Electric Signatures Research Articles

Investigation on the induced electrical wakes generated by underwater vehicles

The induced electrical wake generated by underwater vehicles in the conductive seawater subject to the geomagnetic field can offer significant information for non-acoustic detection. However, the crucial demand in actual detection requires the sophisticated detection technology to capture the variations in the wake, which has not been experimentally explored. In this paper, an underwater measurement platform, which is capable of accurately capturing the weak electrical signature, is developed to verify the detectability of the induced electric field in the wake. Besides, a towing system is designed and established to generate the wake driving the motion of the conducting seawater in the current experiment. The results indicate that the induced electric field distributions in the wake demonstrate prominent peak characteristics. The detectable intensity of the measured electric signatures in the wake can range from 1 to 3 μV/m, primarily concentrated within the extremely low frequency band below 1 Hz. Additionally, the induced electric field intensity is positively correlated with the towing velocity and demonstrates a similar evolutionary trend. Subsequently, the numerical simulations are performed to not only cross-validate the measurement results but also further demonstrate the detailed distribution of the electrical wake evolution. These findings illustrate the feasibility of detection based on the induced electric field disturbance signal in the wake, which holds potential for non-acoustic detection and tracking of underwater targets.

Non‐Geophysical Interhemispheric Asymmetries in Large Magnetic Field Residuals Between Swarm Observations and Earth Magnetic Field Models During Moderate to Quiet Geomagnetic Conditions

AbstractWe present a statistical study of large magnetic field vector residuals between Swarm observations and the 13th generation International Geomagnetic Reference Field (IGRF‐13) model under quiet to moderate geomagnetic conditions. Since the vast majority of data are taken during these conditions, statistics of these large residuals are important for satellite operation when using IGRF‐13 as reference, as well as for magnetosphere‐ionosphere‐thermosphere studies. Residuals with magnitude of vector differences between observations and model estimations larger than 300 nT under Dst > −50 nT are studied from 2014 to 2020. All large residuals appear in the high‐latitude auroral zone region peaking around 70° (−70°) magnetic latitude (MLAT) in the northern hemisphere (southern hemisphere) with also a secondary occurrence peak just below 80° (−80°) MLAT. However, the two hemispheres show clear asymmetries in the magnetic longitude and magnetic local time distribution where both hemispheres show high concentration of large residuals around the geographic poles. Since polar satellite's orbits give rise to highly biased number of observations around the geographic poles, it results in a decrease in occurrence rate with respect to the total number of measurements. We suggest that the asymmetries of large residuals in the two hemispheres around the geographic pole are not geophysical, but due to satellite orbit bias around the geographic poles and difference between IGRF and observations due to model error and geophysical processes not included in the model. Identifying these biases is helpful to better separate out geophysical electric current signatures from non‐geophysical ones.

Identification of cloud-to-ground lightning and intra-cloud lightning based on their radiated electric field signatures using different types of neural networks and machine learning classifiers

The scope of this paper is to test the efficiency of three different neural networks in the area of cloud-to-ground/intra-cloud lightning classification using a not very large data set of electric field data. Effective neural network learning verified for a small lightning event dataset but with whole 2-seconds particular record length is an important feature of fast and efficient procedures implemented into lightning location systems among which discrimination between cloud-to-ground and intra-cloud lightning is one of the most important task. Measurement data obtained at the Lightning Observatory in Rzeszow were used for the analysis. About 100 extremely low/medium frequency bandwidth electric field of cloud-to ground lightning registrations and the same number of intra-cloud lightning events were taken into account. During the study 81% of those registrations were dedicated for learning of neural networks while the remaining 19% were put into the testing set. All data were preselected manually with consideration of the electric field variation and information from the lightning location system database. Lightning events were selected uniformly with the well-pronounced presence of their basic components and reported distance to the registration station which ensured a wide variation of expected lightning electric field signatures. The multilayer perceptron neural network, the radial basis function neural network and the convolutional neural network were tested and compared in different configurations while distinguishing lightning. The results were compared with other conventional classification approaches, such as traditional machine learning methods as well as one a little more contemporary architecture, the long short-term memory neural network. The multilayer perceptron neural network achieved the best detection accuracy overall. Presented research is an extension of lightning identification studies available so far mainly based on the convolutional neural network by the results achieved using the multilayer perceptron neural network, the radial basis function neural network, the long short-term memory neural network and several machine learning-based classification methods.

Propagation Mechanism of Branched Downward Positive Leader Resulting in a Negative Cloud‐To‐Ground Flash

AbstractOur basic knowledge of downward positive lightning leaders is incomplete due to their rarity and limited ability of VHF mapping systems to image positive streamers. Here, using high‐speed optical records and wideband electric field and magnetic field derivative signatures, we examine in detail the development of a descending positive leader, which extended intermittently via alternating branching at altitudes of 4.2 to 1.9 km and involved luminosity transients separated by millisecond‐scale quiet intervals. We show that the transients (a) are mostly initiated in previously created but already decayed branches, at a distance of the order of 100 m above the branch lower extremity, (b) extend bidirectionally with negative charge moving up, (c) establish a temporary (1 ms or so) steady‐current connection to the negative part of the overall bidirectional leader tree, and (d) exhibit brightening accompanied by new breakdowns at the positive leader end. One of the transients unexpectedly resulted in a negative cloud‐to‐ground discharge. Both positive and negative ends of the transients extended at speeds of 106–107 m/s, while the overall positive leader extension speed was as low as 103–104 m/s. Wideband electric field signatures of the transients were similar to K‐changes, with their millisecond‐ and microsecond‐scale features being associated with the steady current and new breakdowns, respectively. For transients with both ends visible in our optical records, charge transfers and average currents were estimated to be typically a few hundreds of millicoulombs and some hundreds of amperes, respectively.

Coating Damage Detection of Vessels Using Machine Learning-Based Underwater Electric Field

There has been extensive research on corrosion electric fields because of their special characteristics. There are apparent distribution characteristics corresponding to different coating damage regions of a vessel. To accurately detect where the coating damage region is located on vessels using underwater electric field signature, in this study, a method called “refined composite multivariate multiscale fluctuation reverse dispersion entropy (RCMMFRDE)” was proposed. This method effectively extracted the multichannel signal in a complex environment of poor stability and high noise. By combining RCMMFRDE and hierarchical prototype (HP), a novel coating damage detection method was developed. First, the accumulated differential value was calculated to determine the side on which the vessel had the damaged coating. Second, RCMMFRDE was used to extract the features of electric field signals. Then, feature vector dimensionality reduction was carried out using pair-wise feature proximity (PWFP). The low-dimensional features were given as input to the HP classifier to detect the damaged region. Finally, a series of numerical and physical scale experiments were conducted to validate the feasibility of the proposed method. The damaged region was efficiently predicted, achieving satisfactory accuracy of 96.67% and 92.00% in numerical and measurement data respectively; thus, it is a suitable alternative to conventional methods, especially when there is a lack of previous environmental information.

Spectral domain analysis of preliminary breakdown pulse train activity during leader electric field signatures of positive cloud-to-ground flash incidents recorded during 2019 thunderstorm season in central part of Poland

Spectral domain analysis of preliminary breakdown pulse train activity during leader electric field signatures of positive cloud-to-ground flash incidents recorded during 2019 thunderstorm season in central part of Poland

Electrostatic Plasma Wave Excitations at the Interplanetary Shocks

Over the last few decades, different types of plasma waves (e.g., the ion acoustic waves (IAWs), electrostatic solitary waves, upper/lower hybrid waves, and Langmuir waves) have been observed in the upstream, downstream, and ramp regions of the collisionless interplanetary (IP) shocks. These waves may appear as short-duration (only a few milliseconds at 1 au) electric field signatures in the in-situ measurements, with typical frequencies of ∼1–10 kHz. A number of IAW features at the IP shocks seem to be unexplained by kinetic models and require a new modeling effort. Thus, this paper is dedicated to bridging this gap in understanding. In this paper, we model the linear IAWs inside the shock ramp by devising a novel linearization method for the two-fluid magnetohydrodynamic equations with spatially dependent shock parameters. It is found that, for parallel propagating waves, the linear dispersion relation leads to a finite growth rate, which is dependent on the shock density compression ratio, as Wind data suggest. Further analysis reveals that the wave frequency grows towards the downstream region within the shock ramp, and the wave growth rate is independent of the electron-to-ion temperature ratio, as Magnetospheric Multiscale (MMS) in-situ measurements suggest, and is uniform within the shock ramp. Thus, this study helps in understanding the characteristics of the IAWs at the collisionless IP shocks.

Characterization of the initial stage in upward lightning at the Gaisberg Tower: 2. Electric field signatures

• Near and far electric field signatures of IS current pulses were analyzed. • Electric field signatures of early-stage-IS bipolar current pulses were characterized. • Such bipolar pulses could be associated with upward leader inception. We examined the characteristics of electric field signatures occurring during the initial stage of 58 flashes measured simultaneously at near (170 m) and far (79 or 109 km) distances from the Gaisberg Tower located near Salzburg, Austria. Of the 340 field signatures measured at the near station, 68 (20%) were associated with current pulses occurring during the initiation and propagation phase (IPP) of the upward leader, and 272 (80%) were associated with pulses that occurred during the mature phase (MP) of the upward leader. Of the 68 field signatures of IPP pulses, 40 were associated with bipolar (IPP-B type) current pulses and 28 were associated with unipolar (IPP-U type) current pulses. Field signatures of IPP-B pulses were only detected at the near measurement station and appear to be associated with currents in relatively short (meter-scale) channel segments formed during the upward leader inception. At the far stations, field signatures of 84 IS pulses were recorded and analyzed. There was modest correlation between the background-to-peak current of IS pulses and near and far electric radiation field changes as well as between radiation field changes recorded at near and far distances.

A New Method for Modeling and Parameter Identification of Positively Charged Downward Lightning Leader Based on Remote Lightning Electric Field Signatures Recorded in the ELF/MF Range and 3D Doppler Radar Scanning Data

The aim of this paper is modeling and parameter identification of the leader stage preceding the positive stroke of natural cloud-to-ground lightning. This research is based on electric field and 3D Doppler radar data recorded during thunderstorms in Poland, as supplied by database information from long range lightning location systems. The numerical simulation performed in the MATLAB platform showed that a high number of assumed model parameters, such as non-uniform leader tip speed and lightning channel charge density, allowed us to obtain greater compliance between simulated and measured electric field signatures than in the classical approach. The proposed model can be implemented in lightning location systems and forest fire warning systems, operating globally to reduce the risk of damages caused by positive cloud-to-ground flashes being one of the most dangerous type of lightning events. An alternative application of the model can be for research, including ground-based lightning data supplementation for the corresponding satellite and airborne registrations.

Learning from Non-experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes

With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm, smart residential environments , also known as smart homes , are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.

ULF Fluctuation of Low‐Latitude Ionospheric Electric Fields During Sudden Commencements

AbstractWe use the C/NOFS satellite observations to provide the direct evidence for ultra‐low‐frequency (ULF) fluctuations in the low‐latitude ionospheric electric field during the sudden commencement (SC) on 8 March 2012. The meridional plasma drift shows a sudden downward motion with a large amplitude after SC onset. This initial westward electric field signature is followed by ULF fluctuations with periods of 28–100 s. Further, C/NOFS observations show that the ionospheric zonal electric field and compressional component of geomagnetic field present the similar ULF fluctuations with a ∼90° phase difference, indicating the existence of a standing compressional mode wave. The fluctuations in the ionospheric electric field and horizontal component of the geomagnetic field on the ground also exhibit a high correlation and the frequency of the ground‐based geomagnetic field shows no dependence with latitudes. These results suggest that the ULF waves in the ionospheric electric field may be associated with a cavity mode. We also show the ULF waves in ionospheric electric fields in other cases. The ULF wave electric fields provide important information on the ionospheric studies and ionosphere‐magnetosphere coupling in low latitudes.

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Quality control is a critical aspect of the modern electronic circuit industry. In addition to being a pre-requisite to proper functioning, circuit quality is closely related to safety, security, and economic issues. Quality control has been reached through system testing. Meanwhile, device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably. Hence, traditional test processes based on manual inspections have become outdated and inefficient. More recently, the concept of Advanced Manufacturing or Industry 4.0 has enabled the manufacturing of customized products, tailored to the changing customers’ demands. This scenario points out additional requirements for electronic system testing: it demands a high degree of flexibility in production processes, short design and manufacturing cycles, and cost control. Thus, there is a demand for circuit testing systems that present effectiveness and accessibility without placing numerous test points. This work is focused on automated test solutions based on machine learning, which are becoming popular with advances in computational tools. We present a new testing approach that uses autoencoders to detect firmware or hardware anomalies based on the electric current signature. We built a test set-up using an embedded system development board to evaluate the proposed approach. We implemented six firmware versions that can run independently on the test board – one of them is considered anomaly-free. In order to obtain a reference frame to our results, two other classification techniques (a computer vision algorithm and a random forest classification model) were employed to detect anomalies on the same development board. The outcomes of the experiments demonstrated that the proposed test method is highly effective. For several test scenarios, the correct detection rate was above 99%. Test results showed that autoencoder and random forest approaches are effective. However, random forests require all data classes to be trained. Training an autoencoder, on the other hand, only requires the reference (anomaly-free) class.

Millisecond observations of nonlinear wave–electron interaction in electron phase space holes

Electron phase space holes (EHs) associated with electron trapping are commonly observed as bipolar electric field signatures in both space and laboratory plasma. Until recently, it has not been possible to resolve EHs in electron measurements. We report observations of EHs in the plasma sheet boundary layer, here identified as the separatrix region of magnetic reconnection in the magnetotail. The intense EHs are observed together with an electron beam moving toward the X line, showing signs of thermalization. Using the electron drift instrument onboard the satellites of the Magnetospheric Multiscale mission, we make direct millisecond measurements of the electron particle flux associated with individual electron phase space holes. The electron flux is measured at a millisecond cadence in a narrow parallel speed range within that of the trapped electrons. The flux modulations are of order unity and are direct evidence of the strong nonlinear wave–electron interaction that may effectively thermalize beams and contribute to transforming directed drift energy to thermal energy.

Probing Molecular Frame Wigner Time Delay and Electron Wavepacket Phase Structure of CO Molecule

The time delay of photoelectron emission serves as a fundamental building block to understand the ultrafast electron emission dynamics in strong-field physics. Here, we study the photoelectron angular streaking of CO molecules by using two-color ( 400 + 800 nm) corotating circularly polarized fields. By coincidently measuring photoelectrons with the dissociative ions, we present molecular frame photoelectron angular distributions with respect to the instantaneous driving electric field signatures. We develop a semiclassical nonadiabatic molecular quantum-trajectory Monte Carlo (MO-QTMC) model that fully captures the experimental observations and further ab initio simulations. We disentangle the orientation-resolved contribution of the anisotropic ionic potential and the molecular orbital structure on the measured photoelectron angular distributions. Furthermore, by analyzing the photoelectron interference patterns, we extract the sub-Coulomb-barrier phase distribution of the photoelectron wavepacket and reconstruct the orientation- and energy-resolved Wigner time delay in the molecular frame. Holographic angular streaking with bicircular fields can be used for probing polyatomic molecules in the future.

The overtaking interaction of electron acoustic solitary waves in a plasma with hot non-extensive electrons

In the present work, we investigate the propagation and overtaking interaction of electron-acoustic solitary waves (EASWs) in a plasma comprising cold electron fluid, hot nonextensive electrons and immobile ions. For this purpose, the Korteweg–de Vries (KdV) equation is derived for this model. Then Hirota’s direct method is used to the KdV equation to obtain multisoliton solution for studying the interaction process. Our one-dimensional plasma model supports rarefactive EASWs. For both single soliton and multisoliton solutions, the amplitude and velocity of the solitons increase with the increase of nonextensivity or superthermality of hot electrons. It is found that the temporal scale of collision between two solitons decreases with the decrease of nonextensive parameter . The choice of propagation vectors on the interaction of the solitons is also significant. The present analysis may be helpful for further study of the modified bipolar or tripolar electric field signatures observed in the auroral zone.

Practical Method to Evaluate the Effects of the Sensor and the Environment on the Measurement of Lightning-Generated Electric Field Signatures

Practical Method to Evaluate the Effects of the Sensor and the Environment on the Measurement of Lightning-Generated Electric Field Signatures

Computation of Lightning Current from Electric Field Based on Laplace Transform and Deconvolution Method

A method for computation of the lightning channel base current from the corresponding vertical component of lightning electric field was presented. The algorithm was developed by applying Laplace transform. The lightning current was estimated from its deconvolution with a special transfer function. The transfer function includes information about geometry and physical properties of entire lightning impulse generation system. The method was verified for a Heidler-type base current and a MTLL model of its propagation within the lightning channel. Research was done for close, middle, and far distance to the lightning strike point. Optimum performance was obtained for the middle distance of several kilometers where the electrostatic, induction, and radiation components of the transfer function were of the same range. An analysis was done for input electric field with and without noise superimposed on its time domain waveform. Relative uncertainties for the electric field and calculated lightning channel base current were similar each other. The presented approach can substantially increase a number of lightning current parameters which can be identified on the basis of its electric field signature. This method can be applied by the lightning location systems using preprocessing which increases the timing efficiency of the transfer function estimation.

Frequency spectra of various events pertinent to lightning cloud flashes obtained from wavelet transform technique and ratified by narrow band measurement technique

In the present study, frequency spectra of the electric field corresponding to the various cloud events, such as, initial breakdown process, regular pulse bursts, chaotic pulse trains and recoil streamers have been analysed. For the purpose, electric field radiated by cloud flashes were obtained simultaneously by a wide bandwidth antenna system and two narrow bandwidth antenna systems tuned at 3 MHz and 30 MHz. The frequency spectra of the broad band electric field signatures were obtained by using the wavelet transform technique and were compared with the magnitudes of the narrow band signals at the given central frequencies. To the best of our knowledge, it is the first study in which frequency spectra is obtained by transforming the time domain signal using wavelet transform technique and ratified by narrow bandwidth system for the cloud flashes. Fifteen cloud flashes pertinent to the Swedish thunderstorms were selected for the purpose. It is found that, the cloud flashes radiate at frequencies as low as 3 kHz to as high as a few tens of Mega Hertz (MHz). Electric field radiation corresponding to the initial breakdown process were found to radiate in the frequency range of 50 kHz to 5 MHz on the average, maximum energy is being radiated in the frequency range of 500 kHz to 5 MHz. Similarly, the final stage corresponding to the regular pulse bursts was found to radiate in the frequency range of 50 kHz to 5 MHz and that corresponding to the chaotic pulse trains was found to be in the range of 100 kHz to 5 MHz. Whereas, the very narrow pulses at the final stage, that can be termed as pulses corresponding recoil streamers (or Q-streamers) were found to radiate in the frequency range of 50 kHz to well above 10 MHz. Q-streamers can be considered as the strongest source of very high frequency and is justified by the simultaneous measurement of the electric fields at very high frequency (30 MHz) narrow bandwidth system. Rectification of the biasness of the conventional wavelet power spectrum has also been performed, however, no significant change in the spectrum was observed. Therefore, this study provides a strong basis for applying the wavelet transform technique without employing large number of narrowband system to acquire frequency domain information of lightning phenomena.

Non-invasive electric arc fault detection based on sliding approximate entropy

In recent years, electrical fires have occurred frequently and have become the first cause of various fires. Among them, the electric arc is one of the important causes of electrical fires. In this paper, the research on non-invasive electric arc fault detection is carried out for low-voltage users, which can discover and detect the electric arc only by analysing the aggregated current measurements from outdoors. In order to achieve this goal, firstly, the abrupt change of the current mode is discovered and detected with sliding window from the total load current signal based on the approximate entropy. Then the current signature samples are extracted around the abrupt change. Finally, according to the pre-set electric arc signature classifier, it is judged whether the abrupt change is caused by the electric arc fault. In addition, under laboratory conditions, an electric arc simulation experiment is carried out for common appliances of actual low-voltage users. The results show the effectiveness of the proposed non-invasive electric arc fault detection method based on approximate entropy.

Analysis and Research on the Influence of Sonar Dome on Ship’s Underwater Electrostatic Field

This paper firstly analyzes the influence of the sonar dome on the ship’s underwater electrostatic field in theory, and then uses boundary element software to simulate and analyze this kind of influence. It is found that the sonar dome can be used as a cathode to participate in the electrochemical corrosion of ships, forming a “propeller-hull-sonar dome” corrosion system. In this case, the “dubble compensation anode” method is proposed in order to control the electric field signature, and the effectiveness of this method is verified by the boundary element simulation software.