Experimental study of core MHD behavior and a novel algorithm for rational surface detection based on profile reflectometry in EAST

Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasma perturbations on the reflect signal is important.The characteristics of the reflect signal from profile reflectometry,the time-of-flight(TOF)signal associated with the MHD instabilities,are investigated in EAST.Using a 1D full-wave simulation code by the Finite-DifferenceTime-Domain(FDTD)method,it is well validated that the local density flattening could induce the discontinuity of the simulated TOF signal and an obvious change of reflect amplitude.Experimental TOF signals under different types of MHD instabilities(sawtooth,sawtooth precursors and tearing mode)are studied in detail and show agreement with the simulation.Two new improved algorithms for detecting and localizing the radial positions of the low-order rational surface,the cross-correlation and gradient threshold(CGT)method and the 2D convolutional neural network approach(CNN)are presented for the first time.It is concluded that TOF signal analysis from profile reflectometry can provide a straightforward and localized measurement of the plasma perturbation from the edge to the core simultaneously and may be a complement or correction to the q-profile control,which will be beneficial for the advanced tokamak operation.

Observation of Doppler shift f_(D) modulated by the internal kink mode using conventional reflectometry in the EAST tokamak

In this paper we present a new experimental observation using a conventional reflectometry technique,poloidal correlation reflectometry(PCR),in the Experimental Advanced Superconducting Tokamak(EAST).The turbulence spectrum detected by the PCR system exhibits an asymmetry and induced Doppler shift f_(D)during the internal kink mode(IKM)rotation phase.This Doppler shift f_(D)is the target measurement of Doppler reflectometry,but captured by conventional reflectometry.Results show that the Doppler shift f_(D)is modulated by the periodic changes in the effective angle between the probing wave and cutoff layer normal,but not by plasma turbulence.The fishbone mode and saturated long-lived mode are typical IKMs,and this modulation phenomenon is observed in both cases.Moreover,the value of the Doppler shift f_(D)is positively correlated with the amplitude of the IKM,even when the latter is small.However,the positive and negative frequency components of the Doppler shift f_(D)can be asymmetric,which is related to the plasma configuration.A simulated analysis is performed by ray tracing to verify these observations.These results establish a clear link between f_(D)and IKM rotation,and are helpful for studying the characteristics of IKM and related physical phenomena.

Deep Learning-Based Program-Wide Binary Code Similarity for Smart Contracts

Recently,security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks.There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts.Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis.However,due to the difference between common programs and smart contract,such as diversity of bytecode generation and highly code homogeneity,directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy,poor scalability and the limitation of binary similarity on function level.Therefore,this paper investigates graph neural network to detect smart contract binary code similarity at the program level,where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts.In particular,two improved Graph Convolutional Network(GCN)and Message Passing Neural Network(MPNN)models are explored to encode the contract graphs into quantitatively vectors,which can capture the semantic information and the program-wide control flow information with temporal orders.Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings.To evaluate the effectiveness and efficient of our proposed method,extensive experiments are performed on two real-world datasets,i.e.,smart contracts from Ethereum and Enterprise Operation System(EOS)blockchain-based platforms.The results show that our proposed approach outperforms three state-of-the-art methods by a large margin,achieving a great improvement up to 6.1%and 17.06%in accuracy.

Feasibility of applying the lower cut-off frequency for the density radial coverage extension in EAST reflectometry measurement

The extraordinary mode(X-mode)lower cut-off frequency is proposed for use in the reflectometry diagnostic on ITER for the electron density profile measurement,which is a trade-off between extreme plasma parameters and the accessible probing frequency.In contemporary experiments,the lower cutoff frequency can be identified at the probing frequency below the electron cyclotron frequency(f_(ce)) under certain plasma conditions.We provide here,for the first time,the experimental validation of the use of the lower cut-off frequency for the density profiles via the reflectometry measurement on EAST.The corresponding group delay of the lower cut-off frequency evolves continuously with the upper one,revealing a reasonable radial coverage extension of reflectometry measurement toward the plasma core.It is concluded that the lower cut-off frequency can be used as a supplement to the upper one in the density profile inversion process,which is of particular interest in the high magnetic field and/or density discharge to extend the radial coverage of reflectometry measurement.