Estimation Performance Research Articles

Evaluating the performance of automatic wave speed estimators of natural shear waves to assess myocardial stiffness

Abstract Introduction Cardiac Shear Wave Elastography is an emerging non-invasive technique using ultrafast echocardiographic imaging. Natural shear waves (SW) occur after mitral valve closure (MVC) and aortic valve closure (AVC), and their propagation velocities are directly related to myocardial stiffness. However, the current assessment of SW velocities lacks a gold standard method, and relies on manual measurements with their inherent variability. Purpose To explore new automated methods for shear wave velocity estimates, aiming to achieve greater accuracy, less variability, and high feasibility. Methods To visualize shear waves, anatomical M-modes were drawn (4±1 cm) along ventricular septum in parasternal long axis views (PLAX), acquired with high-frame-rate echocardiography (1055±159 Hz). M-modes were colour coded for tissue acceleration. SWs appear as tilted green bands after MVC & AVC, the slope of which represents the SW propagation velocity. One hundred-twenty different slopes from healthy volunteers (n=67) and patients (n=53) were measured manually and with 9 different automatic methods. All automatic methods were initiated by indicating the approximate position of the shear wave in the M-mode with a mouse click of the user. Measurements were repeated three times to assess measurement reproducibility. (figure 1, A). In order to determine the influence of display settings, three different acceleration colour scales were tested (0.5, 1, 2 m/s²). (Figure 1, B). Failure of correctly identifying shear waves was counted to assess feasibility. Due to the lack of a gold standard, manual measurements of an experienced reader served as reference. Results Three of nine automatic methods: Radon Transformation (RT), Regression Segmentation Maximum value (RM), Regression Segmentation Weighted Maximum (RMW), revealed the least variability with repeated measurements (0.0±0.0 m/s for all) and colour scales (0.0±0.0, 0.1±0.2 and 0.1±0.2 m/s, resp.). Those three methods showed moderate to good agreement (r=0.66, ICC 0.77 [CI 0.64- 0.85]; r= 0.67, ICC 0.75 [CI 0.50 -0.86]; and r= 0.77, ICC 0.85 [CI 0.73-0.90], resp.) with manual measurements (r=0.96, ICC 0.98 [CI 0.97- 0.99] (figure 2). Conclusion RT, RM, and RMW showed a good reproducibility while RMW showed the best agreement with manual measurements. Our data suggest, that the RMW method could offer an objective alternative for SW velocity estimations. Further research is needed to explore their applicability in clinical scenarios.

Differences in myocardial work across functional class strata in a severe aortic stenosis AHA D1 population. A cross-sectional study

Abstract Background Aortic stenosis is the most prevalent degenerative valvopathy. [1,2] The contractile state of the left ventricle has prognostic value in these patients. Global longitudinal strain (GLS) is the most accurate method for the estimation of myocardial performance. However, its value depends on the left ventricular afterload, [3,4] making GLS values challenging to interpret for decision-making purposes. The global myocardial work index (GWI) is an innovative echocardiographic technique for assessing myocardial function.[5] From these values, other indices representing constructive work, wasted work, and myocardial efficiency can be calculated.[6] Purpose The objective of our study was to measure the GWI and its derivatives using transthoracic echocardiography and relate them to the patient's functional class. Methods We conducted an observational, cross-sectional, and analytical study, including patients with severe aortic stenosis AHA D1, aged over 45 years, hypertensive, without coronary lesions. We exclude patients with other associated valvulopathies and rhythm disorders. All patients underwent transthoracic echocardiography, with measurements of GLS and GWI. Systolic blood pressure was added to the mean gradient of the aortic valve obtained by continuous Doppler. The population was divided into those with NYHA functional classes I/II and those with NYHA III/IV. Results Between May 2022 and August 2023, we included 152 patients, average age was 68 +/- 9 years. Of these, 54% were male, 38% had type 2 diabetes, 36% had dyslipidemia, and 30% had a history of smoking. Regarding functional class, 21% were in NYHA class I, 37% in NYHA II, 32% in NYHA III, and 9% in NYHA IV. The mean LVEF of 60 +/- 5%, GLS strain of -15.1 +/- 3.1; mean GWI was 2251 +/- 595 mmHg%, while the global constructive work index was 2526 +/- 596 mmHg%. The median wasted work index was 198.2 (196-204.2) mmHg%, and the mean myocardial efficiency was 89.9 +/- 4.7%. The global myocardial work index was 2455 +/- 582 in patients with NYHA I/II vs 1978 +/- 527 mmHg% in patients with NYHA III/IV (p<0.001) (Table 1). For the global constructive work, patients in NYHA I/II had a mean of 2708 +/- 590 mmHg, while those in NYHA III/IV had 2281 +/- 542 mmHg% (p<0.001). There were no significant differences in wasted work (190 +/- 11 mmHg%, NYHA I/II vs 210 +/- 113 mmHg%, NYHA III/IV, p=0.27). Marginally statistically significant difference was observed for myocardial efficiency (91 +/- 4.8%, NYHA I/II vs 89 +/- 4.5%, NYHA III/IV, p=0.49). Conclusions Patients with severe aortic stenosis have supranormal myocardial work indices.[7] However, as the disease progresses and patients experience greater clinical deterioration, these indices decrease, showing a significant difference between functional classes I/II and III/IV. Further studies are needed to determine the utility of these indices as prognostic indicators in this patient group.Table 1.Differences in myocardial workBulley

Stealthy Attack Against Distributed State Estimation for Cyber‐Physical Systems

ABSTRACTThis article studies a stealthy attack strategy design for distributed state estimation from the perspective of an attacker in cyber‐physical systems, where both internal and external attack scenarios are considered simultaneously. To enhance robustness against outliers and reduce communication burden, a distributed fusion estimator is developed by fusing the innovation residuals of neighboring smart sensor nodes without any attack. Based on the designed distributed fusion estimator, the evolution of the distributed estimation error covariance is analyzed, and its lower and upper bounds are obtained. Moreover, a stealthy attack framework embedding adjustable parameter is designed to weaken the estimation performance, where the constraints of the adjustable parameter are provided based on the desired attack effect. Finally, a simulation example is provided to manifest the validity of the main results.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

Performance Analysis of Uplink Code Division Multiplexing for LEO Satellite Constellations Under Nonlinear Power Amplifiers.

This paper studies the performance of the communication link between a ground station and the satellites of a LEO constellation, employing code division multiplexing and a non-linear high-power amplifier. The analysis shows that the input power selection at the high-power amplifier of the ground station has a significant impact on overall system performance. The results concerning output power, the challenge of adjusting the back-off with a continuously changing number of satellites, and improved energy efficiency suggest operating in saturation. In this scenario, we can choose to transmit directly the sign of the sum of the signals directed to individual satellites. Analytical exact and simplified results are derived, enabling the estimation of performance as a function of the number of satellites being served when the amplifier operates at saturation. These analytic results are further validated through simulations. A formula to compute the loss across different numbers of satellites is also presented. The performance under saturated amplifier conditions is evaluated, compared, and discussed, providing valuable insights for simplifying the design and operation of satellite uplink communication systems under power amplifier constraints.

On Covariance Switch-Based Invariant Extended Kalman Filter for Multi-Source Fusion

The good estimation consistency of Invariant Extended Kalman Filter (InEKF) is mainly attributed to its excellent trajectory-independent property. However, when the InEKF is applied to multi-source inertial-based navigation that coexists left-invariant and right-invariant observations, the observation matrix would be unavoidably trajectory-dependent and might lead to the degradation of estimation performance. This paper for the first time performs theoretical analyses on the covariance switch between the left error and the right error in multi-source inertial-based navigation. Furthermore, detailed realizations of the covariance switch-based InEKF are formulated in this work, which are lacked in previous works. Numerical simulations and experiments demonstrate that the covariance switch significantly reduces the attitude errors during the transient phase in contrast with the original method using the incompatible error definition.

Reliability analysis of load sharing systems under unequal load-sharing rule with applications

We are examining a setup consisting of several parallel-connected k-out-of-m load sharing systems. The load sharing is modeled using proportional conditional failure rates for component failures and load sharing under the unequal load-sharing rule. We have derived the system reliability for the entire setup. To illustrate this, we examine two cases: one with n systems connected in parallel, each following a 1-out-of-2 configuration, and the other with n systems connected in parallel, each following a 2-out-of-4 configuration. The system components are assumed to be identical with an exponential as well as Weibull baseline distributions. We propose two methods to estimate the baseline parameter and load-sharing parameters. Additionally, we present a confidence interval using bootstrapping techniques, specifically the percentile bootstrap (boot-p). We assess the performance of these parameter estimation and interval estimation methods through simulation studies. Furthermore, we evaluate the practical applicability of the proposed techniques by analyzing two real datasets. The same approach can be applied to a system that includes multiple k-out-of-m load sharing systems, where these systems are connected in either a series or a p-out-of-q configuration.

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework.

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment.

Ghost attentional down net: An effective lightweight top-down network for human pose estimation

Deep learning has demonstrated remarkable advantages in the field of human pose estimation. However, traditional methods often rely on widening and deepening networks to enhance the performance of human pose estimation, consequently increasing the parameter count and complexity of the networks. To address this issue, this paper introduces Ghost Attentional Down network, a lightweight human pose estimation network based on HRNet. This network leverages the fusion of features from high-resolution and low-resolution branches to boost performance. Additionally, GADNet utilizes GaBlock and GdBlock, which incorporate lightweight convolutions and attention mechanisms, for feature extraction, thereby reducing the parameter count and computational complexity of the network. The fusion of relationships between different channels ensures the optimal utilization of informative feature channels and resolves the issue of feature redundancy. Experimental results conducted on the COCO dataset, with consistent image resolution and environmental settings, demonstrate that employing GADNet leads to a reduction of 60.7% in parameter count and 61.2% in computational complexity compared to the HRNet network model, while achieving comparable accuracy levels. Moreover, when compared to commonly used human pose estimation networks such as Cascaded Pyramid Network (CPN), Stacked Hourglass Network, and HRNet, GADNet achieves high-precision detection of human keypoints even with fewer parameters and lower computational complexity, our network has higher accuracy compared to MobileNet and ShuffleNet.

A Unified Framework for Covariate Adjustment Under Stratified Randomisation

ABSTRACTRandomisation, as a key technique in clinical trials, can eliminate sources of bias and produce comparable treatment groups. In randomised experiments, the treatment effect is a parameter of general interest. Researchers have explored the validity of using linear models to estimate the treatment effect and perform covariate adjustment and thus improve the estimation efficiency. However, the relationship between covariates and outcomes is not necessarily linear, and is often intricate. Advances in statistical theory and related computer technology allow us to use nonparametric and machine learning methods to better estimate the relationship between covariates and outcomes and thus obtain further efficiency gains. However, theoretical studies on how to draw valid inferences when using nonparametric and machine learning methods under stratified randomisation are yet to be conducted. In this paper, we discuss a unified framework for covariate adjustment and corresponding statistical inference under stratified randomisation and present a detailed proof of the validity of using local linear kernel‐weighted least squares regression for covariate adjustment in treatment effect estimators as a special case. In the case of high‐dimensional data, we additionally propose an algorithm for statistical inference using machine learning methods under stratified randomisation, which makes use of sample splitting to alleviate the requirements on the asymptotic properties of machine learning methods. Finally, we compare the performances of treatment effect estimators using different machine learning methods by considering various data generation scenarios, to guide practical research.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

Uniformization and bounded Taylor series in Newton-Raphsonmethod improves computational performance for a multistate transition model estimation and inference.

Multistate transition models (MSTMs) are valuable tools depicting disease progression. However, due to the complexity of MSTMs, larger sample size and longer follow-up time in real-world data, the computation of statistical estimation and inference for MSTMs becomes challenging. A bounded Taylor series in Newton-Raphson procedure is proposed which leverages the uniformization technique to derive maximum likelihood estimates and corresponding covariance matrix. The proposed method, namely uniformization Taylor-bounded Newton-Raphson, is validated in three simulation studies, which demonstrate the accuracy in parameter estimation, the efficiency in computation time and robustness in terms of different situations. This method is also illustrated using a large electronic medical record data related to statin-induced side effects and discontinuation.

R152a-R13I1 mixture as an alternative to R1234yf for a mobile air conditioning: an estimation of flammability properties, thermodynamic and environmental performance

R152a-R13I1 mixture as an alternative to R1234yf for a mobile air conditioning: an estimation of flammability properties, thermodynamic and environmental performance

EMG and SSVEP-based bimodal estimation of elbow angle trajectory

Detecting intentions and estimating movement trajectories in a human–machine interface (HMI) using electromyogram (EMG) signals is particularly challenging, especially for individuals with movement impairments. Therefore, incorporating additional information from other biological sources, potential discrete information in the movement, and the EMG signal can be practical. This study combined EMG and target information to enhance estimation performance during reaching movements. EMG activity of the shoulder and arm muscles, elbow angle, and the electroencephalogram signals of ten healthy subjects were recorded while they reached blinking targets. The reaching target was recognized by steady-state visual evoked potential (SSVEP). The selected target’s final angle and EMG were then mapped to the elbow angle trajectory. The proposed bimodal structure, which integrates EMG and final elbow angle information, outperformed the EMG-based decoder. Even under conditions of higher fatigue, the proposed structure provided better performance than the EMG decoder. Including additional information about the recognized reaching target in the trajectory model improved the estimation of the reaching profile. Consequently, this study’s findings suggest that bimodal decoders are highly beneficial for enhancing assistive robotic devices and prostheses, especially for real-time upper limb rehabilitation.

Small area estimators in a simulation test

The Finnish National Forest Inventory produces municipality level results either with an indirect model-based K nearest neighbor estimator or a direct design-based post-stratification estimator. Design-based approach is unbiased, but not always feasible due to low number of field plots. The K nearest neighbor estimator is lacking an analytical estimator for the variance. A composite estimator combining the indirect and direct estimates could be an attractive solution. In this article, estimators for small-area estimation are analyzed in a simulation experiment with varying size small areas and quality auxiliary data. The potential of estimators is assessed based on the true standard errors and RMSEs in the simulation experiment. Direct estimators and composite estimators work reasonably well with varying quality models, but the performance of indirect estimators is dependent on the quality of the model used. The performance of different estimators also depends on the size of the small areas. Linear models in which the weight of plots outside the target domain is smaller than those within the target domain, performed better than an unweighted model, suggesting that localizing the models for the small areas is beneficial. EBLUP approach also performed well, both in connection of a KNN model and a linear model.

New Approaches to Extremal Index Estimation

The extremal index is a parameter associated with the extreme value distributions of dependent stationary sequences. Under certain local dependence conditions, exceedances above a specified threshold tend to occur in isolated clusters. The reciprocal of the extremal index can be interpreted as the limiting size of these clusters. Accurately estimating the size of such clusters is crucial for analyzing real data and can significantly influence decision making processes that impact population well being. The paper presents a recent method for the estimation of the extremal index which starts by the estimation of the parameter itself and, only then, to use that estimate in the cluster mean size estimation. The procedure starts with the estimation of a specific proportion by the corresponding relative frequency. Thus, it is very simple, intuitive, it has good statistical properties, and it does not depend on the method used for the mean cluster estimation. The interpretation of the extremal index as a proportion is known, but it has not been used directly as an estimation method. In recent years, various authors have proposed different estimators for the extremal index. This paper applies some of the latest estimation methods for the extremal index to real data and analyses their performance using training and test samples. The results are compared with other well known estimators, for which R packages are available. The results show a better performance of the Proportion estimator, followed by the Gaps estimator, when compared to the other considered index estimators.

Estimation Network for Multiple Chemical Parameters of Astragalus Leaves Based on Attention Mechanism and Multivariate Hyperspectral Features

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leverage HybridSN and multilayer perceptron (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983, an RMSE of 0.73, an MAE of 0.49, and an RPD of 7.63 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977, an RMSE of 0.27, an MAE of 0.16, and an RPD of 6.62. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. The findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam

Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R2 = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.