esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this study, it was found that a reasonable design and proper selection of the structure parameter for a container crane hydraulic anti-swaying system were the keys to obtaining a good anti-swaying effect for the load. By developing a dynamic model for the hydraulic anti-swaying system and conducting dynamic research in the time and frequency domains, the following important conclusions were obtained:  
1. The action of the high-pressure circuit of the hydraulic system, combined with the anti-swaying rope, could attenuate the swaying angle of the load, thereby achieving the anti-swaying purpose for the load. The structure parameter primarily affected the load anti-swaying effect, and a larger structure parameter produced a better anti-swaying effect. The structure parameter depended on the hydraulic system itself and the installation position of the anti-swaying fixed pulley group;
2. Different structure parameter load ratios could cause the hydraulic anti-swaying system to work in under-damping, critical-damping, or over-damping states. For engineering applications, the hydraulic anti-swaying system could be made to work in the critical-damping state because the critical-damping anti-swaying system produced a good anti-swaying effect with less dependence on changes in the hoisting rope length. The structure parameter load ratio for the critical-damping system was twice the system frequency;
3. When the pressure and size of the anti-swaying hydraulic cylinders in the hydraulic system were determined, the longitudinal and transverse installation distances of the anti-swaying fixed pulleys jointly affected the structure parameter. A larger longitudinal distance and a smaller transverse distance were beneficial for increasing the structure parameter. For engineering applications, the horizontal and longitudinal distances could be selected according to size near that of a standard container;
4. In engineering applications, a good anti-swaying effect could be achieved by obtaining the load mass, hoisting rope length, and swaying angle speed data, then calculating the set pressure for the overflow valve of the hydraulic anti-swaying system in real-time.  
Writing--original draft, B.Z.; Writing--review & editing, L.M.; Formal analysis, H.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by China Postdoctoral Science Foundation (2020M683367).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all the data and models that support the findings of this study are available from the corresponding author upon reasonable request.  
We thank LetPub (www.letpub.com) (accessed on 1 June 2022) for its linguistic assistance during the preparation of this manuscript. We thank the anonymous reviewers and editors for their prolific comments, which significantly improved the quality of their work.  
The authors declare no conflict of interest.
## 6 Conclusions  
The progressive damage process and failure mode of the SiC\({}_{\text{f}}\)/TC\({}_{4}\) composite shaft under forward/reverse torque were thoroughly investigated using cross-scale models, and the following conclusions were obtained.  
1. For the [45\({}^{\circ}\)]\({}_{6}\) model, when the reverse torque increased, the interface was subjected to tensile load. The interface first cracked at \(-6552\) Nm; it exhibited a large number of cracks after expansion to the matrix, resulting in the matrix stiffness reducing, followed by matrix failure. When the matrix failed, the protection of the fibres was lost; the fibres became destabilized, and, finally, fibre fracture occurred. Therefore, the interface failed under a small torque, and the matrix and fibre then failed sequentially, where interface cracking was the main form of failure. The final failure load was \(-11\),\(812\) Nm, and the entire damage process was a slow change process.
2. For the [45\({}^{\circ}\)]\({}_{10}\) model, as the forward torque increased, the interface was subjected to compressive load. When the load reached 9108 Nm, the interface failed, instantly extending transversely to the matrix. Followed by the matrix failure, the failure of the interface and matrix occurred almost simultaneously, primarily owing to fibre fracture. The final failure load was 10,418, and the entire damage process was a fast change process.
3. The composite driving shaft is widely used in aero-engines; under positive torque, the torque magnitude must be relatively small, as operation is frequent. In contrast, under reverse torque, the torque magnitude can be considerably larger, as it is only experienced occasionally.  
Conceptualization, Y.H.; Validation, Y.S.; Formal analysis, J.W.; Data curation, F.Z.; Project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the China Aviation Industry Cooperation Project (No. HFZL2018 CXY019) and the Scientific Research Fund of Liaoning Education Department (Grant No. JYT20220534).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All the data have been reflected in the paper.  
The authors declare no conflict of interest.  
Figure 26: M = 9601.85 Nm matrix MDI: (**a-j**):1 st layer–10 th layer.
## 1 Introduction
However, in the case of an exterior column removal scenario, which is more critical as less alternate load paths can be developed, the effectiveness of the EBM is rarely studied, although the single deformation mode may be more difficult to be ensured. Furthermore, the influence of damping effects on the performance of EBM is not clarified yet in the literature. Hence, both effects (exterior column removal and damping effect) require a further investigation. When designing a structure with respect to robustness, it must also be taken into consideration that progressive collapses caused by extreme events are low-probability high-consequence phenomena [1]. In order to be able to accurately predict the structural response, it is important to study the progressive collapse behavior using a probabilistic approach, as it has been found that there is a significant influence of many uncertainties on the structural behavior, e.g., randomness from material properties, which is neglected by most existing studies [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. However, in the three widely used approaches in relation to the quantification of the structural robustness, i.e., deterministic-based method, probabilistic-based method, and risk-based method [30; 43; 44], both the probabilistic-based method and the risk-based method require a reliability analysis. Several approaches can be adopted in a probabilistic analysis in combination with the finite element method [35; 41; 42; 45], such as the Monte Carlo simulation [42] and the Latin Hypercube sampling technique [45]. Moreover, considering that the EBM is an approximate approach, the quantitative assessment of the model uncertainty associated to the EBM becomes important when the EBM is applied to quantify the reliability or robustness of a RC building structure following a sudden column removal scenario. Such information regarding RC frames is currently lacking in the literature [19]. The model uncertainty information of the EBM is crucial to adequately quantify the failure probability of a system when the EBM is adopted. In light of the above-mentioned state-of-the-art, this paper hence focuses on evaluating the performance of the EBM and quantifying the model uncertainty in relation to the use of the EBM in case of the assessment of RC frames subjected to different sudden column removals. The layout of the paper and research approach is as follows, see Figure 1. Section 2 presents the methodology of the EBM and the procedures on how to quantify the model uncertainty of the EBM is illustrated in Section 3. The numerical modelling techniques are validated based on an experimental result in Section 4. In Section 5, the EBM is then validated by comparing with the results of the NTTHA and both the influences of damping effects and different column removal scenarios are studied. Furthermore, stochastic analyses are carried out and model uncertainties to be considered when using the EBM are calculated in Sections 6 and 7, respectively. Concluding remarks are addressed in Section 8.
## 2 Mobile Cellular Network Basics
### Paging  
Paging can be triggered by a request from a PS or CS network. Paging triggered by a CS network is not in our concern. On a PS network triggered paging, the paging procedure is used only for a UE in the PMM-idle state. Packets that are destined to a UE may come to the GGSN from the Internet or 3G packet networks, such as from another 3G subscriber. If a PDP context exists, packets will be sent to the SGSN via a GTP tunnel. For these packets to reach to the UE, the SGSN should know about the target UE's cell information, _i.e._, the cell that is serving the UE. Therefore, if the SGSN does not have the cell information of the destined UE, it will initiate a paging procedure by sending some packets to the RNC. As mentioned before, this means that paging is realized only if the UE is in the PMM-idle state when packets are sent to it. Figure 6 illustrates the signal flow activity for the PS triggered paging.  
The paging procedure from the GGSN to the UE, given in Figure 6, is described in detail below.  
1. The GGSN sends a packet, destined for the UE in PPM-idle state, to the SGSN;
2. Upon getting packets from the GGSN, the SGSN sends a Paging Request message that includes the IMSI number, RA, _etc._, to the RNC belonging to the RA where the UE is located;
3. The RNC passes the paging requests to the UE;
4. Upon getting the paging request message, the UE's state changes from PMM-idle to PMM-connected. Also, the UE sends paging request packets back to the SGSN.  
After completing the paging procedure, the SGSN knows about the cell, RA and all the other location information of the destination UE. This means that any packet destined from the GGSN to the UE in PMM-idle state can be delivered to the UE.  
While a paging procedure is taking place, mostly the RNC's and SGSN's loads rise. This can be exploited by some DoS or Distributed Denial of Service (DDoS) type of attacks, which is the major focus of this study.
# Similarity-Based Malware Classification Using Graph Neural Networks
## 3 Proposed Problem-Solving Mechanism
### Dataset Construction
There are over 2000 samples that cannot be resolved into function nodes which have no full nodes, and over 1000 samples are resolved into over 1000 function nodes, as a result of the use of different programming languages (C/C++, C#, Visual Basic, and others), different compilers (MinGW, MSVC, Borland, and others), and many development frameworks (MFC,.Net Framework, and others). Moreover, the packer protected sample needs to be unpacked first to obtain the correct function structure. Therefore, the call graph is pruned, and functions that are not called by the entry and export functions are excluded. Finally, samples between 10 and 500 are selected as a dataset. Figure 4: Nodes in call graph. One thousand malware samples for each family were originally corrected. However, in the conversion of samples to graphs, some samples cannot be resolved into function nodes, causing the dataset to contain different numbers of samples of each family. Table 1 presents the distribution of the dataset. The ten malware families are Heodo, Dridex, Quakbot, TrickBot, AveMariaRAT, RemcosRAT, MassLogger, Gozi, CobaltStrike, and IcedID. The total number of samples is 5227. A training dataset, a validation dataset, and a test dataset that are associated with these samples are used in an experiment. For the investigation of the classification model, 50 samples in each family are selected at random to generate a test dataset and the remaining samples are then split 90% into a training dataset and 10% into a validation dataset. In the experiment on the similarity model, a pair-wise dataset is required, which is generated by the following method. Randomly pair off separately with one same family sample and one different family sample for each sample. The paired dataset has double the original number of samples and is balanced.
## 4 Results and Discussion
### Surrounding Distance Monitoring Test  
With reference to the 2 m safe surrounding distance standard specified in Section 2.1, in the range of 15 m, we used the binocular cameras (c1, c2, d1, d2) to perform binocular ranging tests on 20 groups of different distances. The same as in Section 4.2, the distance in the experiment is calculated by binocular ranging algorithms, while the real distance (\(Rd\)) is measured by the laser range finder. The test arrangement is shown in Figure 16. The results are shown in Figure 17.  
As shown in Figure 17, the measurement error of the surrounding distance of the system is controlled within 2% in the range of 5 m, which meets the requirements of error in actual driving. And the accuracy of the binocular ranging is proportional to the target distance too.  
Figure 16: Camera arrangement.  
Figure 15: Analysis of front and rear distance monitoring results. (**a**) Monitoring results; (**b**) error analysis histogram.
## 3 Advances in Control Strategies
Model-free, learning-based approaches (deep RL) improve positional accuracy by \(\approx\)70% and reduce energy by \(\approx\)38% relative to the PID baseline, while still meeting the <25 ms cycle-time constraints required for high-speed assembly. Figure 3: Industry 5.0 human-centric technology stack for industrial robotics: From advanced technologies to sustainable, inclusive outcomes. Digital twin-assisted MPC and quantum-inspired optimization offer the best overall trade-off, achieving \(<\)10 \(\upmu\)m mean error and \(<\)7 \(\upmu\)/cycle energy, which are critical for ultra-precision, energy-regulated production lines. * Bio-inspired evolutionary controllers remain attractive for redundant or highly nonlinear manipulators, but longer cycle times (\(>\)30 ms) currently limit uptake in fast takt-time environments. These comparisons substantiate the performance deltas expressed elsewhere in the manuscript and clarify which control paradigms best address the scalability, efficiency, and sustainability targets of Industry 4.0 (Figure 2) and Industry 5.0 (Figure 3).
# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 4 Experimental Results and Discussion  
Figure 9 illustrates the receiver operating characteristic (ROC) curve of our model, depicting the variation of the true positive rate (TPR) and false positive rate (FPR). The blue dotted diagonal indicates that positive and negative cases are predicted with equal probability (i.e., TPR equals FPR), and if the model only makes random guesses, the ROC curve will be close to the diagonal. The curve shows that the model's performance is superior across the entire range of thresholds, with the area under the curve (AUC) value of 0.92, indicating its robustness and accuracy in discriminatory speech classification. These results reinforce the potential of the model for discriminatory speech classification.  
The improved SMOTE and SVM-RF based models are compared with other base class methods (SVM, RF, DT, KNN (K-Nearest Neighbor), GaussianNB (Gaussian Naive Bayes), and XGB (eXtreme Gradient Boosting)) on the Sina Weibo dataset that we produced. As can be seen in Table 3, the KNN method has the second highest precision, but the recall and F1 scores are low and the overall performance is poor. The performance of the improved SMOTE and SVM-RF based model achieved the highest precision among all models at 90.3%, which is 6.0%, 3.0%, 11.2%, 1.6%, 37.4%, and 11.0% higher than that of SVM, RF, DT, KNN, GaussianNB, and XGB, respectively. In addition, the improved SMOTE and SVM-RF based model also achieved the highest precision in terms of recall and F1 score.  
In order to verify the impact of improved SMOTE and the integration method on model classification, ablation experiments were carried out, as shown in Table 4. It is easy to find that in the presence of improved SMOTE, the precision, recall, and F1 score of SVM and RF are increased to different degrees, indicating that the unbalanced dataset will have an impact on the model, and the model performance is increased after balancing the dataset with improved SMOTE. The performance metrics of the model based on both  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline SVM & 0.843 & 0.862 & 0.852 \\ RF & 0.873 & 0.834 & 0.853 \\ DT & 0.791 & 0.764 & 0.777 \\ KNN & 0.887 & 0.543 & 0.674 \\ GaussianNB & 0.529 & 0.733 & 0.615 \\ XGB & 0.793 & 0.884 & 0.836 \\ Improved SMOTE + SVM + RF & 0.903 & 0.916 & 0.909 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison experiment.  
Figure 9: ROC curve.  
improved SMOTE and SVM-RF are higher than the case without improved SMOTE and a single model; thus, it can be seen that the integrated model of weighted summation of the respective classification results of SVM and RF after normalization of the computed weights by subtracting the maximum value improves the performance of the model.
# Application of Bonded-Block Models to Rock Failure Analysis
## 4 Analysis of Underground Structures
### Selection of Micro-Parameters  
The inner-block size in the tunnel model was chosen to be 0.3 m, providing a fine discretization around the opening. The target Young's modulus was 20 GPa, and the target uniaxial compressive strength was set to 20 MPa. In order to calibrate the micro-properties, a numerical simulation of a uniaxial compression test was performed using a Voronoi pattern of the same size. In this example, the sample size was set to 8 \(\times\) 16 m, as shown in Figure 4a. After a preliminary trial, the joint normal and shear stiffnesses were set to 96.6 GPa/m and 40.3 GPa/m, respectively, to provide a deformation modulus of 20.0 GPa, measured in the initial linear part of the obtained stress-strain curve. After a few trials, the following micro-property strength parameters were selected: tensile strength, 3 MPa; cohesion, 9.5 MPa; and friction, 25\({}^{0}\). The parameters defining the post-peak tensile curve (Figure 2) were \(\beta_{\text{n1}}\) = 0.5, \(\alpha_{\text{n1}}\) = 1.7, and \(\alpha_{\text{n2}}\) = 4.0. In shear, the corresponding parameters were \(\beta_{\text{s1}}\) = 0.5, \(\alpha_{\text{s1}}\) = 1.3, and \(\alpha_{\text{s2}}\) = 2.1. With these micro-properties, the peak stress attained in the uniaxial compression simulation was 20.7 MPa, considered sufficiently close to the target for this study. The failure mode, with the dominant vertical fracture pattern, is also shown in Figure 4b.  
A second model was analyzed by employing a triangular-block pattern (Figure 5a), generated with UDEC's Trigon option [63], instead of the Voronoi polygons. This generation procedure divides each polygon into triangles by inserting a central point. Therefore, it leads to a slightly more refined discretization with more joints meeting at each intersection, requiring different micro-properties to give the desired target macro-properties. In this case, the joint normal and shear stiffnesses were increased to 162 GPa/m and 68 GPa/m, respectively, resulting in a target deformation modulus of 20 GPa. The strength micro-parameters and the shape of the post-peak curve were similar to those of the Voronoi model given above. In this case, the peak stress in the uniaxial compression simulation reached a value of 20.3 MPa, close to the result of the other block pattern. Often, these triangular-block patterns lead to lower strength, requiring an increase of the micro-properties. In the present case, the difference was relatively small, so the micro-strength was maintained. The failure mode obtained with the triangular pattern is presented in Figure 5b.  
Figure 4: Numerical uniaxial compression test. (**a**) Voronoi block pattern; (**b**) failure mode.
# Combined Channel Estimation with Interference Suppression in CPSS
## 2 Signal Model  
NBI is partial band jamming, which consists of a simple unmodulated carrier [22]. Figure 1a is a frequency domain monopulse, which refers to single tone jamming. The mathematical model of single tone jamming is:  
\[J(k_{I},l)=A_{0}\delta(k_{I}-k_{0},l). \tag{1}\]  
Figure 1b refers to multitone jamming, which is a set of single tones. The mathematical model of multitone jamming is:  
\[J(k_{I},l)=\frac{1}{\sqrt{K}}\underset{i=1}{\overset{K}{\sum}}A_{i}\delta(k_{ i}-k_{0},l), \tag{2}\]  
where \(K\) is the number of interfered subcarrier.  
Thereby, the signal with interference at the receiver can be written as:  
\[Y(k,l)=H(k,l)X(k,l)+J(k_{l},l)+W(k,l), \tag{3}\]  
where \(H(k,l)\) is the CIR in frequency domain. \(J(k_{l},l)\) refers to the interference with \(k_{l}\) subcarriers jamming. \(W(k,l)\) is additive white Gaussian noise (AWGN). \(X(k,l)\) is a training sequence based on Zadoff-Chu (ZC) sequence, the modulus of which is 1. The Flourier transformation of ZC sequence is still a ZC sequence.  
To estimate CIR \(H(k,l)\) more accurately, \(J(k_{l},l)\) and \(W(k,l)\) in Equation (3) should be exactly eliminated.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 6 Results and Discussion
### Performance Based on Packet Delivery Ratio  
The performance analysis of packet delivery ratio with CAR-MOSOA and existing methods (FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33]) is illustrated in Figure 6. From Figure 6, it can be seen that CAR-MOSOA achieves better PDR in relation to the existing methods. Moreover, the ratio of PDR turns out to be high once the corresponding communication contains single and binary values. Table 4 shows the comparative analysis of the packet delivery ratio performance. From the table, it can be seen that the proposed CAR-MOSOA delivers more data packets at a specified node when compared with existing techniques.  
Figure 5: Performance based on end-to-end delay.  
Figure 6: Performance based on packet delivery ratio.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{5}{c}{**End-to-End Delay (s)**} \\  & **FDEAM [30]** & **EOMR [31]** & **TSGWO [32]** & **CoCoA [33]** & **Proposed CAR-MOSOA** \\ \hline
100 & 8 & 10 & 8 & 12 & 5 \\
150 & 14 & 17 & 15 & 18 & 9 \\
200 & 15 & 23 & 19 & 27 & 11 \\
250 & 23 & 30 & 26 & 31 & 18 \\
300 & 28 & 35 & 31 & 38 & 20 \\
350 & 32 & 42 & 37 & 45 & 24 \\
400 & 40 & 50 & 44 & 54 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of end-to-end delay.