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We report on a cascade nanosecond CdSe optical parametric oscillator with an output energy of 0.54 mJ at 100 Hz, temperature tuning within 9245–9285 nm, and a narrow (∼6 cm−1) spectral width achieved by a volume Bragg grating acting on the first stage. The CdSe crystal is pumped intracavity by the signal wave from the PPKTP-based first stage, and both stages operate under noncritical phase-matching conditions. The simple, compact, and robust setup produced the highest overall conversion efficiency for an all-solid-state laser system based on CdSe with 1-μm pumping, and the corresponding quantum conversion efficiency exceeded 13%. [ABSTRACT FROM AUTHOR]

The ground‐based microwave radiometer (MWR) retrieves atmospheric profiles with a high temporal resolution for temperature and relative humidity up to a height of 10 km. These profiles have been widely used in the field of meteorological observation. Due to the inherent fragility of neural networks, one of the important issues in this field is to improve the reliability and stability of MWR profiles based on neural network inversion. We propose a deep learning method that adds noise to the BP neural network inversion (NBPNN) process. Comparison of the radiosonde data and NBPNN results shows that if the error of MWR brightness temperature is in the range of −2–2 K, the root‐mean‐square error (RMSE) of the temperature profile is 2.15 K, and the RMSE of the relative humidity profile is 19.46 % inverted by NBPNN. The results are much less than the errors of the temperature profile and relative humidity profile inverted by the traditional backpropagation neural network inverse method. From the comparison, we demonstrated that NBPNN significantly increases the inversion accuracy and robustness under the condition of errors in brightness temperature, which can reduce requirements for BT accuracy of MWR and achieve MWR long‐term stability. Key Points: It's necessary to improve the reliability and stability of inversion for microwave radiometerWe propose a deep learning method that adds noise to the BP neural network inversion processThe new inversion process significantly increases the inversion accuracy and robustness under the condition of errors in brightness temperature [ABSTRACT FROM AUTHOR]

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to the deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated with the core diameter to produce a node-free structure. These features improve the fiber's bending resistance while achieving a larger core diameter. The simulation results show that the radial air–glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core's limiting loss and sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, the diameter of the nested tube is d0 = 29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28 × 10−4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72 × 10−2 dB/m. An anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm was successfully achieved in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band Er, Cr: YSGG laser therapy instrument. [ABSTRACT FROM AUTHOR]