esa-sceva/satcom-chunk-collection huggingface dataset:
# Topological Representative Element Volume of Fractured Rock Mass
###### Abstract  
The representative element volume (REV) of the fractured rock mass is the basis for its homogenization, and is essential for the study of groundwater seepage. In order to determine a simple method for estimating REV, we studied the topological characteristics of the fractured network. We propose the concept of topological representative element volume (TREV) for estimating the fractured rock mass. The topological structure can reflect many properties of the fractured rock mass, to which a great deal of attention has been paid. We examine the applicability of eleven topological parameters as the equivalent parameters of the TREV. According to the selected equivalent parameter, the TREV of twenty-three kinds of fractured rock mass were calculated and then compared with the permeability representative element volume (PREV). The results show that the size of TREV and PREV are essentially the same for a given rock mass. In other words, the PREV can be estimated accurately using the TREV for the rock mass with orthogonal connected fracture networks. The advantage of using TREV to estimate PREV is that there is no need for complex seepage calculations, as the calculation of TREV only needs to account for the geometric characteristics of the fracture network.  
fracture network; fractured rock mass; topological representative element volume; topology; permeability +
Footnote †: journal: Applied sciences
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 3 Understanding the Effect of Splitting a Dynamic Graph Based on Time
### Background: Continuous-Time Dynamic Graphs  
For a set of nodes \(\mathcal{U}\) and maximal time \(T\), a _Continuous-Time Dynamic Graph_ (CTDG) is defined as a stream \(\mathcal{H}=\{(u,v,t)\}\subset\mathcal{U}\times\mathcal{U}\times[0,T]\) of events \((u,v,t)\), each representing an interaction of the source node \(u\) with the destination node \(v\) at timestamp \(t\). We use the term edge to refer to a pair of nodes \((u,v)\) at an unspecified time. In _directed_ graphs, such an edge \((u,v)\) in an event \((u,v,t)\) is an ordered pair of nodes; in other words, this means that node \(u\) sends an interaction to \(v\) at time \(t\). Conversely, _undirected_ graphs treat edges as unordered pairs of nodes \(\{u,v\}\); so an interaction \((u,v,t)\) means that \(u\) and \(v\) interacted at time \(t\). In practice, an undirected edge can be uniquely identified by \((\min(u,v),\max(u,v))\). Further, note that CTDGs allow events to occur at any continuous-valued timestamp \(0\leq t\leq T\) and allow multiple events to happen at the same time.  
To index the collection of all events \(\mathcal{H}\) in the CTDG, we also introduce some helpful shorthand notations.  
We use \(\mathcal{H}^{u}\triangleq\{(u^{\prime},v^{\prime},t)\in\mathcal{H}\mid u^{ \prime}=u\lor v^{\prime}=u\}\) to represent the set of all events in \(\mathcal{H}\) that involve the node \(u\) and \(\mathcal{H}^{(u,v)}\triangleq\{(u^{\prime},v^{\prime},t)\in\mathcal{H}\mid u^ {\prime}=u\wedge v^{\prime}=v\}\) to represent the set of events that involve the edge \((u,v)\). We also define \(\mathcal{H}_{t}\) as the subset of all events in \(\mathcal{H}\) that occur up to a certain time \(t\), i.e., \(\mathcal{H}_{t}\triangleq\{(u,v,t^{\prime})\in\mathcal{H}\mid t^{\prime}<t\}\).  
Overall, our goal is to better inform the evaluation of Dynamic Link Prediction over CTDGs. We hold off on formally introducing this task until Section 4 and first consider a core decision in any machine learning evaluation: how the data are split up into training and testing data. A typical assumption in dynamic graphs is that we will only need to make predictions about future events. Hence, the train-test split is commonly determined by a _cutoff time_\(t_{\text{split}}\) that partitions the set of events \(\mathcal{H}\) into the train set of past, known events \(\mathcal{H}_{\text{train}}=\mathcal{H}_{t_{\text{split}}}\) and the test set of 'future', unknown events \(\mathcal{H}_{\text{test}}=\mathcal{H}\setminus\mathcal{H}_{t_{\text{split}}}\). In what remains of this section, we characterize nodes and edges by whether they are active exclusively in the train set, test set, or in both.
# Research on a Ship Deflection Anti-Collision Method Based on a Water-Jet Interference Flow Field
## 5 Conclusions  
The ship track lines and the ship's maximum drift angle in the numerical simulation and the generalized model test were in good agreement.  
The effect of the interference zone on the yaw ship produces an effective lateral shift distance of 9.1, 5.8, and 6.2 times the ship's width, respectively. The maximum error in the ship's initial offset position is 16.5% in horizontal coordinates and 4.4% in vertical coordinates, and the maximum error in the maximum offset position is 15.8% in horizontal coordinates and 8.3% in vertical coordinates.  
During the process of being affected by the interference zone, the maximum drift angle of the yaw ship was less than 20\({}^{\circ}\), the course of the ship was generally stable, and the drift angle comparison error was a maximum of 10.6%, a minimum of 3.5% and an average error of 6.7%. During the movement of the stalled ship through the interference zone, a negative peak and a positive peak appeared in the ephemeral curve of the ship's yaw moment, and the absolute value of the positive peak was about four times the absolute value of the negative peak. The ship was affected by the negative pressure area of the generator nozzle was the main source of the negative peak. The interference zone's effect of displacement of the ship away from the pier was the main source of the positive peak.  
The ship deflection anti-striking method based on the water-jet interference flow field had a significant impact on the ship's motion state, and the distance between the stalled ship and the pier rapidly increased to a safe distance within the interference zone of anti-striking.  
We verified the results by numerical simulation and generalized model testing. The method can substantially reduce the risk and loss of ships colliding with bridges and improve the safety level of ship bridges in navigable river sections. The results can provide a basis for the development of safe, efficient, and sustainable ship bridge non-destructive anti-striking technology and equipment and improve bridge anti-striking systems.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 4 Experiments  
### Setup  
Off-line experiments were conducted in an approximately rectangular room with dimensions of 4.0 m \(\times\) 3.3 m \(\times\) 3.0 m with reverberation time T60 = 1.5-2.0 s at 100-1000 Hz, i.e., the sound fields and transfer functions were measured in advance and used to calculate the performance of the system. With the reflections of all the surfaces of the room, the sound field in the room is close to the supposed primary sound field mentioned above. A dummy head (B&K, Type 4128C) on a seat was placed in the middle of the room with the primary source in front of it, 1.5 m from the center of the head, as shown in Figure 13a. The primary source was a 5\" loudspeaker with a height of 1 m, the same height as the human ear. The coordinate system was established with the origin at the center of the dummy head, the positive direction of the \(x\)-axis pointing to the primary source, the positive direction of \(y\)-axis points to the left ear, and the \(z\)-axis was perpendicular to the \(x\)-\(y\) plane. A dual-channel system was located near the left ear of the dummy head, and the arrangement of secondary sources, physical microphones and virtual microphones was consistent with the model depicted in Figure 4 for simulations. It should be noted that the measurement microphone and the virtual microphones are connected and fixed to the head using a structure, so that the measurement microphone was 2 cm from the human ear canal and the two virtual microphones were located in front of and behind it at a distance of 1 cm. Their relative position was fixed when the dummy head rotated. The control source and the microphones in front of the ear were denoted as control source 1 and microphone 1,  
Figure 12: Noise attenuation when head rotates with single virtual microphone or two virtual microphones (**a**) 125 Hz, (**b**) 250 Hz and (**c**) 500 Hz.  
while those behind the ear were denoted as control source 2 and microphone 2. Figure 13b,c shows the photographs of the experimental environment.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 2 Literature Review
### Latent Dirichlet Allocation (LDA)  
Latent Dirichlet allocation (LDA) [21] is one of the basic probabilistic models for topic modeling. It identifies latent topics in text data by modeling the data as a mixture of topics, where each topic is a distribution over words. These models rely on input representations (e.g., bag-of-words (BoW), or word frequency) to construct the topic distributions, summarizing term occurrences to facilitate topic extraction.  
LDA has been extensively applied to detect anomalies by identifying log messages with topic distributions that deviate significantly from the normal. For instance, [PERSON] et al. [23] proposed a hybrid anomaly detection model combining LDA with Gaussian mixtures (G-LDA) to identify anomaly packets in network traffic. Similarly, [PERSON] et al. [24] utilized median-based LDA alongside principal component analysis (R1-PCA) to detect anomalies in network traffic logs. These studies highlight LDA's versatility in handling structured textual data (e.g., log messages), where patterns and semantic structures play a crucial role in identifying anomalies.  
The strength of LDA lies in its ability to provide interpretable topic distributions, making it suitable for analyzing log data. Additionally, its probabilistic framework supports extensions for incorporating contextual information, as shown in [PERSON] et al. [25], who applied LDA for contextual anomaly detection in a variety of text forms (i.e., emails, blogs, papers, and video tags). However, LDA is not without limitations. Its performance heavily depends on the length of the documents, as a sufficient number of words is required to generate meaningful topic distributions. When it is applied to very short documents, the word frequency becomes sparse and unreliable, reducing the effectiveness of LDA's probabilistic modeling [26].
## 6 Tests
### Results and Discussion  
For proving the validity of our proposal, we collected measurements of several acceleration and deceleration conditions, more prone to sliding errors in the odometry along the longitudinal axis. The main objective of the tests is to analyze the capability of the filter to represent the vehicle motion at any time, for what we will use the filter consistency techniques introduced in previous sections. Since both Kalman and particle filters using single models show very similar results, only the results of the particle filter will be discussed.  
Figure 1 shows the distribution of particles around the solution in an experiment where the vehicle drives from north to south. Cyan particles are estimated by the SCM while green ones are calculated by the NCM. Even though both solutions are within the possible confidence scenarios, both of them are not the same good. While in the upper cloud, the GPS-EGNOS value matches better the solution that assumes normal conditions, after the slide, the satellite solution fits better the filter output that assumed slides.  
Figure 2 shows how model probabilities change at those instants, precisely during the slides. The MMPF manages to identify the sliding situations, and switches to the SCM model, returning to the NCM only when the slide has disappeared. For the same test, Table 1 provides the consistency results for all model combinations by using the time average correlation. Low values represent better consistency. For a properly tuned filter, the variance should be \(\sigma^{2}=\frac{1}{K}\). In our experiment, \(\sigma=0.0083\), and the 95% probability region results (\(-0.01633\), \(0.01633\)). Both NCM and SCM fall out of this threshold, only satisfied by the MMPF.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Value** \\ \hline NCOMPF & 0.5762 \\ SCMPF & 0.9110 \\ MMPF & 0.0140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time average correlation results for NCOMPF, SCMPF, and MMPF.  
Figure 1: Cloud of particles during an experiment with a slide.  
Similar conclusions can be achieved by using the time average NIS indicator (Figure 3). Even if the MMPF encounters some trouble to model the slide around instant 85, it is still the best solution. Additionally, switching models based on each model probability appears to be sounder than a weighted solution.
## 2 Experiments and Analysis
### Experimental Uncertainty Analysis
#### 2.3.2 Variance Analysis  
In order to distinguish test errors from test index variation caused by changes in test conditions, an analysis of variance is used to estimate the test error that must exist during the test and the result measurement. The sum of the square of test error, \(SSe\), was 0.041 by Equation (3). \(SS_{T}\) is the total sum of square, \(SS_{p,a,t,s}\) is the sum of the square of jet pressure, jet angle, impact time and standoff distance, respectively. With the sum of the square of each factor, the F value and P-value corresponding to each factor can be calculated to determine whether the influence of the factor on erosion depth is significant and reliable. The smaller the P-value, the more significant the effect. In statistics [43], according to P-value is less than 0.05, jet pressure, jet angle and impact time can be considered to have significant and reliable effects on erosion depth, as shown in Table 5. The P-value corresponding to jet pressure is less than 0.01, indicating that the influence of jet pressure is very significant, and much greater than impact time and jet angle. The effect of impact time is also greater than jet angle, which is consistent with the results of the range analysis above. However, the effect of standoff distance is the smallest. Since the P-value of standoff distance is greater than 0.05, this factor can be considered to have no statistically significant effect on erosion depth within the range of 10-15 mm. Therefore, in this paper,jet pressure, jet angle and impact time are three main factors and are determined as the input variables of the prediction model.  
\[\left\{\begin{array}{l}SS_{e}=SS_{T}-SS_{p}-SS_{\alpha}-SS_{t}-SS_{s}\\ \\ SS_{T}=\sum\limits_{j=1}^{n}y_{j}^{2}-\frac{(\sum\limits_{j=1}^{n}y_{j})^{2}}{n} \\ SS_{p,\alpha,t,s}=\frac{1}{n}(\sum\limits_{i=1}^{q}K_{i}^{2})-\frac{1}{n}( \sum\limits_{j=1}^{n}y_{j})^{2}\\ \end{array}\right. \tag{3}\]
## 4 Security Analysis and Simulation Verification
### Informal Analysis of Protocol Security
#### 4.2.2 Interpretation and Result Evaluation  
Security Analysis of SE-UAVAKA (Section Anti-Imitation Attack-Section Known Key Security): An analysis demonstrates that SE-UAVAKA fulfills its specified security properties through the following mechanisms: Resistance to impersonation attacks is achieved by cryptographically binding long-term keys \((y_{a}/x_{a},y_{b}/x_{b})\) with temporary parameters \((r_{a},r_{b})\) during authentication, ensuring hash-value mismatch \((H_{c}\
eq H^{\prime}_{c})\) for adversaries lacking valid credentials. Perfect forward secrecy is realized by derivingsession keys from session-specific random values \(r_{a}\) and \(r_{b}\), thereby isolating historical session-key exposures from long-term key compromises. Replay attack resistance relies on timestamp freshness verification (\(Ts_{a},TS_{b}\)), which invalidates duplicated or delayed messages. Known-key security is ensured through session-unique parameter generation, where each session key exclusively depends on newly generated \(r_{a}/r_{b}\), preventing cross-session key derivation even if prior keys are leaked. These results collectively underscore the robustness of SE-UAVAKA's security design.
## 2 Methodology
### Lifecycle Markov Model  
The Markov model is widely used as a reliability modelling technique. In order to evaluate the impacts of different failure modes on system availability and reliability, a lifecycle Markov model, including normal operation, failure, maintenance, testing and other states, needs to be developed to evaluate engineering systems. First, after all states of the system are determined, define the transfer rates between states. Then, the Markov transition diagram is used to describe the relationship between the states of the system. Moreover, the state transition matrix can be obtained according to the Markov transition diagram. Finally, the reliability of the system is solved by Markov model equation. The state transition matrix with n +1 states is shown below:  
\[A=\left[\begin{array}{ccccc}a_{00}&a_{01}&\cdots&a_{0(n-1)}&a_{0n}\\ a_{10}&a_{11}&\cdots&a_{1(n-1)}&a_{1n}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ a_{(n-1)0}&a_{(n-1)1}&\cdots&a_{(n-1)(n-1)}&a_{(n-1)n}\\ a_{n0}&a_{n1}&\cdots&a_{n(n-1)}&a_{mn}\end{array}\right]. \tag{18}\]  
where \(a_{ii}=-\sum\limits_{\begin{array}{c}j=0\\ j\
eq i\end{array}}^{n}a_{ij}\), and \(a_{ij}\) (\(i\
eq j\)) is the transfer rate from state \(i\) to state \(j\).  
The probability in each state at time \(t\) can be obtained by Equation (19), such as the probability in state \(k\) is \(p_{\rm k}(t)=P(X(t)=k)\), and \(X(t)\) is defined as the state variable at time \(t\). \(P(t)=(p_{0}(t),p_{1}(t),\ldots,p_{n}(t))\) is the probability vector.  
\[\left\{\begin{array}{c}\frac{d}{dt}P(t)=P(t)A\\ \sum\limits_{i=0}^{n}P_{i}(t)=1\\ P(0)=[1,0,0,\ldots,0]\end{array}\right.. \tag{19}\]
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 4 Prediction of Short-Term Ground Deformation
### Deformation Induced by Ground Loss  
According to the derivation by [PERSON] et al. [20], the ground vertical deformation induced by a spherical cavity in the elastic half-space can be written as:  
\[u_{z}=\frac{V_{0}}{4\beta}\left(\frac{z-h}{R_{1}{}^{3}}+\frac{2z}{R_{2}^{3}}- \frac{(3-4\upsilon)(z+h)}{R_{2}^{3}}-\frac{6z(z+h)^{2}}{R_{2}^{5}}\right) \tag{4}\]  
where \(V_{0}\) denotes the volume loss induced by the spherical cavity, \(z\) denotes the point coordinate in the \(z\)-axis (vertical direction), \(h\) denotes the distance between the ground surface (z = 0) to the sphere center, \(\upsilon\) denotes the [PERSON]'s ratio \(R_{1}=\sqrt{r^{2}+(z-h)^{2}}\) and \(R_{2}=\sqrt{r^{2}+(z+h)^{2}}\), where \(r\) denotes the radial distance from any point to \(Z\)-axis in the half-space. Equation (4) was presented based on the virtual image technique and the harmonic function defined by [PERSON] and [PERSON] [21], and more details about it can be found in [20].  
For general subway tunnels, their slopes are usually required to be gentle enough to meet train operation. Thus, the shield buried depth can be simplified as a fixed value within a finite driving length. Figure 7 shows the simplified ground loss distribution along the shield tunneling axis. As drawn in Figure 7, by integrating the Green functions of  
Figure 6: Interaction between the EPB shield machine and soil.  
Equation (4) along the tunnel length from \(-\infty\) to \(-S\), the ground deformation induced by ground loss can be written as:  
\[u_{z}{}^{V}=\int_{-\infty}^{-S}V(x,z)f_{z}(x,y-\xi,z)d\xi \tag{5}\]  
where \(u_{z}{}^{V}\) denotes the vertical ground displacement, \(S\) denotes the shield length, \(V(x,z)\) denotes the area of ground loss in a tunnel section, and \(f_{z}\) denotes the Green functions determined by Equation (4).  
As described in Equation (5), the \(V(x,z)\), i.e., the ground loss distribution in a tunnel section, is of great importance for final vertical ground displacement. Extensive data prove that the distribution of ground losses within the tunnel cross section is nonuniform. A widely used distribution proposed by [PERSON] and [PERSON] [9] is suggested for calculation, which can be expressed as:  
\[V(x,z)=V_{I}\exp\left(-\frac{1.38x^{2}}{\left(H+R\right)^{2}}-\frac{0.69z^{2}} {H^{2}}\right) \tag{6}\]  
where \(V_{I}\) is the volume loss, \(H\) is the shield buried depth, and \(R\) is the
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 4 Comparison with Other Recent Topologies  
The comparison of the proposed converter with similar kinds of existing converters is shown in Table 2. The voltage gain of the proposed is compared with the other converters and presented in Figure 7. The proposed converter can achieve a voltage gain of 16 at a low duty ratio of around 0.6, which is relatively high compared to the other converter presented in Figure 7. The converter proposed in [7] has four inductors, but its gain is much less than the proposed converter. As discussed earlier, the gain of the CQBC presented in [18] is much less than the proposed converter.  
Similarly, the converter proposed in [21] has utilized two switches, and the total number of components used in the converter is also the same as the proposed converter; still, its gain is high. The converter proposed in [30] has twice the gain as compared to CQBC, but even with three inductors and the same number of components, its gain is less than the proposed converter. The normalized voltage stress across the proposed converter switches as a function of voltage gain is plotted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Topology**} & \(\mathbf{N_{L}}\) & \(\mathbf{N_{C}}\) & \(\mathbf{N_{SW}}\) & \(\mathbf{N_{D}}\) & \(\mathbf{M}\) & \(\mathbf{S}\) \\  & (**Inductors**) & (**Capacitors**) & (**Switches**) & (**Diodes**) & (**\(V_{\theta}\)/\(V_{in}\)**) & (\(V_{\theta}\)/\(V_{in}\)**) \\ \hline
[6] & 2 & 4 & 1 & 4 & \(\frac{3+D}{2(1-D)}\) & \(\frac{1}{1-D}\) \\ \hline
[7] & 4 & 1  
The voltage stress across switch S1 is the lowest among all the topologies, and the stress across switch S2 up to a gain of 11 times is also less than the topologies proposed [7; 14; 18; 25; 30]