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A non-periodic "step-like" variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. [ABSTRACT FROM AUTHOR]

Expansive soils pose a significant challenge in geotechnical engineering, especially in coastal areas. While research has mainly focused on their elastic properties, this study explores the overlooked aspect of inelastic subsidence during prolonged droughts, utilizing decade‐long GPS datasets from the University of Houston Coastal Center. Our findings reveal substantial subsidence, approximately one to two dm, during the summer droughts of 2018, 2020, 2022, and 2023, due to compaction within the upper 4 m of expansive soils. Inelastic subsidence constitutes roughly 10% of the total subsidence, resulting in step‐like permanent land elevation loss over time. Notably, drought‐induced subsidence is prominent in open‐field areas with expansive soils but is minor in built‐up areas or in non‐expansive soil regions. The occurrence of inelastic subsidence challenges traditional assessments of relative sea‐level rise and coastal flooding, emphasizing the need to consider it in coastal infrastructure planning for enhanced resilience against climate uncertainties. Plain Language Summary: Expansive soils, often found in coastal regions, are known for causing issues like land shifts and unstable buildings. Our research adds a new dimension: prolonged droughts can lead to significant, irreversible sinking of expansive soils, permanently lowering the elevation of open land areas. Using a decade of GPS data, we found that during droughts, these soils can sink considerably and will not fully recover. Interestingly, in developed regions where pavement covers the soils, thereby minimizing moisture loss, this sinking is observed to be minimal. In the Galveston coastal area, drought‐induced sinking can reach one to two dm, with irreversible subsidence making up about 10% of the total subsidence. This becomes a growing concern as droughts become more frequent due to climate change, especially in coastal areas. Additionally, our results suggest that current methods for estimating sea‐level rise may be missing a key factor: we have underestimated the speed at which uninhabited coastal lands are sinking because we did not account for this irreversible sinking due to droughts. Key Points: Prolonged droughts cause up 2 dm of subsidence in shallow expansive soils (<4 m deep) in Galveston, TX, 10% of which is inelasticProlonged droughts lead to notable subsidence in open fields with expansive soils, but less in built‐up areas or non‐expansive soil areasRecurring droughts lead to permanent land elevation losses in open‐field coast, possibly skewing sea‐level rise and flood risk projections [ABSTRACT FROM AUTHOR]

GNSS phase observable-specific signal bias (OSB) corrections are essential for widespread application of precise point positioning with ambiguity resolution (PPP-AR) or PPP-RTK. However, subject to the orbital error effects, conventional undifferenced (UD) model-derived BeiDou System (BDS) real-time (RT) OSB products are usually unsatisfactory. In this study, a novel OSB-generating method assisted by the double-differenced (DD) model is proposed. The reliable integer UD ambiguities are obtained by converting DD ambiguities with given ambiguity datums, by which the RT orbit error effects on ambiguity fixing can be reduced during the OSB extraction and PPP-AR process. Validated using data from two regional sparse GNSS reference networks in Shaanxi, China, and Europe, results show that the proposed method-derived OSB products can improve RT PPP-AR performance effectively. In the Shaanxi network, the narrow-lane ambiguity residuals for BDS-3 within ± 0.25 cycles are improved by 23.1% and 33.2% compared to those using the UD model and Centre National d’Etudes Spatiales (CNES)-derived OSB products, respectively, and the corresponding values are 15.2% and 43.1% in the European network. A centimeter- or even millimeter-level positioning accuracy can be achieved for BDS PPP using the poposed OSB products in both networks. In the kinematic PPP-AR test within the Shanxi network, the mean RMS of the BDS-2/3 fixed solutions in the east, north, and up directions is 0.9, 0.7, and 2.3 cm, with a decrease of 57.1%, 53.3%, and 46.5% compared to that using OSB derived by UD model. The median Time-To-First-Fix (TTFF) is also shortened from 23.8 to 7.5 min. [ABSTRACT FROM AUTHOR]

For real-time (RT) precise point positioning (PPP), the state space representation (SSR) information is often delayed due to possible communication delays and specific broadcast intervals. In this case, the positioning results will diverge and re-converge due to the increase of SSR products extrapolation errors. In addition, RT orbit and clock offset accuracy, as well as their extrapolation errors, will vary in different systems and satellites. We propose a PPP with ambiguity resolution (PPP-AR) method that combines a time-differenced carrier phase (TDCP) model, in which the characteristics of the orbit and clock are considered. Under normal communication, the PPP-AR solution is obtained by fixing satellites with small SSR product errors. When the communication is abnormal, the TDCP model is utilized to extrapolate user coordinates by considering different extrapolation error characteristics of satellites. The experimental results show that GPS and Galileo SSR products have better accuracy than BDS, with signal-in-space user ranger errors (SISREs) of 2.7, 2.2, and 8.6 cm, respectively. Optimizing the PPP stochastic model based on SISREs can effectively reduce the convergence time. Under 5 min SSR delay, SISREs caused by clock and orbit extrapolation for GPS/Galileo/BDS are 3.5, 1.4, and 2.6 cm, respectively. After optimizing the TDCP stochastic model based on extrapolation errors, the horizontal and vertical positioning accuracies can be maintained at 0.7 cm and 5.0 cm. For multi-GNSS, the combination of the TDCP and PPP-AR can overcome the influence of short delay. After optimizing the stochastic model, the GPS/Galileo/BDS positioning accuracy can be maintained at about 2.4 cm under 3 min delay, showing an accuracy improvement rate of 59.3% compared with the traditional method using only PPP. Additionally, the rapid PPP convergence results can be obtained by inheriting previous filter state information when the communication recovers normally. [ABSTRACT FROM AUTHOR]

The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by n -fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is a chronic renal disease which could eventually develop into renal failure. Though albuminuria and estimated glomerular filtration rate (eGFR) are helpful for the diagnosis of DKD, the lack of specific biomarkers reduces the efficiency of therapeutic interventions.Methods: Based on bulk-seq of 56 urine samples collected at different time points (including 11 acquired from DKD patients and 11 from healthy controls), in corporation of scRNA-seq data of urine samples and snRNA-seq data of renal punctures from DKD patients (retrieved from NCBI GEO Omnibus), urine-kidney specific genes were identified by Multiple Biological Information methods.Results: Forty urine-kidney specific genes/differentially expressed genes (DEGs) were identified to be highly related to kidney injury and proteinuria for the DKD patients. Most of these genes participate in regulating glucagon and apoptosis, among which, urinary PART1 (mainly derived from distal tubular cells) and PLA2R1 (podocyte cell surface marker) could be used together for the early diagnosis of DKD. Moreover, urinary PART1 was significantly associated with multiple clinical indicators, and remained stable over time in urine.Conclusion: Urinary PART1 and PLA2R1 could be shed lights on the discovery and development of non-invasive diagnostic method for DKD, especially in early stages. [ABSTRACT FROM AUTHOR]

The Real-Time Kinematic (RTK) positioning method of the Global Navigation Satellite System (GNSS) has been widely used for landslide monitoring. The stability of its reference station is crucial to obtain accurate and reliable monitoring results. Unstable reference stations due to the geological environment and human activities are difficult to detect and in practical applications often ignored. As a result, it affects the positioning solutions and subsequently the interpretation and detection of landslide motions, which must be addressed in GNSS landslide monitoring. To solve this problem, we propose using the Precise Point Positioning (PPP) technique to analyze the stability of the reference station by verifying its position. The deformations of the monitoring stations are then compensated. First, the reference station coordinates are obtained by the PPP technique and tectonic motion is considered in data processing. The change or breakout of the reference station position is then determined using a cumulative sum control chart method. Finally, each monitoring station's displacements are compensated according to the displacements of the reference station. According to the results of the Tengqing landslide experiment, the PPP technique can be used in GNSS landslide monitoring to analyze the stability of reference stations. With PPP, millimeter-level accuracy for the coordinates of reference stations is achieved. Compared to the traditional deformation series, the compensated displacement series more reliably reflects the landslide motions. This study will increase the reliability of monitoring results and contribute to implementing GNSS in monitoring landslides. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) positioning technology is widely used in landslide monitoring due to its unique real-time and high-precision advantages. However, in complex landslide environments with severe occlusion, multipath error becomes one of the main error sources affecting GNSS real-time kinematic high-precision positioning, as it cannot be eliminated by differential correction. Traditional sidereal filtering methods cannot retain deformation information, which affects the accuracy of landslide early warning. To mitigate multipath error and preserve landslide deformation information, an improved sidereal filtering method based on mutation points and landslide evolution stage recognition is proposed. The method considers the landslide evolution stage, establishes a dataset for sidereal filtering, matches the multi-day correlation data with the sliding section-related data, and constructs a multipath model using averaging. Experimental results show that, compared with the traditional sidereal filtering and conventional segmentation methods, the proposed method reduces the coordinate deviation (QE) in the 3D direction by 25.02% and 22.13% and reduces the fitting deviation (QR) by 4.01% and 36.21% during the stabilization stage. During the critical sliding stage, QE decreases by 48.54% and 23.8%, and QR decreases by 60.12% and 42.54%, respectively. The proposed method has the best smoothness and power spectral density indicators in both stages. It has also been successfully applied to the Heifangtai landslide in Gansu Province, providing more stable and reliable data than traditional methods, avoiding delayed and false alarms, and issuing a red warning 3 h before the landslide failure, demonstrating its practical value. [ABSTRACT FROM AUTHOR]

Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non‐invasive diagnostic tools. We downloaded scRNA‐seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP‐Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP‐Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP‐Genes might be involved in the immune response and extracellular components. Six key EP‐Genes were identified and correlated with renal function. IMC showed that key EP‐Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non‐invasive diagnosis of FSGS. [ABSTRACT FROM AUTHOR]

Non-holonomic constraint (NHC) is a commonly used enhancement technology for GNSS/SINS vehicle positioning. Due to the vehicle's inevitable side slip, bounce, and other special movements during driving and the non-ignorable IMU installation angle, the traditional NHC can no longer satisfy the zero-value assumption for vehicle lateral and vertical velocity. The latest research demonstrates that machine learning (ML) can establish a mapping relationship between IMU output and NHC pseudo-measurements and directly predict the NHC value in the observation domain, which can improve the NHC performance. However, the existing ML-based NHC prediction and corresponding stochastic model are not sufficiently accurate and robust, especially for complex vehicle movements, such as turning. Moreover, the current studies mainly focus on IMU-only dead reckon (DR) positioning and lack feasible verification of NHC-assisted GNSS/SINS integrated positioning performance during the complete travel. Thus, we study a convolutional neural network based on a vehicle motion state classification (VMSC) for NHC prediction and an adaptive stochastic model in the variance domain using the NHC innovation residuals. Additionally, we propose an “end-to-end” framework for adaptive NHC-assisted GNSS/SINS vehicle positioning during the whole traveling, and then, the validation experiment is conducted with the vehicle GNSS/SINS tightly coupled real-time kinematic positioning in the urban environment. Numerical results show that the average predicted accuracy of VMSC-based NHC-Nets can reach approximately 2 cm/s, and by using the new NHC, the positioning error is reduced by around 91.4% compared with the IMU-only DR during a 60-s GNSS outage; for the whole traveling, the GNSS/SINS positioning accuracy achieved a significant improvement, especially in the heavily limited GNSS observation environment. Due to a relatively high DR accuracy, the GNSS ambiguity fixed rate and satellite availability can also be promoted to a certain extent. [ABSTRACT FROM AUTHOR]

Due to the multipath and signal shading effects, there exist biases in the carrier phase measurements, which degrades the reliability of ambiguity resolution in real-time kinematic (RTK) positioning and, subsequently, the positioning accuracy. Integrity monitoring is an effective way to measure, improve, and guarantee the correctness of the RTK positioning solutions, but most integrity monitoring has focused only on the pseudorange faults and further the traditional ambiguity validation methods cannot monitor faulty ambiguity resolution separately. To provide high accuracy and high reliability for safety–critical applications, this study proposes a solution separation (SS)-based method for integrity monitoring of RTK precise positioning, in which the SS failure mode and test statistics are used to monitor the reliability of fixed ambiguity solutions by detecting faulty ambiguity fixes and bound positioning errors in the position domain using protection level (PL). The vehicular test with a low-cost receiver shows that the SS method can effectively detect and exclude fault ambiguity resolutions by 98.35%, and the double-difference residuals of SS are reduced from 6.89 cm to 2.56 cm. The positioning accuracy with the SS method has gained an 83.64% improvement compared with the traditional methods. The horizontal PL and the vertical PL can also effectively bound the positioning errors, with average values of 4.49 cm and 12.25 cm, respectively. The SS PL can achieve 99.17% and 96.14% in the horizontal and vertical directions to provide normal integrity service, respectively. [ABSTRACT FROM AUTHOR]

Smartphones with the Android operating system can acquire Global Navigation Satellite System (GNSS) raw pseudorange and carrier phase observations, which can provide a new way for the general public to obtain precise position information. However, only postprocessing precise orbit and clock offset products in some older smart devices are applied in current studies. The performances of precise point positioning (PPP) with the smartphone using real-time products and newly smartphones are still unrevealed, which is more valuable for real-time applications. This study investigates the observation data quality and multi-GNSS real-time PPP performance using recent smartphones. Firstly, the observed carrier-to-noise density ratio (C/N0), number of satellites and position dilution of precision (PDOP) of GNSS observations are evaluated. The results demonstrate that the C/N0 received by Huawei Mate40 is better than that of the Huawei P40 for GPS, BDS, QZSS and Galileo systems, while the GLONASS is poorer, and the PDOP of the Huawei P40 is slightly better than that of Mate40. Additionally, a comprehensive analysis of real-time precise orbit and clock offset products performance is conducted. The experiment result expresses that the orbit and clock offset performance of GPS and Galileo is better than that of BDS-3 and GLONASS, and BDS-2 is the worst. Finally, single- and dual-frequency multi-GNSS combined PPP experiments using observations received from smartphones and real-time products are conducted; the results indicate that the real-time static PPP using a smartphone can achieve decimeter-level positioning accuracy, and kinematic PPP can achieve meter-level positioning accuracy after convergence. [ABSTRACT FROM AUTHOR]

Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides' strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value. [ABSTRACT FROM AUTHOR]

The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and timeliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7–8 min, and 24 were for GEO satellites with an average detected time of 4–5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files. [ABSTRACT FROM AUTHOR]

Real-time satellite clock offset is a crucial element for real-time precise point positioning (RT-PPP). However, the elapsed time for undifferenced (UD) multi-global navigation satellite system (GNSS) real-time satellite clock offset estimation at each epoch is increased with the growth of stations, which may fall short of real-time application requirements. Therefore, a rapid estimation method for UD multi-GNSS real-time satellite clock offset is proposed to improve the computation efficiency, in which both the dimension of the normal equation (NEQ) and the number of redundant observations are calculated before adjustment; if these two values are larger than the predefined thresholds, the elevation mask is gradually increased until they are less than the predefined thresholds. Then, the clock offset estimation is conducted; this method is called clock offset estimation using partial observations. Totals of 50, 60, 70 and 80 stations are applied to perform experiments. Compared to clock offset estimation using all observations, the elapsed times of clock offset estimation using partial observations can be reduced from 6.80 to 3.10 s, 7.93 to 2.97 s, 12.04 to 3.14 s for 60, 70 and 80 stations, respectively. By using the proposed method, the elapsed time of the clock offset estimation at each epoch is less than 5 s. The estimated clock offset accuracy for GPS, BDS-3, Galileo and GLONASS satellites are better than 0.04, 0.05, 0.03 and 0.16 ns when using the partial observations to estimate clock offset with 50, 60, 70 and 80 stations, respectively. For the multi-GNSS kinematic PPP using the estimated clock offset from 50, 60, 70 and 80 stations with partial observations, the positioning accuracy at 95% confidence level in the east, north and up direction are better than 2.70, 2.20 and 5.60 cm, respectively. [ABSTRACT FROM AUTHOR]

Currently, Global Navigation Satellite System (GNSS) Real-Time Kinematic positioning (RTK) and Precise Point Positioning (PPP) techniques are widely employed for real-time monitoring of landslides. However, both RTK and PPP monitoring techniques have their limitations, such as limited service coverage or long convergence times. PPP-RTK technique which integrates RTK and PPP is a novel approach for monitoring landslides with the advantages of rapid convergence, high-precision, and a wide service area. This study summarizes the limitations of RTK, PPP, and PPP-RTK monitoring techniques and suggests some improved strategies. Their performances are compared and analyzed using real monitoring data. The experiment results demonstrate that RTK is the best option for small-scale (the baseline distance < 15 km) and real-time landslide monitoring without considering the cost. PPP technique converges to centimeter-level accuracy in tens of minutes, only suitable for the stability analysis of reference stations. Over a large area (the baseline distance < 100 km), PPP-RTK can provide excellent horizontal accuracy and adapt the service range in response to the demand for monitoring accuracy, as the vertical accuracy is significantly impacted by the service range and elevation difference. Finally, the characteristics of three techniques are integrated to form a comprehensive landslide monitoring technique that considers intelligence, robustness, and real-time. [ABSTRACT FROM AUTHOR]

Real-time satellite clock offsets are key in realizing real-time precise point positioning (PPP). Typically, tens of minutes are required for re-convergence because many tropospheric and ambiguity parameters are estimated in the clock offset estimation when the interruption of observation data occasionally occurs. This contribution proposes a rapid re-convergence method for multi-global navigation satellite system (GNSS) real-time satellite clock offset estimation. In case of a few seconds of interruption, the dual-thread integer ambiguity resolution method is proposed. The float clock offset, uncalibrated phase delay, fixed and float ambiguities, clock offset with IAR, zenith wet delay (ZWD) and inter-system bias (ISB) are generated in a slow thread, in which the fixed and float ambiguities, ZWD and ISB are used in a quick thread to achieve rapid re-convergence. Observations from 60 globally distributed stations were processed using the proposed method. The experimental results show that rapid re-convergence, fast estimation and high-precision clock offsets can be achieved. The re-convergence time of clock offset estimation was 0.14, 0.15, 0.74, 0.36 and 2.48 min for the GPS, Galileo, BDS-2, BDS-3 and GLONASS, respectively. Most satellite clock offsets can realize instantaneous re-convergence. The clock offset standard deviations are 0.028, 0.024, 0.069, 0.043 and 0.125 ns, respectively. The computation times were 21.4 and 1.7 s for the slow and quick threads, respectively. Compared to the float PPP solutions, the positioning accuracy of the PPP-AR was improved by 53.9, 29.9 and 18.2% from 1.80 to 0.83 cm, 1.57 to 1.10 cm and 3.63 to 2.97 cm for the east, north and up components, respectively. In case of interruptions lasting from a few minutes to tens of minutes, the ZWD, ambiguities and ISB information before interruption are used in the clock offset estimation, and a strong constraint is imposed on the normal equation, i.e., a large value (e.g., 105) is used instead of the diagonal value of the normal equation matrix corresponding to each estimated parameter. Experimental results show that rapid re-convergence can be achieved for GPS and Galileo with an interruption of 10 min, whereas the rapid re-convergence cannot be achieved for BDS-2, BDS-3 and GLONASS because of the impact of satellite orbit accuracy, error model, and neglection of the inter-frequency bias, respectively. [ABSTRACT FROM AUTHOR]

The BeiDou Global Satellite Navigation System (BDS-3) broadcast newly developed B1C and B2a signals. To provide a better service for global users, the vertical phase center offset (PCO) and phase center variation (PCV) are estimated for the B1C/B2a ionospheric-free linear combination of the BDS-3 inclined geostationary orbit (IGSO) and medium earth orbit (MEO) satellites in this study. And considering the traditional PCC estimation method needs two Precise orbit determination (POD) processing, based on the correlation between PCO z-offset and PCV, the theoretical analysis and experimental comparison have been made to discuss whether the POD procedure for the PCO estimation can be omitted. The estimated z-offset time series revealed the inadequacy of the solar radiation pressure (SRP) model for the IGSO satellites and the MEO satellites with Pseudo Random Noise code (PRN) C45 and C46. The PCVraws estimated by the traditional method and the PCO estimation omitted method have the same characteristic. The final PCO z-offsets and PCVs calculated by the two schemes agreed very well with differences can be harmlessly ignored, which confirmed that the PCO estimation can be safely omitted to save computation time. The PCC model proposed in this study has been compared with the Test and Assessment Research Center of China Satellite Navigation Office (TARC/CSNO) released model, the qualities of the orbits and BDS-only precise point positioning (PPP) solutions of the new model both show improvements, except for the IGSO orbits. The analysis of the IGSO orbits further verifies the SRP model is not suitable for the IGSO satellites. [ABSTRACT FROM AUTHOR]

Compared with annual crops, perennial crops with longer growing seasons and deeper root systems can fix more sunlight energy, and have advantages in reducing soil erosion and saving water, fertilizer and pesticide inputs. Rice is one of the most important food crops in the world. Perennial rice can be of great significance for protecting the ecological environment and coping with the shortage of young farmers due to urbanization. Oryza longistaminata (OL) is a rhizomatous wild rice with an AA genome and has strong biotic and abiotic resistances. The AA genome makes OL easy to cross with cultivated rice, thus making it an ideal donor material for perennial rice breeding. Sucrose plays an important role in the development and growth of plants. In this study, OL seedlings were cultured in medium with different concentrations of sucrose, and it was found that sucrose of appropriate concentrations can promote the sprout of basal axillary buds and the subsequent development of rhizomes. In order to explore the molecular mechanism, comparative transcriptome analysis was carried out with OL cultured under two concentrations of sucrose, 20 g/L and 100 g/L, respectively. The results showed that the boost of sucrose to rhizome elongation may be due to the glucose and fructose, hydrolyzed from the absorbed sucrose by vacuolar acid invertase. In addition, the consequent increased osmotic pressure of the cells would promote water absorption, which is benefit for the cell elongation, eventually causing the rhizome elongation. These results may provide a reference for elucidating the regulatory mechanism of sucrose on the rhizome development of OL. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is a core technology that helps to speed up convergence time and increase positioning accuracy for precise point positioning (PPP), and the performance of PPP-AR is based on the quality of ambiguity resolution products. Real-time PPP-AR becomes a reality as users can now obtain publicly accessible real-time observable-specific signal bias (OSB) products from the Centre National d'Etudes Spatiales (CNES). Therefore, an analysis of the quality of OSB products and an evaluation of the performance of PPP-AR are required to promote the application of real-time positioning. For a total of 31 days between day of year (DOY) 121 and 151 in 2021, observation data were collected from 90 stations, and the OSB products were used to assess the experiments. As for the quality of the OSB products, the data availability (DA) of the GPS and Galileo satellites was greater than 97%, whereas that of BDS was less than 60%; the maximum fluctuation value (MAX) and standard deviation (STD) of the GPS, Galileo, and BDS satellites were 0.045 and 0.012; 0.081 and 0.028; and 0.292 and 0.085 cycles, respectively. In terms of ambiguity residuals using the OSB products, the wide-lane (WL) residual percentages within ±0.25 cycles for the GPS, Galileo, BDS-2, and BDS-3 systems were more than 92%, and the narrow-lane (NL) residual percentages within ±0.25 cycles for the four systems were 92%, 89%, 79%, and 60%, respectively. With regard to the performance of PPP-AR, the GPS+Galileo solution showed the best performance in the kinematic positioning mode, in which the mean root mean square (RMS) of positioning accuracy was 1.06, 1.27, and 2.85 cm for the east (E), north (N), and up (U) components, respectively, and the mean convergence time reached 9.6 min. In the static positioning mode, the mean convergence times of the GPS-only and GPS+Galileo solutions were 11.4 min and 8.0 min, respectively, and both of their mean RMS for positioning accuracy reached 0.79, 0.95, and 1.48 cm for the E, N, and U components, respectively. However, the addition of BDS did not further enhance the performance of multi-GNSS PPP-AR in either the kinematic or static positioning mode due to the poor quality of the real-time BDS products. More importantly, a prediction method was proposed to avoid re-convergence and to enhance the reliability of PPP-AR in the event of short-time missing real-time OSB products and to improve the positioning accuracy and the ambiguity fixed rate. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning. [ABSTRACT FROM AUTHOR]

Accurate estimation of tropospheric delay is significant for global navigation satellite system's (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type. [ABSTRACT FROM AUTHOR]

Since BeiDou Navigation Satellite System (BDS) and Japan's Quasi-Zenith Satellite System (QZSS) have more visible satellites in the Asia-Pacific region, and navigation satellites of Global Positioning System (GPS), Galileo satellite navigation system (Galileo), and GLONASS satellite navigation system (GLONASS) are uniformly distributed globally, the service level of multi-mode Global Navigation Satellite System (GNSS) in the Asia-Pacific region should represent the best service capability. Based on the observation data of 10 Multi-GNSS Experiment (MGEX) stations, broadcast ephemeris and precision ephemeris from 13 to 19 October 2021, this paper comprehensively evaluated the service capability of multi-GNSS in the Asia-Pacific region from three aspects of observation data quality, broadcast ephemeris performance, and precision positioning level. The results show that: (1) the carrier-to-noise-density ratio (C/N0) quality of the GPS and Galileo is the best, followed by BDS and GLONASS, and QZSS is the worst. GPS, BDS-2, GLONASS, and QZSS pseudorange multipath values range from 0 to 0.6 m, while Galileo system and BDS-3 pseudorange multipath values range from 0 to 0.8 m. (2) In terms of broadcast ephemeris accuracy, BDS-3 broadcast ephemeris has the best orbit, and the three-dimensional (3D) Root Mean Square (RMS) is 0.21 m; BDS-2 was the worst, with a 3D RMS of 1.99 m. The broadcast ephemeris orbits of GPS, Galileo, QZSS, and GLONASS have 3D RMS of 0.60 m, 0.62 m, 0.83 m, and 1.27 m, respectively. For broadcast ephemeris clock offset: Galileo has the best performance, 0.61 ns, GLONASS is the worst, standard deviation (STD) is 3.10 ns, GPS, QZSS, BDS-3 and BDS-2 are 0.65 ns, 0.75 ns, and 1.72 ns, respectively. For signal-in-space ranging errors (SISRE), the SISRE results of GPS and Galileo systems are the best, fluctuating in the range of 0 m–2 m, followed by QZSS, BDS-3, Galileo, and BDS-2. (3) GPS, BDS, GLONASS, Galileo, GPS/QZSS, and BDS/QZSS were used for positioning experiments. In static PPP, the convergence time and positioning accuracy of GPS show the best performance. The positioning accuracy of GPS/QZSS and BDS/QZSS is improved compared with that of GPS and BDS. In terms of kinematic PPP, the convergence time and positioning accuracy of GPS/QZSS and BDS/QZSS are improved compared with that of GPS and BDS. In addition to GLONASS and Galileo systems, the other combinations outperformed 3 cm, 3 cm, and 5 cm in the east, north, and up directions. [ABSTRACT FROM AUTHOR]

The service areas of the BeiDou Navigation Satellite System (BDS) are China, the Asia–Pacific, and the world via three stages of development. The BDS constellation is designed to maintain a geostationary Earth orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO). These orbital maneuvers yield certain difficulties for data processing, especially for the BeiDou satellites, such as a decrease in the real-time service performance, which causes a missing precise orbit product from the maneuvered satellites. The frequencies of the orbital maneuvers for the GEO and IGSO satellites are higher than those for the MEO satellites. The maneuvering time and strategy cannot be obtained by common users due to secrecy, which can yield a decline in the service performance of the BDS. Thus, in this study, we analyzed the variation in the orbital semimajor axis for the satellites. The long-term variation in the orbital semimajor axis changes linearly. The short-term variation in the orbital semimajor axis also has periodicity, which can be described by the sum of sine functions. According to the long-term variation in the orbital semimajor axis for the satellites, we propose a detection method for in-plane orbital maneuvering of the GEO, IGSO, and MEO satellites. We then propose a detection method for out-of-plane maneuvering of the GEO satellites. BDS and Global Positioning System (GPS) data from the broadcast ephemeris were analyzed to verify the proposed methods. The experimental results from 2013 to 2019 show that the frequency of orbital maneuvering is approximately once a month for the GEO satellites, once every six months for the IGSO satellites, and once every 1.4 years for the MEO satellites. [ABSTRACT FROM AUTHOR]

The atomic clock is the core of the Global Navigation Satellite System (GNSS), which plays an important role in Positioning, Navigation and Timing (PNT) services. However, the long-term characteristics of the global BeiDou navigation system (BeiDou-3) and comparison of satellite clocks performance for the five GNSSs have not yet been revealed. We evaluated and analysed the performance of atomic clocks for BeiDou-3 and other GNSSs using satellite clock offset data. The important contributions are summarised as follows. (1) The phase and frequency sequences of Passive Hydrogen Maser (PHM) and rubidium (Rb) atomic clocks for BeiDou-3 are more stable and continuous than those of the regional BeiDou navigation system (BeiDou-2). The clock offset model precision and average frequency stability at 10,000, 1000, and 100 s for the BeiDou-3 satellite clock offsets are 0.15 ns and 6.8 × 10–14, respectively, exhibiting significant improvements compared with those for BeiDou-2. (2) The performance of cesium (Cs) atomic clocks installed on Global Positioning System (GPS) Block IIF are more inferior than Global Navigation Satellite System (GLONASS), which may be due to the poor level of Cs atomic clock manufacturing technology or obvious noise characteristics. Thus, more attention should be paid to these two satellite clocks. (3) A comparison of various indicators revealed that the satellite clocks for Galileo are the best, followed by BeiDou-3, GPS Block IIF satellites with Rb atomic clocks, Quasi-Zenith Satellite System (QZSS), BeiDou-2, GLONASS, GPS Block IIR, and Block IIR-M. The GPS Block IIF satellites equipped with a Cs atomic clock shows the poorest performance. [ABSTRACT FROM AUTHOR]

Precise point positioning (PPP) with wide-lane ambiguity resolution (PPP-WAR) can quickly achieve decimeter-level positioning accuracy, which is an essential improvement for real-time PPP in terms of reliability and accuracy. However, current implementations of PPP-WAR are based only on the standard Kalman filter (KF) under the assumption of uncorrelated measurements. As Global Navigation Satellite System measurements are often contaminated by colored noises, a KF considering the time-correlation of measurements (CKF) has the potential to improve the performance of PPP-WAR. In this paper, we proposed a real-time cascading PPP-WAR technique based on the CKF model. A real-time kinematic vehicle experiment is conducted to assess the performance of the proposed real-time PPP-WAR method. On the one hand, we analyze and compare the efficiency of CKF-based cascading PPP-WAR with that of KF-based PPP-WAR. The filter output analysis shows that the proposed CKF method is, overall more efficient than the standard KF method in terms of performance indicators, such as wide-lane ambiguity fix-rate, time-to-first-fix, ambiguity residual statistics and correctness of the filter’s stochastic model. On the other hand, we evaluate the convergence time and positioning accuracy of CKF-based PPP-WAR by comparing it with KF-based PPP-WAR. The results show that the fix-rate of the CKF method increased by 26% compared with the KF-based WAR method. Moreover, the CKF-PPP-WAR can achieve 0.5 m (sub-meter) level with 1–4 s faster, 0.2 m level with 2–20 s faster, 0.1 m level with 5–60 s faster compared with float PPP. On average, CKF-PPP-WAR can achieve decimeter-level accuracy by reducing the convergence time 20% than KF-PPP-WAR, and 25% than float PPP. After convergence, the average positioning accuracy of CKF-PPP-WAR is 2–6 cm better with 25% improvement over float PPP and 1–4 cm better with a 23% improvement over KF-PPP-WAR. [ABSTRACT FROM AUTHOR]

Apart from positioning accuracy, the reliability of a navigation system is also significant, especially for safety–critical applications, such as intelligent vehicle navigation. Generally, the Global Navigation Satellite System (GNSS) positioning algorithm assumed that measurement noise is uncorrelated white noise. However, the appearance of colored noise, which comes from various noise sources, does not follow the Gaussian white noise assumption in the Kalman filter and would degrade both the accuracy and reliability of positioning. To deal with this problem, we propose a linear Kalman filter-based integrity monitoring method, which is based on a linear colored Kalman filter (CKF) considering measurement time correlation colored noise by a first-order Gauss–Markov model. Both simulated and real dynamic experiments were conducted to test the proposed algorithm. The results proved that CKF is capable of modeling time correlation with a realistic filter covariance in both simulated static and in-field dynamic tests. Furthermore, the performance of an integrity monitoring system with a protection level based on the CKF has proved to be more feasible and effective to bound position errors. Besides, the simulated results demonstrate that it can typically reduce false alarm by 24.81% in the horizontal direction and 39.47% in the vertical direction in a simulated experiment. The dynamic test shows that the proposed method can reduce the false alarm rate by 22.11% and 15.62% in the horizontal and vertical directions, respectively. [ABSTRACT FROM AUTHOR]

Real-time satellite orbit and clock product is a key prerequisite for real-time precise positioning service based on precise point positioning (PPP). With the rapid development of the multiple global navigation satellite systems (Multi-GNSS), about 120 satellites will be processed for Multi-GNSS real-time clock estimation. Unfortunately, the computation is very time-consuming, especially for quality control since problematic observations are inevitable. Taking advantage of computer technology, sequential least square adjustment with an adapted online quality control procedure is developed to rapidly estimate Multi-GNSS real-time clocks, although various filtering estimators are commonly used now. A globally distributed network including 70 stations tracking mostly satellites of GPS, GLONASS, BDS, and Galileo is employed for experimental validation. The results show that the computation time per epoch is less than 3 s in average and can meet the 5 s update rate of the IGS real-time clock product. Compared to the GeoForschungsZentrum MGEX (GBM) final clock product, the averaged STD values of the estimated clocks of the four GNSS systems are 0.089 ns and 0.153 ns, respectively, for the clock solutions with and without the online quality control, which also confirms the importance of the quality control procedure. The Multi-GNSS kinematic PPP experiment using the estimated clocks with quality control shows that the positioning RMS is about 4 cm and generally 2 cm in vertical and horizontal components, respectively, and the corresponding convergence time is about 15 min. [ABSTRACT FROM AUTHOR]

Currently, resolution of the BeiDou2 satellites differential code bias (DCB) is greatly limited by the number of tracking stations and data quality. To improve the precision of calculated DCBs, an adaptive control method, based on elevation angle weight and posteriori variance factor, is proposed. The feasibility and precision of the proposed method are verified via analysis of the DCBs precision and application of single‐frequency precise point positioning (SF‐PPP). The results show that the long‐term variation of the daily BeiDou2 satellite DCB sequence is stable and the precision of DCBs of BeiDou2 satellites in different constellations is different. In addition, it is found by using periodogram and fast Fourier transform (FFT) methods that weekly and daily periodic signals exist in the daily BeiDou2 satellite DCB products. The results of SF‐PPP show that root mean square (RMS) horizontal residual is reduced by 10% and convergence time is shortened significantly using DCB products. © 2018 Institute of Navigation [ABSTRACT FROM AUTHOR]

The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites' orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites' orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017. [ABSTRACT FROM AUTHOR]

The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS clock offset series, the GPS ultra-rapid clock model, which uses a simple quadratic polynomial plus one periodic is not suitable for BDS. Therefore, an improved prediction model for BDS satellite clocks is proposed in order to enhance the precision of ultra-rapid predicted clock offsets. First, a basic quadratic polynomial model which is fit for the rubidium (Rb) clock is constructed for BDS. Second, the main cyclic terms are detected and identified by the Fast Fourier Transform (FFT) method according to every satellite clock offset series. The detected results show that most BDS clocks have special cyclic terms which are different from the orbit periods. Therefore, two main cyclic terms are added to absorb the periodic effects. Third, after the quadratic polynomial plus two periodic fitting, some evident nonlinear system errors also exist in the model residual, and the Back Propagation (BP) neural network model is chosen to compensate for these nonlinear system errors. The simulation results show that the performance and precision using the improved model are better than that of China iGMAS ultra-rapid prediction (ISU-P) products and the Deutsches GeoForschungsZentrum GFZ BDS ultra-rapid prediction (GBU-P) products. Comparing to ISU-P products, the average improvements using the proposed model in 3 h, 6 h, 12 h and 24 h are 23.1%, 21.3%, 20.2%, and 19.8%, respectively. Meanwhile the accuracy improvements of the proposed model are 9.9%, 13.9%, 17.3%, and 21.2% compared to GBU-P products. In addition, the kinematic Precise Point Positioning (PPP) example using 8 Multi-GNSS Experiment MGEX stations shows that the precision based on the proposed clock model has improved about 16%, 14%, and 38% in the North (N), East (E) and Height (H) components. [ABSTRACT FROM AUTHOR]

In order to estimate the satellite clock offset in a real-time mode, a new algorithm of adaptively robust Kalman filter with classified adaptive factors for clock offset estimation is proposed. Compared with standard Kalman filter clock offset model, the new method can detect and control outliers and clock jumps automatically in real-time. Moreover, the clock model parameters, which contain the clock offset, clock speed and clock shift, are classified to decide the adaptive factors in the new model. Thus, clock jumps with different characteristics can be distinguished more effectively. Meanwhile, the dynamic noise characteristics of clock offset series are used for stochastic modeling. An actual numerical example is presented, which shows that the proposed filter can give a better performance than other commonly used filters. [ABSTRACT FROM AUTHOR]

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value. [ABSTRACT FROM AUTHOR]

Variations in weather conditions have a significant impact on thermokarst lakes, such as the sub-lake permafrost thawing caused by global warming. Based on the analysis of Landsat sensor images by ENVI TM 5.3 software, the present study quantitatively determined the area of the thermokarst lakes and the area of the single selected thermokarst lake in the Beilu River Basin from 2000 to 2016. In an effort to explore the reason for changes in the area of thermokarst lakes, this work used Pearson correlation to analyze the relationship between the area of thermokarst lakes and precipitation, wind speed, average temperature, and relative humidity as obtained from the weather station Wudaoliang. Furthermore, this study used multiple linear regression to comprehensively study the correlation between the meteorological factors and changes in the thermokarst lake area. In this case, the total lake-area changes and the single-area changes exhibited unique patterns. The results showed that the total lake area and the single selected lake area increased year by year. Furthermore, the effects of the four meteorological factors defined above on the total area of typical thermokarst lakes are different from the effects of these factors on the single selected thermokarst lake. While the total area of specific thermokarst lakes exhibited a time lag in their response to the four factors, the surface area of the selected thermokarst lake responded to these factors on time. The dominant meteorological factor contributing to total lake area variations of typical thermokarst lakes is the increasing annual average temperature. The Pearson correlation coefficient between the total area and the annual average temperature is 0.717, suggesting a statistically significant correlation between the two factors. For the selected thermokarst lake, the surface area is related to annual average temperature and wind speed. As a result, wind speed and average temperature could infer the variation law on the thermokarst lake due to the linear fitting equation between area and significant meteorological factors. [ABSTRACT FROM AUTHOR]

The positioning, navigation, and timing (PNT) service of the Global Navigation Satellite System (GNSS) is developing in the direction of real time and high precision. However, there are some problems that restrict the development of real-time and high-precision PNT technology. Satellite orbit maneuvering is one of the factors that reduce the reliability of real-time navigation products, especially the high-frequency orbit maneuvering of geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. The BeiDou Navigation Satellite System (BDS) constellation is designed to contain GEO, IGSO, and medium earth orbit (MEO). These orbit maneuvers bring certain difficulties for data processing, especially for BeiDou satellites, such as decreased real-time service performance, which results in real-time navigation products including unusable maneuvered satellites. Additionally, the performance of real-time navigation products will decrease because the orbit maneuvers could not be known in advance, which diminishes the real-time PNT service performance of BDS for users. Common users cannot obtain maneuvering times and strategies owing to confidentiality, which can lead to a decline in the BDS real-time service performance. Thus, we propose a method to predict orbit maneuvers. BDS data from the broadcast ephemeris were analyzed to verify the availability of the proposed method. In addition, the results of real-time positioning were analyzed by using ultra-rapid orbit products, demonstrating that their reliability is improved by removing maneuvered satellites in advance. This is vital to improve the reliability of real-time navigation products and BDS service performance. [ABSTRACT FROM AUTHOR]

The Mu Us dune field is one of China's four major dune fields, which are ecologically vulnerable areas of northwest semiarid land across Shaanxi, Ningxia, and Inner Mongolia, also very sensitive to the global temperature rise and environmental changes. This paper uses data on the temperature, precipitation, and precipitable water vapor (PWV) in the Mu Us dune field and its surrounding areas to analyze and discuss the time series and spatial distribution characteristics of these three factors in this area. The results of the study show that, in recent years, the trend of temperature increase in the Mu Us dune field has been higher than the average level in China, but this trend has gradually subsided since 2000. The spatial distribution of temperature presents an obvious characteristic of gradual increase from north to south and is affected by latitude, altitude, and topography. The annual cumulative precipitation of the Mu Us dune field is lower than the average level in China. However, in recent years, the rate of the increase in precipitation in this area has been significantly higher than that of the average rate of increase in China. The eastern part of the dune field has the most precipitation, which gradually decreases to the west. The spatial distribution of precipitation is greatly affected by monsoon factors in the region and the distribution of rivers. In the research field, PWV has been rising in recent years, which is greatly related to the increase of vegetation coverage in this region. This demonstrates that the Mu Us dune field has experienced a "warmer and wetter" trend in recent years. [ABSTRACT FROM AUTHOR]

In this study, an uncombined precise point positioning (PPP) model was established and was used for estimating fractional cycle bias (FCB) products and for achieving ambiguity resolution (AR), using GPS, BDS-2, and Galileo raw observations. The uncombined PPP model is flexible and efficient for positioning services and generating FCB. The FCBs for GPS, BDS-2, and Galileo were estimated using the uncombined PPP model with observations from the Multi-GNSS Experiment (MGEX) stations. The root mean squares (RMSs) of the float ambiguity a posteriori residuals associated with all of the three GNSS constellations, i.e., GPS, BDS-2, and Galileo, are less than 0.1 cycles for both narrow-lane (NL) and wide-lane (WL) combinations. The standard deviation (STD) of the WL combination FCB series is 0.015, 0.013, and 0.006 cycles for GPS, BDS-2, and Galileo, respectively, and the counterpart for the NL combination FCB series is 0.030 and 0.0184 cycles for GPS and Galileo, respectively. For the BDS-2 NL combination FCB series, the STD of the inclined geosynchronous orbit (IGSO) satellites is 0.0156 cycles, while the value for the medium Earth orbit (MEO) satellites is 0.073 cycles. The AR solutions produced by the uncombined multi-GNSS PPP model were evaluated from the positioning biases and the success fixing rate of ambiguity. The experimental results demonstrate that the growth of the amount of available satellites significantly improves the PPP performance. The three-dimensional (3D) positioning accuracies associated with the PPP ambiguity-fixed solutions for the respective only-GPS, GPS/BDS-2, GPS/Galileo, and GPS/BDS-2/Galileo models are 1.34, 1.19, 1.21, and 1.14 cm, respectively, and more than a 30% improvement is achieved when compared to the results related to the ambiguity-float solutions. Additionally, the convergence time based on the GPS/BDS-2/Galileo observations is only 7.5 min for the ambiguity-fixed solutions, and the results exhibit a 53% improvement in comparison to the ambiguity-float solutions. The values of convergence time based on the only-GPS observations are estimated as 22 and 10.5 min for the ambiguity-float and ambiguity-fixed solutions, respectively. Lastly, the success fixing rate of ambiguity is also dramatically raised for the multi-GNSS PPP AR. For example, the percentage is approximately 99% for the GPS/BDS-2/Galileo solution over a 10 min processing period. In addition, the inter-system bias (ISB) between GPS, BDS-2, and Galileo, which is carefully considered in the uncombined multi-GNSS PPP method, is modeled as a white noise process. The differences of the ISB series between BDS-2 and Galileo indicate that the clock datum bias of the satellite clock offset estimation accounts for the variation of the ISB series. [ABSTRACT FROM AUTHOR]

The real-time kinematic (RTK) positioning technique of global navigation satellite systems (GNSS) has been widely used for deformation monitoring in the past several decades. The RTK technique can provide relative displacements in a local reference frame defined by a highly stable reference station. However, the traditional RTK solution does not account for reference stations that experience displacement. This presents a challenge for establishing a near real-time GNSS monitoring system, as since the displacement of a reference station can be easily misinterpreted as a sign of rapid movement at the monitoring station. In this study, based on the reference observations in different time domains, asynchronous and synchronous RTK are proposed and applied together to address this issue, providing more reliable displacement information. Using the asynchronously generated time difference of a reference frame, the proposed approach can detect whether a measured displacement has occurred in the reference or the monitoring station in the current epoch. This allows for the separation of reference station movements from monitoring station movements. The results based on both simulated and landslide monitoring data demonstrate that the proposed method can provide reliable displacement determinations, which are critical in deformation monitoring applications, such as the early warning of landslides. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is critical for achieving a fast, high-precision solution in precise point positioning (PPP). In the standard uncombined PPP (S-UPPP) method, ionosphere-free code biases are superimposed by ambiguity and receiver clock offsets to be estimated. However, besides the time-constant part of the receiver code bias, the complex and time-varying term in receivers destroy the stability of ambiguities and degrade the performance of the UPPP AR. The variation of receiver code bias can be confirmed by the analysis in terms of ionospheric observables, code multipath (MP) of the Melbourne–Wübbena (MW) combination and the ionosphere-free combination. Therefore, the effect of receiver code biases should be rigorously mitigated. We introduce a modified UPPP (M-UPPP) method to reduce the effects of receiver code biases in ambiguities and to decouple the correlation between receiver clock parameters, code biases, and ambiguities parameters. An extra receiver code bias is set to isolate the code biases from ambiguities. The more stable ambiguities without code biases are expected to achieve a higher success rate of ambiguity resolution and a shortened convergence time. The variations of the receiver code biases, which are the unmodeled errors in measurement residuals of the S-UPPP method, can be estimated in the M-UPPP method. The maximum variation of the code biases is up to 16 ns within two-hour data. In the M-UPPP method, the averaged epoch residuals for code and phase measurements recover their zero-mean features. For the ambiguity-fixed solutions in the M-UPPP method, the convergence times are 14 and 43 min with 17.7% and 69.2% improvements compared to that in the S-UPPP method which are 17 and 90 min under the 68% and 95% confidence levels. [ABSTRACT FROM AUTHOR]