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The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. [ABSTRACT FROM AUTHOR]

Driven by the scientific objective of geophysical field detection and natural hazard monitoring from space, China launched an electromagnetic satellite, which is known as the China Seismo-Electromagnetic Satellite (CSES-01), on 2 February 2018, into a circular sun-synchronous orbit with an altitude of about 507 km in the ionosphere. The CSES-01 has been in orbit for over 6 years, successfully exceeding its designed 5-year lifespan, and will continually operate as long as possible. A second identical successor (CSES-02) will be launched in December 2024 in the same orbit space. The ionosphere is a highly dynamic and complicated system, and it is necessary to comprehensively understand the electromagnetic environment and the physical effects caused by various disturbance sources. The motivation of this report is to introduce the typical electromagnetic waves, mainly in the ELF/VLF band (i.e., ~100 Hz to 25 kHz), recorded by the CSES-01 in order to call the international community for deep research on EM wave activities and geophysical sphere coupling mechanisms. The wave spectral properties and the wave propagation parameters of those typical EM wave activities in the upper ionosphere are demonstrated in this study based on wave vector analysis using the singular value decomposition (SVD) method. The analysis shows that those typical and common natural EM waves in the upper ionosphere mainly include the ionospheric hiss and proton whistlers in the ELF band (below 1 kHz), the quasiperiodic (QP) emissions, magnetospheric line radiations (MLR), the falling-tone lightning whistlers, and V-shaped streaks in the ELF/VLF band (below 20 kHz). The typical artificial EM waves in the ELF/VLF band, such as power line harmonic radiation (PLHR) and radio waves in the VLF band, are also well recorded in the ionosphere. [ABSTRACT FROM AUTHOR]

The post-midnight irregularities in equator and its adjcent regions, which have remained least explored so far, are investigated using the in situ electron density (Ne) measurements from 2019 to 2021 obtained by the China Seismo-Electromagnetic Satellite (CSES) in the topside ionosphere. The followings are salient points of our findings: 1) Equatorial and low latitude post-midnight irregularities are distributed along the dip equator, exhibiting a wavenumber 4 longitude variation pattern and a seasonal variation pattern characterized by peaks in summer and winter and valleys in spring and autumn during low solar activity (LSA) period in the topside ionosphere, 2) The longitude variation pattern of post-midnight irregularities aligns with that of the background Ne, with irregularities concentrated at the peaks of background Ne, 3) The occurrence rate of post-midnight irregularities increases significantly with higher solar flux, which can be attributed to the rapid increase in the background Ne under LSA conditions. Additionally, the growth rate of occurrence rate varies across different longitudes, with the Pacific and the Atlantic longitudes standing out as prominent regions, and 4) Post-midnight irregularities in the topside ionosphere may have two possible origins. One is irregularities generated at the lower altitudes that drift upward into the topside ionosphere, while the other is the irregularities generated directly in the topside ionosphere. The latter is supported by the presence of conditions conducive to irregularity generation in the topside ionosphere during midnight hours in LSA period. Given the scarcity of research on post-midnight irregularity compared to post-sunset irregularity, further investigations are essential to fully comprehend their generation mechanisms. The abundance of post-midnight Ne measurements from the CSES satellite offers a valuable opportunity for this research, which can enhance our understanding of post-midnight ionospheric dynamics, and their variations with solar activity. [ABSTRACT FROM AUTHOR]

From August to October 2018, a series of strong earthquake (EQ) events occurred in southeast Asia and northern Oceania (22°S to 0°N, 115°E to 170°E) within 50 days. In this paper, we analyze the features of ionospheric plasma perturbations, recorded by the Plasma Analyzer Package (PAP) and Langmuir probe (LAP) onboard the China Seismo-Electromagnetic Satellite (CSES-01), before four EQs with magnitudes of Ms 6.9 to Ms 7.4. The ion parameters such as the oxygen ion density (No+), the ion drift velocity in the vertical direction (Vz) under the conditions of geomagnetic storms, and strong EQs are compared. The results show that within 1 to 15 days before the strong EQs, the No+ and the electron density (Ne) increased while the electron temperature (Te) decreased synchronously. Meanwhile, the Vz significantly increased along the ground-to-space direction. The relative variation of No+ and Vz before the strong EQs is more prominent, and the Vz is not easily influenced by the geomagnetic storm but is susceptible to the seismic activities. Our results suggest that the anomaly of ionospheric plasma perturbations occurring in this area is possibly related to the pre-EQ signatures. [ABSTRACT FROM AUTHOR]

For prediction of urban remote sensing surface temperature, cloud, cloud shadow and snow contamination lead to the failure of surface temperature inversion and vegetation‐related index calculation. A time series prediction framework of urban surface temperature under cloud interference is proposed in this paper. This is helpful to solve the problem of the impact of data loss on surface temperature prediction. Spatial and temporal variation trends of surface temperature and vegetation index are analyzed using Landsat 7/8 remote sensing data of 2010 to 2019 from Beijing. The geographically weighed regression (GWR) method is used to realize the simulation of surface temperature based on the current date. The deep learning prediction network based on convolution and long short‐term memory (LSTM) networks was constructed to predict the spatial distribution of surface temperature on the next observation date. The time series analysis shows that the NDBI is less than −0.2, which indicates that there may be cloud contamination. The land surface temperature (LST) modeling results show that the precision of estimation using GWR method on impervious surface and water bodies is superior compared to the vegetation area. For LST prediction using deep learning methods, the result of the prediction on surface temperature space distribution was relatively good. The purpose of this study is to make up for the missing data affected by cloud, snow, and other interference factors, and to be applied to the prediction of the spatial and temporal distributions of LST. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

The Search Coil Magnetometer(SCM) on China Seismo-Electromagnetism Satellite(CSES), on the basis of Faraday's law of electromagnetic induction, can obtain ULF/ELF/VLF information from variable magnetic field in the ionosphere. The information is used to study anomaly phenomena such as seismic activities, magnetic storm and unknown signals etc.This study is aimed at detecting abnormal phenomena from VLF power spectrum data in SCM ranging from 12.5hz to 25.6khz. VLF signals radiated by artificial VLF transmitters and magnetic storm are defined as abnormal phenomena. Constructing a detection model contributes to search abnormal phenomena in big data quickly and accurately.Firstly, in specific frequency, building training and testing samples, based on the acquired data from both the areas above artificial VLF transmitters (termed as positive samples) and other observed areas (termed as negative samples) respectively, needs to use fixed time window in each orbit to create two-dimensional images through Mel Frequency Cepstrum Coefficient (MFCC). Secondly, deep learning methods used as a detection model, such as CNN, is to detect abnormal phenomena from those samples. Finally, we can use this method to detect each time window in all of the VLF power spectrum data in order to fast find some interesting phenomena. In our experiments, we collect 440 samples including positive samples and negative samples. And we use 110 samples for each class as training data, and the others as testing data. The results show that testing precision of our method reaches 86%. If more data is added to CNN, our method can detect abnormal phenomena more efficiently. [ABSTRACT FROM AUTHOR]