Characteristics of Lightning Activity in Southeast China and its Relation to the Atmospheric Background

Based on the lightning observation data from the Fengyun-4A(FY-4A)Lightning Mapping Imager(FY-4A/LMI)and the Lightning Imaging Sensor(LIS)on the International Space Station(ISS),we extract the“event”type data as the lightning detection results.These observations are then compared with the cloud-to-ground(CG)lightning observation data from the China Meteorological Administration.This study focuses on the characteristics of lightning activity in Southeast China,primarily in Jiangxi Province and its adjacent areas,from April to September,2017–2022.In addition,with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis data,we further delved into the potential factors influencing the distribution and variations in lightning activity and their primary related factors.Our findings indicate that the lightning frequency and density of the FY-4A/LMI,ISS-LIS and CG data are higher in southern and central Jiangxi,central Fujian Province,and western and central Guangdong Province,while they tend to be lower in eastern Hunan Province.In general,the high-value areas of lightning density for the FY-4A/LMI are located in inland mountainous areas.The lower the latitude is,the higher the CG lightning density is.High-value areas of the CG lightning density are more likely to be located in eastern Fujian and southeastern Zhejiang Province.However,the high-value areas of lightning density for the ISS-LIS are more dispersed,with a scattered distribution in inland mountainous areas and along the coast of eastern Fujian.Thus,the mountainous terrain is closely related to the high-value areas of the lightning density.The locations of the high-value areas of the lightning density for the FY-4A/LMI correspond well with those for the CG observations,and the seasonal variations are also consistent.In contrast,the distribution of the high-value areas of the lightning density for the ISS-LIS is more dispersed.The positions of the peak frequency of the FY-4A/LMI lightning and CG lightning contrast with local altitudes,primarily located at lower altitudes or near mountainsides.K-index and convective available potential energy(CAPE)can better reflect the local boundary layer conditions,where the lightning density is higher and lightning seasonal variations are apparent.There are strong correlations in the annual variations between the dew-point temperature(Td)and CG lightning frequency,and the monthly variations of the dew-point temperature and CAPE are also strongly correlated with monthly variations of CG lightning,while they are weakly correlated with the lightning frequency for the FY-4A/LMI and ISS-LIS.This result reflects that the CAPE shows a remarkable effect on the CG lightning frequency during seasonal transitions.

Production and Detection of Ultracold Ground State 85Rb133Cs Molecules in the Lowest Vibrational Level by Short-Range Photoassociation

We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.

Residual current compensation for single-phase grounding fault in coal mine power network

The way of neutral point to earth via full compensation arc suppression coil can solve the problem of residual current compensation in coal mine power network effectively. Based on the analysis on the grounding current detection results of Xieqiao coal mine, the conclusion that harmonic component of grounding current is dominated by higher harmonics with complex harmonic sources in coal mine power network system was obtained. The influences of harmonic source type and fault point position on harmonic voltage and harmonic current were analyzed theoretically. The influences of earthed fault feeder detection result and the estimation errors of parameters to earth on residual current compensation were analyzed. A new thought of residual current prediction and the selections of model method and control method were proposed on this basis. The simulation results prove that harmonic amplitudes of zero sequence voltage and zero sequence current are determined by harmonic source type as well as fault point position in coal mine power network, and also prove that zero sequence voltage detection can avoid the unstable problem of coal mine power network system caused by undercompensation of capacitive current. Finally, the experimental device of full compensation arc suppression coil is introduced.

Measuring ground deformations caused by 2015 Mw7.8 Nepal earthquake using high-rate GPS data

The April 25, 2015 Mw7.8 Nepal earthquake was successfully recorded by Crustal Movement Observation Network of China （CMONOC） and Nepal Geodetic Array （NGA）. We processed the high-rate GPS data （1 Hz and 5 Hz） by using relative kinematic positioning and derived dynamic ground motions caused by this large earthquake. The dynamic displacements time series clearly indicated the displacement amplitude of each station was related to the rupture directivity. The stations which located in the di- rection of rupture propagation had larger displacement amplitudes than others. Also dynamic ground displacement exceeding 5 cm was detected by the GPS station that was 2000 km away from the epicenter. Permanent coseismic displacements were resolved from the near-field high-rate GPS stations with wavelet decomposition-reconstruction method and P-wave arrivals were also detected with S transform method. The results of this study can be used for earthquake rupture process and Earthquake Early Warning studies.

Intelligent identifi cation method for near-surface ground fi ssures based on seismic data

Taking a study area in Jinzhong Basin in Qixian County,Shanxi Province,as an example,this work performs an intelligent interpretation of ground fissures.On the basis of a complete analysis of the regional geological background in the study area,dip-steering cube operation and median filtering of seismic data were performed using fast Fourier transform to improve the continuity of seismic events and eliminate random noise.A total of 200 stratigraphic continuous sample training points and 500 discontinuous training points were obtained from the processed seismic data.Thereafter,a variety of attributes(coherence,curvature,amplitude,frequency,etc.)were extracted as the input for the multilayer perceptron neural network training.During the training period,the training results were traced by normalized root mean square error(RMSE)and misclassifi cation.The training results showed a downward trend during the training period.The misclassifi cation curve was stable at 0.3,and the normalized RMSE curve was stable at 0.68.When the value of the normalized RMSE curve reached the minimum,the training was terminated,and the training results were extended to the whole data volume to obtain the attribute cube of intelligent ground fi ssure detection.The characteristics of ground fi ssures were analyzed and identifi ed from the sections and slices.A total of 11 ground fissures were finally interpreted.The interpretation results showed that the dip angles were 60°-85°,the fault throws were 0-43 m,and the extension lengths were 300-1,100 m in the whole area.The strike of 73%of the ground fi ssures was consistent with the direction of the regional tectonic settings.Specifi cally,four ground fi ssures coincided with the surface disclosed,and the verifi cation rate reached 100%.In conclusion,the intelligent ground fi ssure detection attribute based on the dip-steering cube is eff ective in predicting the spatial distribution of ground fi ssures.

RADAR HRR PROFILING FOR GROUND MOVING TARGET USING PHASE-CODED AND HOPPED-FREQUENCY WAVEFORMS

To obtain the radar High Range Resolution (HRR) profile of the slowly moving ground target in strong clutter background, the Phase-Coded Hopped-Frequency (PCHF) waveform is proposed. By multiple-bursts coherent processing, the HRR profile synthesis, target velocity compensation and clutter compression can be accomplished simultaneously. The new waveform is shown to have good ability to suppress ground clutter and good Electronic Counter-CounterMeasures (ECCM) ability as well. The clutter compression performance of the proposed method is verified by the numerical results.

Coordinate scheduling approach for EDS observation tasks and data transmission jobs

Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.

Highly robust ground moving target detection and relocation method for distributed satellites

The performance of ground moving target detection for distributed satellites will be affected signifi-cantly when there is an image registration error,clutter decorrelation and array error.In this paper,a new approach to moving target detection and relocation is proposed based on multi-channel and multi-pixel adap-tive signal processing in an image domain.First,multi-channel and multi-pixel joint data are equated to a simple array model.Given that there is an image registration error,the real steering vector of the moving target can be estimated through a space projection approach.The optimal beam forming approach is used to cancel clutter,and at the same time the cross-track velocity of the mov-ing target can be determined by searching for the peak value of the cost function.The moving target can then be relocated on the SAR image.The simulation results indicate that this method has a good robustness to image registration error,clutter decorrelation and array error.The detection performance and the estimation accuracy are significantly improved.

High-speed ground moving target detection research using triangular modulation FMCW

The frequency modulated continuous wave(FMCW)radar has the characteristics of low probability of interception,good hidden property and the ability to counter anti-radiation missiles.This paper proposes a new method for high-speed ground moving target detection(GMTD)using triangular modulation FMCW.According to the characteristic of the opposite range shift induced by the upslope and downslope modulation FMCW,the upslope and downslope are imaged,respectively.After compensation of continuous motion of the platform and time difference between upslope and downslope signals for imaging,the moving target can be detected through displaced phase center antenna(DPCA)technology.When the moving target is detected,the moving target image is extracted,and correlation processing is used to obtain the range shift,which can be used to estimate the target radial velocity,and further to find the real position of the target.The effectiveness of this method is verified by the result of computer simulation.

Analytical review and study on object detection techniques in the image

Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.