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Structural equation modeling (SEM) is commonly used in marketing and the social sciences such as education, psychology, and economics. A prominent issue thereby is the assessment of model fit. Although there is a huge variety of fit indices, researchers do not know how well they correspond to the estimation accuracy in terms of discrepancy between the estimated model values and true values of the respondents. By means of a comprehensive simulation study, we show in this paper that our newly developed estimation accuracy fit index (EAFI) evaluates this kind of estimation accuracy significantly better than existing indices and thereby comes closest to the actual relations between the model’s variables. Furthermore, we derive context-specific EAFI orientation points. Our findings provide guidance for researchers in management, psychology, marketing, education, and the medical sector to evaluate the model fit.

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

This research introduces a novel convex methodology for estimating transmission loss coefficients ( B -coefficients) in power systems using a data-driven approach based on power system measurements. To enhance estimation accuracy and practical relevance, the model is evaluated across a wide spectrum of operating conditions, incorporating random variations in active power injections and demand profiles modeled via uniform and Gaussian distributions. A semi-definite programming (SDP) model leveraging p -norm formulations is proposed to derive the B -coefficients efficiently. Numerical evaluations on IEEE 14-, 39-, 57-, and 118-bus test feeders demonstrate the effectiveness and robustness of the approach, yielding average estimation errors between − 6 % and 5 % across diverse scenarios. These results confirm the reliability of the proposed methodology, contributing to improved accuracy in transmission loss modeling and supporting more efficient power system operations.

In order to apply the Z2 statistic to the estimation of the navigation pulsar period and time delay, and thus further improve the accuracy of X-ray pulsar navigation, this paper proposes a method for estimating the pulsar period and time delay with complex profiles based on the improved Zn,bin2 -test. Based on the prior information of the amount of photon data in the observation task, this method adaptively determines the optimal harmonic truncation order under different data qualities. In order to solve the problem of the change rate of complex profile signals being too fast, the binned data is rephased according to the principle of equal photon intensity segmentation within the bin, and the optimal number of bins suitable for the PSR B0531+21 pulsar is selected. Through simulation, a quantitative analysis was conducted on factors that affect the performance of the estimation algorithm, such as observation time, detector area, noise interference, etc. Simulation results show that the proposed estimation method has greater advantages when the observation time is short, the detector area is small, and the interference noise is large. In addition, the observation data of the PSR B0531+21 pulsar is processed and analyzed. The period estimation accuracy of the method proposed is 3.6532 ns, which is 39.57% higher than that of the χ2-test method. The method we proposed has the advantages of being suitable for navigation pulsars, strong environmental adaptability, high estimation accuracy, and strong estimation stability, which can further improve the performance of X-ray pulsar navigation.

China will launch the “Tianwen-IV” mission around 2030, focusing on the orbiting exploration of Jupiter and Callisto, a moon of Jupiter. As part of this ambitious mission, a main satellite will carry another satellite that will be released in the Jupiter system to continue its journey toward Uranus. Considering the current mission planning, we propose an inter-satellite radio-observation mode that differs from the conventional observation mode of tracking from Earth to precisely determine the orbit of the satellites. Given the significance of the Callisto gravity field model in both science objectives and satellite navigation, we have conducted a series of simulation experiments to evaluate the potential of this inter-satellite range-rate data for accurately estimating the Callisto gravity field. The results obtained from the analysis demonstrate that by utilizing 40 days of ground station observations, it is possible to estimate the gravity field model of Callisto up to a degree of 70. Remarkably, when combining these ground station observations with inter-satellite observations, a comparable level of accuracy can be achieved with just 10 days of observations. Furthermore, with reduced inter-satellite observation noise, accuracy improves, enabling estimation up to 80 degrees or higher. Initial inter-satellite distance selection impacts estimation accuracy. These findings serve as a valuable test bed for the future “Tianwen-IV” mission to perform precise orbit determination and gravity field model estimation to reduce reliance on deep space stations.

Multi‐source data merging via weighted average (WA) is widely applied to enhance large‐scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events—undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi‐source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data‐merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA‐based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks. Plain Language Summary Remote sensing (RS) and reanalysis systems are crucial for estimating large‐scale precipitation. Weighted averaging (WA) of different data sets can enhance overall precipitation estimation accuracy and has been widely applied for generating global precipitation data sets. However, WA algorithms often lead to biases for extreme precipitation (EP). Such issues undermine the usefulness of WA‐based precipitation data sets for flood forecasting. This study investigates the sources of EP biases in WA frameworks, based on surface precipitation gauge observations and numerical experiments. Results show that the WA algorithms themselves contribute less than 1% to EP biases. Instead, most EP bias is related to RS/reanalysis data correction procedures. Specifically, current WA methods only adjust the monthly means or general statistical distributions of the input data. However, EP occurrence errors are often neglected during the precipitation correction. This means that the timing and location of EP as estimated by different data sets are not entirely consistent, leading to substantial biases when they are averaged. Therefore, to improve the accuracy of EP estimates, it is important to develop preprocessing methods that better account for both the timing and intensity errors of extreme events. Key Points We investigate sources of bias in extreme precipitation (EP) estimates provided by commonly used data merging frameworks We demonstrate that EP biases arise from the neglect of EP timing error correction and not the merging algorithm Algorithms that iteratively adjust the EP intensities and timing errors should be prioritized in future merging frameworks

Human pose estimation (HPE) has made significant progress with deep learning; however, it still faces challenges in handling occlusions, complex poses, and complex multi-person scenarios. To address these issues, we propose PoseNet++, a novel approach based on a 3-stacked hourglass architecture, incorporating three key innovations: the multi-scale spatial pyramid attention hourglass module (MSPAHM), coordinate-channel prior convolutional attention (C-CPCA), and the PinSK Bottleneck Residual Module (PBRM). MSPAHM enhances long-range channel dependencies, enabling the model to better capture structural relationships between limb joints, particularly under occlusion. C-CPCA combines coordinate attention (CA) and channel prior convolutional attention (CPCA) to prioritize keypoints’ regions and reduce the confusion in complex multi-person scenarios. The PBRM improves pose estimation accuracy by optimizing the receptive field and convolutional kernel selection, thus enhancing the network’s feature extraction capabilities in multi-scale and complex poses. On the MPII validation set, PoseNet++ improves the PCKh score by 3.3% relative to the baseline 3-stacked hourglass network, while reducing the number of model parameters and the number of floating-point operations by 60.3% and 53.1%, respectively. Compared with other mainstream human pose estimation models in recent years, PoseNet++ achieves the state-of-the-art performance on the MPII, LSP, COCO and CrowdPose datasets. At the same time, the model complexity of PoseNet++ is much lower than that of methods with similar accuracy.

We analyze the parameter estimation accuracy that can be achieved for the mass and spin of Sgr A*, the supermassive black hole in our Galactic center, by detecting multiple extremely large mass ratio inspirals (XMRIs). XMRIs are formed by brown dwarfs inspiraling into a supermassive black hole, thus emitting gravitational waves (GWs) inside the detection band of future space-based detectors such as LISA and TianQin. Theoretical estimates suggest the presence of approximately 10 XMRIs emitting detectable GWs, making them some of the most promising candidates for space-based GW detectors. Our analysis indicates that even if individual sources have low signal-to-noise ratios (SNRs; ≈10), high-precision parameter estimates can still be achieved by detecting multiple sources. In this case, the accuracy of the parameter estimates increases by approximately 1–2 orders of magnitude at least. Moreover, by analyzing a small sample of 400 initial conditions for XMRIs formed in the Galactic center, we estimate that almost 80% of the detectable XMRIs orbiting Sgr A* will have eccentricities between 0.43 and 0.95 and an SNR ∈ [10, 100]. The remaining ∼20% of the sources have an SNR ∈ [100, 1000] and eccentricities ranging from 0.25 to 0.92. Additionally, some XMRIs with high SNRs are far from being circular. These loud sources with SNR ≈ 1000 can have eccentricities as high as e ≈ 0.7; although their detection chances are low, representing ≲2% of the detectable sources, their presence is not ruled out.

To further improve the accuracy and speed of real-time dynamic estimation of X-ray pulsar periods, this paper proposes a pulsar period estimation model based on the interlayer phase difference (IPD) of the fast folding algorithm (FFA) and the weighted Z2 (WZ) test. This paper adopts a staged estimation strategy and divides the pulsar period estimation into a fast initial estimation stage and a local refinement search stage. First, in the fast initial estimation stage, an FFA IPD model based on the relationship among phase, time, and period is established. The interlayer phase is used to directly perform a single initial estimation of a large range of periods, thereby improving the period estimation speed. Second, in the local refinement search stage, the response coverage index is proposed for the Z2 test function. The WZ test function is constructed to perform a refinement test on the local candidate period to improve the period estimation accuracy. Meanwhile, for the PSR B0531+21 source, we conducted ablation tests, analyzed influencing factors and simulation performance of the proposed method, and validated its practical application performance using Neutron Star Interior Composition Explorer observation data. We also performed generalization performance tests on other sources such as PSR B0540-69 and SMC X-1. The results show that our method has significant advantages compared to several existing estimation methods. Specifically, for the PSR B0531+21 source, compared to the integrated χ2 test method, our method improves estimation accuracy by 50.21% and reduces computational time by 73.47%.