The importance of conditionally rare taxa for the assembly and interaction of fungal communities in mangrove sediments.

The fungal communities provide the nutrients and drive the cycles of elements in nature, and the rare fungal taxa are proved to be crucial for these communities in many environments. However, the ecological functions of rare taxa for the fungal communities in mangrove ecosystems are poorly assessed until now. This work aims to reveal the importance of rare taxa for the assembly of fungal communities in mangrove sediments by using the amplicon sequencing analysis of different spatiotemporal samples collected from Sanya mangroves, China. The results showed that Ascomycota and Basidiomycota were the dominant phyla in the conditionally rare taxa (CRT). The fungal communities possessed outstanding stability against the spatiotemporal variation and most collected environmental factors. The CRT possessed narrower niches and were more affected by the environmental variables than the abundant taxa. The current work demonstrated that the CRT had significantly higher relative abundances, degrees (the number of adjacent edges), clustering coefficients, and closeness centralities in the top 8 modules of the co-occurrence network (p < 0.05), indicating the important role of the CRT for the interaction of fungal communities in mangrove sediments. These findings indicate the importance of the CRT for the fungal community structures in mangrove sediments, and would deepen our understanding of dynamic functions of mangrove fungi, thereby facilitating the management, utilization, and protection of mangrove ecosystems. KEY POINTS: • Fungal communities in mangrove sediments are stable against environment variations. • The conditionally rare taxa (CRT) possessed narrower niches than the abundant fungal taxa. • The CRT are central for the co-occurrence network and interaction of fungal communities.

Online estimation of Allan variance coefficients based on a neural-extended Kalman filter.

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor.

Online technique for detecting state of onboard fiber optic gyroscope.

Although angle random walk (ARW) of fiber optic gyroscope (FOG) has been well modeled and identified before being integrated into the high-accuracy attitude control system of satellite, aging and unexpected failures can affect the performance of FOG after launch, resulting in the variation of ARW coefficient. Therefore, the ARW coefficient can be regarded as an indicator of "state of health" for FOG diagnosis in some sense. The Allan variance method can be used to estimate ARW coefficient of FOG, however, it requires a large amount of data to be stored. Moreover, the procedure of drawing slope lines for estimation is painful. To overcome the barriers, a weighted state-space model that directly models the ARW to obtain a nonlinear state-space model was established for FOG. Then, a neural extended-Kalman filter algorithm was implemented to estimate and track the variation of ARW in real time. The results of experiment show that the proposed approach is valid to detect the state of FOG. Moreover, the proposed technique effectively avoids the storage of data.