esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of the DRFTM
### Trust Evaluation Based on Random Forest  
In the current context of trust evaluation, different techniques such as Bayesian distribution, subjective logic, fuzzy logic, cloud theory, SVM classification, and various machine learning methods are used [17]. However, this paper proposes a new approach named DRFTM, which utilizes random forest to predict and assess the trustworthiness of sensor nodes. Random forest is a technique introduced by [PERSON] that relies on aggregating multiple decision trees [34]. Compared with a single decision tree, random forest can better process high-dimensional features and large-scale data sets and has strong anti-noise ability. For complex underwater environments where decision trees do not work well, random forest can reduce the impact of noise data generated by underwater acoustic channels on trust evaluation and limit overfitting without significantly reducing prediction accuracy. At the same time, the random forest can automatically select important features and can effectively process missing data. In addition, random forest can also assess the importance of features to provide a more comprehensive reference for trust determination.  
Trust evaluation using the random forest algorithm involves three key steps: (1) preprocessing collected trust evidence data, (2) selecting an appropriate decision tree generation method, and (3) using the random forest algorithm to generate the optimal classification result through a majority vote of multiple decision trees, thereby determining the trust status of each node.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 1 Introduction
Simulation experiments were carried out to evaluate the positioning capabilities of the M-, S-, and MM-estimators against classical LS. In those experiments, the pseudorange observations were contaminated with a percentage of outliers, ranging from 10% to 40 %, of different magnitude. In addition, the Gaussian efficiency and the capability of mitigating the effects of outliers is addressed over different data sizes, to verify the importance of data redundancy for the performance of robust estimators. Finally, the paper is concluded with a set of real data experiments, where standard and robust SPP solutions are compared in a vehicular scenario, which contains intervals of harsh propagation conditions that exemplify the benefits of robust SPP techniques. This paper extends [37] with additional analysis of the robust methods, the definition of the loss-of-efficiency concept for robust PVT estimation, and additional experimental discussions using real data in a vehicular scenario. The rest of the paper is organized as follows. In Section 2, the basics of robust estimation are introduced. Section 3 relates the specific implementation details of using robust techniques in the context of GNSS single point positioning. The concept of loss-of-efficiency for PVT robust estimators is introduced in Section 4. Section 5 presents the results and discusses the performance of robust estimators in both synthetic and real experiments. Finally, Section 6 concludes the paper with an outlook and discussion of future work directions.
# Comparative Test on the Bond Damage of Steel and GFRP Bars Reinforcing Soft Rock Slopes
## 3 Results
### Bond Damage of the Bar Interface under a Constant Load  
When the load of the bar was maintained at a constant value, the axial force-time curves of the bar at different anchorage depths under soft rock condition are shown in Figure 10, including the steel and GFRP bars.  
Under the condition of a constant load, when the axial force-time curve at a certain point trends upwards, it indicates that the interface bond force at this point was triggered and played a role. When the axial force-time at a certain point decreases, it indicates that the interface of the bar body at that point exhibited bond degradation, and the deformation of the bar body extended to the anchorage depth.  
Figure 10a shows that the axial force of the steel bar decreased with time at 0.75 m away from the zero point of the steel bar when the tensile load was 150 kN, indicating that bond degradation of interface occurred at this point on the bar body when the tensile load was less than 150 kN. At 0.75 m away from the zero point of the GFRP bar, the axial force of the GFRP bar increased first to a peak value and then decreased with time, indicating that when the tensile load was 150 kN, the bond state of this point on the bar body appeared to degrade. Therefore, when the tensile load was 150 kN, bond degradation occurred at the bar interface 0.75 m away from the zero point. The bond between the bar and cement was destroyed, and the bond force failed. The load was borne by a friction force or biting force and transmitted along the bar to the depth of the anchorage.  
As shown in Figure 10b, when the bar was under a constant load of 150 kN, the axial force-time curve of the steel bar peaked at 1.25 m away from the zero point and then showed attenuation characteristics, indicating that the bond degradation phenomenon appeared at this point. At 1.25 m away from the zero point, the axial force-time curve of the GFRP bar showed an upward trend, indicating that the bond state at this point on the bar body was in the process of being triggered and would reach a peak or stable value in the future.  
Figure 9: Strain-load relationship curve of the GFRP bar at anchorage depths of 80 cm and 180 cm.  
Figure 10c, shows that the axial force-time process of the steel and GFRP bar was stable at 1.75 m away from the zero point under a constant load of 150 kN, indicating that the bond state of the bar body at this point is stable and had no bond degradation [24].  
According to the above data analysis, the interface bond behaviour of the GFRP bar is better than the steel bar. The elastic modulus of the grouting body is about 30 GPa which is close to the GFRP bar. Because of the small difference in elastic modulus of GFRP bar and the grouting body, the interface bond between the GFRP bar and the grouting body can provide more efficient bond resistance which improves the performance of the anchorage system under constant loading.
## 7 Simulation and Results
### Varying Density of the Network
#### 7.2.1 Packet Success Ratio  
Figure 20 below shows that the hotline assisted routing performs much better as compared to the traditional all wireless routing. As the number of nodes is increasing the packet success ratio in the all wireless routing is decreasing. This is because of the fact that with the increase in density of the network there will be more congestion and interference hence chances of packet loss will be higher [31, 32]. On the other hand, we notice that packet success ratio for the hotline assisted routing remains almost unchanged regardless of the network density. The reason behind this is the permanent wired link between any given two gateways. Therefore it will be fair to conclude that communication between the source and the sink is independent of the density of the network in the case of hotline assisted routing.
## 4 Research Results
### LDA Topic Modeling  
Based on an evaluation of the results with varying numbers of topics and their associated terms, and in alignment with the subsequent educational ecology theory, six topics were determined to be the optimal choice. Figure 7 presents the interactive visualization of the LDA topic modeling. The left side displays the distribution of topics and their correlations, while the right side shows the top 30 most-relevant terms associated with each topic. In terms of distribution, Topic 1 significantly exceeds the other topics, while Topic 5 shows a slight overlap with Topic 6, indicating that certain parts of these two topics are similar or related. The remaining four topics have no overlapping sections. Table 3 records the top 10 most-relevant terms corresponding to each of the six topics.  
Figure 6: Sentiment word cloud.  
Figure 7: LDA topic visualization. Note: Color: Red represents the selected topic, blue represents other topics. Size: The larger the circle, the more frequently this topic appears in the data, and the more important it is. Number: The number in the circle is the topic number, which makes it easier to identify and discuss specific topics.
## 4 Discussion
### Impacts of Unilateral ICA Revascularization on ACoA Hemodynamics Characteristics  
Significant correlations between IA's rupture status and derivatives of WSS have been highlighted by several studies [14; 27; 35; 36; 38; 39; 40; 56; 57]. Under the pulsatile flow conditions, TAWSS generally characterizes the overall WSS levels. The normal TAWSS magnitudes in arteries range from 1.5 to 7 Pa, whereas it could reach 1 to 22 Pa in IAs [58]. Despite both aberrantly high (>7 Pa) [38; 39; 40; 41] and low TAWSS (<1.5 Pa) [27; 34; 35; 36; 37], they have been reported to contribute to the IA rupture via complex mechanobiological mechanisms [14; 17]; associations between low TAWSS and the risk of rupture of large IAs (>5 mm) have been identified by some of the latest investigations [14; 15; 59]. Additionally, high OSI (>0.2), which reflects the elevated local flow instability, was also considered as an important factor in the rupture of IAs by upregulating inflammatory cell-mediated destructive remodeling [14; 56; 60; 61; 62]. Moreover, this corresponds with the lower and stronger oscillatory WSS and the prolonged RRT has been linked with higher rupture risk as well [15].  
Our current results showed that besides the flow patterns, the revascularization of unilateral ICA also greatly affected the magnitudes and distributions of hemodynamics characteristics in the ACoA. The main findings include: (1) the region of low TAWSS elevated with the decreasing unilateral ICA stenosis degree; (2) the overall OSI magnitude as well as high OSI area on the ACoA increased with the decreasing LICA stenosis degree, which is mainly due to the elevated flow complexity that was induced by confluence of bilateral flow from ACA-A1; (3) the RRT was prolonged in the non-stenoic case. These data further support that revascularization procedures would lead to an elevated rupture risk in the cases studied.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### Technical Limitations of SEM and False Positives/Negatives in GSR Analysis  
One of the primary concerns in SEM-based GSR detection is the potential for false positives and false negatives. False positives can occur when environmental particles share morphological and elemental characteristics with GSRs, such as residues from brake linings, fireworks, or welding operations. Studies have reported that environmental contaminantscontaining Pb, Ba, and Sb can be misidentified as GSRs unless additional criteria, such as morphology and particle size distribution, are considered [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. False negatives, on the other hand, arise due to the loss of GSR particles over time. Research indicates that GSR particles can degrade or be transferred away from an individual's hands within hours, reducing the probability of detection. The accuracy of SEM in GSR identification varies, with reported false negative rates ranging between 10 and 30%, depending on the collection method and environmental factors [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. To improve reliability, automated particle classification algorithms and standardized analysis protocols are being developed to enhance detection efficiency and reduce human error in forensic evaluations.
## 4 Results and Discussion
### Flow Field Analysis of Case 3  
Figure 9 shows the streamline of the flow field of the case under actual Martian air conditions. Compared with the former two cases, this case has no vortex ring due to the reason that it is not confined in space. The maximum Mach number is slightly increased due to the lower temperature. It has a simpler vortex structure on the upper surface of the blades. The vortex forms at the tip area and moves to the root area and gradually weakens in intensity. From all streamline figures, it is obvious that the airflow speed reduces rapidly after the vortexes leave the rotor disc; thus, the size of the MAS is large enough to eliminate the influence of the chamber wall on the aerodynamic performance. Although the chamber wall does create vortex rings, the intensity is low.  
Figure 8: Streamline (**a**) and slice Mach number contours (**b**) of the flow field of case 2.  
Figure 7: Pressure contour on the surfaces of a blade.  
Figure 10 shows the vorticity magnitude contour of case 3. It can be seen clearly from the contour that the vorticity at the blade tip is much stronger and more complex than that at the root. Obvious vortexes generated from the leading edge and blade tip can be observed. The vortex generated from the leading edge moves along the chord direction and up and then dissipates quickly after shedding from the trailing edge. The vortex generated from the blade tip moves along the chord direction and span-wise direction toward the root. The vorticity structure at the blade tip is much more complex than that at the center part of the blade. The reason for this is that the high-pressure zone is highly centralized at the leading edge of blade tip area, similar to the lower surface of Figure 7. Therefore, the pressure difference between upper surface and lower surface of the blade is relatively significant, especially along the chord for the tip edge. This may cause a vortex with great intensity, up to 1.2 \(\times\) 10\({}^{7}\), as shown in the contour. While the low-pressure zone above the upper surface covers a large area, the pressure difference is relatively small along the chord and the span; thus, the vortex generated from the leading edge prolongs the forming of a large and stable vortex with a simpler structure. A complex vortex structure with great intensity may consume more energy to generate the same lift, decreasing aerodynamic efficiency. Therefore, one interesting topic worth further investigation would be the designing of a structure to breakdown the highly centralized high-pressure zone, so as to lower the intensity of the vortex at the blade tip area. This also demonstrates that the established method can predict the small and sophisticated flow structure of the airflow with ultra-low air density and a low Reynolds number.  
Figure 10: Contour of vorticity magnitude of case 3.  
Figure 9: Streamline of case 3. (**a**) streamline around the rotor. (**b**) streamline of the whole flow filed.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 2 Related Works
[66] proposed a multi-objective bat algorithm based on decomposition (MOBA/D), which is to decompose multiple problems into sub-problems and then reaggregate. Besides, differential evolution operators are introduced to increase the population diversity and the convergence velocity. According to the trade-off between two objectives in data mining of association rules, [PERSON] et al. [67] proposed the multi-objective bat algorithm based on NSGA-II (Non dominated sorting genetic algorithm-II), and the flight frequency and velocity of bat individuals in the algorithm are improved. As we have known, the observation matrix is an important factor in the signal reconstruction process; however, most scholars ignore the optimization of the observation matrix. Moreover, to achieve better signal reconstruction, according to the RIP theorem, we can obtain the correlation between the transformed base and the observation matrix. However, the experiment of the influence of the choice of different transform bases on the optimization algorithm has not been found in previous works. Therefore, in order to obtain a higher reconstruction accuracy and smaller reconstruction error, an adaptive bat algorithm to optimize the observation matrix is designed. In addition, we also tested the effects of different observation matrix reconstructions and different transform based on the proposed algorithm. Finally, signal reconstruction is achieved.
Future Internet_**2012**, \(4\), 719-736; doi:10.3390/fi4030719  
_future internet_  
ISSN **1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Connectivity Practices and Activity of Greek Political Blogs**  
**[PERSON]**  
Department of International and European Studies, University of Macedonia, Egnatia 156,  
Thessaloniki 54006, Greece; E-Mail: [EMAIL_ADDRESS]; Tel.: +30-2310-891-487; Fax: +30-2310-891-285  
_Received: 1 May 2012; in revised form: 19 July 2012 / Accepted: 30 July 2012 / Published: 14 August 2012_  
**Abstract:** This paper uses Social Network Analysis indexes to study Greek political blogs. The indexes describe bloggers' community recommendations, centrality and bloggers' attempt to form spheres of influence. Five Social Network Analysis indexes are used: incoming links, normalized betweenness, outgoing links, number of 1-cliques a blog belongs to, and size of blog's ego-network. By recording 127 Greek political blogs, the paper finds that there are two distinct blog performance properties regarding connectivity: Only a few blogs serve as authority blogs having many incoming links and centrality, while a few others try to expand their influence territory by having many outgoing links and forming larger 1-cliques and ego-networks. Next, the paper associates the proposed indexes with blogs' and users' community activity. Authority blogs present high blog activity and users' community activity, as well. These are recorded by large numbers of posts and comments to the blog posts, respectively. It is shown that blogs, which strive to expand their network by using many outgoing links are more likely to link to the authority blogs. Content analysis reveals that authority blogs provide news and information and promote discussion to a much higher degree compared to the overall Greek political blogosphere.  
**Keywords:** Greek political blogs; centrality; activity; Social Network Analysis; hyperlinks; influence; content analysis
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### Influence of Ore-Pass Depth \(H\)  
To investigate the influence of ore-pass depth on the total shaft wall damage volume, the ore block size (\(P\) = 400 mm), the shaft diameter (\(D\) = 2 m), and the inclined angle of the chute (\(\alpha\) = 60\({}^{\circ}\)) were kept constant, and the ore-pass depth \(H\) was set between 60 m and  
Figure 8: Initial collision position between the ore and the shaft wall of No. 1 ore pass: (**a**) laser measurement line schematic; (**b**) site diagram of initial collision position.  
Figure 7: Bottom of No. 2 ore pass of the deep mine.  
420 m. The ore passes were divided into five groups according to the ore-pass dip angle \(\theta\) (60\({}^{\circ}\), 65\({}^{\circ}\), 70\({}^{\circ}\), 75\({}^{\circ}\) and 80\({}^{\circ}\)). Figure 9 presents the variation in total shaft wall damage volume \(Q_{\text{tol}}\) determined from Equations (18) and (19) with respect to the ore-pass depth \(H\) across ore-pass dip angles.  
As shown in Figure 9, the ore-pass dip angle \(\theta\) has a significant influence on the \(Q_{\text{tol}}\)-\(H\) relation curve shape. When \(\theta=60^{\circ}\) and \(\theta=80^{\circ}\), \(Q_{\text{tol}}\) remains unchanged with an increase in \(H\). When \(\theta=65^{\circ}\), \(\theta=70^{\circ}\), and \(\theta=75^{\circ}\), \(Q_{\text{tol}}\) increases at different degrees first and then stabilizes with \(H\).  
According to Equation (19), it is discernible that the collision frequency between the ore and the shaft wall increases with an increase in \(H\), resulting in an increase in \(Q_{\text{tol}}\). For ore passes with \(\theta=60^{\circ}\) and \(\theta=80^{\circ}\), as \(H\) varies within the range from 60 m to 420 m, the collision frequency remains constant for 3 times and 1 time, respectively, and \(Q_{\text{tol}}\) remains unchanged. When \(H\) changes from 60 m to 120 m, the collision frequency between the ore and the shaft wall with the ore passes of \(\theta=65^{\circ}\) and \(\theta=70^{\circ}\) increases from 1 time to 2 times. When \(H\) changes from 120 m to 180 m, the collision frequency with the ore pass of \(\theta=70^{\circ}\) increases from 2 times to 3 times. Similarly, as \(H\) varies from 180 m to 360 m, the collision frequency with the ore pass of \(\theta=75^{\circ}\) gradually increases from 1 time to 3 times. Therefore, as \(H\) increases, \(Q_{\text{tol}}\) exhibits the trend presented in Figure 9, corresponding to the increase in collision frequency.  
In summary, it is advisable for mines to judiciously determine the ore-pass depth \(H\) based on the ore-pass dip angle \(\theta\), with the aim of reducing the collision frequency between ores and shaft walls, thereby preventing damage to shaft walls due to multiple collisions.
# Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model
## 5 Discussion
### Calculation Performance Analysis  
In the experiment, we examined the time efficiency of different models on predicting the sememes of unlabeled words. As shown in Table 5, we randomly selected 5000 words as a test task for predicting their sememes with different models and recorded the time consumption of the training process and prediction, respectively.  
Table 5 shows that the SPSY model takes the least amount of time to accomplish this task. It benefited from the fact that the model does not contain training the process of the reference words, synonym, and thus, it does not need to calculate word similarities with word vectors. Actually, all the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nearest Word Number** & **SPWE** & **ASPSW** \\ \hline
10 & 0.5478 & 0.6724 \\
20 & 0.5566 & 0.6762 \\
30 & 0.5587 & 0.6773 \\
40 & 0.5597 & **0.6778** \\
50 & 0.5602 & **0.6778** \\
60 & 0.5605 & 0.6776 \\
70 & 0.5606 & 0.6775 \\
80 & 0.5608 & 0.6777 \\
90 & 0.5609 & 0.6777 \\
100 & 0.5610 & 0.6774 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction accuracy of the SPWE and ASPSW models under setting different values of k:the best results are in bold-face.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Costs (s)** & **Predicting Costs(s)** & **Total (s)** \\ \hline SPWE & NA & 2129 & 2129 \\ SPSE & 6510 & 40 & 6550 \\ SPWE+SPSE & 6510 & 2195 & 8705 \\ \hline SPCSE & 41,191 & 2031 & 43,222 \\ SPWCF & NA & 334 & 334 \\ SPWCF+SPCSE & 41,191 & 2417 & 43,608 \\ CSP & 47,701 & 4656 & 52,357 \\ \hline SPS & NA & 22 & 22 \\ SPSW & NA & 2169 & 2169 \\ ASPSW & NA & 2639 & 2639 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time consumption for predicting sememes of 5000 unlabeled words.  
models without the training process spend less time than the models that contain a training process, because the training process is very time-consuming. Although the SPS model based on matrix decomposition and SPWCF model based on internal character features of words can complete the prediction process in a relatively short time, their prediction accuracy still remains lower.  
In addition, compared with SPSE and SPCSE models, the SPSW and ASPSW model does not require additional time for training. The SPSW model based on fixed weights is similar to the SPWEA model based on word embedding in time consumption. The ASPSW model based on an attention mechanism can also improve the prediction accuracy of sememes without significantly increasing time consumption.