Search

Abstract The development of tumor vaccines represents a significant focus within cancer therapeutics research. Nonetheless, the efficiency of antigen presentation in tumor vaccine remains suboptimal. We introduce an innovative mRNA-lipid nanoparticle platform designed to express tumor antigenic epitopes fused with the transmembrane domain and cytoplasmic tail of the neonatal Fc receptor (FcRn). This novel design exploits FcRn trafficking signals to direct the epitope-FcRn fusion toward endolysosomal degradation, thereby generating epitopes capable of eliciting targeted T cell responses and establishing immune memory. The FcRn-directed presentation of epitopes enhances MHC class I and II antigen presentation, thereby robustly inducing CD4+ and CD8+ T cell responses, which translates to the inhibition of tumor growth and extension of survival in preclinical mouse models. In summary, the deliberate incorporation of FcRn trafficking signals into vaccine design markedly boosts T cell responses, underscoring the promise of this novel strategy in advancing the efficacy of tumor vaccines.

The recruitment of the Low Earth Orbit (LEO) constellation is recognized as an effective way to augment Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) in the near future. Its potential to accelerate PPP convergence has been proved with simulated data. However, the mechanism of how the geometric change of LEO accelerates the convergence of GNSS PPP has not been studied from a theoretical perspective, which hampers the understanding and exploitation of the enhancement of LEO. In this article, the convergence mechanism of LEO enhanced GNSS PPP is investigated in terms of theoretical analysis and simulated verification. To show the characteristics of the ambiguities during convergence, eigenvalue decomposition is used to divide the ambiguities into orthogonal components, named geometric-related component, clock-error-related component, and independent component. The results show that the precision of geometric-related components of ambiguities, which correlates with position parameters, is low at a single epoch, while the precision can be greatly improved with the fast geometric change of LEO. On the other hand, the precision of clock-error-related components of ambiguities, which correlates with clock errors, cannot be improved by fast geometric change of LEO constellation due to its irrelevance to geometry, which causes the precision of each ambiguity to be low. Further investigations show that single-differenced ambiguities could overcome this drawback and are beneficial to ambiguity resolution.

Visual Simultaneous Localization and Mapping (V-SLAM) is pivotal for precise positioning and mapping. However, visual data from crowd-sourced datasets often contains deficiencies that may lead to positioning errors. Despite existing optimization techniques, current algorithms do not adequately adapt to varied data in vehicle driving scenarios. To address this gap, this study introduces a novel SLAM framework (SLG-SLAM). This framework refines trajectories by integrating semantic information, laser point cloud, and global navigation satellite system (GNSS) data into V-SLAM. Initial trajectory estimates are made after filtering out dynamic targets and are subsequently refined with matched laser point clouds, then corrected for scale and direction using GNSS. The efficacy of this approach is assessed using four public datasets and one self-collected dataset, showing significant enhancements across all datasets. The proposed method reduces the mean absolute trajectory error by 43.50% on the KITTI dataset and 14.91% on the MVE dataset compared to the baseline. Unlike the baseline, which fails on three other datasets, the proposed method successfully performs localization and mapping. Additionally, compared to three other single-source methods (DynaSLAM, MCL, MVSLAM), the proposed method consistently outperforms, demonstrating its superior adaptability and effectiveness.

With the fast development of low earth orbit (LEO) enhanced global navigation satellite system (LeGNSS), there is huge demand in LEO real-time precise orbit determination. Real-time service from international GNSS service (IGS) provides the real-time precise GNSS orbit and clock products, which gives an opportunity to make use of kinematic precise point positioning (PPP) method for LEO POD. However, real-time precise GNSS products and onboard GNSS observations interrupt inevitably or even frequently (especially for real-time clock products), resulting in PPP reconvergence as well as large orbit errors in LEO orbit solutions. A new method is put forward to reduce such influence of two types of data interruptions. For interruption of clock products, the Chebyshev extrapolation is used to maintain the continuity of real-time GNSS clock products. For the onboard observation interruption, the predicted dynamic orbits are employed as constraints to overcome the PPP reconvergence. Results show that zero-reconvergence PPP can be realized with our new method, which means there is almost no reconvergence time after data interruption. Moreover, clock prediction can reduce the root–mean-square errors (RMSE) for real-time LEO orbit from 5.40, 4.70, and 7.33 cm to 5.18, 4.55, and 5.99 cm in along-track, cross-track, and radial directions, respectively, while the predicted dynamic orbit constraints reduce the orbit RMSE by 20--30% in cross-track component and 60%–80% in other two components.

The current terrestrial laser scanners (TLS) are generally equipped with digital cameras which can capture the scene along with the scanner. These two types of sensors offer complementary properties in modeling and visualization of real-world scenes. TLSs can provide geometric information of the real scene with accurate 3D coordinates of the point clouds; cameras are used to acquire high-resolution images and provide good texture information of the environment. Fusing the extracted information from these two sensors helps to create a better virtual representation of the real-world. For a TLS with several external cameras, their acquisition centers are not identical and the axis of their coordinate systems are not aligned either. This paper proposes an automatic camera and TLS extrinsic calibration approach using correspondences extracted from both measurements. To overcome the intrinsic difference between back-projected images of point clouds colored by intensities and the RGB camera images, we innovatively generate both magnitude of gradient images, enabling effective image correlation and accurate correspondence extraction. The 3 external cameras mounted on top, side and bottom of Leica RTC360 3D laser scanner are calibrated. Dependent on the distribution of observations, we achieve different calibration accuracy for each camera. With scans from multiple stations, the cameras obtain an offset accuracy of 0.12 – 0.36 mm and angular accuracy of 3.7 – 8.3″. After calibration, the excellent overlap of images from the two sensors further verifies the proposed method's success. The idea of correspondence identification demonstrated in this study can also be applied to the extrinsic calibration/registration of other types of scanner and digital cameras.

Abstract Real-Time Precise Point Positioning (RT-PPP) has been one of the research hotspots in GNSS (Global Navigation Satellite System) community for decades. Real-time precise products of satellite orbits and clocks are the prerequisite for RT-PPP. Thus, it is of great importance to investigate the current multi-GNSS real-time precise products in State Space Representation (SSR) from different analysis centers. In this article, SSR products from 10 analysis centers are comprehensively evaluated by comparing them with the final products and performing the kinematic PPP. The results show that analysis centers CNES (Centre National D'Etudes Spatiales) and WHU (GNSS Research Center of Wuhan University) provide the most complete products with the best quality. Concerning the accuracy of real-time products for the GNSSs, the accuracies of orbit and clock products are better than 5 cm and 0.15 ns, respectively, for Global Positioning System (GPS), followed by Galileo navigation satellite system (Galileo), BeiDou-3 Navigation Satellite System (BDS-3), GLObal NAvigation Satellite System (GLONASS), and BeiDou-2 Navigation Satellite System (BDS-2). Meanwhile, the results of the RT-PPP with quad-system show that the positioning accuracies are 1.76, 1.12 and 2.68 cm in east, north, and up directions, respectively, and the convergence time to 0.1, 0.1, 0.2 m for corresponding directions is 15.35 min.

Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.

With the completion of Chinese BeiDou Navigation Satellite System (BDS), the world has begun to enjoy the Positioning, Navigation, and Timing (PNT) services of four Global Navigation Satellite Systems (GNSS). In order to improve the GNSS performance and expand its applications, Low Earth Orbit (LEO) Enhanced Global Navigation Satellite System (LeGNSS) is being vigorously advocated. Combined with high-, medium-, and low- earth orbit satellites, it can improve GNSS performance in terms of orbit determination, Precise Point Positioning (PPP) convergence time, etc. This paper comprehensively reviews the current status of LeGNSS, focusing on analyzing its advantages and challenges for precise orbit and clock determination, PPP convergence, earth rotation parameter estimation, and global ionosphere modeling. Thanks to the fast geometric change brought by LEO satellites, LeGNSS is expected to fundamentally solve the problem of the long convergence time of PPP without any augmentation. The convergence time can be shortened within 1 minute if appropriate LEO constellations are deployed. However, there are still some issues to overcome, such as the optimization of LEO constellation as well as the real time LEO precise orbit and clock determination.

Abstract Cytosolic inflammasomes are supramolecular complexes that are formed in response to intracellular pathogens and danger signals. However, as to date, the detailed description of a homotypic caspase recruitment domain (CARD) interaction between NLRP1 and ASC has not been presented. We found the CARD–CARD interaction between purified NLRP1CARD and ASCCARD experimentally and the filamentous supramolecular complex formation in an in vitro proteins solution. Moreover, we determined a high-resolution crystal structure of the death domain fold of the human ASCCARD. Mutational and structural analysis revealed three conserved interfaces of the death domain superfamily (Type I, II, and III), which mediate the assembly of the NLRP1CARD/ASCCARD complex. In addition, we validated the role of the three major interfaces of CARDs in assembly and activation of NLRP1 inflammasome in vitro. Our findings suggest a Mosaic model of homotypic CARD interactions for the activation of NLRP1 inflammasome. The Mosaic model provides insights into the mechanisms of inflammasome assembly and signal transduction amplification.

Several global navigation satellite systems (GNSS) broadcast overlapping frequencies to enhance interoperability, allowing tight integration with only one reference satellite for every system with the same frequency. The key to realizing tight integration is estimating and utilizing differential intersystem biases, which allows the integer characteristic of the differenced ambiguity of two satellites from different systems to be retrieved. In this study, a detailed algorithm flow of a tight integration kinematic uncalibrated phase delay (UPD)-based PPP ambiguity resolution (PPP-AR) method, which includes multiple parts, is introduced. Subsequently, PPP-AR numerical experiments were conducted in a high-shade observation environment to determine the performance. In comparison with traditional methods, our results indicate that the average success fix rate improves from 83% to 100% after using the tight integration method in an environment where only three satellites are observed for each system. Moreover, for fewer than nine satellites, the tight integration method can still consistently maintain a fixed state. However, for comparison, the traditional loose integration method could no longer be implemented.

Time correlations always exist in modern geodetic data, and ignoring these time correlations will affect the precision and reliability of solutions. In this paper, several methods for processing kinematic time-correlated observations are studied. Firstly, the method for processing the time-correlated observations is expanded and unified. Based on the theory of maximum a posteriori estimation, the third idea is proposed after the decorrelation transformation and differential transformation. Two types of situations with and without common parameters are both investigated by using the decorrelation transformation, differential transformation and maximum a posteriori estimation solutions. Besides, the characteristics and equivalence of above three methods are studied. Secondly, in order to balance the computational efficiency in real applications and meantime effectively capture the time correlations, the corresponding reduced forms based on the autocorrelation function are deduced. Finally, with GPS real data, the correctness and practicability of derived formulae are evaluated.

The worldwide pandemic of COVID-19 has become a global public health crisis. Various clinical diagnosis methods have been developed to distinguish COVID-19–infected patients from healthy people. The nucleic acid test is the golden standard for virus detection as it is suitable for early diagnosis. However, due to the low amount of viral nucleic acid in the respiratory tract, the sensitivity of nucleic acid detection is unsatisfactory. As a result, serological screening began to be widely used with the merits of simple procedures, lower cost, and shorter detection time. Serological tests currently include the enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), and chemiluminescence immunoassay (CLIA). This review describes various serological methods, discusses the performance and diagnostic effects of different methods, and points out the problems and the direction of optimization, to improve the efficiency of clinical diagnosis. These increasingly sophisticated and diverse serological diagnostic technologies will help human beings to control the spread of COVID-19.

Diagnosed in more than 90% of cervical cancers, the fourth deadliest cancer in women, human papillomavirus (HPV) is currently the most common pathogen responsible for female cancers. Moreover, HPV infection is associated with many other diseases, including cutaneous and anogenital warts, and genital and upper aerodigestive tract cancers. The incidence and prevalence of these pathologies vary considerably depending on factors including HPV genotype, regional conditions, the study population, and the anatomical site sampled. Recently, features of the cervicovaginal microbiota are found to be associated with the incidence of HPV-related diseases, presenting a novel approach to identify high-risk women through both blood and cervical samples. Overall, the HPV repartition data show that HPV infection and related diseases are more prevalent in developing countries. Moreover, the available (2-, 4-, and 9-valent) vaccines based on virus-like particles, despite their proven effectiveness and safety, present some limitations in terms of system development cost, transport cold chain, and oncogenic HPV variants. In addition, vaccination programs face some challenges, leading to a considerable burden of HPV infection and related diseases. Therefore, even though the new (9-valent) vaccine seems promising, next-generation vaccines as well as awareness programs associated with HPV vaccination and budget reinforcements for immunization are needed.

DNA viruses are a source of great morbidity and mortality throughout the world by causing many diseases; thus, we need substantial knowledge regarding viral pathogenesis and the host’s antiviral immune responses to devise better preventive and therapeutic strategies. The innate immune system utilizes numerous germ-line encoded receptors called pattern-recognition receptors (PRRs) to detect various pathogen-associated molecular patterns (PAMPs) such as viral nucleic acids, ultimately resulting in antiviral immune responses in the form of proinflammatory cytokines and type I interferons. The immune-stimulatory role of DNA is known for a long time; however, DNA sensing ability of the innate immune system was unraveled only recently. At present, multiple DNA sensors have been proposed, and most of them use STING as a key adaptor protein to exert antiviral immune responses. In this review, we aim to provide molecular and structural underpinnings on endosomal DNA sensor Toll-like receptor 9 (TLR9) and multiple cytosolic DNA sensors including cyclic GMP-AMP synthase (cGAS), interferon-gamma inducible 16 (IFI16), absent in melanoma 2 (AIM2), and DNA-dependent activator of IRFs (DAI) to provide new insights on their signaling mechanisms and physiological relevance. We have also addressed less well-understood DNA sensors such as DEAD-box helicase DDX41, RNA polymerase III (RNA pol III), DNA-dependent protein kinase (DNA-PK), and meiotic recombination 11 homolog A (MRE11). By comprehensive understanding of molecular and structural aspects of DNA-sensing antiviral innate immune signaling pathways, potential new targets for viral and autoimmune diseases can be identified.

The barrier surfaces of the gastrointestinal tract are in constant contact with various microorganisms. Cytokines orchestrate the mucosal adaptive and innate immune cells in the defense against pathogens. IL-10 and IL-22 are the best studied members of the IL-10 family and play essential roles in maintaining mucosal homeostasis. IL-10 serves as an important regulator in preventing pro-inflammatory responses while IL-22 plays a protective role in tissue damage and contributes to pathology in certain settings. In this review, we focus on these two cytokines in the development of gastrointestinal diseases, including inflammatory bowel diseases (IBD) and colitis-associated cancer (CAC). We summarize the recent studies and try to gain a better understanding on how they regulate immune responses to maintain equilibrium under inflammatory conditions.

Inflammasomes play a crucial role in innate immunity by serving as signaling platforms which deal with a plethora of pathogenic products and cellular products associated with stress and damage. By far, the best studied and most characterized inflammasome is NLRP3 inflammasome, which consists of NLRP3 (nucleotide-binding domain leucine-rich repeat (NLR) and pyrin domain containing receptor 3), ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain), and procaspase-1. Activation of NLRP3 inflammasome is mediated by highly diverse stimuli. Upon activation, NLRP3 protein recruits the adapter ASC protein, which recruits the procaspase-1 resulting in its cleavage and activation, inducing the maturation, and secretion of inflammatory cytokines and pyroptosis. However, aberrant activation of the NLRP3 inflammasome is implicated in various diseases including diabetes, atherosclerosis, metabolic syndrome, cardiovascular, and neurodegenerative diseases; raising a tremendous clinical interest in exploring the potential inhibitors of NLRP3 inflammasome. Recent investigations have disclosed various inhibitors of the NLRP3 inflammasome pathway which were validated through in vitro studies and in vivo experiments in animal models of NLRP3-associated disorders. Some of these inhibitors directly target the NLRP3 protein whereas some are aimed at other components and products of the inflammasome. Direct targeting of NLRP3 protein can be a better choice because it can prevent off target immunosuppressive effects, thus restrain tissue destruction. This paper will review the various pharmacological inhibitors of the NLRP3 inflammasome and will also discuss their mechanism of action.

In Global Navigation Satellite Systems (GNSS) positioning, one often tries to establish a mathematic model to capture the systematic behaviors of observations as much as possible. However, the observation residuals still exhibit, to a great extent, as (somewhat systematic) signals. Nevertheless, those systematic variations are referred to as the unmodelled errors, which are difficult to be further modelled by setting up additional parameters. Different from the random errors, the unmodelled errors are colored and time correlated. In general, the larger the time correlations are, the more significant the unmodelled errors. Hence, understanding the time correlations of unmodelled errors is important to develop the theory for processing the unmodelled errors. In this study, we compare and analyze the time correlations caused by unmodelled errors of Global Positioning System (GPS) and BeiDou signals. The time correlations are estimated based on the residuals of double differenced observations on 11 baselines with different lengths. The results show that the time correlation patterns are different significantly between GPS and BeiDou observations. Besides, the code and phase data from the same satellite system are also not the same. Furthermore, the unmodelled errors are affected by not only the baseline length, but also some other factors. In addition, to make use of the time correlations with more efficiency, we propose to fit the time correlations by using exponent and quadratic models and the fitting coefficients are given. Finally, the sequential adjustment method considering the time correlations is implemented to compute the baseline solutions. The results show that the solutions considering the time correlations can objectively reflect the actual precisions of parameter estimates.

Low Earth Orbit (LEO) satellites have been widely used in scientific fields or commercial applications in recent decades. The demands of the real time scientific research or real time applications require real time precise LEO orbits. Usually, the predicted orbit is one of the solutions for real time users, so it is of great importance to investigate LEO orbit prediction for users who need real time LEO orbits. The centimeter level precision orbit is needed for high precision applications. Aiming at obtaining the predicted LEO orbit with centimeter precision, this article demonstrates the traditional method to conduct orbit prediction and put forward an idea of LEO orbit prediction by using onboard accelerometer data for real time applications. The procedure of LEO orbit prediction is proposed after comparing three different estimation strategies of retrieving initial conditions and dynamic parameters. Three strategies are estimating empirical coefficients every one cycle per revolution, which is the traditional method, estimating calibration parameters of one bias of accelerometer hourly for each direction by using accelerometer data, and estimating calibration parameters of one bias and one scale factor of the accelerometer for each direction with one arc by using accelerometer data. The results show that the predicted LEO orbit precision by using the traditional method can reach 10 cm when the predicted time is shorter than 20 min, while the predicted LEO orbit with better than 5 cm for each orbit direction can be achieved with accelerometer data even to predict one hour.

For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

With the increased availability of regional reference networks, Precise Point Positioning (PPP) can achieve fast ambiguity resolution (AR) and precise positioning by assimilating the satellite fractional cycle biases (FCBs) and atmospheric corrections derived from these networks. In such processing, the atmospheric corrections are usually treated as deterministic quantities. This is however unrealistic since the estimated atmospheric corrections obtained from the network data are random and furthermore the interpolated corrections diverge from the realistic corrections. This paper is dedicated to the stochastic modelling of atmospheric corrections and analyzing their effects on the PPP AR efficiency. The random errors of the interpolated corrections are processed as two components: one is from the random errors of estimated corrections at reference stations, while the other arises from the atmospheric delay discrepancies between reference stations and users. The interpolated atmospheric corrections are then applied by users as pseudo-observations with the estimated stochastic model. Two data sets are processed to assess the performance of interpolated corrections with the estimated stochastic models. The results show that when the stochastic characteristics of interpolated corrections are properly taken into account, the successful fix rate reaches 93.3% within 5 min for a medium inter-station distance network and 80.6% within 10 min for a long inter-station distance network.

An indoor logical network qualitatively represents abstract relationships between indoor spaces, and it can be used for path computation. In this paper, we concentrate on the logical network that does not have notions for metrics. Instead, it relies on the semantics and properties of indoor spaces. A navigation path can be computed by deriving parameters from these semantics and minimizing them in routing algorithms. Although previous studies have adopted semantic approaches to build logical networks, routing methods are seldom elaborated. The main issue with such networks is to derive criteria for path computation using the semantics of spaces. Here, we present a routing mechanism that is based on a dedicated space classification and a set of routing criteria. The space classification reflects characteristics of spaces that are important for navigation, such as horizontal and vertical directions, doors and windows, etc. Six routing criteria are introduced, and they involve: (1) the spaces with the preferred semantics; and/or (2) their centrality in the logical network. Each criterion is encoded as the weights to the nodes or edges of the logical network by considering the semantics of spaces. Logical paths are derived by a traditional shortest-path algorithm that minimizes these weights. Depending on the building’s interior configuration, one criterion may result in several logical paths. Therefore, we introduce a priority ordering of criteria to support path selection and decrease the possible number of logical paths. We provide a proof-of-concept implementation for several buildings to demonstrate the usability of such a routing. The main benefit of this routing method is that it does not need geometric information to compute a path. The logical network can be created using verbal descriptions only, and this routing method can be applied to indoor spaces derived from any building subdivision.

In Global Navigation Satellite System (GNSS) positioning, observation precisions are frequently impacted by the site-specific unmodeled errors, especially for the code observations that are widely used by smart phones and vehicles in urban environments. The site-specific unmodeled errors mainly refer to the multipath and other space loss caused by the signal propagation (e.g., non-line-of-sight reception). As usual, the observation precisions are estimated by the weighting function in a stochastic model. Only once the realistic weighting function is applied can we obtain the precise positioning results. Unfortunately, the existing weighting schemes do not fully take these site-specific unmodeled effects into account. Specifically, the traditional weighting models indirectly and partly reflect, or even simply ignore, these unmodeled effects. In this paper, we propose a real-time adaptive weighting model to mitigate the site-specific unmodeled errors of code observations. This unmodeled-error-weighted model takes full advantages of satellite elevation angle and carrier-to-noise power density ratio (C/N0). In detail, elevation is taken as a fundamental part of the proposed model, then C/N0 is applied to estimate the precision of site-specific unmodeled errors. The principle of the second part is that the measured C/N0 will deviate from the nominal values when the signal distortions are severe. Specifically, the template functions of C/N0 and its precision, which can estimate the nominal values, are applied to adaptively adjust the precision of site-specific unmodeled errors. The proposed method is tested in single-point positioning (SPP) and code real-time differenced (RTD) positioning by static and kinematic datasets. Results indicate that the adaptive model is superior to the equal-weight, elevation and C/N0 models. Compared with these traditional approaches, the accuracy of SPP and RTD solutions are improved by 35.1% and 17.6% on average in the dense high-rise building group, as well as 11.4% and 11.9% on average in the urban-forested area. This demonstrates the benefit to code-based positioning brought by a real-time adaptive weighting model as it can mitigate the impacts of site-specific unmodeled errors and improve the positioning accuracy.

The main challenge of precise point positioning (PPP) applications is the long convergence time of typically a half hour, or even more, to achieve centimeter accuracy. Even when the multi-constellation is involved and ambiguity resolution is implemented, it still requires about ten minutes. It is becoming a hot spot to incorporate the low Earth orbit (LEO) satellite constellation for enhancing the Global Navigation Satellite System (GNSS), named here as LEO-enhanced GNSS (LeGNSS). In this system, the LEO satellites cannot only receive GNSS signals, but also serve as GNSS satellites by transmitting similar navigation signals to the ground users, but with higher signal strength and much faster geometric change due to their low altitude. As a result, the convergence time of PPP is expected to be shortened to a few minutes, or even seconds. Simulation software is developed to simulate GNSS and LEO observations for ground stations taking into account tropospheric delay, satellite clock errors, observation noises, as well as other error sources. Then the number of visible satellites, the geometry dilution of precision (GDOP), and the convergence time of the kinematic mode of PPP are evaluated on a global scale compared to those of GNSS systems. The simulation results show that LeGNSS can decrease the PPP convergence to 5 min. If there are more LEO satellites included in the LeGNSS, it is expected that the initialization of PPP can be further shortened.

In the construction of super-tall buildings, it is rather important to control the verticality. In general, a laser plummet is used to transmit coordinates of reference points from the ground layer-by-layer, which can effectively control the verticality of super-tall buildings. However, the errors in transmission will accumulate with increasing height and motion of the buildings in construction. This paper presents a global navigation satellite system (GNSS)-based method to check the results of laser plumbing. The method consists of four steps: (1) Computing the coordinate time series of monitoring points by adjusting the GNSS monitoring network observations at each epoch; (2) Analyzing the horizontal motion of super-tall buildings and its effect on vertical reference transmission; (3) Calculating the deflections of the vertical at the monitoring point using an Earth gravity field model and a geoid model. With deflections of the vertical, the static GNSS-measured coordinates are aligned to the same datum as used by the laser plummet; and (4) Finally, validating/checking the result of laser plumbing by comparing it with static GNSS results corrected by deflections of the vertical. A case study of a 438-m high building is tested in Guangzhou, China. The result demonstrates that the gross errors of baseline vectors can be eliminated effectively by GNSS network adjustment of the first step. The two-dimensional displacements can be measured at millimeter-level accuracy; the difference between the coordinates of the static GNSS measurement and laser plumbing is less than ±2.0 cm after correction with the deflections of the vertical, which meets the design requirement of ±3.0 cm according to the Technical Specification for Concrete Structures of Tall Buildings in China.

An empirical tropospheric delay model, together with a mapping function, is commonly used to correct the tropospheric errors in global navigation satellite system (GNSS) processing. As is well-known, the accuracy of tropospheric delay models relies mainly on the correction efficiency for tropospheric wet delays. In this paper, we evaluate the accuracy of three tropospheric delay models, together with five mapping functions in wet delays calculation. The evaluations are conducted by comparing their slant wet delays with those measured by water vapor radiometer based on its satellite-tracking function (collected data with large liquid water path is removed). For all 15 combinations of three tropospheric models and five mapping functions, their accuracies as a function of elevation are statistically analyzed by using nine-day data in two scenarios, with and without meteorological data. The results show that (1) no matter with or without meteorological data, there is no practical difference between mapping functions, i.e., Chao, Ifadis, Vienna Mapping Function 1 (VMF1), Niell Mapping Function (NMF), and MTT Mapping Function (MTT); (2) without meteorological data, the UNB3 is much better than Saastamoinen and Hopfield models, while the Saastamoinen model performed slightly better than the Hopfield model; (3) with meteorological data, the accuracies of all three tropospheric delay models are improved to be comparable, especially for lower elevations. In addition, the kinematic precise point positioning where no parameter is set up for tropospheric delay modification is conducted to further evaluate the performance of tropospheric delay models in positioning accuracy. It is shown that the UNB3 model is best and can achieve about 10 cm accuracy for the N and E coordinate component while 20 cm accuracy for the U coordinate component no matter the meteorological data is available or not. This accuracy can be obtained by the Saastamoinen model only when meteorological data is available, and degraded to 46 cm for the U component if the meteorological data is not available.