which is unsuitable for micro-satellite platforms. Furthermore, since these algorithms rely on star camera measurements for correction, their precision is directly influenced by the observation precision of the star camera. Therefore, it is of great engineering value to enhance the accuracy of single-star camera attitude determination by modeling its observations and fully utilize inter-star information to reduce errors.

To address the technical challenges outlined above, we first analyze the principles of traditional star camera attitude determination methods. This analysis reveals that accuracy primarily depends on four factors: the precision of navigation star vectors, the accuracy of matching observed stars with navigation stars, the accuracy of observed star vectors, and the weighting of star points. Navigation star vector accuracy can be improved by correcting star positions using stellar motion models, while matching accuracy can be improved via optimized star map recognition algorithms. In this paper, we focus on improving attitude accuracy by refining the precision of observed vector and optimizing the distribution of star point weights. First, considering observational noise, certain stars within the field of view (FOV) may exhibit lower measurement accuracy and negatively influence attitude results. To mitigate this, a threshold is introduced that dynamically adjusts based on real-time noise analysis of in-orbit star images, ensuring a balance between data retention and error elimination under varying conditions. Second, since the weight of a star point in the observation model correlates with its accuracy, we propose a method that evaluates observation vectors based on angular distance errors invariant across coordinate systems. Cross-validation of all FOV data enables optimal weight allocation. Finally, we build a topological model linking multiple stars, use redundant observations to construct constraint equations, and iteratively correct measurement errors using adjustment algorithms. The refined vectors and weights are then input into the quaternion estimator (QUEST) algorithm to determine the current frame’s attitude.

To evaluate the performance of the proposed method, we assess its sensitivity to errors through comparative simulations with traditional algorithms and verify its robustness under observation noise. In addition, we confirm the method’s effectiveness in practical applications using in-orbit star images captured by star camera A and star camera B on board the Wuhan-1 satellite. Simulation parameters are set based on the actual optical system design specifications of the star cameras (Table 1). The precision of star observation vectors is affected by multiple coupled error sources, which collectively act as deviations on star centroid extraction positions. These disturbances are simulated by adding positional noise of different magnitudes to theoretical star imaging positions. To reduce the influence of star distribution and density on attitude determination accuracy, all-sky imaging scenarios are simulated by randomly selecting the star camera’s pointing directions. Simulation results (Fig. 2) demonstrate that, at varying error levels, the proposed method achieves higher attitude determination accuracy and superior noise resistance compared to traditional algorithms, maintaining high precision even under poor imaging conditions. For practical validation, we process the in-orbit star images captured by the Wuhan-1 satellite’s star cameras using the proposed method. Prior to attitude determination, it is necessary to calibrate the optical parameters of the star cameras in orbit according to the actual star images. Star screening thresholds are established using star maps under both normal (Fig. 5) and high-noise imaging conditions (Fig. 6), with Starpoint retention criteria determined by measurement errors. Since we have no way of knowing the true attitude pointing of the real star images, we assess the algorithms’ attitude determination accuracy based on the following two dimensions: inter-frame attitude stability of a single star camera and optical axis angle stability between two star cameras. For inter-frame attitude stability evaluation, we analyze 3600 consecutive frames of in-orbit star images by different attitude determination algorithms. The accuracy on the X and Y axes for a single star camera, as determined by the proposed algorithm, is better than 0.55″, a 15% improvement over the traditional algorithms. For optical axis angle stability between two star cameras, data from star camera A and star camera B during four 30-s in-orbit missions are analyzed, demonstrating that the proposed method achieves precision better than 0.5″, representing a 50% improvement over traditional methods.

In this paper, we present a high-precision star camera-based attitude determination algorithm suitable for micro-satellite platforms. The proposed algorithm leverages redundant observed stars in the attitude determination process, integrates the star camera imaging model, and assesses the credibility of star observation results through the invariant characteristics of interstellar angular distances across different reference frames. The star centroid positions are corrected to achieve precise attitude determination using the star camera. Simulation experiments and real-star image measurements validate the robustness and effectiveness of the proposed method, achieving sub-arcsecond accuracy in in-orbit star image attitude determination. This paper introduces a novel technical approach for high-precision post-mission attitude determination.

With the advancement of Earth observation technology, there is an increasingly urgent need to develop remote sensing edge intelligence applications. These applications aim to perform object detection and analysis directly on edge devices such as satellites or drones, thereby conserving transmission bandwidth, processing time, and resource consumption. Deep learning, renowned for its powerful feature extraction capabilities, has been extensively researched and applied in optical remote sensing image object detection. However, the continuous pursuit of higher detection accuracy has led to deep learning object detection models grappling with issues such as high complexity, a large number of parameters, massive scale, and low algorithmic efficiency. Due to constraints on volume, weight, and power consumption, edge devices often lack large storage and computational resources, limiting the deployment and application of many high-precision deep learning models on them. Therefore, the design of intelligent algorithm models that are faster, more accurate, and more lightweight, has attracted more and more attention in the field of remote sensing. We focus on edge intelligence applications and address the lightweight optimization problem in existing object detection tasks in optical remote sensing images. We pay attention to the detection of diverse object shapes in remote sensing images and propose a deformable convolution-based lightweight model (DCBLM), based on deformable convolution, using YOLOv8n as the baseline model. By employing deformable convolution for feature extraction, optimizing multi-scale feature fusion strategies, and introducing the minimum-point-distance-based intersection over union (MPDIoU) loss function to address shortcomings of the original loss function, the model achieves lightweight optimization while enhancing accuracy. DCBLM reduces the number of model parameters, computational complexity and memory usage, and improves the deployment flexibility of the model in practical applications.

We propose a lightweight model, DCBLM, based on deformable convolution, using the lightweight network YOLOv8n as the baseline model. The C2f deformable convolution feature extraction (C2f_DCFE) module enables the network to dynamically adapt to varying shapes, sizes, and positions of objects, achieving efficient feature extraction while reducing the number of parameters. The cross-scale feature fusion module (CFFM) effectively integrates multi-level features, addressing the issue of feature redundancy in the neck network, thereby enhancing the efficiency of feature fusion and significantly decreasing the number of parameters. The improved MPDIoU loss function specifically mitigates the failure of the loss function when the predicted bounding box and the ground truth bounding box share the same aspect ratio, effectively improving the detection accuracy of the model.

As is shown in Table 4, DCBLM outperforms other lightweight detection methods in the selected dataset. Compared to the baseline model YOLOv8n, DCBLM achieves a 0.8-percentage-point improvement in detection accuracy, while reducing number of parameters, computational load, and model size by 39.5%, 22.2%, and 36.5%, respectively. Table 6 illustrates that for drone-based multi-angle livestock detection in grassland environments, DCBLM also excels, with a 0.9-percentage-point increase in detection accuracy and reductions in number of parameters, computational load, and model size by 39.5%, 22.2%, and 36.5%, respectively. These improvements significantly improve the model’s deployment flexibility. This is attributed to the enhanced C2f_DCFE module, which enables dynamic adaption to varying shapes, sizes, and positions of objects, achieving efficient feature extraction with fewer number of parameters. The CFFM effectively integrates multi-level features, further reducing the number of parameters. Additionally, the MPDIoU loss function enables more accurate object localization, effectively improving detection accuracy. Visualization results in Figs. 6 and 7 demonstrate DCBLM’s superiority over YOLOv8n across different scenarios, validating proposed improvements. Furthermore, inference experiments on an unknown dataset show that DCBLM exhibits lower maximum utilization of graphics processing unit (GPU) than YOLOv8n, indicating that it reduces computational demands, alleviates computational bottlenecks, and enhances inference efficiency. Moreover, DCBLM achieves a mean average precision (mAP) above 60% across all three datasets, with an improvement of over 2 percentage points compared to YOLOv8n. These results highlight that DCBLM offers superior detection accuracy and lightweight performance, with enhanced capabilities for detecting densely distributed small objects and morphologically diverse objects. The model demonstrates excellent applicability for both general and specialized object detection tasks.

In this study, a lightweight model DCBLM based on deformable convolution is proposed. The C2f_DCFE module enables the optimized backbone network to dynamically adapt to varying shapes, sizes, and positions of objects, acquiring more accurate feature information while reducing the number of parameters. The CFFM enhances the efficiency of feature fusion by uniformly reducing the number of channels in feature maps at different scales, achieving effective integration of multi-level features and further reducing the number of parameters. The MPDIoU loss function specifically addresses the issue of loss function failure when the predicted bounding box and the ground truth bounding box share the same aspect ratio, effectively improving detection accuracy and simplifying computations. Experimental results demonstrate that DCBLM exhibits superior detection accuracy and lightweight performance, showing excellent applicability for both general and specialized object detection tasks. Future work will involve optimization and validation across multiple domains and scenarios based on practical application requirements, aiming to further improve the performance and adaptability of the model.

Substations are critical components of power systems, serving as essential hubs for power transmission and distribution. Intrusions caused by human activity pose significant threats to substations, potentially leading to severe safety issues and substantial economic losses. The security and protection of these facilities are paramount, as any intrusion or damage can result in widespread power disruptions and compromise grid stability. Traditional perimeter security methods such as video surveillance and infrared detection systems face several limitations: video surveillance systems often have blind spots and lack automatic alarm capabilities, while infrared detection systems require flat installation environments and suffer from poor reliability. To enhance performance, distributed acoustic sensing (DAS) technology based on phase-sensitive optical time-domain reflectometry (φ-OTDR) is employed for perimeter security in this paper. By deploying optical fiber in the designated area, vibration signals are collected, and intrusion events are classified using a feature extraction and classification model. These systems offer distinct advantages, including strong anti-electromagnetic interference capabilities, high sensitivity, compact size, and excellent stability in harsh environments. Moreover, DAS enables continuous, real-time monitoring along the entire fiber length, effectively eliminating blind spots. However, accurate identification and classification of different intrusion events using DAS signals remains challenging due to signal complexity and environmental interference. Conventional recognition systems primarily rely on extracting features from signals in either the time or frequency domain, followed by classification using methods such as support vector machines, neural networks, or other deep learning models. These approaches face challenges such as underutilization of signal information across both domains, redundant classification parameters, and computational complexity. To address these issues, we propose a novel intrusion event identification method that combines DAS with advanced signal processing and deep learning techniques. The method integrates the Gramian angular difference field (GADF) for signal encoding and a multi-scale convolutional neural network (MSCNN) enhanced with a cross attention fusion module (CAFM). This approach aims to improve the accuracy of substation perimeter security monitoring by overcoming the limitations of conventional single-scale neural networks and enhancing feature extraction capabilities.

A DAS system is implemented using a narrow-linewidth laser (1550.12 nm) with a pulse repetition rate of 1 kHz and pulse width of 40 ns. The sensing fiber (155 m) is deployed in an S-shaped configuration, with vibration events simulated at the 8-m end section. The system collects vibration signals for five different scenarios: no invasion, striking, climbing, trampling, and shoveling. Raw vibration signals are obtained through in-phase/quadrature (I/Q) demodulation, and the signals are segmented into 1-s frames (1000 data points). The data is then resampled to 224 points. The GADF technique converts time-domain signals into 224 pixel×224 pixel images, which are used as input to the CAFM-MSCNN. After feature extraction through the first convolution layer, the model performs convolution operations using kernels of sizes 3, 5 and 7. The multi-scale fusion framework is built using these different kernel sizes. Each convolutional channel contains several convolutional layers and max pooling layers to extract features and capture complementary information of different scales. The features extracted from the three channels are then fused into feature vectors. The spliced feature vectors are classified by a classifier layer to determine the vibration categories. The model is built based on the PyTorch framework, and the program is written in Python 3.9. The optimal parameters are determined through repeated experimentation: batch size is set to 32, the maximum number of epochs is set to 100, the learning rate is set to 0.001, and the Adam optimizer is used. The confusion matrix is adopted as the evaluation metric of the model.

The experimental results show that the classification accuracies for the five types of vibration events by the proposed model are 100%, 97.65%, 97.22%, 98.32%, and 99.25% (Fig. 9), respectively. Compared with convolutional neural networks (CNN), long short term memory (LSTM) networks, time convolutional neural networks (TCN), CNN-LSTM, and MSCNN, the classification accuracy of the proposed model improves by 3.27 percentage points, 15.16 percentage points, 8.06 percentage points, 7.12 percentage points, and 0.83 percentage points, respectively (Table 4).

We propose a novel approach for substation perimeter security using DAS combined with GADF-CAFM-MSCNN architecture. The method achieves high accuracy in identifying different types of intrusion events, with an average accuracy of 98.49%. The integration of multi-scale feature extraction and cross-attention mechanisms effectively addresses the limitations of traditional approaches. The system’s robustness under various noise conditions, along with its improved performance over existing methods, demonstrates its potential for practical substation security applications. The proposed method offers a new and effective technical solution for enhancing the safety and reliability of intrusion detection.

Compared with traditional acoustic communication technologies, underwater vertical wireless optical communication (UVWOC) offers several advantages, including high bandwidth, low latency, compact device size, and energy efficiency. These qualities make it highly promising for applications in high-speed data transmission, multimedia content distribution, and real-time marine communication. However, the performance of UVWOC is significantly affected by the combined effects of absorption, scattering, and turbulence in seawater, all of which vary considerably with depth. Existing simulation methods face critical limitations: Monte?Carlo (MC) techniques are commonly used for modeling absorption and scattering effects, while phase screen approaches are typically employed for turbulence simulation. However, optical turbulence fundamentally arises from random variations in the refractive index along the light propagation path, driven by depth-dependent fluctuations in temperature and salinity. These environmental parameters exhibit strong stratification in ocean environments, leading to complex vertical heterogeneity that cannot be adequately captured by conventional decoupled modeling approaches. In this study, we develop an integrated photon transport model that captures the continuous interplay between particulate interactions and refractive turbulence in stratified marine environments. By unifying these physical processes within a single MC framework, we enable accurate simulation of optical signal degradation across the entire water column. The model incorporates empirical data from oceanic sensors to ensure a realistic representation of vertical stratification effects.

The MC simulation framework developed in this study employs a multi-layer photon transport model to characterize light propagation in stratified underwater optical channels. Photon packets are initialized with spatial and angular distributions that match practical laser diode outputs, which feature beam divergence angles ranging from 0.1 to 50 mrad. During propagation, each photon packet undergoes energy attenuation and trajectory deviation due to combined absorption, scattering, and turbulence effects. The model implements wavelength-dependent absorption coefficients derived from empirical seawater databases, with scattering effects calculated using the Henyey?Greenstein phase function. Turbulence is simulated using a refractive cell approach, which vertically discretizes the water column into 0.1?1 m thick layers. Each layer contains spherical turbulence elements, with refractive index fluctuations determined by local temperature and salinity gradients. The receiver module incorporates a 0.2 m aperture diameter and a 120° field-of-view constraint. Photon tracking continues until either successful detection within the receiver criteria, energy falling below the detection threshold, or divergence beyond the effective propagation range. Model validation employs three complementary approaches: first, confirming that simulated light intensity distributions under pure turbulence conditions conform to lognormal statistics; second, implementing controlled verification by comparing pure turbulence channels against composite channels with scattering artificially disabled (scattering coefficient set to 0); third, comparing with field measurements from South China Sea waters.

The simulation results demonstrate three key characteristics of underwater vertical optical channels through a comprehensive parametric analysis. Under pure turbulence conditions, scintillation index analysis reveals that the link distance contributes approximately 60% to the overall turbulence intensity, followed by refractive index variations (~30%) and layer spacing (~10%) (Fig. 6). The research defines threshold criteria for turbulence regimes of weak, moderate, and strong (Fig. 7). Path loss measurements show that absorption and scattering dominate signal attenuation, with coastal waters exhibiting a 10 dB higher loss than that of clear oceanic waters, while turbulence introduces an additional 1 dB penalty due to beam wander and distortion (Fig. 10). Comparative analysis between pure turbulence and composite channels reveals significant nonlinear interactions between scattering and turbulence effects. In turbid coastal waters (scattering coefficient >1.5 m^-1), the presence of multiple scattering amplifies turbulence-induced signal fluctuations by 35%?40% compared to clear ocean conditions, as quantified by the enhanced scintillation index values. The vertical stratification effects are particularly pronounced in thermocline regions (100?700 m depth), where temperature and salinity gradients cause scintillation indices to fluctuate between 0.8?1.3, compared to the more stable mixed layer (0?100 m, σ_SI=0.04?0.08) and deep-water regions (>700 m, σ_SI=0.05?0.1) (Fig. 12). The model’s accuracy is confirmed through excellent agreement (R²>0.9) with lognormal distributions in turbulence-only scenarios and successful reproduction of field measurement data from South China Sea campaigns, particularly in predicting the nonlinear relationship between water depth and signal degradation.

We develop a MC-based simulation framework for underwater vertical wireless optical communication (UVWOC) that systematically integrates absorption, scattering, and turbulence effects in stratified marine environments. The model demonstrates high fidelity in characterizing channel behavior, with validation results confirming its accuracy in predicting both turbulence-induced signal fluctuations (scintillation index) and beam wander effects. Key findings reveal that link distance (L) dominates turbulence intensity, which contributes approximately 60% to the observed scintillation index (σ_SI), while refractive index variation (Δn) and turbulent layer spacing (Δz) account for 30% and 10%, respectively. The research defines threshold criteria for different turbulence regimes: weak turbulence (σ_SI<0.15) occurs when refractive index variation Δn<1.8×10^-4 and turbulent layer spacing Δz>0.50 m, primarily found in optically stable surface mixed layers; moderate turbulence (0.15≤σ_SI≤1) emerges at Δn=1.8×10^-4?2.6×10^-4 with Δz=0.25?0.50 m, typically observed in thermocline transition zones; while strong turbulence (σ_SI>1) dominates when Δn>2.6×10^-4 and Δz<0.25 m. In composite channel simulations, absorption and scattering are identified as the primary drivers of power attenuation, with coastal waters exhibiting 10 dB higher path loss compared to clear oceanic conditions. The integration of real-world Argo float temperature-salinity profiles confirms the model’s applicability across distinct oceanic layers—mixed layer (0?100 m), thermocline (100?700 m), and deep water (>700 m)—where turbulence characteristics vary significantly with depth. This framework offers a robust tool for optimizing UVWOC systems in challenging scenarios such as deep-sea exploration and cross-layer communication. Future enhancements will incorporate machine learning for real-time turbulence prediction and expand experimental validation through controlled underwater trials, which further improves the model’s predictive reliability in dynamic marine environments.

Abruptly autofocusing beams exhibit a sudden and significant increase in intensity at the focal point, with enhancements reaching several orders of magnitude. This effect is achieved without relying on conventional lenses or nonlinear effects, while the beam maintains a low-intensity profile prior to focus. This unique property has demonstrated significant potential for diverse applications ranging from optical manipulation to optical trapping and biomedical therapy. In recent years, regulating and optimizing the autofocusing characteristics of beams through light field design has attracted extensive attention. While traditional Airy beams have been extensively studied for their exceptional self-accelerating, self-bending, and self-healing properties, butterfly beams have emerged as a novel research focus due to their controllable parametric properties and stable higher-order focal dispersion structures. The propagation dynamics of the new beam combining these two autofocusing beams with additional vortex-phase modulation are highly anticipated. In this paper, we propose a novel circular butterfly Airy vortex beam (CBAVB) and systematically investigate its autofocusing properties in free-space propagation. The results are expected to provide a reference for the application of CBAVB in optical communication, optical trapping, and biomedical therapy.

Initially, we utilize the split-step Fourier algorithm to numerically simulate the propagation of CBAVB in free space. Subsequently, the influence of different parameters on the autofocusing characteristics of the beam is investigated. Furthermore, the energy flow of the beam and the influence of optical vortices are analyzed using the Poynting vector and angular momentum density vector, respectively. Finally, we analyze the autofocusing performance of CBAVB through a comparative study.

Through numerical simulations of the beam propagation dynamics, the superior autofocusing characteristics of CBAVB are demonstrated. The incorporation of optical vortices can significantly improve the focusing performance coefficient of CBAVB (Fig. 2). By adjusting the position of the optical vortex and the size of the topological charge, the autofocusing behavior can be flexibly controlled while maintaining the position of maximum intensity (Fig. 3). Simultaneously altering the transverse scale factor and spatial offset factor can enhance the beam’s focusing performance coefficient while effectively regulating the focus position (Fig. 4). The autofocusing mechanism of CBAVB (Fig. 5) and the influence of optical vortices on the beam (Fig. 6) are analyzed based on the beam’s Poynting vector and angular momentum density vector. In addition, compared with CAVB and CBVB, CBAVB demonstrates superior autofocusing performance (Fig. 7).

In this paper, we propose a novel autofocusing circular butterfly Airy vortex beam (CBAVB), whose propagation in free space is numerically simulated using the split-step Fourier algorithm. The effects of topological charge, optical vortex position, transverse scale factor, and spatial offset factor on the autofocusing characteristics of the beam are investigated. Furthermore, the propagation dynamics of CBAVB are further analyzed through the Poynting vector and angular momentum density vector. The research results show that the incorporation of optical vortices significantly promotes the maximum focusing performance coefficient of CBAVB. By adjusting the position of the optical vortex and the size of its topological charge, the transverse intensity distribution of CBAVB can be flexibly regulated, and the beam’s focusing performance coefficient can be improved. Altering the transverse scale factor and spatial offset factor can also regulate the focusing position and effectively enhance the beam’s autofocusing performance. Compared with the CAVB and CBVB, CBAVB demonstrates superior autofocusing performance. These results suggest the promising potential of CBAVB for applications in free-space optical communications, biomedical imaging, optical manipulation, and related fields.

Infrared optical systems possess excellent penetrability and offer advantages in applications such as target tracking. Particularly, cooled long-wave infrared optical systems exhibit superior transmittance and detection performance, enabling all-weather operations. Combined with continuous zoom functionality, these systems can seamlessly transition from wide-field-of-view search to narrow-field-of-view detailed inspection while maintaining image stability and clarity. As a result, cooled long-wave infrared optical systems are widely used in fields such as coastal defense and ground-based air defense. To adapt to detectors with smaller pixels and avoid the use of binary surfaces, while also achieving a larger zoom ratio, the general rules of power distribution and the conditions for cold aperture matching based on the theory of mechanical compensation zoom are discussed. A method is proposed for rapidly obtaining initial structures by studying the Gaussian layout of cooled long-wave infrared optical systems, which can significantly improve optical design efficiency. Additionally, the irradiance values of thermal stray light introduced onto the image plane by the system itself are calculated. This effectively shifts the subsequent stray light analysis to the optical system design stage, allowing for the anticipation and avoidance of potential risks.

By integrating the rear fixed group into the secondary imaging system, the structure of the cooled long-wave infrared optical system is simplified while still achieving 100% cold shield efficiency and avoiding system vignetting. Based on the theory of mechanical compensation zoom systems, we propose a design method for rapid zooming to meet the requirement of a large zoom ratio. The core of this method is the allocation of optical power among different lens groups and the smooth zoom transition, which can be achieved by solving a quadratic equation. The quantitative relationships are provided between the first-order parameters and the general principles for determining these parameters. By tracing ideal rays, we study Gaussian structure layouts, which can quickly and in real-time verify the rationality of the solved first-order parameters, greatly improving design efficiency. Considering the inherent thermal stray light problem in long-wave infrared systems, we establish a linear mapping relationship between the surface temperature of the optical system and the irradiance on the image plane from the same surface. When the optical surface temperature changes, new irradiance values can be obtained without performing a new round of ray tracing. Moreover, this method of thermal stray light analysis can also be extended to the thermal stray light analysis of general optomechanical systems.

With the reasonable allocation of optical power and research on Gaussian structure layouts, the initial structure can be obtained immediately. Further optimization by implementing the CODE V software results in an optical system that meets all the technical requirements. The materials chosen for the lenses are Germ, ZnS, Germ, Germ, ZnS, and GaAs (Table 2), and the introduction of sulfur glass helps control chromatic aberrations. In this optical system, four even-ordered aspherical surfaces are employed (Table 3), with the remaining surfaces being standard spherical surfaces. The modulation transfer function (MTF) at all focal lengths is close to the diffraction limit (Fig. 8). The root mean square (RMS) diameter is within the pixel size at all focal lengths for all fields of view (Fig. 9). Meanwhile, the distortion is also well corrected, with a maximum value of 1.66% (Fig. 10). Considering the needs of manufacturing and alignment, the zoom cam has been optimized to ensure that the rise angles of the two zoom paths can be controlled between 5.19° and 30.47° (Fig. 7). The design example is a cooled long-wave infrared continuous zoom optical system with a magnification ratio of 20, an image plane size of 9.60 mm×7.68 mm, a maximum focal length of 400 mm, a constant F# of 3, and distortion and chromatic aberrations corrected, which provides new ideas and insights for the design of such optical systems. The results of the thermal stray light analysis indicate that zooming does not significantly affect the irradiance on the image plane.

For the cooled long-wave 640 pixel×512 pixel infrared detector with pixel dimensions of 15 μm×15 μm, a cooled long-wave infrared zoom optical system with a smooth zoom transition has been designed to meet the need for reducing axial dimensions. The system achieves MTF values close to the diffraction limit at all focal lengths, which indicates excellent image sharpness. It features a compact structure, minimal aberrations, and high overall imaging quality. The system achieves a zoom ratio of 20, a large relative aperture (with a constant F# of 3), a short and smooth zoom curve, and excellent correction of various aberrations. Additionally, the irradiance values formed on the image plane by the thermal stray light of the optical system itself have been calculated, which can serve as supplementary data for subsequent calculations of dynamic range, contrast, and other parameters. The optical system designed in this paper can be applied in fields such as surveillance, reconnaissance, and air defense.

All-inorganic CsPbX? perovskite quantum dots (QDs) are ideal materials for high-quality single-photon sources in quantum information applications, as the performance of single-photon sources is closely related to the size of perovskite QDs. However, the lack of effective methods to reduce the size of CsPbX₃ QDs remains one of the major obstacles to achieving single-photon emission. In this study, we employ an efficient and low-cost doping synthesis strategy, directly introducing ammonium bromide (NH₄Br) into the lead precursor via the hot-injection method, successfully preparing CsPbBr₃ perovskite QDs. By utilizing NH₄Br to regulate crystal growth kinetics and passivate surface defects, we effectively suppress the Ostwald ripening process, significantly reducing the average size from the original 10.07 nm to 6.87 nm while improving size uniformity. The size reduction enhances the quantum confinement effect, leading to a blue shift in the photoluminescence (PL) emission peak from 520 nm (undoped) to 505 nm. Additionally, autocorrelation tests reveal that the g2(0) value of the doped QDs decreases from 0.45 to 0.22, which indicates a significant improvement in single-photon purity. We present an innovative and straightforward synthesis strategy, successfully producing CsPbBr₃ perovskite QDs with a narrow size distribution. The incorporation of NH?Br enhances the single-photon purity of the QDs, which provides an ideal material system for single-photon emission applications and lays an important foundation for their commercialization.

In our study, CsPbBr? QDs, and NH?Br-doped CsPbBr? QDs are synthesized using the hot-injection method to achieve size reduction and improved size uniformity. The synthesis process consists of two main steps. Firstly, the cesium precursor is prepared by heating a mixture of cesium carbonate, oleic acid, and 1-octadecene in an inert atmosphere at 120 ℃ for 2 h, followed by increasing the temperature to 160 ℃. The resulting solution is then cooled to room temperature and stored under sealed conditions. Secondly, the synthesis of CsPbBr? QDs and NH?Br-doped CsPbBr? QDs is carried out by heating a mixture of lead bromide, ammonium bromide, oleic acid, oleylamine, and 1-octadecene in an inert atmosphere for 2 h, with the temperature raised to 150 ℃. Subsequently, 0.4 mL of cesium oleate precursor is rapidly injected. After 5 s of reaction, the mixture is immediately cooled using an ice-water bath. Well-dispersed QD solutions are obtained by high-speed centrifugation. Throughout the synthesis process, QDs with different doping concentrations are prepared by controlling the amount of ammonium bromide added.

The prepared NH₄Br-CsPbBr₃ QDs, with the Br/Pb molar ratio less than 7, exhibit a distinct decreasing trend in particle size as the Br/Pb molar ratio increases. The average particle size of CsPbBr? QDs significantly decreases from 10.07 to 6.87 nm, which results in a blue shift of the PL emission peak from 520 nm (undoped) to 505 nm (Br/Pb molar ratio is 7). Statistical analysis of the same number of grains further shows that the particle size distribution narrows from 4?19 nm to 5.5?9 nm, which confirms that ammonium bromide doping effectively improves the morphological uniformity of the QDs. However, when the Br/Pb molar ratio exceeds 7, excess ammonium bromide disrupts the controllability of the QD morphology, which leads to distortion of the cubic structure. Therefore, the effective doping range of ammonium bromide is limited to a Br/Pb molar ratio of less than or equal to 7. Furthermore, the doping of ammonium bromide strengthens the covalent bonding of Pb-Br, which enhances the stability of the QD crystal structure and increases the quantum yield of the QDs from 31.22% to 61.65%, while the fluorescence lifetime extends from 1.15 ns to 2.80 ns. The reduction in QD size induced by NH?Br doping enhances the quantum confinement effect. This enhanced quantum confinement leads to a more discrete energy level structure in the QDs, which significantly reduces the probability of multiphoton emission, thereby improving the purity of single-photon emission. After doping, the second-order autocorrelation function g2(0) of the QDs decreases from the original value of 0.45 to 0.22, which indicates that ammonium bromide doping effectively improves the single-photon emission purity of the QDs.

Our study systematically reveals the effect of NH?Br doping on the size control of CsPbBr? perovskite QDs, its intrinsic mechanisms, and the effect on single-photon properties. Experimental results show that within the doping concentration range where the Br/Pb molar ratio is less than or equal to 7, the QD size decreases in a regular pattern as the doping concentration increases, with the average particle size significantly reducing from 10.07 nm (undoped) to 6.87 nm (at a Br/Pb molar ratio of 7). More importantly, the uniformity of the doped QDs’ size is improved, with the particle size distribution narrowing from 4?19 nm to 5.5?9 nm. This improvement primarily stems from the bromine-rich environment provided by ammonium bromide, which not only effectively fills the bromine vacancies on the QD surface [as confirmed by X-ray photoelectron spectroscopy (XPS) quantitative analysis, showing an increase in the Br/Pb molar ratio from 3.85 to 4.21] but also narrows the size distribution by suppressing Ostwald ripening and through the synergistic coordination of NH4+ ions with the [PbBr₆]^4- octahedral structure. We find that after doping, the g2(0) value of the QDs decreases from 0.45 to 0.22, which indicates that NH₄Br doping helps improve the single-photon purity of the QDs. The innovative findings of this study open new avenues for the application of perovskite crystal QDs in single-photon sources, which is of great significance for advancing the development of single-photon technology in cutting-edge fields, such as quantum communication, quantum computing, and quantum information processing.