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Bohai Sea is China's inland sea, its complex marine and atmospheric optical properties pose a challenge to the application of satellite data to retrieve chlorophyll-a (CHLA) concentration with high accuracy. The high accuracy retrieval results of CHLA require simultaneous consideration of the adaptability of atmospheric correction algorithms and CHLA retrieval models. In this study, four atmospheric correction methods [the standard atmospheric correction algorithms of GDPS1.4.1 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA (Seadas_Default), and management unit of the North Sea mathematical models (Seadas_MUMM)] and four CHLA retrieval models (OC2, YOC, OC3G, and OC2M-HI) were selected in the process of applying geostationary ocean color imager (GOCI) data to retrieve CHLA in Bohai Sea. Based on the in situ data, the adaptability of their pairwise combinations in retrieval of CHLA in Bohai Sea was evaluated. The results indicate that the OC2 and OC3G models significantly overestimated the CHLA. The combination of the Seadas_Default atmospheric correction algorithm with the YOC CHLA retrieval model, or the combination of the Seadas_MUMM atmospheric correction algorithm with the YOC CHLA retrieval model, is more suitable for the retrieval of CHLA using GOCI data in Bohai Sea. In addition, this study shows that the CHLA obtained based on the data from eight-scene GOCI data were different to the data obtained based on single-scene GOCI data (approximating traditional polar-orbiting satellite sensor data) in daily, monthly, and yearly average results. The monthly mean difference between the two is the most significant, ranging from -0.66 to 1.49 μg/l.

Computer technology has rapidly advanced the use of computer-assisted refereeing in sports. However, motion image segmentation presents several challenges, including image blurring due to high-speed motion and the impact of ambient light on image accuracy. These challenges can affect the referee’s judgment of the game. To address this issue, the research utilizes sinusoidal mapping and the simplex method to enhance the cuckoo algorithm. Additionally, the optimization accuracy is improved through sinusoidal adaptive discovery probability. To tackle the problem of the fuzzy clustering algorithm’s weak global search ability, the clustering center is optimized by enhancing the cuckoo algorithm. To optimize the objective function, domain space information is introduced into the fuzzy clustering algorithm to address its sensitivity to noise. As the number of iterations approached 500, the experimental findings showed that the convergence accuracy of the particle swarm algorithm, the sparrow algorithm, the modified cuckoo algorithm, and the cuckoo algorithm were, respectively, 0.99, 0.89, 0.85, and 0.73. The enhanced algorithm model, enhanced cuckoo-fuzzy C-mean algorithm model, cuckoo-fuzzy C-mean algorithm model, and fuzzy C-mean algorithm model had delineation effects of 0.80, 0.72, 0.70, and 0.61, in that order. The outcomes show that the suggested algorithm can more accurately segment images from soccer matches. This provides a valuable reference for future computer-assisted match discrimination.

Water transparency is one of the important parameters to describe the optical properties of seawater. It is a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. First, this article verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Second, the water transparency estimation results retrieved from Geostationary Ocean Color Imager (GOCI) using four atmospheric correction (AC) algorithms were compared and analyzed with in-situ data in the study area. The four AC algorithms are the standard AC algorithms of Korea Ocean Satellite Center (KOSC) GOCI GDPS1.3 and GDPS2.0, the standard near-infrared AC algorithm of NASA, and management unit of the North Sea mathematical models. At last, GOCI data on Mar 09, 2018, were used to analyze the differences in hourly changes of transparency caused by different AC algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R2>0.8, MAPE = 16.4%). The difference in transparency value caused by the AC algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency is presented based on different AC algorithms, while the moment when the minimum value of transparency is presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different AC algorithms are not completely consistent.

Large Language Models (LLMs) such as OpenAI's ChatGPT have achieved surprisingly huge progresses in the field of Natural Language Processing (NLP). This paper aims to present an immersive introduction to LLMs from the perspective of generative models. The main components of the training process of LLMs are explained, and an example of LLMs for AI-generated contents is given. This short paper is a summary of the interactive full paper online available at IEEE Xplore, in which detailed examples interactively demonstrate the training and working mechanisms of LLMs. [ABSTRACT FROM AUTHOR]