Estimation Algorithm Research Articles

A hybrid fractional order LMS algorithm for power system harmonic estimation

This article proposes a novel multi-innovation fractional order least mean square algorithm for estimating harmonic parameters in power systems with unknown amplitudes and phases. The algorithm incorporates fractional order calculus with variable initial values, which, in contrast to the fixed initial value approach, overcomes the non-locality issue, and converges to its true extreme value. In addition, by extending scalar innovation term to a vector form, the algorithm fully utilizes innovation within a fixed window to improve computational accuracy. The convex combination between integer order and novel fractional order ensures that the performance of the algorithm will not change significantly with the fractional order. Finally, the effectiveness is verified by numerical simulations and comparisons.

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO.

A deep learning-based method for estimating the main stem length of sweet potato seedlings

The length of the main stem of sweet potato seedlings is a crucial criterion for determining whether to harvest it. However, accurately estimating the main stem length is challenging due to two growth forms: leaf occlusion and self-curvature, which complicate automated harvesting and grading. This paper considers the morphological characteristics of sweet potato seedlings and proposes a main stem reconstruction and length estimation algorithm based on a two-stage classifier and feature extraction and search. Semantic segmentation is used to obtain masks of the stems. This is followed by image processing to segment clustered skeletons into singular lines, facilitating feature analysis of the main stem. Object detection is employed to identify regions of interest such as roots, growth joints, and stem-leaf intersections. Using the category and centroid information obtained from object detection, a prioritized search relationship is established among the line segments extracted through image processing. Subsequently, the main stems are classified and reconstructed based on constraints including direction, distance, and curvature. Finally, utilizing stereo depth information, the main stem is divided into four parts to estimate its length. Experimental results demonstrate that the algorithm achieves effective main stem reconstruction under four conditions: un-occluded, partially occluded, curved main stem, and low-density clustered multi-seedling states. The algorithm achieves a median length estimation error of 1.7 cm, with maximum errors of 3.2 cm and minimum errors of 0.3 cm. It provides an automated solution for length estimation during sweet potato seedling harvesting and grading.The length of sweet potato seedling main stems is critical for harvest decisions, but accurately measuring it is challenging due to leaf occlusion and self-curvature. This study proposes a method for stem reconstruction and length estimation using a two-stage classifier, feature extraction, and semantic segmentation to obtain stem masks. Image processing separates clustered skeletons into singular lines for detailed analysis. Object detection identifies roots, stem-leaf intersections, and establishes a search relationship among segments. Stems are reconstructed based on directional, distance, and curvature constraints, then divided into four parts for length estimation using stereo depth information. Experimental results demonstrate effectiveness under various conditions: un-occluded, partially occluded, curved stems, and low-density clusters. Median length estimation error is 1.7 cm, with maximum and minimum errors of 3.2 cm and 0.3 cm respectively. This automated method provides a reliable solution for sweet potato seedling harvest and grading.

Semi-Supervised Learning for Multi-View Data Classification and Visualization

Data visualization has several advantages, such as representing vast amounts of data and visually demonstrating patterns within it. Manifold learning methods help us estimate lower-dimensional representations of data, thereby enabling more effective visualizations. In data analysis, relying on a single view can often lead to misleading conclusions due to its limited perspective. Hence, leveraging multiple views simultaneously and interactively can mitigate this risk and enhance performance by exploiting diverse information sources. Additionally, incorporating different views concurrently during the graph construction process using interactive visualization approach has improved overall performance. In this paper, we introduce a novel algorithm for joint consistent graph construction and label estimation. Our method simultaneously constructs a unified graph and predicts the labels of unlabeled samples. Furthermore, the proposed approach estimates a projection matrix that enables the prediction of labels for unseen samples. Moreover, it incorporates the information in the label space to further enhance the accuracy. In addition, it merges the information in different views along with the labels to construct a consensus graph. Experimental results conducted on various image databases demonstrate the superiority of our fusion approach compared to using a single view or other fusion algorithms. This highlights the effectiveness of leveraging multiple views and simultaneously constructing a unified graph for improved performance in data classification and visualization tasks in semi-supervised contexts.

Raindrop size distribution characteristics of pre-monsoon precipitation observed over eastern India

The knowledge of raindrop size distribution (DSD) is crucial for understanding the microphysical processes involved with the precipitation. Different empirical relationships established with DSD parameters, like radar reflectivity– rainfall rate (Z–R) relationships and shape–slope (μ–Ʌ) relationships, can progress the rainfall estimation algorithms and cloud modeling simulations. In the present study, long-term (2018–2021) measurements of a Laser Precipitation Monitor (LPM) disdrometer installed at the National Institute of Technology, Rourkela, India is used to investigate the DSD characteristics of pre-monsoon (March–May) rainfall. Along with the disdrometer data, auxiliary parameters like convective available potential energy (CAPE), total column water vapor (TCWV), vertical profiles of temperature and relative humidity from reanalysis data sets of ECMWF (European Centre for Medium-Range Weather Forecasts) fifth-generation reanalysis (ERA5) are also used in this study. Based on standardized rainfall anomaly, the pre-monsoon precipitation days are classified into strong, moderate, and weak rainy days, and they contributed to 58.69%, 32.7%, and 8.61% of total rainfall, respectively. The average DSD indicated noteworthy variations among strong, moderate, and weak rainy days with maximum (minimum) concentration of raindrops in strong (weak) rainy days. The mean value of rain rate (R), normalized intercept parameter (Nw), and mass-weighted mean diameter (Dm) is maximum during days of strong rainfall. Strong rainy days showed high-value CAPE, TCWV and vertical profile of relative humidity. The majority of R is contributed by moderate-sized raindrops with a significant difference in the Z–R and μ–Λ relationships among three types of rainy days.

Mass estimation of tractor-semitrailer systems: An approach of dynamics and data fusion-driven in real environments

Accurate mass estimation is crucial for improving vehicle active safety control performance. In the context of tractor-semitrailer systems, the precise estimation of mass presents a formidable challenge. This challenge is rooted in traditional methods affected by model uncertainty, model mismatch, and insufficient training data; furthermore, there is also a lack of algorithm generalization to account for real-world traffic scenarios. To address these challenges, a hybrid algorithm for vehicle mass estimation based on bidirectional long short-term memory (BiLSTM) networks and square-root cubature Kalman filter (SCKF) is proposed in this study. Driven by vehicle time series data, the vehicle mass estimator based on the BiLSTM network is constructed to address model uncertainty. To enhance both estimation accuracy and robustness, the hybrid BiLSTM-SCKF method is constructed by incorporating the BiLSTM mass estimation as the initial state value and measured value in the SCKF algorithm while also formulating a fusion rule. The experimental results under real environments show that the hybrid BiLSTM-SCKF algorithm outperforms single algorithms, particularly under medium and low loads, and the estimated error remains consistently below 10% across all working conditions. Additionally, this proposed method exhibits adaptability to various types of semitrailers, effectively enhancing algorithmic accuracy, reliability, robustness, and interpretability.

Memristive Crossbar Array-Based Probabilistic Graph Modeling.

Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu0.3Te0.7/HfO2/Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs.

Helmert variance component estimation for mixed additive and multiplicative random error model

Abstract With the development of surveying and mapping science, the object of measurement adjustment has been extended from a single observation of the same kind in the past to different precisions of the same kind, or different kinds of observations. There are additive and multiplicative errors related to electronic instrument measurements, which affect the quality of the adjustment results. A single additive error model is difficult to meet the accuracy requirements, so a mixed additive and multiplicative random error model (MAMREM) is needed. Aiming at the problem of inaccurate MAMREM stochastic model, this paper proposes Helmert Variance Component Estimation (VCE) to determine the weight matrix in different types of observations under MAMREM. In this paper, the formula and iterative algorithm of Helmert variance component estimation applied to MAMREM are derived, and the weights of different kinds of observations in the adjustment process are estimated. In order to verify the effectiveness of the method, the digital elevation model experiment and the side network experiment are used to verify the method. The results prove the effectiveness of the method.

Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most cellular systems, such as Bluetooth indoor positioning systems, due to multipath and non-line-of-sight (NLOS) propagation, indoor positioning accuracy is severely affected. In this paper, a comprehensive algorithm that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates is investigated, which is obtained using particle filtering. This algorithm allows us to explore new optimized methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by approximately 25.7% on average.

Evaluation and improvement of parameterization methods for estimating cloudy-sky downwelling surface longwave radiation from geostationary satellite data

ABSTRACT Estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions presents a significant challenge, the parameterization methods used to estimate cloudy-sky DSLR may yield disparate results for the same scenario. Hence, it is imperative to undertake a comparative and validation study of these methods to comprehend their applicability. This study compares and validates five parameterized schemes for estimating cloudy-sky DSLR using data from the Fengyun-4A (FY-4A) and Himawari-8 geostationary satellites. Additionally, an improved algorithm for cloudy-sky DSLR estimation is proposed, integrating the radiation effect of the entire cloud layer and a nonlinear parameterization algorithm. The verification results demonstrate that the nonlinear parameterization algorithm exhibits higher accuracy compared to those reliant on cloud-base temperature. Among these, the improved algorithm has high accuracy similar to the nonlinear algorithms in BSRN (Baseline Surface Radiation Network) and TPDC (National Tibetan Plateau Data Center) sites, its RMSE are 29.62 and 30.09 W/m2, respectively. Sensitivity analysis reveals that cloud type and cloud-base temperature exert a pronounced influence on DSLR estimation, particularly within parameterization algorithms based on cloud-base temperature, warranting thorough consideration. Furthermore, the influence of land cover type and surface elevation, especially in high-altitude regions with bare surfaces, should not be disregarded in DSLR estimation.

A framework for co-existence of radar and communication with joint design

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms.

Development of a Cancer-associated Fibroblast Signature for Evaluating Immunotherapy Response and Prognosis of Hepatocellular Carcinoma.

Alcohol abuse, non-alcoholic fatty liver disease (NAFLD), and hepatitis B and C are the main pathogenic factors of hepatocellular carcinoma (HCC). Though the current understanding of risk factors for HCC has been improved, patients with this type of cancer are normally diagnosed at advanced stages, posing significant challenges to effective treatment. This study analyzed the HCC datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database (GSE14520 and GSE116174). Stromal and immune cell infiltration in the tumor microenvironment (TME) was quantified by the ESTIMATE algorithm. To identify gene modules associated with cancer-associated fibroblasts (CAFs), weighted gene co-expression network analysis (WGCNA) was performed to develop gene co-expression networks. A CAF prognosis score (CAFPS) model was established based on the prognostic CAF genes screened by univariate and multivariate Cox regression analyses. To determine the role of the genes in the vital module in HCC, we conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, the relationship between CAFPS and drug sensitivity was analyzed using Genomic Data for Cancer Drug Sensitivity (GDSC). In this study, we found significant differences in immune scores, stromal scores, CAFs scores, and CD4/8 T-cell scores between normal samples and samples with different TNM staging. In particular, the proportion of CAFs was higher than all other cells in normal samples. Gene modules related to CAFs were identified by developing a gene co-expression network using WGCNA analysis. The lightyellow and greenyellow modules showed the highest correlation with CAF scores. Univariate COX analysis identified 12 genes related to HCC prognosis from a total of 191 genes in the two modules. The Kaplan-Meier (KM) survival analysis revealed that a high expression of these genes was associated with a lower survival chance. Based on the 12 genes obtained by univariate COX analysis, multivariate COX analysis was performed to construct a risk score model for the characteristics of CAFs (CAFPS). The KM survival curves of patients in the high CAFPS and low CAFPS groups showed that patients in the low CAFPS group had better survival. CAFs played a crucial role in the pathogenesis and treatment response of HCC. Targeting the CAFs milieu may provide therapeutic benefits, highlighting the importance of CAFS in developing a personalized treatment for HCC patients. Further studies are required to verify the current findings and explore their implications in clinical settings.

Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing

IntroductionHepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.MethodThe GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.ResultsAn ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.ConclusionThe consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Research and Implementation of Indoor Positioning Algorithm Based on Bluetooth 5.1 AOA and AOD

With the addition of Bluetooth AOA/AOD direction-finding capabilities in the Bluetooth 5.1 protocol and the introduction of antenna array technology into the Bluetooth platform to further enhance positioning accuracy, Bluetooth has gradually become a research hotspot in the field of indoor positioning due to its standard protocol specifications, rich application ecosystem, and outstanding advantages such as low power consumption and low cost compared to other indoor positioning technologies. However, current indoor positioning based on Bluetooth AOA/AOD suffers from overly simplistic core algorithm implementations. When facing different application scenarios, the standalone AOA or AOD algorithms exhibit weak applicability, and they also encounter challenges such as poor positioning accuracy, insufficient real-time performance, and significant effects of multipath propagation. These existing problems and deficiencies render Bluetooth lacking an efficient implementation solution for indoor positioning. Therefore, this paper proposes a study on Bluetooth AOA and AOD indoor positioning algorithms. Through an analysis of the principles of Bluetooth’s newly added direction-finding functionality and combined with research on array signal DOA estimation algorithms, the paper ultimately integrates the least squares algorithm to optimize positioning errors in terms of accuracy and incorporates an anti-multipath interference algorithm to address the impacts of multipath effects in different scenarios. Experimental testing demonstrates that the indoor positioning algorithms applicable to Bluetooth AOA and AOD can effectively mitigate accuracy errors and overcome multipath effects, exhibiting strong applicability and significant improvements in real-time performance.

Sensorless control method of induction motors with new feedback gain matrix and speed adaptive law for low speed range

Abstract The conventional adaptive full-order observer-based speed sensorless control method for induction motor is prone to instability at low-speed region as improper feedback gain matrix and imprecise speed adaptive law are often used. To address these problems, a new feedback gain matrix design approach as well as a new speed adaptive law design technique are proposed in this paper. Firstly, considering that the d-axis current estimation error in the traditional feedback gain matrix design method has a great influence on the stability of the speed estimation algorithm, especially at low speeds, a new feedback gain matrix design method is proposed to minimize the d-axis current estimation error. Secondly, a new speed adaptive law design technique is studied based on the conventional method, which only requires the d-axis and q-axis current estimation error with a weight coefficient to be designed, simplifying the conventional speed adaptive law. Thirdly, the transfer function from the speed observation error to the proposed adaptive error is analyzed by Routh stability criterion theory, and the weight coefficient suitable for full range stable operation is determined by MATLAB software. Fourthly, the stability of the proposed method in this paper is analyzed using the poles distribution maps. Finally, the proposed method is experimentally verified based on a 2.2 kW induction motor experimental platform. The experimental results show that the proposed method can make the induction motor operate steadily at low-speed and zero-speed region with rated load. In addition, the proposed method has better anti-disturbance performance than the existing method.

High-Performance Binocular Disparity Prediction Algorithm for Edge Computing

End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the condition of adaptation operator. Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume. It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy. Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%. The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability.

Ultra-Efficient MCMC for Bayesian Longitudinal Functional Data Analysis

Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bayesian inference with these models only provide either scalable computing or accurate approximations to the posterior distribution, but not both. We introduce a new MCMC sampling strategy for highly efficient and fully Bayesian regression with longitudinal functional data. Using a novel blocking structure paired with an orthogonalized basis reparameterization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate-specific random effects functions. Crucially, the joint sampler optimizes sampling efficiency for these key parameters while preserving computational scalability. Perhaps surprisingly, our new MCMC sampling algorithm even surpasses state-of-the-art algorithms for frequentist estimation and variational Bayes approximations for functional mixed models—while also providing accurate posterior uncertainty quantification—and is orders of magnitude faster than existing Gibbs samplers. Simulation studies show improved point estimation and interval coverage in nearly all simulation settings over competing approaches. We apply our method to a large physical activity dataset to study how various demographic and health factors associate with intraday activity. Supplementary materials for this article are available online.

Modification of algorithm for accurate estimation of Hp(3) based on response of head TLD badge calibrated using ISO cylindrical phantom.

In India, the prevailing approach to eye lens dosimetry is the placement of an existing dosemeter on the forehead region after slight modification serves as a dedicated Eye Lens Dosemeter. A methodology for estimating the eye lens dose in terms of the Hp(3) has been previously explored employing an algorithm based on the response characteristics of this dosemeter using ISO slab phantom. It was observed that the performance of the dosemeter in terms of Hp(3) using previous algorithm showed under response at higher angles of incidence and photon beams of energy < 200keV. Further, study was conducted to modify the algorithm following the latest ISO recommendations. This involved generation of data from the response of existing dosemeter on a cylindrical phantom. The results of this study revealed better performance of the newly established algorithm in estimating eye lens dose in terms of Hp(3) when compared to the earlier algorithm.

New Perspective to Evaluate the Carbon Offsetting by Urban Blue-Green Infrastructure: Direct Carbon Sequestration and Indirect Carbon Reduction.

Urban blue-green infrastructure (BGI) offers a multitude of ecological advantages to residents, thereby playing a pivotal role in fortifying urban resilience and fostering the development of climate-resilient cities. Nonetheless, current research falls short of a comprehensive analysis of BGI's overall potential for carbon reduction and its indirect carbon reduction impact. To fill this research gap, we utilized the integrated valuation of ecosystem services and trade-offs model and remote sensing estimation algorithm to quantify the direct carbon sequestration and resultant indirect carbon reduction facilitated by the BGI within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (China). To identify the regions that made noteworthy contributions to carbon offsets and outliers, spatial autocorrelation analysis was also employed. The findings of this study reveal that in 2019, the BGI within the study area contributed an overall carbon offset of 1.5 × 108 t·C/yr, of which 3.5 × 107 and 11.0 × 107 t·C/yr were the result of direct carbon sequestration and indirect carbon reduction, respectively. The GBA's total CO2 emissions were 1.1 × 108 t in 2019. While the direct carbon sequestration offset 32.0% of carbon emissions, the indirect carbon reduction mitigated 49.9% of potential carbon emissions. These results highlight the critical importance of evaluating BGI's indirect contribution to carbon reduction. The findings of this study provide a valuable reference for shaping management policies that prioritize the protection and restoration of specific areas, thereby facilitating the harmonized development of carbon offset capabilities within urban agglomerations.

Research on Inter-Satellite Laser Ranging Scale Factor Estimation Methods for Next-Generation Gravity Satellites

The scale factor serves as a ruler for converting raw phase measurements into physical displacements and significantly impacts the preprocessing of data from the Laser Ranging Interferometer (LRI) in laser ranging systems. In the current GRACE Follow-On (GRACE-FO) mission for low–low tracking gravity satellites, most of the existing LRI scale factor estimation algorithms heavily rely on cross-calibration between instantaneous/biased ranges from the Ka-Band Ranging Interferometer (KBR) and the LRI. However, due to the nonlinearity of the objective function (which includes terms involving the product of scale and time shifts), the scale factor estimation may absorb errors from timing noise. Moreover, future gravity missions or gravity detection tasks may no longer incorporate KBR ranging instruments. To address these challenges, this paper proposes an energy-based method for scale factor estimation using inter-satellite baseline solutions. Comparative analysis indicates that the proposed method effectively disentangles two parameters in the objective function and can be applied in scenarios where KBR data are unavailable, demonstrating promising prospects for practical application. The experimental results show that when the KBR validation residuals are lower than 0.8 mm, the SYSU LRI1B V01 products exhibit residuals below the payload design accuracy thresholds in the frequency band of 2 mHz to 0.1 Hz. Additionally, the stability of the scale factors obtained from the baseline can reach 10−7. Although this is still below the required precision of better than 10−8 for the laser frequency stability in next-generation gravity satellites, advancements in orbit determination technology and the enhanced stability of GPS receivers offer potential for future precision improvements. Currently, this method appears suitable for roughly extracting the scale factor as a stochastic mean over several months or serving as a backup validation strategy for future missions, but it is not well suited to measure day-to-day variations.