Search

Global navigation satellite system (GNSS) spoofing technology is an effective way to protect sensitive facilities and control foreign objects. To realize effective distributed GNSS forwarding spoofing for multiple receiver sensors in the area, the following research work is carried out: first, the GNSS forwarding spoofing model is established, including a forwarding spoofing mathematical model and an asynchronous traction spoofing method; second, the function scope of forwarding spoofing is fully evaluated; third, two forwarding spoofer area deployment algorithms for multi-spoofer multi-target (MSMT) are proposed—the multi-target same-point spoofing algorithm (MSPSA) is suitable for the same-point spoofing of multiple receiver sensors, and the multi-target different-point spoofing algorithm (MDPSA) is suitable for the different-point spoofing of multiple receiver sensors; and four, the experimental tests of MSPSA and MDPSA for MSMT were carried out. The experimental results show that MSPSA and MDPSA can respectively give the most appropriate deployment scheme of spoofing according to the various needs of the spoofer. For example, the number of spoofing devices is 1, the total distance between each spoofer and each receiver is 40,345.1 m, and the critical spoofing rate (CSR) and spoofing success rate (SSR) are both 100%. The performance of the spoofing deployment scheme can meet the needs of the spoofer. [ABSTRACT FROM AUTHOR]

Satellite navigation spoofing technology has become a hotspot of interference technology research because of its significant threat and high concealment. In a spoofing scenario, suppressive interference is typically used to ensure that the target receiver sensor is in the unlocked and reacquisition state, and then spoofing is implemented. This method has a high feasibility, and the power of the spoofing signal affects the concealment and efficiency of spoofing. Currently, there is limited research involving the GNSS spoofing signal power control. Moreover, there is no systematic complete power control scheme, most of which is limited to qualitative or simulation, and the actual application effect is still unclear. Therefore, a new GNSS spoofing signal power control algorithm under the power constraints of the receiver sensor in the acquisition phase and the subsequent control is proposed. The experimental platform is designed to prove that compared with the conventional spoofing signal high power control algorithm, the new GNSS spoofing signal power control algorithm shortens Doppler frequency fluctuation time by 72.2% and reduces the range by 75.9%. The carrier-to-noise ratio of the received signal is less than the threshold of the receiver sensor, and the range of three-dimensional coordinates of Earth-Centered, Earth-Fixed (ECEF) is significantly reduced during the spoofing signal taking over receiver sensor, this shows that the new design of the GNSS spoofing signal power control algorithm can make spoofing behavior more hidden, and it will make it more difficult for the target receiver sensor to detect spoofing behavior. The designed algorithm can take over the receiver sensor stealthily with the help of suppressing interference and then pull the bias positioning results, which has good feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightly-coupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved. [ABSTRACT FROM AUTHOR]

When satellite navigation terminal sensors encounter malicious signal spoofing or interference, if attention is not paid to improving their anti-spoofing ability, the performance of the sensors will be seriously affected. The global navigation satellite system (GNSS) spoofing has gradually become a research hotspot of the jammer because of its great harm and high concealment. In the face of more and more sensors coupling GNSS and inertial measurement unit (IMU) to varying degrees and configuring a variety of anti-spoofing techniques to effectively detect spoofing, even if the spoofer intends to gradually pull the positioning results, if the spoofing strategy is unreasonable, the parameters of the coupled filter output and spoofing observation measurement will lose their rationality, which will lead to the spoofing being detected. To solve the above problems, in order to effectively counter the non-cooperative target sensors of assembling loosely coupled GNSS/IMU using GNSS spoofing, based on the analysis of the influence mechanism of spoofing on the positioning of loosely coupled GNSS/IMU, a slowly varying spoofing algorithm to avoid loosely coupled GNSS/IMU with multiple anti-spoofing techniques is proposed in this paper, and a measurement deviation determination method to avoid multiple anti-spoofing techniques is proposed, which can gradually pull the positioning results of the coupled system and successfully avoid the detection of anti-spoofing techniques of innovation sequence monitoring and a rationality check on parameters. Simulation experimental results show that the proposed algorithm gradually changes the positioning of loosely coupled GNSS/IMU, the north and east displacements achieve the purpose of spoofing, and error with expected offset is −0.2 m and 2.3 m, respectively. Down displacement also basically achieves the purpose of spoofing, and error with the expected offset is 13.2 m. At the same time, the spoofer avoids the detection of multiple anti-spoofing techniques, does not trigger the system alarm, and realizes the purpose of spoofing; thus, the effectiveness and high concealment of the spoofing algorithm are verified. [ABSTRACT FROM AUTHOR]

In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network. [ABSTRACT FROM AUTHOR]

The Ultra-Wide Bandwidth (UWB) signal has received much attention due to its penetrability and high positioning accuracy with the increasing demand for indoor positioning, in which Global Positioning System (GPS) signal is challenging. In practice, there are two main problems with indoor 3D positioning based on UWB. One is that the quality of Time Difference of Arrival (TDOA) measurements varies in different observation environments. Namely, the time delay generated by Non-Line-of-Sight (NLOS) causes an enormous deviation from the real distance and cannot be well distinguished from the measurement reducing the accuracy of positioning. The other problem is that the height estimates, which are calculated using the conventional least square method, are extremely unstable due to the limitation of the Base Station (BS) layout. To address these problems, this paper presents Robust Ridge Estimation (RRE) for UWB positioning. Firstly, NLOS errors are detected, and the weights of each measurement are automatically adjusted in accordance with their quality, which is represented by the residuals between the estimated measurements and real observations. Then, the ridge estimation algorithm is applied iteratively for position estimation based on a robust estimation framework, which updates the weight of the measurements at each iteration. This approach transforms unbiased estimation to biased estimation by adding constraints that minimize the weighted quadratic sum of some parameters. As a result, the impact of NLOS can be reduced. The experimental result shows an improvement of RMSE in positioning with 45.71% when compared with ridge estimation in an NLOS/Line-of-Sight (LOS) mixed environment and an increase of robustness to NLOS with 56.11%. [ABSTRACT FROM AUTHOR]

Background/Aims: Serum 25-hydroxyvitamin D [25(OH)D] levels proved to be associated with prognosis of patients with colorectal cancer or breast cancer, but its prognostic role in hematological malignancies was still unclear. A systematic review and meta-analysis was performed to comprehensively evaluate the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. Methods: We searched Pubmed, Embase, Web of Science, and Google Scholar for studies evaluating the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. The hazard ratios (HR) with 95% confidence intervals (95%CI) for overall survival (OS) or relapse-free survival (RFS) were pooled using meta-analysis. Results: Seven studies with a total of 2,643 patients with hematological cancer were finally included into the meta-analysis. Overall, compared with normal serum 25(OH)D levels, low serum 25(OH)D levels were significantly associated with both poorer OS (HR = 1.85, 95% CI 1.54-2.23, P <0.001) and poorer RFS (HR = 1.45, 95% CI 1.25 to 1.70, P <0.001) in hematological malignancies. Subgroup analysis further showed that low serum 25(OH)D levels were significantly associated with poorer OS and RFS in both lymphoma and leukemia. Conclusion: Low serum 25(OH)D levels are significantly associated with poorer prognosis in patients with hematological malignancies including lymphoma and leukemia. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Cancer remains a highly lethal disease globally. The approach centered on REDOX-targeted mitochondrial therapy for cancer has displayed notable benefits. Plant polyphenols exhibit strong REDOX and anticancer properties, particularly by affecting mitochondrial function, yet their structural instability and low bioavailability hinder their utility. To overcome this challenge, researchers have utilized the inherent physical and chemical characteristics of polyphenols and their derivatives to develop innovative nanomedicines for targeting mitochondria. This review examines the construction strategies and anticancer properties of various types of polyphenol-based biological nanomedicine for regulating mitochondria in recent years, such as polyphenol self-assembly, metal–phenol network, polyphenol–protein, polyphenol–hydrogel, polyphenol–chitosan, and polyphenol–liposome. These polyphenolic nanomedicines incorporate enhanced features such as improved solubility, efficient photothermal conversion capability, regulation of mitochondrial homeostasis, and ion adsorption through diverse construction strategies. The focus is on how these polyphenol nanomedicines promote ROS production and their mechanism of targeting mitochondria to inhibit cancer. Furthermore, it delves into the benefits and applications of polyphenolic nanomedicine in cancer treatments, as well as the challenges for future research. [ABSTRACT FROM AUTHOR]

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction. [ABSTRACT FROM AUTHOR]

Simple Summary: Intraguild predation between the introduced enemy Neoseiulus barkeri (Hughes) and the native enemy Scolothrips takahashii (Priesner) frequently occurs when target prey is scarce. It is hypothesized that intraguild predation is an adaptative strategy for predators, ensuring the persistence of the intraguild predators during periods of prey scarcity. Therefore, we undertook a study to evaluate the effects of the intraguild prey on the survival, development and reproduction of intraguild predators in a reciprocal predator guild with N. barkeri and S. takahashii. Our results support the hypothesis, showing that intraguild prey enhanced the survival, development and oviposition of intraguild predators, further confirming the effectiveness of intraguild predation as an adaptative strategy. The predatory mites Neoseiulus barkeri (Hughes) and the predatory thrips Scolothrips takahashii (Priesner) are known as potential biocontrol agents for the two-spotted spider mite Tetranychus urticae (Koch). These two predator species occur simultaneously on crops in agricultural ecosystems and are proved to be involved in life-stage specific intraguild predation. The intraguild prey may play a role in securing the persistence of the intraguild predators during food shortage periods. To understand the potential of intraguild prey as food source for intraguild predators in the N. barkeri and S. takahashii guild at low T. urticae densities, the survival, development and reproduction of both predators was determined when fed on heterospecific predators. The choice tests were conducted to determine the preference of the intraguild predator between the intraguild prey and the shared prey. Results showed that 53.3% N. barkeri and 60% S. takahashii juveniles successfully developed when fed on heterospecific predators. Female intraguild predators of both species fed on intraguild prey survived and laid eggs throughout the experiment. In the choice test, both intraguild predator species preferred their extraguild prey T. urticae. This study suggested that intraguild prey served as an alternative prey for intraguild predators prolonged survival and ensured the reproduction of intraguild predators during food shortage, ultimately decreasing the need for the continual release of the predators. [ABSTRACT FROM AUTHOR]

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be unreliable in dynamic or degraded environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of lazer sweeps (i.e., geometric, intensity and temporal characteristics). The specific content of this work includes method innovation and experimental verification. With respect to method innovation, we propose the cylindrical-image-based feature extraction scheme, which makes use of the characteristic of uniform spatial distribution of lazer points to boost the adaptive extraction of various types of features, i.e., ground, beam, facade and reflector. We propose a novel intensity-based point registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out in the resulting points map. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. With respect to experimental verification, extensive tests are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy with respect to the state-of-the-art in normal driving scenarios and outperforms geometric-based LO in unstructured environments. [ABSTRACT FROM AUTHOR]

Traffic situation awareness is the key factor for intelligent transportation systems (ITS) and smart city. Short-term traffic flow prediction is one of the challenging tasks of traffic situation awareness, which is useful for route planning, traffic congestion alleviation, emission reduction, and so on. Over the past few years, ubiquitous location acquisition techniques and sensors digitized the road networks and generated spatiotemporal data. Massive traffic data provide an opportunity for short-term traffic flow prediction in a data-driven manner. Most of the existing short-term traffic flow prediction methods can be divided into two categories: nonparametric and parametric. Traditional parametric methods failed to obtain accurate prediction, due to the nonlinear and stochastic characteristics of short-term traffic flow. Recently, deep learning methods have been studied widely in the fields of short-term prediction. These nonparametric methods yielded promising results in practical experiments. Motivated by the current study status, we dedicate this paper to a short-term traffic flow prediction approach based on the recurrent mixture density network, the combination of recurrent neural network (RNN), and mixture density network (MDN). This approach is implemented on real-world traffic flow data and demonstrates the prominent superiority. To the best of our knowledge, this is the first time that the recurrent mixture density network is applied to a real-world short-term traffic flow prediction task. [ABSTRACT FROM AUTHOR]