Actual Signal Research Articles

Terahertz Non-Destructive Testing of Porosity in Multi-Layer Thermal Barrier Coatings Based on Small-Sample Data

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life.

Instrumentation techniques for the measurement of gunfire (Part 3)

This paper is the third in a series concerning near-field measurement of sound pressure from small arms gunfire. It has been long established that exposure to gunfire events can lead to permanent threshold shift for an unprotected shooter. Close to a shooter's ear, the peak sound pressure level from a rifle or pistol can be 155-to-160 decibels with a pulse rise time on the order of ten microseconds. The U.S. Department of Defense has considered two approaches for assessing exposure limits from small arms gunfire - one involves the unweighted peak sound pressure and the other a series of A-weighted sound exposure events integrated to obtain an equivalent eight-hour dose. This dose is then compared to hearing damage risk criteria established for continuous noise signals. The first approach places great demands on the measurement system since the necessary bandwidth extends well into the ultrasonic range, thereby requiring fast slewing from the amplifiers combined with high sampling rates from the digitizer. The second approach requires the A-weighting filter to have specified time and frequency domain properties in the ultrasonic region. The paper discusses some laboratory experiments using actual gunfire signals to simulate the outcome from both approaches.

Wavelet-based arcing signal source localization algorithm using a compact multi-square microstrip antenna

Ensuring power system safety involves effective arc fault detection and localization. Existing devices struggle to differentiate between normal and abnormal conditions, especially in confined spaces, posing precision challenges. Strategically placing antennas around the arc helps detect electromagnetic radiation, even in limited areas, enabling valuable data collection for real-time monitoring. To address these challenges, this paper proposes integrating experimental work using a compact multi-square microstrip antenna and signal processing techniques. The study compares the effectiveness of two signal processing approaches: Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT). These techniques separate genuine arc signals from background noise by identifying unique characteristics and isolating the dominant frequency. The Time of Arrival (ToA) is measured and used in Least Square and Gauss-Jordan Elimination methods to calculate the arc source location. The outcomes illustrate the precision of the proposed model in detecting and pinpointing arc source signals, with error margins ranging from 0.0615 to 0.0713 m for the CWT technique, 0.0688 to 0.0789 m for the DWT technique, and 0.0799 to 0.0844 m from the actual signal captured. These results hold promise for enhancing the integration of experimental approaches in assessing arcing conditions, thereby addressing challenges in insulation systems.

Apply Honey Barger Algorithm to double integral sliding mode observer for stable load frequency in multi-area power system

A randomness of load demand and plant uncertainties causes frequency deviation in linked power plants. Based on the double-integral sliding mode observer (DISMO) integrated Honey Badger algorithm (HBA), a novel LFC for a multi-area power plant (MAPP) is introduced in this article. The main aim is to minimize frequency offset, finite time, and aggregate instability of controllable loads subject to the balance of the power on the MAPP. Firstly, the suggested observer is developed to ensure an exact estimation of the actual signal for the controller input signal. Secondly, a double-integral sliding surface (DISS) is introduced based on estimated signals the designed observer gains. Then, a new SMC rule is designed to ensure finite time reachability and eliminate the chattering and oscillating troubles. Thirdly, in this regard, the proposed sliding surface is designed with four selected parameters. Regarding this, to maximize the ability of the suggested SMCer, the optimized controller parameters are determined by HBA. The robustness of the suggested scheme is examined by various case studies on the LFC models in a single-area power plant (SAPP) and MAPP under different load disturbances and parameter uncertainties.

Rotating machinery early fault detection integrating variational mode decomposition and multiscale singular value decomposition

Abstract Security and reliability are important issues that must be paid attention to during the operation of rotating machinery. If defects can be found in the early stage, there will be enough time to take maintenance measures and realize the stable operation of equipment. However, the presence of noise, shaft rotation signals, gear meshing signals, and other interfering factors often obfuscate fault signals, rendering the early detection of defects an arduous undertaking. Against this backdrop, this study presents an advanced approach for early defect detection, integrating the virtues of variational mode decomposition (VMD) and multiscale singular value decomposition (MSVD). Initially, a novel evaluation index is constructed by combining envelope entropy and envelope spectrum sparsity. Based on this a method is proposed to adaptively determine the critical parameters of VMD, enabling the adaptive decomposition of vibration signals into a series of modal components. The optimal sensitive components are then discerned utilizing the characteristic frequency intensity coefficient index. Subsequently, to address the limitations of single VMD methods in effectively suppressing low-frequency noise, the MSVD method is proposed for effective noise reduction, which reconstructs the signal after SVD of the signal within each segment through the operation of successive signal segmentation. Ultimately, envelope spectrum analysis is conducted on the reconstructed signal, facilitating the precise extraction of fault characteristic frequency information and enabling early fault identification. The efficacy of this novel methodology is evaluated through simulations and actual vibration signals, successfully discerning early faults afflicting rotating machinery.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network.

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.

Enhancing microgrid production through particle swarm optimization and genetic algorithm

<p>The growing demand for sustainable and efficient energy solutions has led to research on optimizing renewable energy sources within microgrid systems. This study presents a comparative analysis of two prominent optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA), to enhance solar photovoltaic PV and wind production in microgrids. The aim is to achieve a balanced and efficient energy generation that closely matches the load demand, thereby minimizing energy wastage and ensuring a reliable energy supply. The two algorithms are employed using data representing PV and wind production, as well as load consumption, over a 24-hour period. The results are evaluated based on their ability to reduce the gap between energy production and load demand. Our findings reveal compelling insights into the performance of GA and PSO in the context of microgrid optimization. To validate the results obtained from the simulation, the PSO algorithm is implemented on an actual cart Digital Signal Processor DSP platform, using a processor-in-the-loop (PIL). This successful real-world application highlights the practical viability of utilizing PSO to improve solar PV and wind energy generation within microgrids.</p>

Signal simulation based on improved doppler filter

Abstract In satellite communication, the signal is transmitted through a wireless channel, and distorted signals will appear under the influence of the actual environment. In this paper, an improved Doppler filter is proposed by mathematically fitting the actual signals, and the similarity of the time-frequency map of the signals output by this improved filter reaches 94.357% of the standard value, realizing the simulation of a kind of aberration appearing in the actual environment on the time-frequency image and finally realizing the self-adaptation.

A cost-effective field-programmable-gate-array-based pulse processor for biomedical imaging applications

ObjectiveThis paper presents the design and evaluation of a cost-effective, high-spatial resolution, multichannel, field-programmable gate array (FPGA)-based readout system for Positron Emission Tomography (PET) detectors suitable for clinical and pre-clinical work in cancer and other diseases. A key challenge in PET imaging is the precise measurement of the time of arrival and energy of gamma photons originating from positron annihilation, along with the position of interaction within the detector. These parameters enhance image quality by improving coincidence detection, excluding Compton-scattered photons, and improving system spatial resolution. We developed a 16-channel readout system using single silicon photomultiplier (SiPM) element readout for efficient and precise energy, time, and position measurements. Each channel incorporates an FPGA-based sigma-delta analog-to-digital converter (ΣΔ-ADC) and tapped delay line (TDL) based time-to-digital converter (TDC) for timing and energy estimation. Performance characterization of the system was performed through simulation, electronically generated waveform, and actual PET detector signals from three different single scintillation crystals of face dimensions 3 × 3 mm2 and depths of 5 and 20 mm coupled to a 3 × 3 mm2 SiPM. We also constructed and tested a modular PET detector with a 10x10 array of 1.2x1.2x14 mm³ LSO crystals coupled to a 4x4 SiPM array of 3 × 3 mm2 elements. Best energy resolution was 9.3% at the 511 KeV photopeak and best coincidence timing resolution (CTR) was 210 ± 3.5 ps. Our system balances cost-effectiveness with performance and contributes to developments in PET signal acquisition and detector technology.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

Innovative real-time deloading approach to wind systems with applications in primary frequency control

The integration of wind systems into the electrical grid faces a significant challenge due to the absence of inertia, a fundamental characteristic of this system. This lack of internal inertia directly impacts frequency stability. To address this challenge, various techniques have been developed, including storage systems, inertia emulation, and deloading methods, among others. Among these approaches, the latter method appears to be more suitable. This study introduces a novel deloading approach that offers several advantages. The proposed strategy is an online technique that is easy to implement. It does not necessitate mathematical models, estimations, prior knowledge of wind turbine (WT) characteristics, or the use of an extra sensor, thereby simplifying the approach. This approach is based on a WT cycle consisting of two consecutive operating modes: a maximum power measurement (MPM) mode and a power reserve (PR) mode. These modes are controlled by two controllers: a pitch angle controller (PAC) and a perturb and observe (P&O) algorithm controller. The cycle is managed and initiated using a monitoring signal with multiple configurations, providing various solutions and implementation possibilities. The effectiveness of this approach has been verified through numerous simulation tests conducted on both finite and isolated grids. These simulations employ theoretical and actual wind speed signals in the MATLAB Simulink environment, confirming the practicality and reliability of the proposed method.

<i>In situ</i> and <i>in silico</i> modeling of the hematopoiesis-inducing effect of chelidonic acid

The current trend in regenerative medicine, in the context of an aging population, is the search for new ways and means to optimize tissue bioengineering. One of the convenient models for in situ studying bone marrow regeneration is the subcutaneous ectopic osteogenesis test on scaffolds that imitate the architecture of bone tissue. Chelidonic acid (CA), a small molecule, is capable of participating in various cellular processes and metabolic pathways, and it can activate the osteogenic differentiation of mesenchymal stem cells. However, the molecular mechanisms behind the regulatory effects of CA remain unknown. The aim of this study was to investigate the modulatory effect of CA on the in situ formation of hematopoietic foci, as well as to predict target genes and intracellular signalling pathways involved in the hematopoietic activity of CA. An aqueous solution of CA, isolated from an extract of the Saussurea controversa plant. Course (daily for 35 days) oral administration of CA. Ectopic osteogenesis testing in Balb/c mice. Morphometric analysis of histological sections after 45 days and in silico modelling of gene expression with statistical analysis. CA, when administered orally in a low dose (10 mg/kg), threefold increases the normalized area of bone marrow in the composition of bone tissue plates grown in situ in a test of ectopic subcutaneous osteogenesis in mice. This effect is associated essentially (a probability of CA activity Pa 0.5 and a probability of inactivity Pi 0.5) with enhanced expression of 358 hematopoiesis-related genes, as predicted by in silico analysis. The top list with the highest Pa value included 10 target genes, such as GATA1, CITED2, SFRP1, EP300, LGALS9, VNN1, IL10RB, RARA, CD83, and HMOX1. CA has a significant ability to enhance the reparative remodelling of hematopoietic tissue in situ. The next phase of research will be to test actual target genes and signalling pathways that mediate the regulatory effect of HC on hematopoiesis both in vitro and in vivo, as well as in clinical settings.

Open-source synthetic photoplethysmographic signal generator with analog output.

The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

Deep Learning for Dynamic Modeling and Coded Information Storage of Vector‐Soliton Pulsations in Mode‐Locked Fiber Lasers

AbstractSoliton pulsations are ubiquitous feature of non‐stationary soliton dynamics in mode‐locked lasers and many other physical systems. To overcome difficulties related to a huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non‐stationary solitons, a two‐parallel bidirectional long short‐term memory recurrent neural network (TP‐Bi_LSTM RNN) is proposed, with the main objective to predict dynamics of vector‐soliton pulsations (VSPs) in various complex states, whose real‐time dynamics is verified by experiments. For two examples, viz., single‐ and bi‐periodic VSPs, with period‐21 and a combination of period‐3 and period‐43, the prediction results are better than provided by direct simulations – namely, deviations produced by the TP‐Bi_LSTM RNN results are 36% and 18% less than those provided by the simulations, respectively. This means that predicted results provided by the neural network are better than numerical simulations. Moreover, the prediction results for unstable VSP state with period‐9 indicate that the optimization of training sets and the number of training iterations are particularly important for the predictability. Besides, the scheme of coded information storage based on the TP‐Bi_LSTM RNN, instead of actual pulse signals, is realized too. The findings offer new applications of deep learning to ultrafast optics and information storage.

Synchronous match-reassigning transform: A method for extracting time-varying features of planetary gearbox

Instantaneous frequency (IF) time-varying closely spaced and severe background noise can introduce fake components into the time-frequency representation (TFR) of most time-frequency analysis (TFA) methods, especially for the vibration signals of variable-speed planetary gearboxes. These fake components lead to a decrease in the readability of the TFR, making it difficult to extract the true IF. To address this issue, a new TFA method called Synchronous Match-Reassigning Transform (SMRT) is proposed. Firstly, a polynomial chirp rate with a single parameter adjustment is constructed to achieve precise matching of the IF. Secondly, a frequency reassignment operator is constructed for the fine refinement of the IF ridge of Synchronous Match-Reassigning Operator (SMRO). Finally, SMRO is combined with VKF to achieve adaptive reconstruction of the true IF and to remove fake components. The TFR of SMRT has high readability and low energy leakage. The superiority and effectiveness of this method have been verified through synthetic signals and actual signals.

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

Mixing efficiency optimization of Tesla-type flow channel for total temperature simulation device

The space vehicle total temperature simulation device introduces the actual gas total temperature signal into the vehicle development process. It enables the simulation of flight environments, reduces development time, and decreases overall costs. The uniform and stable temperature signal is paramount in accurately simulate the vehicle's real flight speed and altitude. However, the existing ground test facilities for space vehicles, characterized by their large scale, face significant challenges including insufficient uniformity in gas mixing and inadequate simulation accuracy. The Tesla-type flow channel (TFC) is widely applied for its excellent mixing capabilities in gas and liquid mixing across various domains. In this paper, from the working principle of the total temperature simulation device of space vehicle, according to the characteristics of TFC, a high mixing efficiency optimization design method of TFC is proposed by using RBF neural network response surface and NSGA-II algorithm. The optimized TFC is implemented in the mixing chamber of the total temperature simulation device to enhance the mixing efficiency. This improvement ultimately leads to enhanced accuracy in simulating the flight environment of space vehicles during semi-physical simulations. By utilizing the Pareto optimal solution, the optimal pressure drop is 985.50 Pa, while the standard deviation of temperature is 39.37 K. The results demonstrate a significant improvement in mixing efficiency within the total temperature simulation device due to the introduction of the TFC. This study serves as a valuable reference for enhancing the mixing performance of the total temperature simulation device for space vehicles, while also addressing the need for total temperature simulation in smaller laboratory environments.

An enhancement filtering method based on CEEMDAN-EP-WTD for TDLAS gas sensing

In tunable diode laser absorption spectroscopy (TDLAS) gas detection, the accuracy of the detection system is reduced due to the impact of nonlinear noise on the second harmonic. In this paper, a noise reduction method CEEMDAN-EP-WTD was proposed. Combining wavelet threshold method and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) via evaluation parameter (EP) and sample entropy (SE). The CEEMDAN-EP-WTD can evaluate the noise level of the (intrinsic mode function) IMF component. It calculates the unknown noise standard deviation of the IMF component in the actual signal, which is related to the wavelet threshold. Based on simulation and experimental analysis, the signal-to-noise ratio of the harmonic signal is improved from 4.41 to 28.8596. The minimum detection limit of the experimental system is 4.72 ppm and the detection accuracy is 4.4 %. The results confirm that the algorithm successfully suppresses system noise and enhances the performance of the detection system.

Assessing the Accuracy of Eddy‐Covariance Measurement at Different Source Emission Scenarios

AbstractThe eddy‐covariance (EC) method assumes a homogeneous underlying surface. However, recent studies increasingly examining on EC measurements across diverse surfaces, raising concerns about measurement precision and accuracy. This study evaluates the impacts of altering the emission height and rate on the EC measurements through utilizing an artificial source emission system. The results demonstrated a significant impact of changes in the emission height and rate on the EC measurements. Higher emission height may lead to the underestimation of the measured EC fluxes, attributed to the variations in the footprint area and turbulent transport. Traditional data quality control methods may discard effective EC data during sudden changes in the emission rate. Therefore, to secure effective data and accurately observe emissions, it was practical to analyze the auxiliary factors, such as environmental factors, such as vapor pressure deficit (VPD). An unresolved issue would persist with the correction of the EC method for accurately capturing the actual emission signals when there was a sudden increase in the data range or deviation. Furthermore, comparing the footprint model estimations with the actual emissions demonstrated the necessity of footprint analyses, offering a valuable reference for the data calibration when the uncertainties arose owing to inhomogeneous underlying surfaces. Although EC fluxes across the three averaging periods indicated no significant differences, the footprint model suggested that 15‐min interval was the optimal. Further validation experiments are required for the EC measurements in locations with complex source conditions to enhance our understanding of land‐atmosphere flux exchange.