esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Intelligent Tire Force Estimation Method
### Analysis of the Characteristics of Lateral Force-Sensitive Response Signal Curves
#### 5.2.2 Lateral Displacement Response Signal in the Left Sidewall Area  
Figure 12a illustrates the variation in the lateral displacement response signal in the left sidewall area with respect to lateral force. When no lateral force is applied, the lateral displacement response signal only exhibits a single peak at the center of contact patch area B, with negligible displacement observed in the left sidewall area on either side of the ground deformation area. As the lateral force increases, two local minima gradually appear in the lateral displacement response signal as the tire enters contact patch area A and exits contact patch area C. Simultaneously, the original maximum shifts downward, transitioning from a positive to a negative value. It is observed that as the lateral force increases, the amplitude of this shift progressively decreases. Seven characteristic values are extracted from the lateral displacement response signal curve in the left sidewall area, including the baseline value \(h_{0}\); the amplitudes of the extreme points \(h_{1}\), \(h_{2}\), and \(h_{3}\); as well as the distances between them, namely \(S\), \(S_{1}\), and \(S_{2}\), as shown in Figure 12b.
## 3 Classic Numerical Scheme Test Cases  
### Solving the One-Dimensional Euler Equations under Riemann Initial Conditions  
The Riemann problem is a fundamental scenario in computational fluid dynamics, often used to study the behavior of shock waves and discontinuities in fluid flow. It involves solving the Euler equations with piecewise constant initial conditions that include a discontinuity, leading to complex wave interactions within the fluid medium. This problem is crucial for understanding the dynamics of shock waves, rarefaction waves, and contact discontinuities, which are essential for accurately modeling fluid behavior under various conditions.  
In the current study, we consider one-dimensional Euler equations with specific initial conditions defined over the domain \(x\in[-5,5]\), as shown in Equation (7). The computational domain is discretized using 200 grid points.  
\[\frac{\partial}{\partial t}\begin{pmatrix}\varrho\\ \varrho u\\ E\end{pmatrix}+\frac{\partial}{\partial x}\begin{pmatrix}\varrho u\\ \varrho u^{2}+p\\ u(E+p)\end{pmatrix}=0. \tag{7}\]  
The initial conditions for the Riemann problem are given by Equation (8), where \(T=1.6\). The initial conditions are set to create a discontinuity, which then evolves over time, providing insights into the fluid's response to abrupt changes in pressure, density, and velocity. The study of Riemann problems is pivotal for validating numerical methods and enhancing the accuracy of simulations involving shock waves and other discontinuous phenomena in fluid dynamics.  
\[(\varrho,u,p,\gamma)^{T}=\begin{cases}(0.445,0.698,3.528,1.4)^{T},&x\in[-5,0 ),\\ (0.5,0,0.571,1.4)^{T},&,x\in[0,5].\end{cases} \tag{8}\]  
In Figure 1, the horizontal axis represents the range of the independent variable, with units in millimeters (mm), while the vertical axis represents the dimensionless density. And the exact solution is represented by a solid line, while solutions from different numerical schemes are denoted by various symbols. It can be observed that the new WENO scheme exhibits advantages in resolving discontinuities, showing better stability and resolution compared to the WENO3-JS format. Additionally, the superiority of the new WENO scheme in terms of stability and resolution at discontinuities becomes even more pronounced when compared to WENO4 and WENO5.
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.4 Alcohols

Table 5: Concentrations (\(\text{ug}/\text{L}\)) of norisopenreoids found in Malvazija istarska white wines produced by fermentation with _Saccharomyces cerevisiae_ (SCE) and _Saccharomyces paradoxes_ (SPA) yeasts in two harvest years determined by untargeted two-dimensional gas chromatography with time-of-flight mass spectrometry (GC \(\times\) GC/TOF-MS). the Ehrlich pathway is a primary source. It includes the transamination of amino acids into \(\alpha\)-keto acids, their decarboxylation into aldehydes, and reduction of aldehydes into higher alcohols [48]. Major higher alcohols produced in fermentation form a basis of wine aroma. In moderate amounts, they add depth and complexity to the wine's character. However, when their total concentration exceeds 350 mg/L, they can have a negative impact with their medicinal and solvent-like odours. Numerous previous studies have demonstrated a significant impact of yeast species on higher alcohol production in wine fermentations [1]. C\({}_{6}\)-alcohols, on the other hand, arise from the breakdown of lipids, specifically long-chain fatty acids. This process occurs through a series of enzymatic reactions, primarily during grape harvesting and the initial processing steps. C\({}_{6}\)-alcohols are carriers of herbaceous odours that generally have little impact on wine aroma due to their relatively high perception thresholds. Several major fermentation alcohols, such as 1-propanol and isobutanol in 2021, 2-phenylethanol in 2022, and isoamyl alcohol in both harvest years, were found in higher concentrations in SCE wines (Table 8). This suggests that _S. cerevisiae_ exhibited greater activity for some or all enzymes involved in the Ehrlich pathway, such as transaminases, decarboxylases, and alcohol dehydrogenases. It also implies that the mentioned higher alcohols may have a stronger influence on the aroma of _S. cerevisiae_ compared to _S. paradoxus_ determined wines. However, because the impact of yeast varied for each higher alcohol
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 4 Results of Surface Discharge Simulations
### Effect of Length of the Discharge Path  
The length of the current filament determines the discharge path over which the charge is deposited. The results presented in the third row of Figures 9 and 10 show a proportional increase in UHF signal magnitude with the PD path for a given pulse width and intensity. With the shortest UF sensor distance, XLE showed slightly lower signal magnitudes compared with Teflon and Acrylic for the non-uniform field configuration (Figure 9). For the uniform field configuration, the Teflon samples showed the smallest magnitudes, with the difference between materials increasing for higher intensity discharges.
## 3 Results  
An analysis of the manual anterior tibial translation testing results based on the Lachman test revealed no abnormalities in any of the studied participants. The pivot shift was negative in all of the studied participants. Between-Limb and Between-Group Comparison of Obtained Vertical Hop Landing Vertical Ground Reaction Force Normalized to Body Mass (vGRF BM) Values  
The analysis of the whole group of ACL-reconstructed patients indicated statistically significant differences between limbs in the vGRF BM values obtained during the double-leg and single-leg vertical hop landing (Table 2). In contrast to the ACL-reconstructed patients, there were no statistically significant differences noted between the dominant and nondominant limbs in the vGRF BM values in the control group (Table 2). Statistically significant between-limb differences were noted in the double-leg and single-leg vertical hop landing vGRF BM values in the patients who underwent less than months of supervised postoperative physiotherapy (Table 3). On the other hand, for the patients who underwent six or more months of supervised postoperative physiotherapy, the vGRF BM values were comparable in the ACL-reconstructed and contralateral limb. The double-leg vertical hop landing vGRF BM values obtained in the involved limb in the patients with a shorter duration of supervised physiotherapy were significantly lower than in the involved limb in the patients with a longer duration of supervised physiotherapy, and in the dominant limb in the control group (Table 3). The results of the patients who underwent longer supervised physiotherapy were comparable to the control group. The double-leg vertical hop landing vGRF BM values did not significantly differ between the uninvolved limbs in the ACL-reconstructed patients and the nondominant limb in control group. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Vertical Hop Landing vGRF BM (N-kg\({}^{-1}\))**} \\ \hline \multicolumn{3}{c}{**Total Number of ACL-Reconstructed Patients**} \\ \hline \hline  & **Double-leg** & **Single-leg** \\ \hline Involved limb & 20.17 (18.30, 22.05) & 26.52 (24.37, 28.66) \\ Uninvolved limb & 21.83 (19.93, 23.73) & 27.53 (25.10, 29.96) \\ \(p\) & \(\leq\)0.001 & 0.003 \\ \hline  & Control group & \\ \hline  & **Double-leg** & **Single-leg** \\ \hline Dominant limb & 24.27 (22.23, 26.30) & 31.09 (29.04, 33.14) \\ Nondominant limb & 23.96 (21.97, 25.94) & 31.45 (29.41, 33.49) \\ \(p\) & 0.483 & 0.313 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Between-limb comparison of vertical ground reaction force values obtained during the double-leg and single-leg vertical hop landing in ACL-reconstructed patients and a control group; ACL- anterior cruciate ligament; \(p\): level of significance of the between-limb difference; vGRF BM: vertical ground reaction force normalized to body mass. Values expressed as the arithmetic mean and 95% confidence interval.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 6 Conclusions  
We considered the rateless coded transmission scheme for two-user uplink in C-RAN with two RRHs under a block fading channel. In the proposed scheme, both users keep transmitting until the BBU pool feeds back ACKs, where instant CSI is not required by the users. We also proposed a scheme to find the universally good degree profiles for both users under all possible channel states, with only statistics information of the channel matrix. In detail, the degree profiles are optimized to minimize the threshold SNR under the conditions that the average code length is fixed and the message of each user can be correctly recovered at the BBU pool in all possible channel states. It can be seen from the simulation that the optimized degree profiles have better performance for both BER and system throughput, compared with the BEC profile. Moreover, the achieved throughput has only about \(11\%\) loss from the theoretical limit.  
Note that although only a two-user case is considered in this paper, the extension to the multi-user case is not difficult. One can simply extend the iterative receiver structure in Figure 2, where the output LLR of the MU detector can be calculated accordingly. Using EXIT analysis, the degree profile optimization problem can also be formulated, and DE can be applied.  
Figure 8: Achieved throughput with the optimized profiles and benchmark profile for the cases of the three-bit quantizer and the eight-bit quantizer.  
Funding: This research was funded in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY17F010014, the National Natural Science Foundation of China (No. 61871348, No. 61871349, No. 61803336), and by the open project of Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, China. Conflicts of Interest: The authors declare no conflict of interest.
# Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis
## 5 Conclusions  
The complexity and multipath effect of the mining machine vibration lead to the nonlinear mixing of signal sources, and additive noise was introduced by the sensor when collecting the vibration signal. Hence, useful vibration characteristics for revealing the health status of the mining machine were completely submerged in the background noise and disturbance. In order to separate the mining machine vibration fault signal from the nonlinear mixed signal with additive noise, this paper proposes a novel wavelet-nonlinear ICA model to extract distinct source components of machine faults. Through analysis of the actual vibration signal of a mining machine, the effectiveness of the proposed diagnostic model was verified. The main conclusions are drawn as follows.  
(1) Wavelet de-noising can effectively suppress additive noise in the nonlinear mixed observations to improve the separation performance of the ICA models.  
(2) In the case of hybrid gear failures, the nonlinear degree of observation has exceeded the capacity of linear ICA. In this situation, the linear assumption of linear ICA separation cannot be satisfied, so the fault detection accuracy is lower than that of the nonlinear ICA model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mother Wavelet** & **Correctness** \\ \hline db10 & 93.3\% \\ db17 & 91.1\% \\ sym5 & 91.1\% \\ Sym7 & 93.3\% \\ haar & 88.9\% \\ bior3.3 & 91.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different mother wavelets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wavelet-Nonlinear ICA** & \multicolumn{2}{c}{**Nonlinear ICA**} & \multicolumn{2}{c}{**Wavelet-FastICA**} & \multicolumn{2}{c}{**FastICA**} \\
**Correctness** & **Misdiagnosis** & **Correctness** & **Misdiagnosis** & **Correctness** & **Misdiagnosis** \\ \hline
93.3\% & 2.7\% & 87.8\% & 4.3\% & 88.9\% & 3.7\% & 82.2\% & 6.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of diagnostic performance of different diagnosis methods.  
(3) The diagnostic model proposed in this paper is sensitive to the vibration source of the mining machine and can accurately extract the fault vibration source. As a result, it is very suitable for feature extraction and pattern recognition of early faults of mining machine.  
[PERSON] proposed the idea and methodology; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] conducted investigations and guidance; [PERSON] wrote the paper; [PERSON] edited writing and polished language.  
This research was funded by the Shanxi Science and Technology Foundation Condition Platform Project, grant 201805D141002 and the Joint Training Base for Postgraduate Students in Shanxi Province, grant 2018D15.  
Thanks are due to Taizhong Coal Machinery Co., Ltd. for providing experimental equipment and environment.  
The authors declare no conflict of interest.
# Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case +
## 4 \(\mathcal{O}(1)\)-Time \(\mathcal{ASYNC}\) Complete Visibility Algorithm
For \(N=3\), if the robots are not collinear after at least \(x\) (or \(y\)) moved once, we already have a polygon with three corners. However, if all three robots \(y,z,x\) in \(\mathcal{Q}\) are again collinear after \(x,y\) are moved and colored ready, the middle robot \(z\) sets its color to ready and moves orthogonal to line \(\overline{xy}\) for \(\delta>0\). Since \(x,y\) are already colored ready, they do not make the perpendicular move again, and the move of \(z\) guarantees that the collinear configuration translates to a triangle configuration. When \(N=1\), the only robot \(x\) can simply terminate since it sees no other robot. If \(N=2\), one robot sees the light of other robot ready and figures out that there are only two robots in \(\mathcal{Q}\) and terminates. This happens at the second (and final) round of the algorithm. **Lemma 8**. : _At the end of Stage 0, one of the following holds: (i) for \(N=1\), the only robot simply terminates with color start; (ii) for \(N=2\), both the robots terminate changing their color to ready from start; (iii) for \(N=3\), all three robots are in the vertices of a triangle with color ready; and (iv) for \(N>3\), there exists a hull \(\mathbf{P}\) such that all robots in \(\mathcal{Q}\) are in the corners, sides, and the interior of \(\mathbf{P}\) with color \(\in\{\texttt{start},\texttt{ready}\}\)._  
**Theorem 3**. : _Stage 0 finishes in (at most) three epochs._  
Proof. : For \(N=1\), it is immediate that the algorithm terminates in one epoch. For \(N>3\), any collinear configuration translates to a non-collinear configuration in the first epoch,  
Figure 8: Examples of initially linear configurations. since the two endpoint robots move orthogonal to the line segment in that epoch. For \(N=2\), the only two robots change their color to start_moving in the first epoch. In the second epoch, they again find themselves in a line and change their color to ready. In the third epoch, each realizes that the other is the only robot in the system and terminates. For \(N=3\), by the third epoch, the middle robot realizes that it is the only robot between two endpoint robots in the line segment and moves orthogonal to the line by \(\delta>0\) setting its color to ready. Since the endpoint robots have color ready and do not move, this gives a non-collinear (triangle) configuration by the end of that epoch.
# Address Space Layout Randomization Next Generation
## 2 ASLR Taxonomy  
In this section, we present our novel taxonomy which consider three different dimensions to assess practical ASLR implementations. This taxonomy revealed the security issues described in Section 5 and are were used to develop the assessment tool presented in Section 4.  
Depending on the exact implementation details there may be important differences in the final operation and effectiveness of the ASLR, and so in order to understand and compare these differences between ASLR implementations, it is necessary to have a detailed definition of all memory objects and how they can be randomized.  
In what follows, a memory object is classed as a block of virtual memory allocated to a process, examples of which include the stack, the executable, a mapped file, an anonymous map and the vDSO. For our purposes, the size and the base address of each object are the most relevant attributes.  
Several objects may be allocated together (in consecutive addresses) with respect to a random address base, which will be referred to as the **area** or as the **zone**. For example, in Linux, all objects allocated via the mmap() system call are placed side by side in the mmap_area area.  
We have categorized the ASLR into three main dimensions: (1) When, (2) What and (3) How. The first dimension defines how often randomization takes place, the second determines which objects are randomized and the last one defines how and how much the objects are randomized.
## 2 Related Works
### Existing Computing Resources and Storage Resources  
In the existing research based on mobile communication network scenarios, most of the research work only takes into accounting the problem of computing offloading (such as References [7; 8]), or data caching (such as References [9; 10]). In joint resource management, computing offloading is only combined with radio resource optimization (such as References [11; 12; 13; 14]). For example, in the literature [11], the author proposes an effective radio and computing resource allocation scheme to minimize the total processing time of multiple tasks. Considering the local processing, each mobile user divides the computing task into unloading task and local task, which realizes the effective utilization of wireless power resources and computing resources. In the literature [12], the cache decision and radio and computing resource allocation of video service in mobile edge computing (MEC) are optimized to maximize the system revenue. In order to tolerate the uncertainty of network traffic and avoid the \"hard constraint\" based on constant content request rate, this paper uses robust optimization to obtain the optimal cache decision, and then allocates radio and computing resources for video trans-coding. In Reference [13], an online combined wireless and computing resource management algorithm for multi-user mobile edge computing system is proposed, which reduces the terminal energy consumption by assigning the CPU frequency, transmitting power and bandwidth of the mobile terminal.  
In most mobile applications, the computing and storage requirements of the terminal are tightly coupled. That is, both of the computing process work and the data access work are included in one task, and the two are interrelated. Therefore, the joint optimization of computing and storage resources needs to be further explored in the research of resource management technology.
## 1 Introduction
The proposed method is in line with our previous multi-mode linear ultrasonic signal method [17] but has additional features to extract and analyze the nonlinear ultrasonic response from micro-cracks. In other words, this study demonstrates a new guided-wave NUT technique for micro-crack detection and monitoring by using nonlinear response of multi-mode guided wave ultrasonic signals, generated and received by single-element transducers and simple electronics [17]. When the multi-mode guided waves diffusely propagate through any physically-connected structure with arbitrarily complex geometry [18; 19], all available guided wave modes in any interrogated zone of the structure are automatically down-selected by the medium through attenuation, dispersion, or filtering. Such remaining modes efficiently transfer energy (for example, to their second harmonic modes) when they encounter micro-cracks even in the case of irregular geometries. Thus, our proposed multi-mode ultrasonic inspection technique can be used to ultrasonically interrogate microstructural defects in an entire structure with virtually any complex geometry. Also, to avoid interference of nonlinearities in instrumentation this study generates and compares a pair of ultrasonic signals with two opposite phases for nonlinear response extraction. It enables the proposed method to analyze nonlinear ultrasonic response for micro-crack monitoring. This manuscript is organized as follows: The analysis methodology and underlying theory used in this work are introduced in Section 2. Finite element simulations of multi-mode ultrasonic waves in a beam are presented in Section 3. This is followed by results of experimental studies in the beam in Section 4. Section 5 summarizes the results from both the numerical simulations and the experiments.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 3 The Proposed Method
### Local Memory Encoder
#### 3.3.2 Local Memory Representation  
In order to obtain the temporal features of entities and relations in adjacent historical knowledge graphs, following RE-GCN [12], GRU is adopted to update the representations of entities and relations. The update of entity embedding representations is as follows:  
\[\mathrm{H}_{\mathrm{t+1}}=\mathrm{GRU}\Big{(}\mathrm{H}_{\mathrm{t}},\mathrm{H }_{\mathrm{t}}^{\mathrm{HT-GNN}}\Big{)}, \tag{10}\]  
where \(\mathrm{H}_{\mathrm{t+1}}\) is the entity embedding at time \(\mathrm{t+1}\). \(\mathrm{H}_{\mathrm{t}}^{\mathrm{HT-GNN}}\) is the entity embedding obtained after aggregating the knowledge graph using HT-GNN.  
Similarly, in order to obtain the temporal embedding representation of the relationship, the following method is used for calculation:  
\[\mathrm{R}_{\mathrm{t+1}}=\mathrm{GRU}\big{(}\mathrm{R}_{\mathrm{t}},\mathrm{ R}\big{)}, \tag{11}\]  
\[\mathrm{\hat{R}}=\mathrm{pooling}(\mathrm{H}_{\mathrm{t}}), \tag{12}\]  
where \(\mathrm{R}_{\mathrm{t+1}}\) is the relation embedding at time \(\mathrm{t+1}\). \(\mathrm{pooling}(\cdot)\) represents mean pooling operation to retain all entity embeddings related to the relationship r and average them.