esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Delay Inversion and Jitter Optimizations
### Window Jitter  
Window jitter is a small modification of the uniform distribution of jitter, as recommended by RFC 5148 [9]. It introduces a minimum amount of jitter that must be incurred in each hop. Jitter values are, then, instances of a random variable \(\textit{TJ}_{\textit{W}}\sim\text{Uniform}\left[\alpha J_{m},J_{m}\right]\), where \(\alpha\in(0,1)\) and \(\alpha J_{m}\) is a minimum jitter value. Note that \(\alpha=0\) corresponds to the uniform jitter distribution specified in RFC 5148 [9]; \(\alpha=1\) would imply a deterministic delay (of length \(J_{m}\)). The fact that \(\alpha\
eq 0\) entails that the lower bound for the RREQ delay grows linearly with the length of the traversed path.  
Window jitter reduces the randomness and increases the (deterministic) dependency of the total RREQ delay to the length \(n\) of the traversed path. When assigning jitter values according to the distribution of random variable \(J_{w}\), the total delay caused by jitter in a path of \(n\) hops belongs to the interval \([n\alpha J_{m},nJ_{m}]\) (\(\alpha\
eq 0\)). The trade-off between randomness and path length deterministic dependence can be controlled by way of parameter \(\alpha\in(0,1)\): the closer \(\alpha\) is to one, the more deterministic becomes the total delay of an RREQ packet with respect to the path length.  
Under the window jitter distribution, each additional hop in the path traversed by an RREQ packet causes at least an additional delay of \(\alpha J_{m}\). Intuitively (see Section 4.1.2 for a more rigorous analysis), this makes it less likely that a larger number of hops is traversed by RREQ packets in a shorter time. The implicit assumption is that \"shorter\" paths (in the number of hops) are preferable to \"longer\" paths, considered worse for routing (hop count metric).
## 3 Results and Discussion
### Sensory Evaluation  
The sensory scores of the lamb odour, roasted flavour, colour, tenderness, juiciness, and overall acceptance of the roasted lamb are shown in Figure 4A. The average sensory scores of tenderness and juiciness in the PT30 samples were 5.98 and 6.06 (Table A1), respectively, which were the closest to those of the CT samples and higher than those of the NT samples. The sensory scores of the roasted flavour in the PT30, PT45, and NT samples were higher than that of the CT sample. Table 3 shows that pyrazine compounds with a roasted aroma were found in all treatments, and the contents and kinds of pyrazines in the NT, PT30, and PT45 samples were higher than those of the CT samples, which contributed to a higher roasted flavour score in the NT, PT30, and PT45 samples compared with the CT samples. Hexanal and 1-octen-3-ol, derived from lipid oxidation, are responsible for rancid and oxidized flavours in meat products [39]. The contents of these two aldehydes were higher in the CT samples compared with those of the NT and PT samples, which might decrease the roasted flavour in the CT samples. Meanwhile, the lamb odour scores of the PT30 and PT45 samples were lower than the CT samples, which are closely related to the contents of short chain fatty acids such as 4-methylconcatenoicacid, 4-4-methylnonanoicacid, and 4-ethylcatenoicacid [40]. [PERSON] et al. [41] reported that a 15 min plasma treatment with a 45-kHz frequency might decompose the fatty acids and then decrease their contents in sea bream fillets. Most likely, the plasma treatment for roasted lamb in this study reduces the contents of short chain fatty acids related to the lamb odour and thus partly leads to their higher overall acceptance compared with the CT samples. In addition, the colour scores of the PT30, PT45, and NT samples from the sensory evaluation were higher than those of the CT samples. The result of instrumental colour showed that the redness (\(a^{*}\)) values of the NT, PT30, and PT45 samples were significantly (\(p<\) 0.05) higher than those of the CT samples, which might lead to higher colour scores in the PT30, PT45, and NT samples.  
The orthogonal partial least squares discrimination analysis (OPLS-DA) bioplot of the sensory score is shown in Figure 4B. In the OPLS-DA bioplot, the distance between the treatment group and the sensory attribute was smaller, which means that the roasted lamb in the treatment group has a higher sensory score. The length of the distances among the different treatment groups indicated the degree of difference of samples in those groups. The result showed that the NT samples had a higher colour score, and the PT30 samples had higher overall acceptance and roasted flavour scores compared with other treatments. In the bioplot, the shortest distance among all treatments was between the PT30 group and the NT group, which means the sensory attributes were similar between these two treatments. Therefore, the sensory attributes of roasted lamb were enhanced by the 30 min plasma treatment.
## 1 Introduction
[PERSON] et al., in their critical review, indicated that the equilibrium between carbonate rock, formation water and crude oil is disturbed when low salinity water, containing more \(SO_{4}^{2-}\) ions than formation water, was injected [23]. This leads to the dissolution of calcite and a change in the wettability of the rock surface from oil-wet to water-wet. [PERSON] et al. showed that carbonated water injection led to a change in rock wettability from neutral to water-wet [24]. This change in wettability also led to a shift in the relative permeability curves and showed an increase in oil mobility after carbonated water injection. One of the important factors influencing the characteristics of two-phase flow in porous media are pore filling and snap-off, which depend on the topology and pore sizes [25]. They can lead to trapping of the non-wetting phase, which decreases its mobility. [PERSON] and [PERSON] showed a significant effect of snap-off on relative phase permeabilities and residual saturations [26]. Under highly wetting conditions, snap-off significantly reduces the relative permeability and increases the residual saturation of the non-wetting phase. In addition, snap-off shifted the intersection point of the relative permeabilities to lower saturations of the wetting phase. [PERSON] et al., using nuclear magnetic resonance, demonstrated that the process of calcite dissolution is capable of increasing pore sizes and improving the connection between micro- and macropores [27]. The authors hypothesized that changes in wettability due to calcite dissolution could help increase oil production. However, a literature review has shown that there is a gap in the study of the influence of changes in the pore structure of carbonate rocks during rock dissolution on relative phase permeabilities. This study aimed to examine the effect of changes in the pore structure of two cylindrical carbonate samples during HCl injection on relative phase permeability curves and residual saturations using \(\upmu\)CT images and pore-scale simulations. For this purpose, 20 sub-volumes were extracted from both samples, which were scanned before and after the injection of HCl solutions using X-ray \(\upmu\)CT with a spatial resolution of 18 \(\upmu\)m.
## 3 The Experimental Results and Discussion
### Measurement of Adhesion under Different Detaching Velocities and Preload  
As shown in Figure 5, the maximum pull-off force was measured at different peel velocities (detaching velocity) varying from 10 \(\upmu\)m/s to 300 \(\upmu\)m/s, in which high peel speed (300 \(\upmu\)m/s) resulted in strong adhesion, while low peel speed (10 \(\upmu\)m/s) resulted in weak adhesion. Obviously, there is a strong correlation (proportional relationship) between the maximum pull-off force and the peel velocity.  
We measured the maximum adhesion force at different preload from 0.5 to 3 \(\upmu\)N (the peel speed was fixed at 10 \(\upmu\)m/s). The proportional relationship between the maximum pull-off force and the preload is shown in Figure 6.  
The experimental results show that the preload has a great influence on the adhesion, which is different from the previous research: \"Unlike the effects of material property of PDMS, the maximum pull-off force has similar value regardless of the initial indentation force between the tip and the flexible substrate\". Our theoretical result is consistent with our experimental result but different from the literature.  
It Is hard to compare the experimental results to results from Equations (8)-(10). The equations show a positive proportion relationship between contact area and the maximum pull-off force. However, the real contact area between the \(\mu\)LED and PDMS or other  
Figure 5: The measured maximum pull-off forces with respect to peel velocity.  
substrates could not be measured. Therefore, it is impossible to directly compare the quantity of theoretical value and experimental value absolutely, but only relatively.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 4 MAuth-CAN
### System and Adversary Assumptions
#### 4.1.1 System Assumptions  
First, the system dedicates to the authenticator an ECU with two CAN controllers for CAN message authentication. The dedicated ECU is assumed to be assigned to the highest priority CANID, which is open to anyone. Second, we assume that it is possible to compute the maximum acceptable communication delay for a given application running and communicating over CAN. Third, we assume that the ECU, i.e., the authenticator, for authentication is very hard for the attacker to compromise so as to drop the assumption that CAN is a victim of a single point failure (SPF) where all points lose a specific security once a point is compromised. This assumption can be achieved by applying lightweight tamper-resistance hardware such as SMART [28] and TrustLite [29] into the authenticator.
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 2 Theoretical Analysis
### Dynamic Analysis of Riser Structure  
The simplified stress model of the traditional riser (hinged at both ends) is shown in Figure 1 (left). The main subject of this paper is the deep-sea mining pipe. The deep-sea mining pipe model can be simplified into the Euler-Bernoulli beam model with one end hinged and one end free (z-direction constraint), and the top end subject to preload, as shown in Figure 1 (right).  
Figure 1: Model sketch (**left**: traditional riser; **right**: deep-sea mining pipe).  
By discretizing the deep-sea mining pipe into a finite number of three-dimensional units, the structural motion control equation can be discretely expressed as follows:  
\[[\mathrm{M}]\big{\{}\ddot{\mathrm{x}}(\mathrm{t})\big{\}}+[\mathrm{C}]\big{\{} \dot{\mathrm{x}}(\mathrm{t})\big{\}}+[\mathrm{K}]\big{\{}\mathrm{x}(\mathrm{t}) \big{\}}=\{\mathrm{F}(\mathrm{t})\}, \tag{8}\]  
In this formulation, the matrix, \([\mathrm{M}]\), represents the total mass matrix, while \([\mathrm{C}]\) denotes the comprehensive damping matrix, where Rayleigh damping is implemented; \([\mathrm{K}]\) signifies the complete stiffness matrix; the vector \(\{\mathrm{F}(\mathrm{t})\}\) encapsulates the total external forces acting on the structure as a column vector; and the displacement of all finite element nodes within the tube is captured by the vector \(\mathrm{x}(\mathrm{t})\).  
Natural frequency usually affects structural damping by using Rayleigh damping matrix, \([\mathrm{C}]\). The calculation formula is as follows:  
\[[\mathrm{C}]=\alpha[\mathrm{M}]+\gamma[\mathrm{K}], \tag{9}\]  
In this context, \(\alpha\) and \(\gamma\) represent the coefficients for Rayleigh damping and are derived based on the tube's natural frequency and damping ratio. These coefficients are computed as per the formula presented in Equation (10):  
\[\begin{bmatrix}\alpha\\ \gamma\end{bmatrix}=\frac{2\zeta}{\omega_{n1}+\omega_{n2}}\begin{bmatrix} \omega_{n1}\omega_{n2}\\ 1\end{bmatrix}, \tag{10}\]  
where \(\zeta=0.03\), \(\omega_{n1}\) takes the first order natural frequency, and \(\omega_{n2}\) takes the second order natural frequency.  
In this paper, the Newmark-beta method is used to solve the control equation of a deep-sea mining pipe structure by self-compiling UDF.
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 1 Introduction
Thus far, researchers have developed ground-motion-selection software or frameworks based on different methods [27, 28, 29]; however, procedures for developing ground motion simulations using advanced methodologies are lacking. Although some methods have been proposed for synthesizing ground-motion records that meet some of the statistical characteristics, the current theoretical approach does not consider user-oriented applications [31]. The current user-oriented ground-motion simulation records cannot meet user requirements for more accurate acceleration time history records. The user-friendly interface and visualization allows users to focus on other aspects of their research, and software or plug-ins have been developed to facilitate their use [32]. Thus, it is necessary to develop user-friendly software that provides a graphical user interface (GUI) with visualization functions and applies a more rational and advanced ground-motion methodology [33, 34, 35]. Machine learning methods have been successfully applied in various fields in recent years [36]. The proposed software employs a machine learning synthesis method based on an actual ground-motion database that not only matches the target response spectrum, but also ensures constraints on the duration using constraints. This resolves the significant variations in the input response caused by the diversity of the matching results mentioned above. This method first extracts the characteristic ground motions from the actual ground-motion database using principal component analysis (PCA), and then matches the amplitude, spectrum, and duration. To determine the most reasonable ground motions, the extracted characteristic ground motions are combined using a multi-objective genetic algorithm known for its successful application in numerous studies [37]. This algorithm efficiently determines the optimal solution by considering multiple constraints, enabling the linear combination of the extracted characteristic ground motions. The software is a MATLAB-based application that clearly identifies target areas based on engineering parameters provided by researchers and engineers, obtains amplitude fields visualized at different periods, and generates acceleration time history records on demand, which enables researchers to focus on subsequent research. This paper introduces ground-motion simulation methodology used by the software and demonstrates its application.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 1 Introduction
Moreover, they considered a ranging problem based on hybrid RSS and TOA quantities, where the main goal was to estimate the distance of each link in line-of-sight (LOS) environments only. In huge contrast to [20], here we consider target localization problem, i.e., our goal is to determine the estimation of target's location. To do so, both RSS and TOA observations are employed and mixed LOS/NLOS environments are investigated. We exploit the state-of-the-art estimators in [18; 19; 20; 21; 22] for the problem of interest and we study their performance in different scenarios and compare it with their counterparts that employ RSS-only and TOA-only measurements. In addition, we derive a novel heuristic approach to select _the best_ measurement for each link based on relative difference in RSS and TOA measurements, which we test on the estimator presented in [21]. This heuristic approach does not depend on the knowledge about noise powers, and can match or even better the performance of the best individual option, which highly differs in considered scenarios.
# Manufacturer Channel Encroachment and Evolution in E-Platform Supply Chain: An Agent-Based Model
## 3 Model
### Manufacturers' Behaviour  
The manufacturers' state chart is shown in Figure 3. In the _Initialize_ state, the manufacturer establishes two sales channels and randomly sets the price of the product for the first period, and informs the consumer. The consumers evaluate the utility of the product and return the purchase decision to the manufacturers, who then enter the _Sell_ state and record sales and profits. In the _Check_ state, the manufacturers calculate the accumulated wealth and determine whether to give up channel encroachment, i.e., whether to quit the direct sales channel. In the _Gasc/GAdc_ state, the manufacturer uses the genetic algorithm to obtain the optimal price decision of single-channel or dual-channel for the next period. In the _Reset Price_ state, the manufacturers update the optimal price obtained by the GA algorithm and enter a new period.  
Figure 1: Different sales modes.  
Figure 3: Manufacturers’ state chart.  
Figure 2: Agents’ actions in one period.  
The manufacturer's objective function is as follows:  
\[\begin{array}{c}\text{Maximize }P_{i}^{t}=Pgt\left(RP_{Hi}^{t},RP_{Li}^{t},DP_{Hi}^{t},DP_{Li}^{t}\right)\\ \text{s.t. }RP_{Hi^{t}}^{t},RP_{Li^{t}}^{t},DP_{Hi^{t}}^{t},DP_{Li}^{t}\geq 0\end{array} \tag{1}\]  
Table 1 shows the expressions of the profits, and Table 2 shows the notations and explanations.  
The logic of the manufacturer's channel decision is as follows. At the \(t\) period, the manufacturer in the _Check_ state calculates the cumulative profit for the previous \(t-1\) periods. A manufacturer chooses to exit the direct sales channel when its cumulative profit in the channel is less than the cost of maintaining the channel for one cycle (\(\sum_{t=0}^{t}DP_{i}^{t}<C\)). This means that manufacturers will abandon channel encroachment when their accumulated profits are not sufficient to cover the cost of maintaining the channel in the next period. Thus, in period \(t+1\), the manufacturer will only operate the retail channel based on the e-commerce platform.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 3 Proposed Methodology
### Geo-Clustering Algorithm
#### 3.1.2 Region Description  
For each tile, we exploit \(T_{i,j}\) in order to create its semantic representation \(D_{i,j}\). We expect that among the user-generated tags, we shall encounter some that describe it by means of locality (e.g., _Thiseio_) or landmark(s) (e.g., _Acropolis_). Even though users tend to add \"personal\" tags (e.g., a name), we expect that a subset of the most \"popular\" tags (i.e., selected by the majority of users) will be able to describe a tile in a discriminable way. More specifically, for \(R_{i,j}\) the region description \(D_{i,j}^{L}\) is the set of the \(L\) most \"important\" tags, where importance may be measured in three ways: (a) the number of users that have used a specific tag within the given region; (b) the occurrences of the tag within the given region and (c) the average percentage of a tag among those selected by the user for the given region. In Section 4, these cases will be referred to in short as (U), (T) and (TU), respectively.
## 3 Results and Discussion
### Lesions' Ellipticity Analyses  
The lesion ellipticity (determined as described in Section 2.8) results were initially plotted on a quantile-quantile (q-q) plot to confirm whether they followed a normal distribution. The four data sets were considered normally distributed, as only minor deviation at the extremes was observed. These were then fitted to a normalised probability density function that follows a Gaussian distribution. The resultant plots are shown in Figure 9. Dark/light red plots show the ellipticity of ablation achieved with annealed antennas, whilst the dark/light blue plot resulted from non-annealed antennas. Solid and dotted lines distinguish the different measurement methods. Subtle ellipticity improvement is observed with both methods for the annealed antennas. With the fitted ellipse method, the mean decreased from 0.62 to 0.54, whereas with the tailing method, it decreased from 0.68 to 0.64. This shows that the annealed antennas produced more spherical lesions, as desired.
## 3 Test and Result Analysis of Three-Dimensional Bending Test
#### 3.1.1 Curvature Continuity Algorithm
For complex curves, linear interpolation often makes it difficult to achieve the ideal interpolation effect. The cubic spline interpolation method uses the cubic function to ensure that each interpolation interval can be smooth and continuous. As a result, the accuracy of this method is higher than that of the linear interpolation method, and it also has a better curve-fitting effect in the case of complex curve bending.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 5 Discussion
### Potential Applications per Class
#### 5.2.4 Target Software  
The identification of target software--be it firmware, OS, data, or control logic--is instrumental in shaping both immediate recovery actions and long-term defensive strategies. From a scientific standpoint, this knowledge facilitates the selection of approaches to system recovery. For instance, attacks on firmware may require different recovery tools and approaches compared to those targeting OS layers. In cases of data-focused attacks, recovery might prioritize data decryption and restoration, often involving specialized software and techniques.  
Additionally, the identification of the target software aids significantly in vulnerability assessment and management. It allows for a targeted analysis of existing vulnerabilities within similar systems or software components. This analysis is fundamental in the development of patches and updates, addressing specific vulnerabilities exposed by the PDoS attack. Such targeted updates are crucial in fortifying systems against similar future attacks, thereby enhancing the overall security posture.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Active Acoustic Signal Detection System of ULAHW  
The ULAHW uses an active acoustic sonar system to detect, locate, and track targets. In active sonar systems, the emission signal is known. If the additive noise is white Gaussian, the best detector given by the likelihood ratio test is a cross-correlator or matched filter. The detection background consists of marine environmental noise, self-noise, circuit noise, and reverberation. To reduce noise and reverberation interferences as much as possible, we adopt beamforming before detection and the time-varying gain control (TVG) method, respectively. A typical active sonar signal processing structure is shown in Figure 3.