We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method c...We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method cannot be used when observation geometry is poor or observations are insufficient, but also avoids solving the ambiguity in the zero-difference reduced dynamic method. As the epoch-difference method is not sensitive to the impact of phase cycle slips, it can lower the difficulty of slip detection in phase observation preprocessing. In the solution strategies, we solve the high-dimensional matrix computation problems by decomposing the long observation arc into a number of short arcs. By gravity recovery and climate experiment （GRACE） satellite orbit determination and compared with GeoForschungsZentrum （GFZ） post science orbit, for epoch-difference reduced dynamic method, the root mean squares （RMSs） of radial, transverse and normal components are 1.92 cm, 3.83 cm and 3.80 cm, and the RMS in three dimensions is 5.76 cm. The solution＇s accuracy is comparable to the zero-difference reduced dynamic method.展开更多
We have derived a set of field equations for a Weyssenhoff spin fluid including magnetic interaction among the spinning particles prevailing in spatially homogeneous, but anisotropically cosmological models of Bianchi...We have derived a set of field equations for a Weyssenhoff spin fluid including magnetic interaction among the spinning particles prevailing in spatially homogeneous, but anisotropically cosmological models of Bianchi type V based on Einstein Cartan theory. We analyze the field equations in three different equations of states specified by p =(1/3)ρ, p =ρ and p =0. The analytical solutions found are non singular provided that the combined energy arising from matter spin and magnetic interaction among particles overcomes the anisotropy energy in the Universe. We have also deduced that the minimum particle numbers for the radiation ( p =(1/3)ρ ) and matter ( p =0) epochs are 10 88 and 10 108 respectively, the minimum particle number for the state p =ρ is 10 96 , leading to the conclusion that we must consider the existence of neutrinos and other creation of particles and anti particles under torsion and strong gravitational field in the early Universe.展开更多
The effect of Earth precession angle on a climate is presented here. It is shown that the glaciation epochs occurred only when the precession angle was low. After the continental glaciation formed in the Northern hemi...The effect of Earth precession angle on a climate is presented here. It is shown that the glaciation epochs occurred only when the precession angle was low. After the continental glaciation formed in the Northern hemisphere, Earth’s spherecal symmetry was disrupted and its precession angle increased drastically. As a result, a drastic and rapid climate warm-up occurred, the glaciers melted down and an interglacial stadial1 began. Subsequently, affected by the Lunar-Solar gravity pull on the Earth’s equatorial swelling, the precession angle gradually decreased and a new cooling-down phase occurred. As a result, there was nonlinear oscillation of Earth’s climate with periods on the order of 100 - 120 MY.展开更多
The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is amajor challenge. Because of the distinctive nature, unpredictabi...The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is amajor challenge. Because of the distinctive nature, unpredictability, and greatchangeability in the reality of toxins and particulates, detecting air quality is apuzzling task. Simultaneously, the ability to predict or classify and monitor airquality is becoming increasingly important, particularly in urban areas, due tothe well documented negative impact of air pollution on resident’s health andthe environment. To better comprehend the current condition of air quality, thisresearch proposes predicting air pollution levels from real-time data. This studyproposes the use of deep learning techniques to forecast air pollution levels.Layers, activation functions, and a number of epochs were used to create the suggested Long Short-Term Memory (LSTM) network based neural layer design. Theuse of proposed Deep Learning as a structure for high-accuracy air quality prediction is investigated in this research and obtained better accuracy of nearly 82% compared to earlier records. Determining the Air Quality Index (AQI) and danger levelswould assist the government in finding appropriate ways to authorize approaches toreduce pollutants and keep inhabitants informed about the findings.展开更多
基金National Natural Science Foundation of China （61002033, 60902089） Open Research Fund of State Key Laboratory of Astronautic Dynamics （2011ADL-DW0103）
文摘We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method cannot be used when observation geometry is poor or observations are insufficient, but also avoids solving the ambiguity in the zero-difference reduced dynamic method. As the epoch-difference method is not sensitive to the impact of phase cycle slips, it can lower the difficulty of slip detection in phase observation preprocessing. In the solution strategies, we solve the high-dimensional matrix computation problems by decomposing the long observation arc into a number of short arcs. By gravity recovery and climate experiment （GRACE） satellite orbit determination and compared with GeoForschungsZentrum （GFZ） post science orbit, for epoch-difference reduced dynamic method, the root mean squares （RMSs） of radial, transverse and normal components are 1.92 cm, 3.83 cm and 3.80 cm, and the RMS in three dimensions is 5.76 cm. The solution＇s accuracy is comparable to the zero-difference reduced dynamic method.
文摘We have derived a set of field equations for a Weyssenhoff spin fluid including magnetic interaction among the spinning particles prevailing in spatially homogeneous, but anisotropically cosmological models of Bianchi type V based on Einstein Cartan theory. We analyze the field equations in three different equations of states specified by p =(1/3)ρ, p =ρ and p =0. The analytical solutions found are non singular provided that the combined energy arising from matter spin and magnetic interaction among particles overcomes the anisotropy energy in the Universe. We have also deduced that the minimum particle numbers for the radiation ( p =(1/3)ρ ) and matter ( p =0) epochs are 10 88 and 10 108 respectively, the minimum particle number for the state p =ρ is 10 96 , leading to the conclusion that we must consider the existence of neutrinos and other creation of particles and anti particles under torsion and strong gravitational field in the early Universe.
文摘The effect of Earth precession angle on a climate is presented here. It is shown that the glaciation epochs occurred only when the precession angle was low. After the continental glaciation formed in the Northern hemisphere, Earth’s spherecal symmetry was disrupted and its precession angle increased drastically. As a result, a drastic and rapid climate warm-up occurred, the glaciers melted down and an interglacial stadial1 began. Subsequently, affected by the Lunar-Solar gravity pull on the Earth’s equatorial swelling, the precession angle gradually decreased and a new cooling-down phase occurred. As a result, there was nonlinear oscillation of Earth’s climate with periods on the order of 100 - 120 MY.
文摘The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is amajor challenge. Because of the distinctive nature, unpredictability, and greatchangeability in the reality of toxins and particulates, detecting air quality is apuzzling task. Simultaneously, the ability to predict or classify and monitor airquality is becoming increasingly important, particularly in urban areas, due tothe well documented negative impact of air pollution on resident’s health andthe environment. To better comprehend the current condition of air quality, thisresearch proposes predicting air pollution levels from real-time data. This studyproposes the use of deep learning techniques to forecast air pollution levels.Layers, activation functions, and a number of epochs were used to create the suggested Long Short-Term Memory (LSTM) network based neural layer design. Theuse of proposed Deep Learning as a structure for high-accuracy air quality prediction is investigated in this research and obtained better accuracy of nearly 82% compared to earlier records. Determining the Air Quality Index (AQI) and danger levelswould assist the government in finding appropriate ways to authorize approaches toreduce pollutants and keep inhabitants informed about the findings.
