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Emissions of atomic oxygen (1304 A), atomic carbon (1657 A), and atomic hydrogen (1216 A) from Comet Kohoutek were observed with ultraviolet cameras carried on a sounding rocket on Jan. 8, 1974. Analysis of the Lyman alpha halo at 1216 A gave an atomic hydrogen production rate of 4.5 x 10 to the 29th atoms per second.
A total reduction procedure, notable for its use of a computer-controlled microdensitometer for semi-automatically tracing curved spectra, is applied to distorted high-dispersion echelle spectra recorded by an image tube. Microdensitometer specifications are presented and the FORTRAN, TRACEN and SPOTS programs are outlined. The intensity spectrum of the photographic or electrographic plate is plotted on a graphic display. The time requirements are discussed in detail.
The soft (10-100 A) X-ray spectrum of an M-class solar flare was observed with a high-resolution (0.02 A) rocket-borne spectrograph on 1982 July 13. The spectrum samples an area of 600/sq arcsec on the sun, centered on or near the brightest X-ray feature of the flare. Several hundred emission lines characteristic of temperatures from about 0.5 to 7 x 10 to the 6th K have been photographically recorded. All but three of the stronger lines have been identified. It is argued that previous identification of the line at 17.62 A as iron Ly-alpha is incorrect. Spectral lines from nickel, iron, chromium, calcium, sulphur, silicon, aluminium, magnesium, neon, oxygen, nitrogen, and carbon are tabulated and discussed with extensive reference to earlier work. Absolute line intensities are given and the calibration of the telescope-spectrograph is discussed.
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Changes in chromium, manganese, and nickel abundances derived from singly ionized lines are incorporated into the elemental abundance of Adelman and Hill (1987) in order to provide more accurate gf values and damping constants for several atomic species. An improved agreement with the values from neutral lines of the same element is found. In the second part, the method is applied to an elemental abundance analysis of three mercury-manganese stars, and correlations are found between the derived abundances and the effective temperature.
The elemental abundances of three mercury-manganese stars, Nu Cancri, Iota Coronae Borealis, and HR 8349, were found to be consistent with previous analyses of this series. As Iota CrB is a double-lined spectroscopic binary with a small velocity amplitude for most of its period, its study required determining whether the observed lines were produced in the primary or secondary or both. The derived abundances and effective termperatures were used along with those of mercury-manganese stars previously analyzed in order to extend the study of probable correlations between abundances, with the effective temperature and surface gravity in accordance with radiative diffusion explanations.
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Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.
Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.
Compress and clean raw data file for permanent storage We have identified various error conditions/types and developed algorithms to get rid of these errors/noises, including the more complicated noise in the newer data sets. (status = 100% complete). Internet access of compacted raw data. It is now possible to access the raw data via our web site, http://www.jhuapl.edu/Aurora/index.html. The software to read and plot the compacted raw data is also available from the same web site. The users can now download the raw data, read, plot, or manipulate the data as they wish on their own computer. The users are able to access the cleaned data sets. Internet access of the color spectrograms. This task has also been completed. It is now possible to access the spectrograms from the web site mentioned above. Improve the particle precipitation region classification. The algorithm for doing this task has been developed and implemented. As a result, the accuracies improved. Now the web site routinely distributes the results of applying the new algorithm to the cleaned data set. Mark the classification region on the spectrograms. The software to mark the classification region in the spectrograms has been completed. This is also available from our web site.
Most of the traditional attosecond pulse retrieval algorithms are based on a so-called attosecond streak camera technique, in which the momentum of the electron is shifted by an amount depending on the relative time delay between the attosecond pulse and the streaking infrared pulse. Thus, temporal information of the attosecond pulse is encoded in the amount of momentum shift in the streaked photoelectron momentum spectrogram S(p,τ), where p is the momentum of the electron along the polarization direction and τ is the time delay. An iterative algorithm is then employed to reconstruct the attosecond pulse from the streaking spectrogram. This method, however, cannot be applied to attosecond pulses generated from free-electron x-ray lasers where each single shot is different and stochastic in time. However, using a circularly polarized infrared laser as the streaking field, a two (or three)-dimensional angular streaking electron spectrum can be used to retrieve attosecond pulses for each shot, as well as the time delay with respect to the circularly polarized IR field. Here we show that a retrieval algorithm previously developed for the traditional streaking spectrogram can be modified to efficiently characterize single-shot attosecond pulses. The methods have been applied to retrieve 188 single shots from recent experiments. We analyze the statistical behavior of these 188 pulses in terms of pulse duration, bandwidth, pulse peak energy, and time delay with respect to the IR field. Furthermore, the retrieval algorithm is efficient and can be easily used to characterize a large number of shots in future experiments for attosecond pulses at free-electron x-ray laser facilities.
Core-collapse supernovae are among the astrophysical sources of gravitational waves that could be detected by third-generation gravitational-wave detectors. Here, we analyze the gravitational-wave strain signals from two- and three-dimensional simulations of core-collapse supernovae generated using the code FORNAX. A subset of the two-dimensional simulations has non-zero core rotation at the core bounce. A dominant source of time changing quadrupole moment is the l = 2 fundamental mode (f- mode) oscillation of the proto-neutron star. From the time-frequency spectrogram of the gravitational-wave strain we see that, starting ~ 400 ms after the core bounce, most of the power lies within a narrow track that represents the frequency evolution of the f-mode oscillations. The f-mode frequencies obtained from linear perturbation analysis of the angle averaged profile of the protoneutron star corroborate what we observe in the spectrograms of the gravitational wave signal. We explore the measurability of the f-mode frequency evolution of protoneutron star for a supernova signal observed in the third-generation gravitational-wave detectors. Measurement of the frequency evolution can reveal information about the masses, radii, and densities of the proto-neutron stars. We find that if the third generation detectors observe a supernova within 10 kpc, we can measure these frequencies to within ~90% accuracy. Here, we can also measure the energy emitted in the fundamental f-mode using the spectrogram data of the strain signal. We find that the energy in the f-mode can be measured to within 20% error for signals observed by Cosmic Explorer using simulations with successful explosion, assuming source distances within 10 kpc.
Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. The most straightforward denoising approach, using spectral filtering, is not effective when noise and seismic signal occupy the same frequency range. Neural network approaches have shown success denoising local signal when trained on short-time Fourier transform spectrograms (Zhu et al 2018; Tibi et al 2021). Scalograms, a wavelet-based transform, achieved ~15% better reconstruction as measured by dynamic time warping on a seismic waveform test set than spectrograms, suggesting their use as an alternative for denoising. We train a deep neural network on a scalogram dataset derived from waveforms recorded by the University of Utah Seismograph Stations network. We find that initial results are no better than a spectrogram approach, with additional overhead imposed by the significantly larger size of scalograms. A robust exploration of neural network hyperparameters and network architecture was not performed, which could be done in follow on work.
Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.
This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.
Photometric and/or spectroscopic observations have been obtained of 52 late-type stars which are suspected or known to be chromospherically active. Although not all types of observations were obtained for each star, these observations include all-sky BVRI Johnson photometry, ultraviolet spectrograms, low-dispersion blue-wavelength spectrograms, and high-dispersion red-wavelength spectrograms. From the spectroscopic observations v sin i's, radial velocities, and the appearance of the Ca II H and K emission lines have been determined as well as the H-alpha line. The photometric observations indicate that chromospherically active stars have V - R and V - I color excesses. Such excesses will affect the surface fluxes determined with the surface brightness-color relationship. On the other hand all-sky BVRI photometry appears to be an excellent way to identify chromospherically active stars. A small group of moderately rapidly rotating, possibly single G8-K2 giants has been found. These stars have very modest chromospheric activity and so are not FK comae stars. A number of other unusual chromospherically active stars are identified.
Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.
JET’s frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). Here, in this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela et al., has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.