esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary  
An AFRBF-SMC vehicle stability control uncertainty term based on ADUKF sideslip angle estimation is proposed. Given the difficulty in estimating vehicle sideslip angle, we use ADUKF to compute sideslip angle. After that, given the vehicle uncertain disturbance caused by parameter perturbation or the external environment, a nonlinear vehicle dynamic model involving system uncertainty disturbance and additional yaw moment is established. A vehicle stability SMC based on the SMC theory is designed. Given the severe system vibration due to the large robustness in the SMC, a FRBFNN is designed and used to approximate the uncertainty, which thereby decreases the robustness in SMC and lowers the system vibration, making the controlling process more stable. The adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of the system are validated. Simulations under the steering wheel sinusoidal input condition and the DLC condition are conducted on MATLAB/Simulink-carsim and compared with SMC and the control-less condition. (1) As for estimation of sideslip angle, ADUKF is precise and can meet the demand of vehicle stability control. (2) When a stability controller is needed, SMC and AFRBF-SMC both can ensure vehicle stability compared with the absence of controllers, but AFRBF-SMC can better trace the ideal values of the control parameters and decrease the system vibration. The overall control effect of AFRBF-SMC is better than traditional vehicle stability smcs.  
Conceptualization, Z.Z. and J.L.; methodology, Z.Z.; validation, Z.Z., C.G. and J.Z.; formal analysis, J.Z. and Z.Z.; resources, L.C. and J.L.; funding acquisition, J.L.; supervision, L.C.; writing--original draft preparation, Z.Z.; writing--review and editing, C.G. and L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R & D Plan of China, grant number 2018 YFB0105905-02.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Vibration in the Tunnel Arch
### Attenuation Law of the Blasting Vibration Velocity  
To describe the attenuation law of the blasting vibration velocity, the Sadowski formula is a common method. The Sadowski formula is:  
\[V=K\cdot\left(R/Q^{1/3}\right)^{\alpha} \tag{1}\]  
where \(V\) is the PPV (cm/s); \(Q\) is the number of explosives (kg); \(R\) is the distance from the blast centre (m); and \(K\) and \(\alpha\) are the coefficient and attenuation index related to terrain and geological conditions, respectively. Reference [17] shows that the Sadowski formula determined by the nonlinear regression method has higher accuracy, and then, \(K\) and \(\alpha\) can be obtained. The PPVs of the vertical, horizontal tangential and horizontal radial directions with proportional distances at the tunnel vault, arch waist, and sidewall are fitted.  
Figure 3 and Table 1 show that the PPV at the vault was always larger than at the arch waist, which was greater than at the sidewall regardless of the vertical, horizontal tangential, or horizontal radial direction. With an increasing proportional distance, the PPV gradually approaches, which indicates that the three-way vibration velocity may overlap or even change the size relationship with a further increase in the proportional distance. The vertical, horizontal radial, and horizontal tangential \(K\) of the vault vibration velocity are all larger than the corresponding components of other test positions. The vertical vibration velocity is the largest. Although the shock wave is attenuated by the propagation path, the residual blasting energy of the vault is higher than that of the other test positions. The \(\alpha\) of the three components of the vibration velocity of the measuring points at the same position is close. In addition, the absolute values of the vertical, horizontal radial, and horizontal tangential \(\alpha\) of the vault are larger than the corresponding components of other blasting holes, indicating that the vibration velocity of the vault decays faster.  
Figure 3: Three-component vibration velocity attenuation law and regression curve. (**a**) At vault; (**b**) At arch waist; (**c**) At sidewall.
## 1 Introduction
The first method here presented is the evaluation of streamer repeatability. Corona discharge is featured with a strong randomness, which can contribute to the overall CCV. Depending on discharge parameters (mainly the electrode voltage [21; 32; 34]), pressure and turbulence in the combustion chamber, and mixture composition, streaming behavior is different. Phenomena like branching, transition to arc, and lack of streamers might occur. Moreover, in nominally identical conditions, the discharge can also be different, as happens with every discharge system [35]. Among all the streamer features, the parameter chosen to investigate the repeatability of the discharge was the max penetration inside the combustion chamber. A higher penetration denotes a higher amount of air-fuel mixture involved in early flame generation (volumetric effect), which is crucial to ensure a robust combustion especially with lean mixtures [11]; there is evidence that links the streamer penetration to the thermal energy released inside the combustion chamber [21] and to the production of radicals and excited species [36]. Thus, a methodology to assess corona discharge variability, in terms of streamer max penetration, is reported. A further step is the determination of the very first frames in which flame, generated by the streamer, is present, at least in the visible range; a method to capture the transition from pure discharge to flame onset, based on the binarized area evolution, is here presented. This method can contribute to a better understanding of how corona ignition is capable of triggering combustion, thereby providing valuable information not obtainable via a more common indicating system because of the limited pressure rise at the very beginning of the combustion onset. Note that streamer discharge, which is also stochastic by nature in quiescent conditions [37], is affected by a further degree of variability depending on in-cylinder phenomena like charge motion and mixture composition. The characterization here presented is dependent not only on the igniter _as it is_, but also on these factors. The transition into flame is the following process, just after the discharge onset. Thus, it can be affected by the streamer variability defined before, but it can contribute even by itself to overall engine CCV. The further combustion evolution was out the scope of this work, which was focused on the very first ignition and combustion frames. The outcomes of this work can be used as methodology for extensive experimental campaigns aimed at determining how discharge conditions, together with in cylinder flows and mixtures, affect the overall combustion process, including the overall CCV. Finally, the obtained data can be used as validation data for ignition models, as up-to-date 3D-CFD codes are missing robust and predictive LTP models [38].
## 1 Introduction
analyzed the probability of load settlement and the design parameters of bored piles using the t-z approach [19]. Through field experiments, the Australian company Vico-Pile determined that with silty clay, sandy, or clay silt (SPT = 5-23), and gravel soil (SPT > 35) as the underlying layer, the bearing and deformation characteristics of bored piles were significantly superior to those of long spiral grouting piles [9]. Bauer Company in Germany found, through field full-scale experiments, that in clay and sandy soil foundations, the ultimate bearing capacity of a bored pile is roughly double that of a long spiral grouting pile and that bored piles are suitable for sand, silt, clay, and other compressible soil layers with SPT < 30 and CPT < 10 MPa [10]. China introduced this technology in 2012; the Jingye Engineering Technology Company in China developed the bored piles construction method, special drilling tools, and drilling equipment, and through field full-scale testing, it was determined that the compaction range of bored piles in clay is four times the pile diameter, and the greater the compressibility of the soil, the greater the squeezing range [11]. The preceding engineering practice demonstrates that bored piles offer numerous benefits, including no noise, no vibration, no mud pollution, no mud transport, a broad range of appropriate strata, high economic benefits, and a large increase in bearing capacity [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. However, their research and engineering applications in areas with collapsible loess have not been reported. Consequently, this study chose a typical collapsible loess site and conducted a field test to investigate the squeezing effect of bored piles in a collapsible loess site and the bearing characteristics before and after immersion to provide a design basis and technical reference for the application of bored piles in a collapsible loess area.
## 3 Results
### Plasma Actuator Case
#### 3.2.2 Applied Voltage Frequency  
The applied voltage frequency used for the DBD plasma actuator was 3 kHz. As at this frequency, the DBD actuator could vary from 1-10 kHz [2], the analysis was done to understand the significance of the applied voltage frequency in the plasma body force. It should be noted that the plasma body force was proportional to the applied voltage frequency, and hence, an increase in frequency would increase the body force. However, this increased induced body force may not guarantee improvement in flow separation. To observe the effects and flow field conditions, skin friction coefficient analysis was done as displayed in Figure 22. Figure 22 indicates that the high frequency created no separation as the yellow line representing 5000 Hz does not cross the \(C_{f}=0\) line. From the current analysis based on applied voltage frequency variation, it was concluded that the DBD plasma actuator with 5 kHz was more effective for flow separation control on the hump model and merited the need for future DBD plasma actuator simulations.  
Figure 21: Coefficient of skin friction comparisons for different plasma actuator locations.  
Figure 22: Coefficient of skin friction comparisons for different voltage frequencies.  
As stated earlier, a total of five test cases were run for the plasma actuator simulations, shown in Table 2. The summary of these five cases' results is shown below in Table 4. Test Cases 3 and 5 gave us improved performances for the DBD plasma actuator.
## 4 Numerical Simulation Analysis
### Influence of Burial Depth  
Burial depth is one of the important factors in the seismic response of the underground structure. The same underground structures may have different seismic responses in different burial depths. To study the influence of different burial depths on the seismic response of an underground substation in the horizontal direction, five conditions including different burial depth at 1.2 m (M1), 3 m (M2), 5 m (M3), 7 m (M4) and 9 m (M5) were chosen for the samples.
###### Abstract
[PERSON] et al. (2021) [20] applied the SDD model proposed by [PERSON] et al. (2020) [21] to Sentinel-2 to calculate the SDD of the Yangtze River mainstream and analyze its spatio-temporal variations from 2017 to 2020 [20]. Sentinel-2 has been used to analyze the water color variations of Baiyangdian Lake [22]. Furthermore, Sentinel-2 has been used to calculate the water hue angle \(\alpha\) and determine the threshold for screening water color anomalies, which achieved good results in Heilongjiang and Hebei province [23]. The Development Plan for the GBA was issued on 18 February 2019, and officially announced the construction of the GBA, in which Guangzhou and Shenzhen are two important cities. Therefore, we analyzed water color variations of 41 inland lakes and reservoirs (larger than 0.5 km\({}^{2}\)) in Guangzhou and Shenzhen after the construction of the GBA. We used Sentinel-2 to produce monthly, quarterly and annual FUI products, and analyzed their spatial distributions, seasonal and inter-annual variations, and driving factors.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 2 Literature Review
### Direct and Indirect Metrics for Trust Assessment  
Trust assessment in IoT systems leverages both direct and indirect metrics to enhance reliability and security. [PERSON] et al. [21] proposed a Context-Based Trust Evaluation System (CTES) for the Industrial Internet of Things (IIoT), which calculates trust scores for edge nodes by combining direct trust from user satisfaction scores and indirect trust from node recommendations. The system dynamically adjusts the weights of these trust components based on contextual factors, aiming to filter out malicious nodes effectively. In [22], the authors introduce a framework called the Intelligent Trust Collaboration Network System (ITCN), which employs mobile vehicles and UAVs for decentralized data collection in IoT networks. The system uses a trust evaluation mechanism that compares data collected by UAVs and vehicles, optimizing data collection efficiency while maintaining security through direct and indirect trust metrics. [PERSON] et al. [23] present a trust evaluation method that integrates direct trust metrics, such as honesty and energy efficiency, with indirect trust derived from neighboring node recommendations. This approach continuously updates and propagates trust values to enhance the security and reliability of IoT network routing. The authors of [24] develop a T-Safe framework for evaluating sensor node trustworthiness in intelligent transport systems. The framework assesses trust through direct metrics like security and data quality, combined with indirect trust from referrals by neighboring nodes. Trust values are updated using a sliding window approach to ensure accurate and reliable service provisioning. The model proposed by [30] evaluates entities using a combination of multi-context quality of service, quality of provider, and market value. Trust is calculated dynamically through the IoT-TESM algorithm, which uses an auto-scale weighting (ASW) technique to adjust trust metrics over time. The model outperforms other frameworks like ReGreT and SloT in selecting reliable service providers. However, limitations include the need for
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Pre-Processing
#### 3.2.1 QRS Extraction  
The first pre-processing step consists in extracting all the QRS complexes from each of the \(Q\) ECGs, \(x_{q}[n]\) for \(q=1,\ldots,Q\). To attain this goal, we followed the approach described in [30]:  
1. Apply a 4 th order Butterworth bandpass filter with cut-off frequencies \(f_{c1}=1\) Hz and \(f_{c2}=40\) Hz to remove noise and interferences. Forward-backward filtering, with an appropriate choice of the initial state to remove transients [31], is used to avoid phase distortion.
2. Locate the positions of the R waveforms using the Pan-Tompkins QRS detector [32].
3. Determine the fiducial points that mark the beginning and end of the QRS complexes by tracking backwards and forward from the R peaks, estimating the QRS onset and offset points using the minimum _radius of curvature_ technique, as described in Section 4.2 of [30].  
Figure 2: Block diagram of the pre-processing stage applied before the creation of the dictionary.  
This approach resulted in \(Q^{\prime}=44\) valid subjects (i.e., \(\approx\)84.6% out of the \(Q=51\) available individuals), for which a significant and variable number of QRS complexes (\(y_{q,i}[n]\) for \(1\leq q\leq Q^{\prime}\), \(1\leq i\leq P_{q}\) and \(0\leq n\leq L_{q,i}-1\)), with a variable length of samples for each of them, were extracted for the database used. A total of 6266 QRS complexes were extracted, implying an average of 142.4 QRS complexes per patient (with a maximum of 194 QRS complexes obtained from a single subject) with lengths from 90 to 124 samples (i.e., from 90 to 124 ms).
## 3 Results and Discussion
### Kinetic Modelling
#### 3.5.2 Parameters Estimation  
Once the kinetic model was defined, it was fitted to diverse sets of experimental data with the aid of Aspen Custom Modeler v 12.1 software. We used implemented algorithms based on least square estimation for non-linear regression and in the Euler method for ODEs integration, as explained in detail in Section 2.2.6. This methodology was used to obtain optimal values for each parameter, starting the calculations using reasonable initial value given for them by drawing on our experience from modeling other reaction systems [50,51].  
The kinetic parameters related to the above model were calculated by nonlinear adjustment coupled with the numerical integration of this model with the experimental data. First, a regression was performed at different concentrations of catalyst (C\({}_{\text{cat}}\)) at 80 \({}^{\circ}\)C, to determine the kinetic constant k and a parameter K\({}_{\text{cat}}\). Once this last value is retrieved, it was fixed and the same kinetic model was fitted to a second set of experiments at three molar ratios between EC and glycerol from 1.5 to 3. As these runs were performed at 100 \({}^{\circ}\)C, the kinetic constant k value increased in an exponential way, as expected. These values are collected in Table 3, together with their errors at 95% confidence. Table 4 compiles all goodness-of-fit parameters, and the goodness of the kinetic model fit to experimental data can also be appreciated in Figure 15. We can appreciate that the model fits perfectly to data contained in the two datasets: RSE and S\({}_{\text{e}}\) values are very low while VE% and F are very high.
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study
### Failure Probability Analysis
#### 3.6.1 Evaluation Framework Based on Fragility Analysis
For the case that the downburst footprint is parallel to the transmission line, according to the fragility curve calculated above, it can be known that the critical collapse wind speed in the \(0^{\circ}\) direction is about 30 m/s, and the downburst footprint length of Andrews air force base is about 5.5 km. The length of the downburst footprint under different direction angles can be obtained [34].
## 3 Testing and Results
### Test-2: Hovering Close to a Tall Wall
In contrast, the zoom-in in Figure 7c highlights the case of a false fix bringing an error of about 40 cm in height (around 10:55), which cannot be highlighted by looking at the related HRMS. The time span considered in Figure 7c is in general characterized by a lower number of  
Figure 7: (**a**): The top chart in reports the time series of the up coordinates recorded in ambiguous fixed conditions. The estimated vertical precision is shown in the middle plot, whereas in the bottom, the time series is the number of GNSS satellites in sight of the receiver during the test. (**b**) is a zoom-in of the runs where the estimated error VRMS is well correlated to the real error in height, while (**c**) highlights the test results in a period with fewer satellites in sight and some trouble (see minute 10:54) for the VRMS in perceiving false fix solutions. satellites in sight; this also caused a slightly less stable estimation of the height position even for the good solutions, as properly evidenced by the VRMS. Figure 8 reports histograms and statistics of the time the system took to lose and re-gain the fixing condition. To re-fix the solution, the receiver took on average 13 s, with a rather small variability in the data, with a standard deviation of less than one second. These values were calculated considering the time from the instant the cart passed the average position where the GNSS fixing were lost in the one-way runs.
# Research on Key Technologies of 260 \({}^{\circ}\)C/210 MPa Ultra-Deep Logging Equipment
## 3 Research on Key Technologies of Ultra-High-Temperature and High-Pressure Logging
### Data Transmission over Ultra-Long Cables
The digital component multiplies the digital signal by an exponentially varying coefficient based on the frequency of the transmission signal. This serves as a signal compensation mechanism during the transmission of analog signals within the cable's bandwidth [14; 15], enabling the automatic gain control (AGC) circuit at the receiver to have more adjustment ranges. Simultaneously, the relevant peak intervals are statistically analyzed to automatically adjust the pre-emphasis parameters. Real-time modulation constellation allocation design  
Signals are transmitted using the 6-85 kHz range, with different dimensional modulation signals allocated to different carriers in real-time based on the cable characteristics to improve the signal transmission reliability and balance during high-speed transmission. For lower-frequency sub-carriers with better channel characteristics and less signal attenuation, a higher-dimensional 64 QAM modulation mode is used to increase the data transmission speed. For higher frequency sub-carriers, where the signal attenuation varies, lower-dimensional 16 QAM and 4 QAM modulation modes are selected in real time to enhance the transmission reliability, achieving a high-speed remote transmission rate of 300 kbps [16]. PID synchronization for clock signals  
The ground clock synchronization algorithm was improved from the original minimum error adjustment algorithm [17; 18; 19] to a proportional-integral-derivative (PID) algorithm. This algorithm not only minimizes errors but also incorporates real-time error variation parameters (derivative) and historical variation parameters (integral), allowing the ground clock to more stably track the changes in the downhole clock. This ensures more stable clock adjustments and enhances the reliability of the system data transmission. Channel characteristic evaluation statistics  
By continuously evaluating and statistically analyzing the channel characteristics, the system can dynamically adjust transmission parameters to optimize the data integrity and transmission efficiency under varying conditions. A 13,000 m long cable communication indoor test was conducted, with a downlink speed of 51.2 kbps and an upload speed of 268.8 kbps. The 13,000 m long cable was successfully operated in an 8000 m deep well at Deep Taco 1 well. The logging instrument included seven types (seven nodes), with 1516 words of valid data uploaded per frame, a CAN interrupt interval of 160 ms, a measurement speed of 370 m/h, and a smooth communication process. Figure 13: Test waveform of the 13,000 m cable at 100 kHz input (green), output (blue) and FFT frequency amplitude waveform (midline).
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 1 Introduction
### Contributions  
In this paper, our contributions are listed as follows: Firstly, the multilayer MPC controller that can automatically adjust the velocity according to path information is proposed. Our proposed controller is compared with the switching MPC controller and the nonlinear MPC controller. The performance of the multilayer MPC controller is very good. Secondly, two controller topology schemes are proposed, and we prove that the parallel layout can further improve the real-time performance of multilayer MPC. Thirdly, the performance of the multilayer MPC controller under the impact of positioning error is verified.  
The content of this paper is arranged as follows: The multilayer MPC-based controller of articulated vehicles is introduced in Section 2. In Section 3, the simulation results are detailed. Finally, in Section 4 we conclude the present work.