A Deep Mantle Source for the Late Neoarchean Metamorphosed Basalts in Eastern Hebei,North China Craton:Insights from Whole-Rock Geochemistry and Sm-Nd Isotopes,and Zircon U-Pb-Hf Isotopes

The basalts within the greenstone belt worldwide serve as an ideal target to decipher the nature of Archean mantle sources and further to extend the understanding of the early stages of Earth's evolution.To provide important insights into the issues,we carried out a detailed investigation of whole-rock geochemistry and Sm-Nd isotopes,and zircon U-Pb-Hf isotopes for the Late Neoarchean metamorphosed basalts in eastern Hebei,North China Craton.U-Pb isotopic dating using the LA-ICPMS on zircons reveals that the basalts in eastern Hebei erupted at ca.2.48-2.51 Ga and subsequently experienced multiple regional metamorphic events at 2477 and 1798 Ma,respectively.The metamorphosed basalts are featured by low SiO_(2),MgO,K_(2)O+Na_(2)O,and high Fe O contents,endowed with the subalkaline and high-Fe tholeiitic affinities.The radiogenic initial Nd and Hf isotope values and correlations among V,Ni and Cr contents strongly imply that the basalts experienced significant clinopyroxene and olivine fractionation and minor crustal contamination during magma evolution.They are also characterized by the relatively low total REE contents and exhibit significant depletions to moderate enrichments in the LREE contents,indicating the derivation from a deep mantle source in an Archean proto-mantle plume setting.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning

The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph structure.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning.

An MAC Layer Aware Pseudonym (MAP) Scheme for the Software Defined Internet of Vehicles

This paper proposes a cross-layer design to enhance the location privacy under a coordinated medium access control(MAC) protocol for the Internet of Vehicles(Io V). The channel and pseudonym resources are both essential for transmission efficiency and privacy preservation in the Io V. Nevertheless, the MAC protocol and pseudonym scheme are usually studied separately, in which a new MAC layer semantic linking attack could be carried out by analyzing the vehicles' transmission patterns even if they change pseudonyms simultaneously. This paper presents a hierarchical architecture named as the software defined Internet of Vehicles(SDIV). Facilitated by the architecture, a MAC layer aware pseudonym(MAP) scheme is proposed to resist the new attack. In the MAP, RSU clouds coordinate vehicles to change their transmission slots and pseudonyms simultaneously in the mix-zones by measuring the privacy level quantitatively. Security analysis and extensive simulations are conducted to show that the scheme provides reliable safety message broadcasting, improves the location privacy and network throughput in the Io V.