2829results about "Electromagentic field characteristics" patented technology

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention relates to the technical field of electromagnetic environment detection, and discloses a multi-mode electromagnetic environment detection system and method. The system acquires multi-modal data such as electromagnetic field intensity and spectrum distribution through a multi-modal sensor array to generate a data set, and realizes interference source identification and dynamic adjustment of detection parameters through nonlinear signal processing, depth feature extraction based on a convolutional neural network, topology analysis of a graph neural network and adaptive optimization of reinforcement learning. And an electromagnetic field simulation module is also arranged to simulate electromagnetic wave propagation and early warn abnormal radiation, and an abnormal mode library is constructed to identify potential interference types. The system can comprehensively and accurately detect an electromagnetic environment, efficiently position an interference source, adaptively adjust detection parameters, improve detection accuracy, real-time performance and system stability, and effectively meet complex electromagnetic environment monitoring requirements.

The invention discloses a booster station monitoring operation and maintenance system, and relates to the technical field of data management. A plurality of heterogeneous intelligent sensors are deployed in a booster station to monitor the operation state of equipment in real time, an electromagnetic field sensor is utilized to sense the electromagnetic interference intensity, and when abnormal high-frequency pulses are detected, a data anomaly detection mode is triggered to detect the abnormal high-frequency pulses; calculating an error compensation value according to the abnormal degree, correcting abnormal data, ensuring the accuracy of the data, performing equipment state evaluation according to the recovered data, and if potential safety hazards are found, sending intelligent early warning to operation and maintenance personnel and providing suggestions for adjusting monitoring strategies or optimizing shielding measures; according to the invention, the anti-interference capability and accuracy of booster station monitoring data are effectively improved, the risk of operation and maintenance misjudgment caused by electromagnetic interference is reduced, the reliability of intelligent monitoring in an ultrahigh-voltage power transmission scene is enhanced, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The invention provides a transformer substation monitoring method and system based on the Internet of Things, and the method comprises the steps: obtaining an original electromagnetic disturbance signal of a transformer substation, and constructing an electromagnetic disturbance feature matrix; carrying out the anomaly detection of the electromagnetic disturbance signal through employing a self-adaptive attention mechanism in combination with asymmetric anomaly detection loss; inputting the time sequence input vector into a fault classification model to perform fault type identification, outputting a fault type label and severity, and performing fault propagation path calculation on the fault type label and severity to obtain a fault propagation path; and simulating fault evolution through a digital twin model, predicting a fault development trend, a secondary fault influence range and a future state vector, and finally generating a maintenance priority, a time window and a resource scheduling scheme. The system breaks through the limitation of a traditional monitoring technology, improves the intelligent level of a transformer substation, reduces the maintenance cost, and improves the operation safety and reliability of a power grid.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The invention relates to the technical field of unmanned aerial vehicle application, and discloses a multi-mode electromagnetic field distribution surveying and mapping unmanned aerial vehicle system and an application method thereof. The system comprises a dynamic adjustment module, a multi-band acquisition module, a hybrid positioning module, a three-dimensional modeling module and a multi-modal fusion module. The dynamic adjusting module adjusts the distance between the electromagnetic sensor and the rotor wing according to the rotor wing electromagnetic interference suppression rule; the multi-band acquisition module acquires and processes electromagnetic signals; the hybrid positioning module integrates TDOA and AOA algorithms to position the radiation source; the three-dimensional modeling module generates a three-dimensional electromagnetic field intensity distribution diagram; the multi-modal fusion module fuses the visible light image and the electromagnetic data. The system can also evaluate the risk of the radiation source and plan a flight path. The application method comprises the steps of distance adjustment, data acquisition and processing, radiation source positioning, model construction and the like. According to the method, rotor interference is effectively suppressed, the positioning precision is improved, multi-modal data fusion is realized, and the method has a wide application prospect in the fields of electric power inspection, urban electromagnetic environment evaluation and the like.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

An apparatus (100) for detecting an Electro-Magnetic, EM, field, said apparatus comprising a: Rydberg-Atom EM Field Detector (107), RAEM-FD, configured to detect an EM field (170) of a given frequency; and Multi-Plate WaveGuide (105), MPWG, comprising: at least three overlapping and non-intersecting plates (180); and at least two waveguides (190), each formed by an adjacent pair of the at least three plates, and wherein each waveguide has a separation distance suitable (h) for supporting propagation of the EM field; and wherein the MPWG is arranged so as to direct the EM field towards the RAEM-FD.

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention discloses an efficient information extraction method based on Rydberg atom low-frequency detection and a measurement system thereof, and the extraction method is based on the avoidance of background noise by a coherent detection technology, converts the modulation of a low-frequency electromagnetic field into quantum coherent phase change by using Rydberg atoms, and adopts the means of homodyne detection and heterodyne detection. The amplitude information and the phase information of the low-frequency electromagnetic field are accurately extracted, and the measurement sensitivity of the weak low-frequency electromagnetic field is remarkably improved.

The invention discloses a magnetotelluric signal noise suppression method and system based on complexity driving, and the method comprises the steps: firstly calculating the complexity feature CS of a signal segment, and then constructing and training a noise reduction model based on a multi-path attention residual denoising network, the network is formed by connecting a plurality of feature recovery modules with heterogeneous structures in parallel to form different main branches, different main branches are respectively provided with a shallow convolution module CBL, a channel structure modeling module MACNet and a global attention modeling module DHVT, each main branch is provided with a complexity sensing gating unit taking a complexity feature CS as input, and an output signal of the complexity sensing gating unit is input into a feature recovery module of the main branch in cooperation with a noisy signal segment; the path scheduling mechanism guides whether the feature recovery module of the main branch participates in data processing or not for an output result of the complexity sensing gating unit on each main branch; and finally, performing noise reduction on the actually measured magnetotelluric signal by using the trained multi-path attention residual denoising network.

The invention belongs to the technical field of geophysical monitoring, and relates to an electromagnetic interference detection method for earthquake monitoring, which comprises the following steps: deploying a multi-dimensional electromagnetic field sensing unit and an earthquake signal acquisition unit and realizing high-precision synchronization; synchronously acquiring multi-dimensional electromagnetic field and seismic physical signal data; performing multi-feature and noise feature extraction on the two; inputting the features into a pre-trained machine learning / deep learning model for identification, and outputting information such as interference type, intensity and azimuth angle; and carrying out influence evaluation and quality marking on the seismic data according to the output, and selectively inhibiting or compensating. Through the above scheme, multi-dimensional perception and characterization, high-precision synchronization and correlation analysis, intelligent identification and spatial positioning are realized, and the reliability and accuracy of earthquake monitoring data are significantly improved.

This invention provides an electromagnetic field detector, a system comprising the electromagnetic field detector, and a method of operating an electromagnetic field detector, the electromagnetic field detector comprising a first optical signal interface configured to receive, from one or more remote optical sources, a first optical signal; an electromagnetic field transmitter configured to transmit a local oscillator electromagnetic field generated from the first optical signal, the electromagnetic field transmitter comprising: a photocurrent generator configured to generate a photocurrent from the first optical signal; and an antenna interface configured to supply the photocurrent to an antenna to transmit the local oscillator electromagnetic field; a second optical signal interface configured to receive, from the one or more remote optical sources, a probe optical signal and a coupling optical signal; and a transmission medium configured to be excited by the probe optical signal and further excited by the coupling optical signal, wherein the probe optical

The invention relates to the technical field of meteorological monitoring, in particular to a ring main unit lightning micrometeorological online monitoring system and method, and the system comprises a feature extraction module, a frequency spectrum detection module, a frequency band pool screening module, a path adjustment module and an interference avoidance module. The method comprises the following steps: collecting an electric field intensity extreme value sequence, calculating a change rate difference ratio, identifying thunder and lightning precursor characteristics, combining a frequency spectrum energy density map, analyzing an energy peak frequency band and a drop point of a frequency hopping signal, judging a thunder and lightning frequency signal concentration area, performing frequency spectrum interference filtering, and combining a dynamic time warping algorithm. The time deviation between a lightning stroke time point and frequency hopping scheduling is analyzed; a frequency hopping path is adjusted in real time; the response timeliness is improved; the difference value before and after electric field disturbance is monitored; and support is provided for monitoring stability and response adaptability of the thunder and lightning risk environment.

The invention provides a lightning proximity prediction method based on a space-time separation convolutional neural network. The method comprises the following steps: S1, acquiring satellite infrared brightness temperature observation data and lightning positioning data; s2, performing spatial resolution adjustment and standardization processing on the satellite infrared brightness temperature observation data; gridding and de-noising processing is carried out on the lightning positioning data; s3, defining a prediction task; s4, establishing a space-time sequence prediction model; based on the processed data, storing the processed data in a numpy file format according to a time point sequence, dividing the processed data into a training set, a verification set and a test set, and loading the training set, the verification set and the test set to a space-time sequence prediction model for model training to obtain an optimization model; and based on the prediction task, using the optimization model to carry out lightning approaching prediction to obtain lightning prediction data. According to the method, the development of cumulus cloud is captured by using satellite infrared brightness temperature observation data and brightness temperature difference data, so that the problem that thunder and lightning inception cannot be effectively predicted is solved.

The invention discloses a response time conduction type testing device and method for a radio frequency channel protection device. The response time conduction type testing device comprises a transient field intensity generating device, an isolator, a directional coupler, a first attenuator, a second attenuator, a third attenuator, a first power meter, a second power meter, a high-speed oscilloscope, a vector network analyzer and a radio frequency channel protection device to be tested. According to the method, the response performance of the protection device under the influence of the transient strong field can be comprehensively, safely and accurately evaluated, the amplitude limiting function of the protection device can still be effectively exerted under the action of the transient strong field with an extremely narrow rising edge, and then subsequent devices, circuits and equipment are prevented from being damaged.

The invention relates to a lightning nowcasting method based on multi-source satellite data, and belongs to the technical field of lightning nowcasting. The method comprises the steps of obtaining and preprocessing satellite data of a multi-source channel and corresponding lightning data, constructing a U-Net lightning prediction model, collecting a historical multi-source satellite image sequence, constructing a PredRNN satellite image prediction model and predicting the lightning occurrence probability. According to the method, the PredRNN and the U-Net model are combined, the lightning distribution can be predicted at the future moment only by using the satellite image, the dependence on historical lightning data is broken through, and the method has a wide application prospect in the field of lightning nowcasting.

The invention relates to the technical field of intelligent inspection, and discloses an outdoor substation unmanned aerial vehicle safety inspection distance calculation method, which comprises the following steps: scanning an outdoor substation by adopting three-dimensional laser scanning equipment, and constructing a three-dimensional model of the outdoor substation after processing; analyzing an electromagnetic field generated by the operation of the high-voltage live equipment in the outdoor substation, obtaining the distribution condition of the electromagnetic field of the outdoor substation, and determining the safety limit values of the electric field and the magnetic field; calculating an alarm distance of the unmanned aerial vehicle based on the safety limit values of the electric field and the magnetic field and the comprehensive influence factors; and based on the positioning precision of the unmanned aerial vehicle and the influence of environmental factors, calculating the safety inspection early warning distance of the unmanned aerial vehicle. According to the invention, the safety of unmanned aerial vehicle inspection can be improved, and a scientific basis is provided for the large-scale application of the unmanned aerial vehicle in outdoor transformer substation inspection.

The invention provides a photovoltaic power station communication equipment interference assessment method, medium and system, and belongs to the technical field of communication equipment interference, and the method comprises the following steps: establishing a communication equipment interference basic database by collecting communication equipment operation data and environmental electromagnetic data in a photovoltaic power station; performing time domain analysis on the data, converting the data into frequency domain characteristic parameters, establishing an interference evaluation index system, and constructing a neural network model; and collecting operation data of to-be-evaluated communication equipment, extracting characteristic parameters, inputting the characteristic parameters into the model, obtaining an interference evaluation result, and determining an anti-interference level. Decomposing a stable component and a variable component of the interference signal time domain characteristic parameter, and establishing an equation set through an interference source influence coefficient matrix to calculate an interference source influence coefficient matrix and a communication equipment anti-interference coefficient matrix. And finally, correcting the evaluation result according to the anti-interference coefficient matrix to obtain a corrected interference evaluation value, and establishing an early warning threshold value for real-time monitoring and early warning.

The application discloses a thunderstorm monitoring device and path planning method of unmanned aerial vehicle based on electric field and magnetic field detection. The device comprises a quadrature magnetic antenna array, a vertical differential electric field sensor, a signal processing module, a positioning algorithm module, a distance estimation module, an autonomous decision module, a GNSS module and an inertial measurement module. The quadrature magnetic antenna array is composed of two orthogonally arranged magnetic antennas, which are at a 45° angle with the flight direction of the unmanned aerial vehicle, and is used for receiving the horizontal magnetic field signal of lightning radiation. The vertical differential electric field sensor comprises two groups of upper and lower electrode plates and measures the differential value of the vertical electric field intensity. Through the cooperative measurement of the magnetic antenna array and the electric field sensor, the lightning azimuth (θ) is calculated by combining the magnetic field intensity ratio, and the quadrant is corrected by using the electric field polarity. Based on the magnetic field amplitude attenuation model and the waveform rise time correction factor, a distance estimation model is constructed. The autonomous decision module generates an approach path or an obstacle avoidance path according to the lightning azimuth and distance, and drives the flight control system to adjust the heading.

The invention, which belongs to the technical field of electromagnetic signal monitoring, relates to a rapid judgment method for a spatial spill electromagnetic signal, and the method specifically comprises the following steps: S1, receiving a spill electromagnetic signal in a space; s2, dividing the monitoring total frequency band into a plurality of sub-frequency bands with the same width, rapidly analyzing the obtained electromagnetic signal frequency spectrum in real time based on multi-scale spectrum entropy analysis, and positioning the frequency band where the characteristic signal is located; and S3, converting the frequency spectrum of the frequency band where the characteristic signal is located into a time domain signal, and simultaneously displaying a spectrogram and a time domain waveform. Through spectrum segmentation, multi-scale spectrum entropy analysis, adaptive threshold judgment and frequency domain-time domain combined display, the problems of poor real-time performance, weak anti-noise capability and single dimension in the prior art are solved.

The invention provides a lightning early warning system for a port dangerous cargo major dangerous source tank field, and relates to the field of lightning early warning. The lightning early warning system uses a data processing technology based on artificial intelligence to carry out data set splitting on a time sequence data set of lightning characteristic parameters; performing relative position prediction based on time sequence characteristics on the time sequence data set of the split lightning position, and performing message constraint transfer based on the time sequence characteristics on the time sequence data set of the atmospheric electric field intensity value at the same time; therefore, the lightning early warning level is automatically determined according to the predicted value of the lightning relative position data and the atmospheric electric field intensity time sequence fluctuation coding characteristics after time sequence transmission. Therefore, by monitoring the change of the intensity of the atmospheric electric field in real time and accurately capturing the position and the moving path of the lightning, the occurrence time and the intensity of the lightning can be predicted more accurately, and then a reliable safety guarantee can be provided for a dangerous cargo tank field of a port.

The invention provides a lightning frequency nowcasting method and system, and relates to the technical field of lightning nowcasting, and the method comprises the steps: obtaining the actual measurement data of severe convective weather of a target region in a current time period, the severe convective weather actual measurement data at least comprise weather radar echo actual measurement data, lightning frequency actual measurement data and stationary satellite multichannel actual measurement infrared brightness temperature; and based on a preset time forecasting interval, inputting the severe convective weather measured data into a multi-branch coding lightning frequency forecasting model to obtain weather radar echo forecasting data and lightning frequency forecasting data of the target area in the next time period output by the multi-branch coding lightning frequency forecasting model, the multi-branch coding lightning frequency forecasting model is obtained through training based on severe convection weather sample data marked with severe convection weather labels. According to the invention, lightning frequency forecasting is realized, and the accuracy of lightning forecasting is improved.

The invention discloses a thunder and lightning positioning method and system based on multi-source data fusion, and belongs to the technical field of thunder and lightning monitoring and positioning. The method comprises the following steps: in a preset time window, collecting first and second arrival time of a lightning event received by each ground lightning detection device, and calculating the arrival time difference; and extracting a lightning detection distance, a capture time difference and an observation included angle in combination with the lightning area image, obtaining an electric field change rate, a radar reflectivity and an equipment distance, substituting into a combined lightning positioning formula, and calculating a space distance between a lightning striking point and the first receiving equipment. Through the position information of the equipment, the geographical coordinate of the lightning striking point is calculated. According to the scheme, the spatial resolution and accuracy of lightning positioning can be effectively improved, positioning errors caused by a traditional radar or single-point monitoring method are reduced, simultaneous positioning of a plurality of lightning striking points is supported, and accurate data support and spatial decision basis are provided for subsequent lightning risk early warning, power equipment protection and disaster traceability.

The invention discloses a sea water area karst cave group transient electromagnetic collaborative investigation data processing method and system, and the method comprises the steps: transmitting a wide frequency spectrum excitation signal to the seabed through a shipborne transient electromagnetic transmitting system, and synchronously collecting a secondary field attenuation signal responded by a seabed medium through a pull-type receiving array, meanwhile, a distributed sensor network is arranged at a preset seabed node to collect background noise base data; performing motion attitude compensation on the secondary field attenuation signal to generate transient electromagnetic response data; constructing an adaptive filter based on the background noise base data, performing noise suppression processing on the transient electromagnetic response data, and outputting noise suppression data; and performing space-time alignment on the noise suppression data of the towed array and the seabed node data to form a three-dimensional space sampling data set. According to the method, high-precision fusion of deep and shallow detection data and quantitative evaluation of the stability of the karst cave are realized, and the detection reliability and the engineering risk early warning capability of the complex ocean karst area are remarkably improved.

The invention discloses a railway LEU-transponder transmission system lightning stroke fault risk assessment method, and the method comprises the steps: determining a lightning current invasion path through building a lightning stroke transient electromagnetic disturbance calculation model of an electromagnetic sensitive equipment port, and calculating the voltage and current at the port when incoming waves invade. And building an electromagnetic sensitivity test experiment platform of the railway signal system based on the lightning current invasion path and the voltage and current at the port. And performing label category prediction on the to-be-predicted sample point through each support vector machine model in the target model set, and voting each prediction result. According to the invention, a complex transmission relation between lightning transient electromagnetic disturbance and equipment failure risk is established through deep learning. The fault risk of the signal system under the lightning transient electromagnetic disturbance action is predicted through transient electromagnetic effect experimental data of real equipment of the railway signal system, and powerful support is provided for improving the electromagnetic sensitivity of the signal system and guaranteeing stable operation of a railway.

A Rydberg sensor may include a probe laser source and a quantum radio frequency (QRF) cavity downstream from the probe laser source and configured to define a path for a plurality of probe laser beam passes within the QRF cavity. A Rydberg sensing region may be within the QRF cavity and in the path of the probe laser beam passes. An optical amplifier may be within the QRF cavity and in the path of the probe laser beam passes. A detector may be downstream from the QRF cavity.