This study presents an experimental investigation of the flow and combustion dynamics of a single LOX/CH 4 liquid-centered swirl injector under high-pressure transcritical conditions, conducted on the cryogenic MASCOTTE test bench. As a preliminary step, the interaction between hydrodynamic and combustion phenomena in a stable operating regime is examined. The structure and evolution of the flame and dense phase are analyzed using synchronized high-speed imaging at 13 kHz, with OH* chemiluminescence capturing the reactive zone and backlighting visualizing the dense phase. Time-averaged and instantaneous fields are provided to highlight the flame structure and flow behavior. Spectral analysis, conducted via Fast Fourier Transform (FFT), is performed, and complemented by video filtering using a combination of averaged Inverse Discrete Fourier Transform (IDFT) and phase-averaging techniques. The backlighting recordings reveal a dominant symmetric mode aligned with the preferred instability mode frequency of the dense jet. The corresponding Strouhal number, ranging between 0.25 and 0.5, is consistent with classical hydrodynamic predictions and is identified as a symmetric Kelvin–Helmholtz instability. Similar modal behavior is observed in the OH* chemiluminescence signal, sharing the same dominant frequency and mode shape. These coherent structures are confined to the reaction zone and dense phase region, with no significant modes persisting downstream. In the absence of significant pressure oscillations, it is inferred that the flame dynamics are primarily driven by the dense phase hydrodynamics. These findings enhance the understanding of coupled hydrodynamic-combustion instabilities in transcritical swirl injectors and offer insights relevant to the design and control of next-generation rocket propulsion systems. Novelty and significance statement The novelty of this work lies in its contribution to the very limited experimental data on transcritical LOX/CH 4 swirl flame dynamics available in the literature. Backlighting imaging and OH* chemiluminescence measurements reveal a dominant symmetric mode in the flow oscillations. Additionally, the state-of-the-art Strouhal number definition for non-reactive swirl flows provides a good estimate of the corresponding dominant frequency, which aligns with the preferred instability frequency of the dense jet. This analytical frequency estimation enhances understanding of the coupling between hydrodynamic and combustion instabilities in transcritical swirl injectors, representing a key step toward the development of safe and reliable liquid rocket engines.

A predictive model for birth delivery mode utilizing shape characteristics of uterine contractions from electrohysterographic signals and obstetric parameters

Accurate long-term electrical load forecasting is required for reliable smart grid operation, yet it remains difficult due to multi-scale periodic patterns and non-stationary temporal variations across different prediction horizons. This paper presents MoE-Transformer, a dual-domain forecasting framework that learns to route representations in both the time and frequency domains through reinforcement learning. To mitigate spectral misalignment in multi-step forecasting, we introduce an Extended Discrete Fourier Transform (Extended DFT) that aligns the input spectrum with the frequency grid of the full prediction window. The proposed model incorporates parallel Mixture-of-Experts modules in the time and frequency domains (T-MoE and F-MoE), where domain-specific experts capture complementary temporal dynamics and spectral structures. Expert routing in each domain is modeled as an independent Markov Decision Process and optimized using reinforcement learning to jointly consider forecasting accuracy, routing consistency, and balanced expert utilization. Experiments on five benchmark datasets, including ETTh1, Electricity, and Traffic, across four forecasting horizons show that MoE-Transformer achieves MSE reductions of 50.9-56.9% relative to state-of-the-art baselines under matched training protocols. Relative to a same-capacity dense Transformer baseline on NVIDIA RTX 4090, sparse top-1 expert activation reduces peak GPU memory by [Formula: see text] and single-sample inference latency by [Formula: see text] (mean ± std over 5 runs), with measured absolute batched latency of [Formula: see text] ms per sample, supporting real-time forecasting deployment. Ablation results confirm the individual effects of Extended DFT, dual-domain modeling, and reinforcement-based routing, yielding performance gains of 5.8%, 4.6%, and up to 47.2%, respectively.

This paper proposes disturbance detection algorithms to mitigate the oscillations in smartphone camera module actuators induced by external shocks (e.g., drop events). Smartphone camera modules operate under volumetric constraints with inter-component trade-offs. Specifically, the limited space leads to insufficient performance because actuators are unstable under external disturbances. To optimize actuator function, we define the dynamic model of a voice coil motor (VCM) actuator, a controller model, and a shock disturbance model and perform worst-case operational analysis with MATLAB/Simulink (R2015a) simulations. Moreover, we propose two disturbance detection techniques: a phase-based detection algorithm that statistically analyzes the phase difference between the control input and the position feedback signal to detect disturbances and a frequency-based detection algorithm that uses discrete Fourier transform (DFT) to identify the characteristic spectral component of disturbances at 500 Hz. According to the simulation results, both methods reduce recovery time upon disturbance. Furthermore, the frequency-based algorithm achieves faster recovery performance than the phase-based detection algorithm. The phase-based detection method offers low computational complexity but increased processing latency, while the frequency-based detection method requires more memory capacity. The proposed techniques are anticipated to improve the recovery time of smartphone camera modules under disturbances, thereby enhancing system robustness and contributing to a more stable user imaging experience by mitigating image blur.

The uncertainty of distributed resource (DR) aggregation responses has become a key bottleneck restricting the reliable scheduling of virtual power plants (VPPs). To mitigate this uncertainty, this paper explores the strong coupling between residential behavior and the adjustable capability of distributed resources, and proposes a dynamic aggregation strategy that accounts for such coupling. First, the concept of Behavior-sensitive Distributed Resources (BDRs) is proposed and defined, where resources whose adjustable capability is significantly influenced by the number of residential users are categorized as BDRs. By applying the Discrete Fourier Transform (DFT), this study, for the first time, reveals the daily periodic characteristics of BDRs. Subsequently, a segmented confidence interval estimation method is introduced, which, in conjunction with the daily periodicity of BDRs, enables precise characterization of adjustable capability across different time periods, thus improving the accuracy of response capability modeling. An improved two-stage greedy aggregation algorithm is then designed to simultaneously reduce the uncertainty of VPP adjustable capability after aggregation and optimize economic performance. Finally, the strategy is validated using real-world data from a resource set that includes three representative types of BDRs: electric vehicle (EV) charging stations, commercial lighting (CL), and office building heating, ventilation, and air conditioning (HVAC) systems. The results demonstrate that, compared to existing approaches, the proposed aggregation strategy reduces VPP response uncertainty by over 22.63% and lowers aggregation cost by an average of 8.44%, thereby confirming its advantages in enhancing both grid reliability and economic efficiency.

Abstract Quantum kernel methods are promising for near-term quantum machine learning, yet their behavior under data corruption remains insufficiently understood. We analyze how quantum feature constructions degrade under controlled additive noise. We introduce Spectral Phase Encoding (SPE), a hybrid construction combining a discrete Fourier transform (DFT) front-end with a diagonal phase-only embedding. Within a unified framework, we compare the resulting DFT-based quantum-kernel pipeline () against alternative quantum variants based on principal component analysis (PCA) and random projections (RP), as well as classical support vector machine (SVM) baselines, under identical clean-data hyperparameter selection. Robustness is quantified via dataset fixed-effects regression with wild cluster bootstrap inference across heterogeneous real-world datasets. Across the matched quantum family, DFT-based preprocessing yields the smallest degradation rate as noise increases, with statistically supported slope differences relative to PCA and RP. Compared with classical baselines, shows degradation comparable to linear SVM and more favorable than radial basis function (RBF) SVM under the present tuning protocol. Hardware experiments further confirm that the resulting overlap-estimation primitive remains executable and numerically stable. Taken together, these results support a robustness-first view of quantum kernel evaluation and show that, within the controlled pipeline family studied here, structure-aligned spectral preprocessing combined with a shared diagonal quantum embedding can yield slower degradation under corruption.

Detection of trace gases with high sensitivity and weak excitation power is highly desired for long-range remote sensing. Here, we report the detection of the greenhouse gas nitrous oxide (N2O) with the power of excitation light down to picowatts, by converting the mid-infrared laser to near-infrared photons through an intra-cavity-enhanced sum-frequency upconversion system. The intra-cavity-enhanced pumping power of 1064.0 nm reaches about 200.0 W, resulting in the conversion of the 4514.6 nm mid-infrared laser to 861.1 nm with an efficiency up to 73.4% under optimal conditions. The upconverted light is then detected by a single-photon avalanche detector, followed by a time-correlated single-photon counting module, which can measure the arrival time of each upconverted photon. By performing discrete Fourier transformations of the arrival time of the detected photons, the frequency spectrum can be determined. By using frequency modulation, this method can suppress background noise significantly. Consequently, the excitation power can be brought down to about 100 pW with the concentration of N2O being 10 ppm. As a demonstration of application, the presented system is also used for N2O sensing in an open-path geometry, highlighting the potential for stand-off leak detection. Our proposal offers promising applications to monitor trace gases over long distances with weak excitation powers.

Rock masses naturally exhibit significant anisotropy, however, majority of the analytic solution of a half-plane with a hole are based on the isotropic assumption, which would result in the larger difference from the real scenario. Hence, the purpose of present study is to derive the general form of the stress functions in an anisotropic medium because it can be easily used to solve the problem of the unbalanced forces by tunnel excavation. Since the affine transformation is used, the computing model may become asymmetrical and conventional mapping functions are difficult to be obtained. Without loss of generality, decoupling conformal mapping functions are introduced. By adopting Discrete Fourier Transform, all coefficients of the stress function can be determined. Compared with the numerical results, the analytical solution of stress around the unlined tunnel is higher precision. In the present study, the effects of the depth, the tectonic stress and the anisotropic parameters are discussed on the stress field and the displacement field. When compared to the isotropic case, it is unfavorable for the safety and stability of tunnel if the problem is solved under the isotropic condition.

Abstract Inferring network architecture from partial behavioral observations is central to computational studies of complex systems, with implications for detecting covert actors in terrorist networks, localizing unobserved brain regions, and revealing latent molecular regulators. Focusing on evolutionary game dynamics, we uncover an empirically consistent frequency-domain regularity: the aggregated magnitude of the discrete Fourier transform (DFT) of a node's payoff time series scales linearly with its structural degree, S ∝ k, across tested topologies and parameter regimes. Leveraging this structure-behavior scaling, we develop a deterministic inference framework that (i) estimates degrees directly from DFT spectra via a single global-slope fit, and (ii) uses brief, identifiability-oriented local interventions ("suppress-and-measure") to reveal adjacencies through intervention-induced spectral drops. Discrepancies between estimated degree and revealed neighbors deterministically enumerate external edges to hidden components and yield tight lower/upper bounds on the number of unobserved nodes. We analyze the conditions under which the scaling holds, quantify deviations under structural heterogeneity, and characterize sample-length dependence. Experiments on synthetic and empirical networks demonstrate accurate topology recovery, robustness to stochasticity, and scalability to thousand-node graphs, with consistent gains over causality-, correlation-, information-, and likelihood-based baselines. These results establish a DFT-based route to structure discovery under partial observability, translating local spectral measurements into actionable structural information.

Damped sinusoid fitting algorithm for parameter estimation of real-valued noisy damped sinusoids

Digital Signal Processing (DSP) is a very significant research area in communication. Finite impulse response (FIR) filters are one of the most efficient and commonly used practical filters for digital signals, which can remain stable all the time and tend to implement different frequency responses ideally with greater flexibility. In this research, the windowing method is used to design the digital filters due to its simplicity, which scales each sample in impulse response. By using this method, the discrete-time system produces the continuous spectra. In addition, Discrete Fourier Transform (DFT) is used to provide discrete spectra instead. However, the DFT can be efficiently computed with the Fast Fourier Transform (FFT), which is a very widely used and practical algorithm. Two samples are analyzed by using the FIR digital filter design with the window method and FFT in this paper, which are the audio signal and satellite transmission signal respectively. The audio signal is from a 5-second recorded voice, and the satellite transmission signal. The property and effect of the FIR filter can then be finally found by using MATLAB to process the signal in this research.

The multi-channel fiber optic communication network, crucial for long-distance digital signal transmission, faces linear interference from orthogonal frequency division multiplexing. To address the challenge of linear anti-interference in digital signal transmission, this paper integrates Discrete Fourier Transform and Wavelet Transform techniques to precisely identify and locate linear interference signals during the transmission process of Orthogonal Frequency Division Multiplexing systems, and then specifically suppress and mitigate them. Firstly, this paper transforms multiple subcarrier digital signals in multi-channel transmission into linear interference signals. For these interference signals, wavelet decomposition is performed using wavelet transform technology, decomposing the noise signals into coefficients at different levels. Wavelet coefficients that are either above or below the threshold values are sequentially subjected to thresholding processing, thereby achieving denoising. Finally, by combining DFT and WT, the DFT-WT-LAJ algorithm is proposed. This algorithm searches for linear interference signals by employing both DFT and DMFT algorithms, and filters out all reconstructed linear interference signals from the transmitted signals. Experimental data show that the algorithm controls the amplitude below 10 Hz, achieves a synchronization probability of 0.7 at a signal-to-noise ratio (SNR) of 35 dB, and maintains an interference-to-signal ratio above 31 dB, significantly enhancing signal quality and transmission reliability.

Seasonal influenza poses severe global health and economic burdens, demanding reliable long-term (3-6 months) forecasts for proactive public-health interventions. However, influenza surveillance data exhibits four key idiosyncrasies-quasi-periodicity with drifting phase, sharp asymmetric peaks, collinear seasonal exogenous drivers, and temporal inconsistency in non-autoregressive (NAR) decoding-that existing methods address in isolation, lacking a unified solution. We propose SpecFlu-Net, a lightweight frequency-aware neural architecture for long-term influenza transmission forecasting. It integrates two core components: (1) a frequency-domain encoder, which lifts historical incidence data to the complex frequency domain via learnable discrete Fourier transform (DFT) to preserve phase information (critical for peak timing) and denoise signals through energy compaction; (2) an NAR decoding framework enhanced by temporal-dependency tuning (TDT) loss, which penalizes deviations between predicted and ground-truth first differences and adaptively balances training focus between absolute accuracy and epidemic shape. Theoretically, the complex-valued multi-layer perceptron (MLP) layer in SpecFlu-Net equals a time-domain global convolution (ensuring interpretability and parameter efficiency), and TDT loss prevents gradient flow into historical data for stable training. Evaluations on three real-world influenza datasets across 3-24 weeks horizons show SpecFlu-Net outperforms state-of-the-art baselines consistently. SpecFlu-Net provides a unified solution to influenza data challenges, delivering epidemiologically coherent long-term forecasts to support proactive public health, and is adaptable to other seasonal infectious diseases.

Time series classification (TSC) is a critical area with broad applications. In the field of evidence theory, quantum evidence theory (QET) offers a promising framework for onedimensional TSC tasks, leveraging the capabilities of quantum basic probability amplitude (QBPA) to capture two-dimensional uncertainty. However, as the first step for the application of QET to TSC, how to construct QBPA still remains an open issue. In this paper, a novel approach to generate QBPA is devised. Specifically, we first apply the discrete Fourier transform (DFT) to the original data, extracting two-dimensional features embedded in the magnitude and phase from the frequency domain based on the front-few multi-frequency components, achieved by setting a threshold frequency index (TFI) to limit the frequencies considered. Next, we introduce the complex dual gaussian fuzzy number (CDGFN) as a carrier for QBPA, effectively representing two-dimensional uncertainty in the data. A CDGFN-based multisource information fusion (CDGFN-MSIF) algorithm for decision-making is proposed to combine information from different frequency components. Finally, the decisionmaking algorithm is validated on multiple time series datasets. Experimental results highlight the superior performance of the proposed approach over other state-of-the-art models, demonstrating its effectiveness and enhanced classification accuracy.

A class of Boris-like second-order volume-preserving algorithms (VPAs) for simulating charged particle motion in electromagnetic fields have been generalized to a rotating angle formulation by matrix notation. The phase stability of this class of VPAs has been analyzed by utilizing discrete Fourier transformations (DFT) technique. It is found that two prominent VPAs, namely the and the well-known Boris algorithm, exhibit optimal phase precision for high-frequency (gyro motion) and low-frequency dynamics (transit/bounce motion), respectively. These findings have been empirically verified through numerical experiments. The insights gained from this study enable the selection of an appropriate VPA for practical simulations based on the characteristic frequencies of specific physics problems, which can substantially enhance numerical accuracy and improve computational efficiency for long-term calculations.

Accurately capturing the evolving temporal correlations between unstructured textual features and multi-modal parameter data is pivotal for robust equipment health assessment. Conventional multimodal fusion methods typically fail to capture temporal variations across modalities: text features exhibit stage-specific changes, such as slight abnormal noise in the early fault stage and severe vibration in the late degradation stage, while parameter data contains latent temporal patterns like wavelet energy accumulation in specific frequency bands during the fault precursor period. The attention mechanism is a highly promising architecture to address this issue. This study proposes a dynamic attention-driven multimodal feature fusion method for equipment health status assessment. This method integrates a hybrid time-frequency encoding framework, combining wavelet packet decomposition (WPD), fast Fourier transform (FFT), and discrete Fourier transform (DFT) with textual feature extraction based on bidirectional encoder representations from transformers (BERT). On the Case Western Reserve University (CWRU) bearing fault dataset, the proposed method improves classification accuracy by 7.2% compared with conventional symmetric attention models and achieves an AUC-ROC of 0.951. The proposed method captures evolving multimodal signals, allowing for more accurate and interpretable health assessment, thereby providing valuable technical support for real-time monitoring and preventive maintenance of equipment.

Electricity load forecasting is of high importance for electricity management. Modern power systems are complex and diverse, resulting in increased randomness and nonlinear factors of electricity load data, which greatly increases the difficulty of forecasting. This paper proposes a hybrid-deep-learning-based load forecasting method, named DCFformer (DFT-CNN-FEDformer), for short-term load forecasting (STLF) tasks. The method first employs the discrete Fourier transform (DFT) to denoise time-sequence data on electricity load, so that fluctuations caused by incidents can be reduced. Secondly, it utilizes a convolutional neural network (CNN) that produces sequences of local features extracted from the denoised time sequences. Thirdly, a FEDformer network is applied to perform load forecasting by using extracted feature sequences. In the experiments, we utilize datasets from three regional power systems or apparatuses to compare the proposed DCFformer with other approaches, and the results show that, under the same conditions, DCFformer outperforms the competitors in forecasting precision, which proves the significance of its performance and practicality.

Properties related to the discreteness of the phases of the Discrete Fourier Transform (DFT) of pitch-class (pc)-sets in the 12-tone universe were studied. We primarily investigated the conditions where the DFT phases are integer multiples of π / 12 for pc-sets of up to six tones (effectively covering all the pc-sets in the 12-tone universe). Pc-sets were classified into five classes according to whether or not each DFT component’s phase was an integer multiple of π / 12 . It was also found that phases of non-integer multiples of π / 12 tend to be associated with larger Fourier components. Additionally, discontinuities associated with pc-sets with indeterminate phases were also investigated by tracking the phase changes in certain chord progressions.

ABSTRACT Deepfake videos, employing artificial intelligence techniques to create highly realistic but fabricated content, have emerged as a major concern for the society. Proposed paper develops deep learning model called, EnsembleResnet, that ensembles multiple feature descriptors, and then judicially select optimal features for deepfake video detection. Initially, individual features such as DFT (Discrete Fourier Transform), DCT (Discrete Cosine Transform), SIFT (Scale-Invariant Feature Transform), and Gabor Filter are trained on ResNet-18 model to classify real and fake videos. Afterward, ensemble learning of optimal feature descriptors is trained on the same model to improve detection accuracy. Experimental evaluations on diverse datasets: FF++ and Celeb-DF show potency of ensemble learning in identifying real and fake videos. Experimental results show an accuracy with paired feature descriptors (SIFT + Gabor + DCT) on datasets FF++ (raw data, high quality, low quality) is (99.80%, 99.25%, 99.79%), and on Celeb-DF dataset is 98.01%, respectively.

Abstract Monte Carlo radiation transfer simulations are widely used to generate mock galaxy images based on hydrodynamical simulation outputs, which is important for connecting physics considered in hydrodynamical simulations to galaxy observables. These images unavoidably contain random noises because practical Monte Carlo calculations can only perform finite size statistical sampling. The straightforward way of suppressing this kind of noises is to use large number of photon packages (≥ 10 9 ) in radiation transfer calculations, but this leads to heavy computational costs, which limit the potential of applying these simulations
onto large theoretical galaxy samples. In this work we investigate another way, namely suppressing these noises through various image filtering methods, including mean, median, Gaussian and bilateral filtering, and filtering based on discrete Fourier transformation and discrete Haar wavelet transformation, with both amplitude and frequency thresholds.We first estimate the noise levels of images from simulations of low number of photon packages (10 7 ), and then observe how they change with the applications of various filtering methods. Several methods can obviously reduce the image noise levels, but not sufficient to replicate the results of simulations run with 10 9 photon packages. An even better filtering method is needed, and it can be obtained probably through adopting a spatial kernel or wavelet more sophisticated than what considered in this work.