Calibration of the Radical Installation Limit Error of the Accelerometer in the Gravity Gradient Instrument

Gravity gradient instrument (GGI) is the core of the gravity gradiometer, so the structural error of the sensor has a great impact on the measurement results. In order not to affect the aimed measurement accuracy, limit error is required in the installation of the accelerometer. In this paper, based on the established measuring principle model, the radial installation limit error is calibrated, which is taken as an example to provide a method to calculate the other limit error of the installation under the premise of ensuring the accuracy of the measurement result. This method provides the idea for deriving the limit error of the geometry structure of the sensor, laying the foundation for the mechanical precision design and physical design

Mathematical Modeling of the Working Principle of Gravity Gradient Instrument

Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton's law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement

Clinical Value of Spectral Imaging Combined with MAR for CTA after Embolization of Intracranial Aneurysms

Objective: To evaluate the application value of combining spectral imaging and metal artifact reduction (MAR) in head and neck CTA after the embolization of intracranial aneurysms. Methods: We collected 37 patients who experienced embolization of intracranial aneurysms then received spectral imaging of head and neck CTA. Monochromatic images with energy ranging from 70～140 keV, 120 kVp-like mixed energic images, 70～140 keV MAR images, and 120 kVp-like MAR images were generated. The region of interest was placed on the area near the coil and with the most serious metal artifact. CT attenuation and standard deviation were measured, and artifact index (AI) and signal-noise ratio (SNR) were calculated. Two radiologists independently subjectively evaluated the metal artifact and the display of surrounding vessels using Likert 5 scales. The subjective scores and objective parameters between MAR and non-MAR images were compared. The Wilcoxon ranking test, paired sample t test, and independent sample t test were utilized to compare parameters between the groups. Results: MAR images had significantly lower AI than did non-MAR images for all eight monochromatic energies. When energies ranged from 80~110 keV, SNR was higher for MAR images than for non-MAR images, and the difference was statistically significant. With same energies, MAR images had higher artifact and vessel display scores than did non-MAR images. For non-MAR images, the different coil diameters did not make a statistical difference in AI and vessel display scores. For MAR images, a larger coil diameter (>8.79 mm) led to higher AI and lower vessel display scores than did normal diameters (≤8.79 mm). Conclusion: The combination of spectral imaging and MAR could effectively reduce the metal artifact of implants for the embolization of intracranial aneurysms and improve the surrounding vessel display. Moreover, the metal artifact reduction effect was more significant for the coils with smaller diameters

Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing

Satellites will play a vital role in the future of the global Internet of Things (IoT); however, the resource shortage is the biggest limiting factor in the regional task of massiveequipment in the IoT for satellite service. Compared with the traditional isolated mode of satellite resources, the current research aims to realize resource sharing through satellite cooperation in satellite edge computing, to solve the problems of limited resources and low service quality of a single satellite. We propose a satellite resource pool architecture-oriented regional task in satellite edge computing. Different from fixed servers in ground systems, the satellite orbital motion brings challenges to the construction of the satellite resource pool. After the capacity planning of the satellite resource pool for regional tasks is given, an algorithm based on search matching is proposed to solve the dynamic satellite selection problem. A ground semi-physical simulation system is built to perform experiments and evaluate the performance of three modes of satellite resource sharing: isolated mode, cooperative mode, and pooled mode. The results show that the pooled mode, compared with the isolated mode, improves the task success rate by 19.52%, and at the same time increases network resources and energy consumption in the same scenario. Compared with the cooperation mode, the performance of task success rate and resource utilization rate is close to that of the pooled mode, but it has more advantages in response time and load balancing of satellite resources. This shows that in the IoT, the resource pool is of great benefit as it improves the task response time and improves the load balance of satellite resources without degrading the performance, which makes sense in task-demanding scenarios

Data from: Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal‐dominated alpine coniferous forests

Fine root traits vary greatly with environmental changes, but the understanding of root-trait variation and its drivers is limited over broad geographical scales, especially for ectomycorrhizal (ECM)-dominated conifers in alpine forests. Herein, the covariation patterns of and environmental controls for fine root traits among ECM-dominated conifers were examined to test whether and how climate and soil nutrients differentially affect fine root trait variations. Eight traits of first- and second-order roots were measured, i.e., root diameter (RD), specific root length (SRL), branching intensity (BRI), root tissue density (RTD), mycorrhizal colonization rate (MCR), and concentrations of carbon (C), nitrogen (N) and phosphorus (P), across 76 alpine coniferous populations on the eastern Tibetan Plateau, China. Our results showed that variations of the fine root traits fell into two major dimensions: the first dimension (32.39% of the total variance) was mainly represented by RD and SRL, potentially conveying a tradeoff between root lifespan and efficiency of resource foraging; the second dimension (23.70% of the variance) represented coordinated variation for root nutrients (i.e., N and P) and RTD, which depicts the conservation-acquisition tradeoff in resource uptake, i.e., root economic spectrum (RES). Variations in RD and SRL were mainly driven by climatic variables, characterized by a significant increase in RD and a decrease in SRL with increasing mean annual precipitation. In contrast, variations in fine root nutrients (i.e., N and P) and RTD were primarily driven by soil fertility, showing a significant increase in root N and P concentrations but a decrease in RTD with increasing soil resource levels. Synthesis. Our study clearly shows two distinct dimensions of the variation of fine root traits in ECM-dominated alpine coniferous forests, providing further evidence of the inherent multidimensionality of root traits. Moreover, our findings highlight different roles of climatic and soil variables in driving the variation of fine root traits, potentially leading to the multidimensionality of root traits. This study provides new insights for understanding and predicting shifts in plant belowground strategies in climate-sensitive alpine forests worldwide