Observation of HⅠaround three Satellite Galaxies of M31 with FAST:AndromedaⅡ,NGC 205,and NGC 185

With the exceptional sensitivity of the Five-hundred-meter Aperture Spherical radio Telescope,we conducted observations of the neutral hydrogen(HⅠ)in the circumgalactic medium of Andromeda’s(M31)satellite galaxies,specifically AndromedaⅡ,NGC 205,and NGC 185.Initially,three drift scans were executed for these satellites,with a detection limit of 4×10^(18)cm^(-2)(approximately 1.88×10^(3)M_Θof HⅠmass),followed by a more in-depth scan of a specific region.We discovered a C-shaped HⅠarc structure sharing a position and line-of-sight velocity similar to a stellar ring structure around AndromedaⅡ,hinting at a potential connection with AndromedaⅡ.In the context of NGC 205,we identified two mass concentrations in the northeast direction,which could be indicative of tidal streams resulting from the interaction between this galaxy and M31.These new lumps discovered could be very helpful in solving the missing interstellar medium problem for NGC 205.Observations regarding NGC 185are consistent with previous studies,and we did not detect any additional HⅠmaterial around this galaxy.These observational results enhance our understanding of the evolution of these satellite galaxies and provide insight into their historical interactions with the galaxy M31.

HiFAST:An HI Data Calibration and Imaging Pipeline for FAST.Ⅱ.Flux Density Calibration

Accurate flux density calibration is essential for precise analysis and interpretation of observations across different observation modes and instruments.In this research,we first introduce the flux calibration model that incorporated in Hi FAST pipeline,and designed for processing HⅠ21 cm spectra.Furthermore,we investigate different calibration techniques and assess the dependence of the gain parameter on the time and environmental factors.A comparison is carried out in various observation modes(e.g.,tracking and scanning modes)to determine the flux density gain(G),revealing insignificant discrepancies in G among different methods.Long-term monitoring data shows a linear correlation between G and atmospheric temperature.After subtracting the G-Temperature dependence,the dispersion of G is reduced to<3%over a one-year timescale.The stability of the receiver response of Five-hundred-meter Aperture Spherical radio Telescope(FAST)is considered sufficient to facilitate HⅠobservations that can accommodate a moderate error in flux calibration(e.g.,>~5%)when utilizing a constant G for calibration purposes.Our study will serve as a useful addition to the results provided by Jiang et al.Detailed measurement of G for the 19 beams of FAST,covering the frequency range 1000-1500 MHz,can be found on the Hi FAST homepage:https://hifast.readthedocs.io/fluxgain.

Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.

Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

In this study,an optimized long short-term memory(LSTM)network is proposed to predict the reliability and remaining useful life(RUL)of rolling bearings based on an improved whale-optimized algorithm(IWOA).The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing.To provide covariates for reliability assessment,a kernel principal component analysis is used to reduce the dimensionality of the features.A Weibull distribution proportional hazard model(WPHM)is used for the reliability assessment of rolling bearing,and a beluga whale optimization(BWO)algorithm is combined with maximum likelihood estimation(MLE)to improve the estimation accuracy of the model parameters of the WPHM,which provides the data basis for predicting reliability.Considering the possible gradient explosion by training the rolling bearing lifetime data and the difficulties in selecting the key network parameters,an optimized LSTM network called the improved whale optimization algorithm-based long short-term memory(IWOA-LSTM)network is proposed.As IWOA better jumps out of the local optimization,the fitting and prediction accuracies of the network are correspondingly improved.The experimental results show that compared with the whale optimization algorithm-based long short-term memory(WOA-LSTM)network,the reliability prediction and RUL prediction accuracies of the rolling bearing are improved by the proposed IWOA-LSTM network.

Development of series SQUID array with on-chip filter for TES detector

A cold preamplifier based on superconducting quantum interference devices(SQUIDs)is currently the preferred readout technology for the low-noise transition edge sensor(TES).In this work,we have designed and fabricated a series SQUID array(SSA)amplifier for the TES detector readout circuit.In this SSA amplifier,each SQUID cell is composed of a first-order gradiometer formed using two equally large square washers,and an on-chip low pass filter(LPF)as a radiofrequency(RF)choke has been developed to reduce the Josephson oscillation interference between individual SQUID cells.In addition,a highly symmetric layout has been designed carefully to provide a fully consistent embedded electromagnetic environment and achieve coherent flux operation.The measured results show smooth V-Φcharacteristics and a swing voltage that increases linearly with increasing SQUID cell number N.A white flux noise level as low as 0.28μφ;/Hz;is achieved at 0.1 K,corresponding to a low current noise level of 7 pA/Hz;.We analyze the measured noise contribution at mK-scale temperatures and find that the dominant noise derives from a combination of the SSA intrinsic noise and the equivalent current noise of the room temperature electronics.

Denoising Method for Partial Discharge Signal of Switchgear Based on Continuous Adaptive Wavelet Threshold

Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection in power plant switchgear,a method based on continuous adaptive wavelet threshold switchgear PD signals denoising is proposed in this paper.By constructing a continuous adaptive threshold function and introducing adjustment parameters,the problems of over⁃processing of traditional hard threshold functions and incomplete denoising of soft threshold functions can be improved.The analysis results of simulated signals and measured signals show that the continuous adaptive wavelet threshold denoising method is significantly better than the traditional denoising method for the PD signal.The proposed method in this paper retains the characteristics of the original signal.Compared with the traditional denoising methods,after denoising the simulated signals,the signal⁃to⁃noise ratio(SNR)is increased by more than 30%,and the root⁃mean⁃square error(RMSE)is reduced by more than 30%.After denoising the real signal,the noise suppression ratio(NRR)is increased by more than 40%.The recognition accuracy rate of PD signal has also been improved to a certain extent,which proves that the method has a certain practicability.

State Estimation for Non-linear Sampled-Data Descriptor Systems:A Robust Extended Kalman Filtering Approach

This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.

Sensor Fault Diagnosis Observer Design for Linear Sampled-Data Descriptor System with Time-Vary Delay

In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model with time-vary delay is proposed and transformed into a discrete-time non-singular one. Then, a robust sensor fault diagnosis observer is proposed based on the state estimation error and the measurement residual, this observer can guarantee the robustness of the residual against the augmented disturbance and the sensor fault, which means the H∞ performance index is satisfied. As the confining matrix of the designed observer parameters does not meet the Linear Matrix Inequality (LMI), a cone complementary linearization (CCL) algorithm is proposed to solve this problem. The decision logic of the residual is obtained by the residual evaluation function. Simulation results show the effectiveness of the method.

HiFAST:An HIdata calibration and imaging pipeline for FAST

The Five-hundred-meter Aperture Spherical radio Telescope(FAST)has the largest aperture and a 19-beam L-band receiver,making it powerful for investigating the neutral hydrogen atomic gas(HI)in the universe.We present HiFAST(https://hifast.readthedocs.io),a dedicated,modular,and self-contained calibration and imaging pipeline for processing the H?data of FAST.The pipeline consists of frequency-dependent noise diode calibration,baseline fitting,standing wave removal using an FFT-based method,flux density calibration,stray radiation correction,and gridding to produce data cubes.These modules can be combined as needed to process the data from most FAST observation modes:tracking,drift scanning,On-The-Fly mapping,and most of their variants.With HiFAST,the root-mean-square(RMS)noises of the calibrated spectra from all 19 beams were only slightly(~5%)higher than the theoretical expectation.The results for the extended source M33 and the point sources are consistent with the results from Arecibo.The moment maps(0,1 and 2)of M33 agree well with the results from the Arecibo Galaxy Environment Survey(AGES)with a fractional difference of less than 10%.For a common sample of 221 sources with signal-to-noise ratio S/N>10 from the Arecibo Legacy Fast ALFA(ALFALFA)survey,the mean value of fractional difference in the integrated flux density,Sint,between the two datasets is approximately 0.005%,with a dispersion of 15.4%.Further checks on the integrated flux density of 23 sources with seven observations indicate that the variance in the flux density of the source with luminous objects(Sint>2.5 Jy km s-1)is less than 5%.Our tests suggest that the FAST telescope,with the efficient,precise,and user-friendly pipeline HiFAST,will yield numerous significant scientific findings in the investigation of the H?in the universe.

Highly Sensitive and Fast Hydrogen Detection Based on Light-Induced Thermoelastic Spectroscopy

As a new energy source,hydrogen(H_(2))detection is a hot topic in recent years.Because of the weak absorption characteristic,laser spectroscopy-based H_(2)detection is challenging.In this paper,a highly sensitive H_(2)sensor based on light-induced thermoelastic spectroscopy(LITES)technique is demonstrated for the first time.A continuous-wave,distributed feedback diode laser with emission in the 2.1μm region was adopted as the excitation source to target the strongest H_(2)absorption line of 4,712.90 cm^(−1).A Herriott multipass cell with an optical length of 10.1 m was chosen to further improve the H_(2)absorption.With the feature of processing the raw input data without data preprocessing and extracting the desired features automatically,the robust shallow neural network(SNN)fitting algorithm was brought in to denoise the sensor.For the LITES-based H_(2)sensor,the concentration response was tested,and an excellent linear response to H_(2)concentration levels was achieved.A minimum detection limit(MDL)of~80 ppm was obtained.On the basis of implementation of the H_(2)-LITES sensor,a heterodyne H_(2)-LITES sensor was further constructed to realize a fast measurement of resonance frequency of quartz tuning fork and H_(2)concentration simultaneously.The resonance frequency can be retrieved in several hundred milliseconds with the measurement accuracy of±0.2 Hz,and the result of 30,713.76 Hz is exactly same as the experimentally determined value of 30,713.69 Hz.After the SNN algorithm was applied,an MDL of~45 ppm was achieved for this heterodyne H_(2)-LITES sensor.