The adaptive practical prescribed-time (PPT) neural control is studied for multi-input multi-output (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. Firstly, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator.

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This work addresses the contribution of the wavelength composition of the spectrum of the rail support stiffness profile to the expected long-term settlement. To that aim, purely harmonic stiffness variations of different wavelength are studied. The frequency-domain model with a double periodicity level previously developed by the first and last authors is adopted to embed the stiffness profile in one of the periodicity layers. Additional resonance velocities at which the resonance frequency of the track system coincides with the support-passing frequency or its multiples are found. The susceptibility to degradation is assessed both by quantifying the mechanical energy dissipated in the substructure under a moving train axle within one wavelength of the support stiffness variation, and the work performed by the wheel-rail contact force. It is shown that shorter wavelengths and larger standard deviations of varying ballast/subgrade stiffness result in an increasing energy dissipation in the substructure, and increase the work performed by the wheel-rail contact force, therefore leading to a reduced lifetime of the track. The energetic quantities increase for lower mean values of the stiffness profile, confirming the proneness of tracks on soft soils to degradation. The influence of varying stiffness vanishes for wavelengths of approximately 16 times the sleeper span, which is equivalent to a track length of about 10 m. High railpad stiffness values result in increased energy dissipation but the influence is limited. In general, an increasing train velocity amplifies the rate of track degradation, with no stabilizing trend in the high-speed regime (300 km/h).

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Modeling password distributions is a fundamental problem in password security, benefiting the research and applications on password guessing, password strength meters, honey password vaults, etc. As one of the best segment-based password models, WordPCFG has been proposed to capture individual semantic segments (called words) in passwords. However, we find WordPCFG does not address well the ambiguity of password segmentation by maximum matching, leading to the unreasonable segmentation of many password and further the inaccuracy of modeling password distributions. To address the ambiguity, we improve WordPCFG by maximum probability segmentation with A*-like pruning algorithm. The experimental results show that the improved WordPCFG cracks 99.26%–99.95% passwords, with nearly 5.67%–18.01% improvement.@en

The coupled vehicle-track system (CVTS) dynamics have been extensively investigated for decades. However, the calculation accuracy of prevailing vehicle-track coupling models needs to be improved in the high frequency range due to the inappropriate model simplification and neglect of material nonlinearity. In this study, we propose a refined numerical model of the CVTS that considers the nonlinear properties of the railpads and primary suspension using the fraction derivative Zener model. Furthermore, we more realistically simulate the wheelset, rail and railpad configuration with the elastic axle, solid finite element and surface-support models, respectively, and improve the computation efficiency by employing the mode superposition method. The results demonstrate that the refined CVTS model is more accurate than the classical model in simulating vehicle-track coupling dynamics above 2 kHz. In particular, there are significant differences in the dynamic response of the elastic wheelset model compared to the rigid model over a broad frequency range, with an 11% difference in the bogie acceleration response at the first dominant frequency. When the railpads are modeled using the surface-support model, the rail acceleration differences exceed 41% near 1 kHz and 44% near 2650 Hz, compared to the point-support model. Additionally, the rail response at various locations across the rail cross section can be calculated using the finite element method in this refined model. Overall. the proposed CVTS model provides high accuracy and efficiency for random vibration analysis, especially in the high frequency domain.

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Password-only authentication is one of the most popular secure mechanisms for real-world online applications. But it easily suffers from a practical threat - password leakage, incurred by external and internal attackers. The external attacker may compromise the password file stored on the authentication server, and the insider may deliberately steal the passwords or inadvertently leak the passwords. So far, there are two main techniques to address the leakage: Augmented password-authentication key exchange (aPAKE) against insiders and honeyword technique for external attackers. But none of them can resist both attacks. To fill the gap, we propose the notion of <italic>honey PAKE (HPAKE)</italic> that allows the authentication server to detect the password leakage and achieve the security beyond the traditional bound of aPAKE. Further, we build an HPAKE construction on the top of the honeyword mechanism, honey encryption, and OPAQUE which is a standardized aPAKE. We formally analyze the security of our design, achieving the insider resistance and the password breach detection. We implement our design and deploy it in the real environment. The experimental results show that our protocol only costs 71.27 ms for one complete run, within 20.67 ms on computation and 50.6 ms on communication. This means our design is secure and practical for real-world applications.

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Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), actual cloud shadow flags (ACSFs), and spectral cloud shadow flags (SCSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. Because we find indications of the wavelength dependence of cloud shadow extents in the UV, the SCSF is a wavelength-dependent alternative for the ACSF at the wavelengths of TROPOMI's air quality retrievals. We validate the PCSF and ACSF with true-colour images made by the VIIRS instrument on board Suomi NPP orbiting in close proximity to TROPOMI on board Sentinel-5P. We find that the cloud evolution during the overpass time difference between TROPOMI and VIIRS complicates this validation strategy, implicating that an alternative cloud shadow detection approach using co-located VIIRS observations could be problematic. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF and SCSF can be used to select pixels for the scientific analysis of cloud shadow effects.@en

To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.@en

The vertical vibration and transmission characteristics of ballast are key factors that affect the dynamic stability of railway track structures and control the settlement of ballasted beds. Therefore, the following study was conducted to explore this topic. Firstly, through an impact hammer test on a ballast sensor with embedding chip, the vertical vibration data of the ballast was accurately measured. Therefore, the vertical vibration characteristics of a single ballast can be studied. Then, the vertical vibration characteristics at different positions in the stack were obtained by embedding ballast sensors into a ballasted stack. Finally, combined with field tests, a discrete element numerical model was established, then the vibration transmission speed and diffusion angle in a ballasted stack were calculated. The results of this study show that the damping ratio of ballast particles is less than 0.1, and the natural frequency is above 1000 Hz. The damping ratio and natural frequency of ballasts are greatly affected by their shape. The damping ratio of a ballasted stack is greater than that of ballast particles, and its natural frequency is lower. This indicates that the ballasted stack has the attributes of a soft material. The vertical acceleration transmission rate of ballasts is lower at frequencies below 257.94 Hz. This shows that the vibration suppression ability of the ballasted bed is better in the lower frequency range. As the depth increases, the vertical vibration transmission speed of the ballast gradually decreases, as does the accumulated external force. In the impact hammer test of a ballasted box, the average vertical vibration transmission speed was calculated to be 0.88 mm/μs, and the ballast vibration was transmitted downward at a diffusion angle of 35.32°–54.51° from the direction of gravity.

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Sintered nano-silver die-attach materials have been widely used in high-power electronics packaging because of their high thermal and electrical conductivities. In this study, we characterized the tensile properties of sintered nano-silver particles over a range of strain rates and temperatures, and established the constitutive models. First, 50 nm nano-silver particles were sintered at 275 °C for 50 min as test samples, and their tensile tests were conducted under a dynamic thermomechanical analyzer (DMA Q800) and an IBTC 300SL in-situ mechanical test system respectively with different strain rates and ambient temperatures. Then, both Anand and variable-order fractional models (VoFM) were adopted to analyze the obtained stress-strain data and we studied their fitting accuracy and applicability. The results showed that: (1) The Young's modulus of the sintered nano-silver particles decreased with increasing temperature. In addition, the tensile strengths declined under lower strain rates and higher temperature conditions; (2) both the Anand model and VoFM characterized the tensile stress-strain properties of the sintered nano-silver material well. Compared to the Anand model, the VoFM utilized a simpler formula with fewer parameters and higher precision.@en

Passwords have been widely used in online authentication, and they form the front line that protects our data security and privacy. But the security of password may be easily harmed by insecure password generator. Massive reports state that users are always keen to generate new passwords by reusing or fine-tuning old secrets. Once an old password is leaked, the users may suffer from credential tweaking attacks. We propose a password reuse model PassTrans and simulate credential tweaking attacks. We evaluate the performance in leaked password datasets, and the results show that 67.51% of accounts is breakable under 1,000 guesses, indicating our model is accurate in capturing password reuse behavior.@en

To investigate the train-induced ground vibration, an explicit time-domain, three dimensional (3D) finite element (FE) model is developed. The train, track, embankment, pile-board structure and nearby ground soils are all fully coupled in this model. The complex geometries involving the track components and pile-board structure are all modelled in detail, which makes the simulation of wave propagation more realistic from the train to the ground. The model is validated with in situ tests data collected in the Beijing-Shanghai high speed railway line. Good agreements have been achieved between the numerical results and experimental results both in time domain and frequency domain. The proposed model is thus capable of reproducing the dynamic ground response induced by a typical high speed train. Soil responses induced by different number of vehicles are compared. With more vehicles, the spectral peaks of soil responses are more prominent at the integral multiples of the vehicle passing frequency. Too few vehicles will not bring about such phenomenon, thus sufficient number of vehicles should be included in a train to properly model train-induced ground vibration. With the proposed model, the influence of the pile-board foundation on the ground vibration is investigated. It is found that the pile-board foundation can significantly attenuate the low frequency ground vibration. The attenuation of the ground vibration as a function of distance from the track is simulated and the influential factors to the local vibration amplification are investigated. It is found that soil Young's modulus and soil impedance contrast are the two main factors influential to the local vibration amplification. The softer the natural soil, the larger the amplification. The larger soil impedance contrast makes the amplification more obvious. The soil stratification and geometric discontinuity at ground surface are not the main cause of the local vibration amplification in this work.

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Password vault applications allow a user to store multiple passwords in a vault and choose a master password to encrypt the vault. In practice, attackers may steal the storage file of the vault and further compromise all stored passwords by offline guessing the master password. Honey vaults have been proposed to address the threat. By producing plausible-looking decoy vaults for wrong master passwords, honey vaults force attackers to shift offline guessing to online verifications. However, the existing honey vault schemes all suffer from intersection attacks in the multi-leakage case where an old version of the storage file (e.g., a backup) is stolen along with the current version. The attacker can offline identify the decoys and completely break the schemes. We design a generic construction based on a multi-similar-password model and further propose an incremental update mechanism. With our mechanism, the attacker cannot get any extra advantages from the old storage, and therefore degenerates to an attacker only with knowledge of the current version. To further evaluate the security in the traditional single-leakage case where only the current version is stolen, we investigate the theoretically optimal strategy for online verifications, and propose practical attacks. Targeting the existing schemes, our attacks crack 33%-55% of real vaults via only one-time online guess and achieve 85%-94% accuracy in distinguishing real vaults from decoys. In contrast, our design reduces the values of the two metrics to 2% and 58% (close to the ideal values 0% and 50%), respectively. This indicates that the attackers needs to carry out 2.8x-7.5x online verifications to break our scheme.

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Multi-factor authentication (MFA) has been widely used to safeguard high-value assets. Unlike single-factor authentication (e.g., password-only login), t-factor authentication (tFA) requires a user always to carry and present t specified factors so as to strengthen the security of login. Nevertheless, this may restrict user experience in limiting the flexibility of factor usage, e.g., the user may prefer to choose any factors at hand for login authentication. To bring back usability and flexibility without loss of security, we introduce a new notion of authentication, called (t, n) threshold MFA, that allows a user to actively choose t factors out of n based on preference. We further define the &#x201C;most-rigorous&#x201D; multi-factor security model for the new notion, allowing attackers to control public channels, launch active/passive attacks, and compromise/corrupt any subset of parties as well as factors. We state that the model can capture the most practical security needs in the literature. We design a threshold MFA key exchange (T-MFAKE) protocol built on the top of a threshold oblivious pseudorandom function and an authenticated key exchange protocol. Our protocol achieves the &#x201C;highest-attainable&#x201D; security against all attacking attempts in the context of parties/factors being compromised/corrupted. As for efficiency, our design only requires 4+t exponentiations, 2 multi-exponentiations and 2 communication rounds. Compared with existing tFA schemes, even the degenerated (t, t) version of our protocol achieves the strongest security (stronger than most schemes) and higher efficiency on computational and communication. We instantiate our design on real-world platform to highlight its practicability and efficiency.

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In high-power electronics packaging, nano-silver sintering technology has been widely applied due to its excellent electrical and thermal conductivity and its low-temperature packaging and high-temperature operation. In this study, 50-nm nano-silver particles are sintered at 275°C for 50 min and placed under a dynamic thermomechanical analyzer (DMA Q800) with three strain rates (0.001%s-1, 0.01%s-1, and 0.1%s-1) and seven ambient temperatures (-40°C, 0°C, 25°C, 60°C, 120°C, 150°C, and 185°C). Both the variable-order fractional constitutive model and Anand model are adopted to characterize the tensile behaviors of sintered nano-silver particles. The results show that (1) the tensile strength of sintered nano-silver particle samples declines under the lower strain rate and higher temperature; and (2) both the variable-order fractional model and Anand model can well represent the tensile mechanical properties of sintered nano-silver. According to the root mean square error (RMSE) calculation, the fitting accuracy of the variable-order fractional model is slightly better than that of the Anand model. Furthermore, the variable-order fractional model involves fewer parameters, which makes it easier to fit than the Anand model.

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Probabilistic context-free grammars (PCFGs) have been pro-posed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG. The WordPCFG using word extraction method can precisely extract semantic segments (called word) from passwords based on cohesion and freedom of words. We evaluate our WordPCFG on six large-scale datasets, showing that WordPCFG cracks 83.04%–95.47% passwords and obtains 12.96%–71.84% improvement over the state-of-the-art PCFGs.@en

Embedded rail system (ERS) is a new type of track structure with many advantages due to its continuous rail support. The rapid development of urban rail transit all over the world renders its application prospect broad. However, the cracks and debonds in ERS present a threat to the traffic safety and a possibility for high maintenance costs. In this work, a longitudinal pushing experiment was designed to explore the damage development process in ERS in order to help structural optimization and performance maintenance. The first order derivative of displacement-longitudinal force curve indicates that the damage process of ERS could be divided into three stages: linear elasticity, damage initiation and damage acceleration stages. The surface deformation of the elastic poured compound (EPC) was analyzed with the particle velocimetry and it is shown that the damage is possibly localized in a small EPC part. Statistics of the absolute displacements of a large number of interrogation areas show that their percentage distribution changes in agreement with the increment of rail displacement, which could be the basis for monitoring of EPC deformation in the breathing zone of continuous welded rail. The analysis of the deformation of EPC from side views, together with the qualitative analysis with finite element method, reveals that the large shear strain of rubber strip and the intense shear strain of EPC at rail foot are the main causes of damage initiation and growth in ERS under longitudinal force.

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The goal of this study is to develop a vibration-based electromagnetic energy harvesting prototype that provides power to rail-side monitoring equipment and sensors by collecting wheel-rail vibration energy when the train travels. This technology helps power rail–side equipment in off-grid and remote areas. This article introduces the principle, modeling, and experimental test of the system, including (1) an electromagnetic energy harvesting prototype with DC-DC boost converter and lithium battery charge management function, (2) wireless sensor nodes integrated with accelerometer and temperature/humidity sensor, and (3) a vehicle-track interaction model that considers wheel out-of-roundness. Field test results, power consumption, Littlewood–Paley wavelet transform method, and feasibility analysis are reported. An application case of the technology is introduced: the sensor nodes of the wireless sensor network are powered by the electromagnetic energy harvester and lithium battery with DC-DC boost converter, thereby continuously monitoring the railway track state; based on the Littlewood–Paley wavelet analysis of measured railway track acceleration data, the abnormal signal caused by the wheel out-of-roundness can be detected.

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