Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and the local single-temporal information restoration (LSIR) stage. It adopts the fusion feature of global multi-temporal to restore the thick cloud occlusion information of local single temporal images. Then, the featured global–local structure is created in both two stages, fusing the global feature capture ability of Transformer with the local feature extraction ability of CNN, with the goal of effectively retaining the detailed information of the remote sensing images. Finally, the local feature extraction (LFE) module and global–local feature extraction (GLFE) module is designed according to the global–local characteristics, and the different module details are designed in this two stages. Experimental results indicate that the proposed method performs significantly better than the compared methods in the established data set for the task of multi-temporal thick cloud removal. In the four scenes, when compared to the best method CMSN, the peak signal-to-noise ratio (PSNR) index improved by 2.675, 5.2255, and 4.9823 dB in the first, second, and third temporal images, respectively. The average improvement of these three temporal images is 9.65%. In the first, second, and third temporal images, the correlation coefficient (CC) index improved by 0.016, 0.0658, and 0.0145, respectively, and the average improvement for the three temporal images is 3.35%. Structural similarity (SSIM) and root mean square (RMSE) are improved 0.33% and 34.29%, respectively. Consequently, in the field of multi-temporal cloud removal, the proposed method enhances the utilization of multi-temporal information and achieves better effectiveness of thick cloud restoration.

The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model’s accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing.

Semiconductor quantum dots (QDs) are a promising luminescent phosphor for next-generation lightings and displays. In particular, QD-based white light-emitting diodes (WLEDs) are considered to be the candidate light sources with the most potential for application in displays. In this work, we synthesized quaternary/ternary core/shell alloyed CdZnSeS/ZnSeS QDs with high bright emission intensity. The QDs show good thermal stability by performing high temperature-dependent experiments that range from 295 to 433 K. Finally, the WLED based on the CdZnSeS/ZnSeS QDs exhibits a luminous efficiency (LE) of 28.14 lm/W, an external quantum efficiency (EQE) of 14.86%, and a warm bright sunlight close to the spectrum of daylight (Commission Internationale de l'éclairage (CIE) coordinates 0.305, 0.371). Moreover, the photoluminescence (PL) intensity, LE, EQE, and correlated color temperature (CCT) of as-prepared QD WLED remained relatively stable with only slight changes in the luminescence stability experiment.

Space gravitational wave detection uses a three-satellite formation scheme, with the distance between satellites reaching hundreds of thousands or millions of kilometers. According to the principle of laser heterodyne interferometry, the distance change between the inter-satellite inertial references caused by the gravitational wave event is converted into the phase change of the heterodyne interference signal. The payload for measuring the phase change information is the phasemeter. The mission requires that the phasemeter’s ranging accuracy is 1 picometer, and the corresponding phase measurement accuracy is required to reach 2π μrad/Hz^1/2 @(0.1 mHz–1 Hz). Due to the inter-satellite Doppler effect, the dynamic range of the interference signal frequency reaches 5 MHz to 25 MHz. Due to the sampling jitter noise of the interference signal, it is necessary to suppress the noise through a single pilot tone. This paper introduces the development of the phasemeter, which uses a single pilot tone to suppress sampling jitter noise. The test results show that when the dynamic range of the interference signal frequency is 5 MHz to 25 MHz, the phasemeter meets the mission indicator requirement of 2π μrad/Hz^1/2 @(0.1 mHz–1 Hz).

As the primary electronic payload of laser interferometry system for space gravitational wave detection, the core function of the phasemeter is ultra-high precision phase measurement. According to the principle of laser heterodyne interferometry and the requirement of 1 pm ranging accuracy of the phasemeter, the phase measurement noise should reach 2π μrad/Hz^1/2@(0.1 mHz–1 Hz). The heterodyne interference signal first passes through the quadrant photoelectric detector (QPD) to achieve photoelectric conversion, then passes through the analog-to-digital converter (ADC) to achieve analog and digital conversion, and finally passes through the digital phase-locked loop (DPLL) for phase locking. The sampling timing jitter of the heterodyne interference signal caused by the ADC is the main noise affecting the phase measurement performance and must be suppressed. This paper proposes a sampling timing jitter noise suppression system (STJNSS), which can set system parameters for high-frequency signals used for inter-satellite clock noise transmission, the system clock of the phasemeter, and the pilot frequency for suppressing ADC sampling timing jitter noise, meeting the needs of the current major space gravitational wave detection plans. The experimental results after the integration of SJNSS and the phase meter show that the phase measurement noise of the heterodyne interferometer signal reaches 2π μrad/Hz^1/2@(0.1 mHz–1 Hz), which meets the requirements of space gravitational wave missions.

Brain tumor is a common neurological surgical disease, where surgical resection is the primary treatment method. Neurosurgeons need to accurately determine the location of the tumor during tumor resection surgery, but existing clinical tumor identification technologies face numerous challenges, such as high equipment costs, long processing times, a certain degree of invasiveness, and insufficient image clarity. In this work, we propose a hyperspectral image detection algorithm based on the fusion of multiple features to maximize the determination of tumor boundaries. The algorithm establishes the machine learning models of Support Vector Machine (SVM) and Random Forest (RF) by integrating data features from optimal wavelengths, spectral indices, and textural features. Experimental results show that on different datasets, the classification accuracy of the three-feature fusion model is significantly higher than that of models using only two features or a single feature. Hyperspectral tumor image recognition can effectively help distinguish the tumors from the surrounding tissue, thereby enhancing the safety and thoroughness of tumor surgery.