esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 5 Derailment Process Analysis
### Wheel Lifting Height  
Under the critical derailment speed, the wheel lifting height on the inner rail side is shown in Figure 8. When the vehicle enters the transition curve, the wheel flange on the inner side of the track has a tendency to climb up the rail due to the superelevation, and the wheel lifting height increases rarely. Based on the centrifugal force, the wheel pair moves to the outer side of the track quickly. In order to restore the alignment position, the wheel shakes left and right during the balancing process, resulting in fluctuation of the wheel lifting height. When the radius of the curve is 800 m, the train wheels along to the inner side derail on the transition curve. While on other lines, the wheel lifting height increases suddenly after the train enters the circular curve until the train turns over.  
Figure 7: Wheel–rail contact spot variation diagram.  
Figure 9 shows the wheel lifting height on the outer rail side. When the train runs on the line of group A, the lateral swing displacement of the wheel is large, and the wheel lifting height changes obviously. On the line with a radius of 800 m, the wheel of the outer rail does not return to alignment position after one lateral swing, and the flange falls over the top of the rail. The change trend of the wheel lifting height on the outer rail side of the vehicle in group B is the same. The wheel lifting height of trains which run in the curve of group B is smaller than that in group A. With the increase in the curve radius, the train derailment occurs later.
## 2 Materials and Methods
### Model Performance Evaluation  
The performance evaluation of the model adopts a multi-index comprehensive evaluation system, including the determination coefficient of the calibration set (\(R_{C}^{2}\)), the determination coefficient of the prediction set (\(R_{P}^{2}\)), the root mean square error of the calibration (RMSEC) and the root mean square error of the prediction (RMSEP). Residual prediction deviation (RPD) is used as a criterion for model quality [32]. The specific calculation formulas of each evaluation index are shown in Equations (4)-(6).  
\[R_{C}^{2},R_{P}^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2}}}}{ \sum\limits_{i=1}^{n}{{{(y_{i}-y_{m})}^{2}}}} \tag{4}\]  
\[RMSEC,RMSEP=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2}}}} \tag{5}\]  
\[RPD=\frac{SD}{RMSEP}\frac{\sqrt{\frac{1}{n-1}\sum\limits_{i=1}^{n}{{{(y_{i}-y _{m})}^{2}}}}}{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2 }}}}} \tag{6}\]  
where \(y_{i}\) and \(\hat{y_{i}}\) represent the measured and predicted values of the SSC of the i-th blueberry sample, respectively, \(y_{m}\) denotes the mean value of the SSC of the blueberry sample and \(n\) denotes the number of blueberry samples in the calibration or prediction set. In general, an ideal model should have higher determination coefficients and smaller root mean square errors. The range of RPD values reflects the predictive ability of the model: with RPD \(<\) 1.5, the model is unavailable; with 1.5 \(\leqslant\) RPD \(<\) 2.0, the model can distinguish between high and low values of the target variable; with 2.0 \(\leqslant\) RPD \(<\) 2.5, the model has the ability to make quantitative predictions; and with RPD \(\geqslant\) 2.5, the model has an excellent predictive performance, and the quality of the model is improved with the increase in the RPD [33; 34].
# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 4 Results and Discussion
### Comparison with Other Works  
In this section, we compare the performance of the proposed Adaptive CAPTCHA with four other models. The first model is the baseline model, Deep CAPTCHA, as mentioned before. The second is the Multiview Network (MultiviewNet) proposed by [PERSON] et al., which applies two different filters to the same image and then sends them to two CNN paths for processing before making predictions. In this experiment, median filtering and bilateral filtering were used. The third and fourth models are ConvNet proposed by [PERSON] et al., and Capsule, proposed by [PERSON] et al.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Models** & **FPS** & **MACs** & **Params** & **AASR** & **AASR** & **CEPOCH** & **CEPOCH** \\  & & & & & **M-Dataset** & **P-Dataset** & **M-Dataset** & **P-Dataset** \\ \hline \multirow{3}{*}{O} & Deep CAPTCHA & \multirow{3}{*}{126} & \multirow{3}{*}{193.1 M} & \multirow{3}{*}{6.46 M} & \multirow{3}{*}{85\%} & \multirow{3}{*}{85\%} & \multirow{3}{*}{120} & \multirow{3}{*}{120} \\  & (Baseline) & & & & & & & \\ \cline{1-1} \cline{5-10} A & Baseline + CRNN & 109 & 276.1 M & 3.82 M & 98\% & 99\% & 110 & 110 \\ B & A + AFFN & 77 & 319.1 M & 3.82 M & 99\% & 98\% & 30 & 100 \\ E & B + R (T0) & 77 & 319.1 M & 3.82 M & 99\% & 99\% & 50 & 30 \\ F & B + R (T0, 13, 23) & 71 & 320.2 M & 3.82 M & 99\% & 99\% & 70 & 20 \\ G & B + R (T0, T1) & 67 & 319.6 M & 3.82 M & 99\% & 99\% & 70 & 70 \\ H & B + R (T0, T1, 13, 23) & 66 & 320.7 M & 3.82 M & 99\% & 99\% & 60 & 20 \\ I & B + R (T1) & 72 & 319.6 M & 3.82 M & 99\% & 99\% & 40 & 20 \\ I & (Adaptive CAPTCHA) & & & & & & & \\ J & B + R (T1, 13, 23) & 70 & 320.7 M & 3.82 M & 99\% & 98\% & 80 & 40 \\ L & B + R (T1, 23) & 70 & 320.3 M & 3.82 M & 99\% & 99\% & 70 & 80 \\ M & O + AFFN & 81 & 236.1 M & 6.46 M & 93\% & 84\% & 100 & 120 \\ N & O + STN & 67 & 226.2 M & 6.53 M & 62\% & 95\% & 120 & 60 \\ O & O + AFFN + STN & 60 & 269.2 M & 6.53 M & 89\% & 97\% & 110 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study.  
It can be seen from Figure 21 that ConvNet and Adaptive CAPTCHA have the highest AASR exceeding 99%, but the convergence of ConvNet is faster under the same learning rate. The AASR of MultiviewNet and Capsule is between 90% and 95%, while Deep CAPTCHA is about 85%.  
Table 2 lists other evaluation metrics of the different models. The chart clearly indicates that while ConvNet exhibits the highest AASR, its PARAMs and MACs are suboptimal, being almost four times more than those of Adaptive CAPTCHA. Besides, the FPS of ConvNet is the lowest, only around 22.5. It is worth noting that Capsule is the lightest framework with PARAMs of 691.26 K and has the lowest MACs among all models, but its inference speed is slower than Deep CAPTCHA and Adaptive CAPTCHA. The AASR of MultiviewNet is about 95% with small PARAMs, but the FPS is indeed significantly low. Adaptive CAPTCHA is in the first two on all indicators, which indicates that the proposed model does have comprehensive performance advantages.
## 4 Holochain Experiment for Healthcare IoT Network
### Results and Comparison  
In this experiment, Node 1 published health data, Node 2 retrieved it, and Node 3 updated the data. We compared Holochain with blockchain in terms of validation latency and throughput. The blockchain experiment was conducted using Ethereum with Proof of Authority (PoA). PoA is preferred for private or permissioned blockchain networks, which are common in IoT and enterprise environments [62; 63]. A smart contract was deployed to store and retrieve health data, and getn (Ethereum CLI) was used for interaction. Our preliminary results, presented in Table 2, show that Holochain achieves a latency of around 50 ms to publish data and 30 ms to retrieve it, with a throughput of around 20 TPS for a single node. In contrast, blockchain exhibits higher latency (around 200 ms to publish and 100 ms to retrieve) and lower throughput (around 10 TPS).  
To evaluate scalability, we simulated an increasing number of nodes, from 3 up to 10 (Figure 5). The results show that Holochain maintains relatively stable performance as the network grows. At 10 nodes, Holochain sustains an average throughput of approximately 15 TPS, with only a modest increase in publish and retrieve latencies (up to 65 ms and 45 ms, respectively), while the blockchain system experiences significant performance degradation with increasing network size. By 10 nodes, its throughput drops to three TPS, and latencies increase substantially, publish latency exceeds 280 ms, and retrieve latency approaches 160 ms. These results highlight the scalability limitations of traditional blockchain platforms in latency-sensitive healthcare IoT applications.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Performance Metric** & **Holochain** & **Blockchain** \\ \hline Latency & 50 ms (publish), 30 ms (retrieve) & 200 ms (publish), 100 ms (retrieve) \\ \hline Throughput & 20 TPS (stable) & 10 TPS (decrease with more nodes) \\ \hline Network Overhead & Low (P2P/DHT) & High (Global broadcast) \\ \hline Storage Growth & Linear per agent (Local + shared DHT) & Exponential (full ledger replication) \\ \hline Consensus Mechanism & Agent-centric validation & Global PoA consensus \\ \hline
10-Node Test & 15 TPS (+20\% latency) & \textless{}5 TPS (2\(\times\) latency) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Holochain vs. blockchain comparison summary.
## 4 The SMA-LRBs
### The Hysteresis Model of SMA Wires
#### 4.2.5 Force-Displacement Curves  
In the next section, the radius of the SMA wire is found for each configuration through an optimization algorithm. However, the optimized radius is different for each arrangement. Thus, to observe and compare the sole effect of various arrangements on the hysteresis diagram of the SMA wires, the force-displacement curves for the three wire's configurations with a fixed wire radius of 2.5 mm, under three different lateral displacement scenarios, are plotted in Figure 9. As illustrated,for specified excitation and wire radius, the stiffness of the double-cross configuration is more than the straight one and more than the cross one. By increasing the radius of the wire in each arrangement, the stiffness could be increased. Thus, it is expected that SMA-LRBs with cross arrangement need thicker wires, and with double-cross arrangement, require the thinnest wire. Additionally, the cross wires create less flag-shaped hysteresis because of their lower levels of strain in the SMA wires (see Figure 8). The input displacements are extracted from [PERSON] and [PERSON] [24] and are named in the same way as E5, E6, and E7. They selected these displacement scenarios in a way to follow different functions like ramp, step, sinusoidal, and a combination of them with various peak amplitudes to assess the model in any condition. As mentioned before, the written code in this study is based on the algorithm developed by [PERSON] and [PERSON] [24] and was verified in a previous paper by [PERSON] et al. [23].  
Figure 8: Strain in SMA wires due to shear strain of SMA-LRB for the three different wires arrangement.  
Figure 9: Excitations (E5, E6 and E7) in terms of shear strain versus time, and corresponding shear hysteresis responses of the three SMA wire configurations in terms of force versus displacement.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 4 Proposed Mechanism
### Proposed Framework  
Figure 3 illustrates the workflow of the [PERSON] decision policy. Firstly, all vehicles' speeds and directions of motion are transmitted to the data center. Then, the expected RSS from the two kinds of base stations is first obtained from the existing vehicle state that is based on our proposed method. As per the [PERSON] decision policy, the two kinds of signal strength are used as a benefit to base station selection. Then, all vehicles are arranged in order. After that, all vehicles compete with the femtocell base station resources. Finally, the best selection strategy is obtained through different permutations and combinations.  
Figure 2: Basic idea of base station selection, presenting the objective, subproblems, and methods. C-V2X, cellular vehicle-to-everything.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 4 Diagnosability Design Based on Structural Analysis
### Actual Diagnostic Design
#### 4.2.3 Fault-Tolerant Control Based on Structural Analysis  
It is claimed that system failures are reconfigurable; currently, this is the case only if the target state has at least one reconfigurable path in addition to the failure path. The current aspect of fault-tolerant control based on structural analysis is still at the stage of theoretical analysis.  
[PERSON] designed a graph-theory-based system reconfigurability method to define a causal diagram of the system based on causal matching and to analyze the structural analysis, controllability, and other properties. He expressed the reconfigurability as multiple controllable paths in the causal diagram of the system [77]. [PERSON] addressed the critical issues of the diagnosability and reconfigurability of an electric vehicle drive system driven by a permanent-magnet synchronous motor. Firstly, a structural analysis model was established. The definitions of fault structural detectability and isolability were given based on DM decomposition. Secondly, the structural reconfigurability determination conditions were presented based on the structural controllability and observability. Finally, based on the tram-driven model, the reconfigurability was analyzed for sensor failure [78]. [PERSON] proposed a fault-tolerant control architecture based on structural analysis and gave the conditions for structural observability. The possibility of sensor fusion was explored from the perspective of structural observability based on the reconstruction of the bipartite-directed graph. The necessary conditions for structural observability were established [79].
## 3 Numerical Validation
### FSI with Free Motion
#### 3.2.1 Flutter of NACA 64A010 Airfoil  
The two free-degree structural systems are in free motion in response to the aerodynamic forces, namely, pitch and plunge. The structural parameters for this case are \(x_{\alpha}=1.8\), \(r_{\alpha}^{2}=3.48\), \(\omega_{h}=\omega_{\alpha}=100\) rad/ sec, and \(\mu=60\). \(a=-2.0\) indicates that the pivot axis lies on a semi-chord ahead of the leading edge of the NACA 64A010 airfoil.  
The common practice [57; 58] is to force the airfoil sinusoidally in pitch around the elastic axis until three full cycles are completed with the frequency \(\omega\) close to the first mode of vibration. It is notable that the angle of attack is set as \(1^{\circ}\) for an obvious initial disturbance.  
Figure 17 compares the flutter boundaries between the present work calculated by ALE-LBFS and previous references [59; 60; 61]. It is apparent that the current algorithm predicts the \"transonic dip\" phenomenon well. In addition to the flutter boundary in viscid flow, the ALE-LBFS also predicts the inviscid flutter boundary in the Euler simulations. The bottom of the dip is placed at the speed index of \(V_{f}^{*}=0.75\) at a Mach number \(Ma_{\infty}=0.825\) while it is \(V_{f}^{*}=0.55\) in the inviscid flow. As can be seen in Figure 17, viscosity is vital when predicting the flutter. The inviscid and viscous simulations agree well with each other until the bottom of the transonic dip. Due to the consideration of viscosity, the critical flutter velocities are lifted in the region of Ma = 0.8-0.9 and the S shape flutter boundary in inviscid simulations disappears. For higher Mach numbers, the predicted viscous flutter boundary has visible discrepancies compared with other references, which is caused by the use of numerical schemes [15]. Figure 18 presents several responses in different pairs of a Mach  
number and flutter speed velocity, namely, damped response, divergent response, and limit cycle oscillation (LCO). Figure 19 depicts four instantaneous snapshots in a circle of LCO and their phase angles correspond to \(\omega=0,90^{\circ},180^{\circ},270^{\circ}\). Forced by shocks moving along the surface of the airfoil, the structure pitches and plunges in a periodic process.  
Figure 17: Flutter speed index \(V_{f}^{*}\) as a function of a free-stream Mach number for NACA 64A010.  
Figure 18: History of plunge and pitch for (**a**) damped response; (**b**) divergent response; (**c**) limit cycle oscillation (LCO).
###### Abstract  
The goal of this work is to show how machine learning models, such as the random forest, neural network, gradient boosting, and AdaBoost models, can be used to forecast the fatigue life (N) of plain concrete under uniaxial compression. Here, we developed our final machine learning model by generating the following three data files from the original data used in the work of [PERSON] et al.: (a) grouped data with the same input variable value and different output variable log\(N\) value, (b) data excluding outliers selected by three or more outlier detection methods; (c) average data excluding outliers, created by averaging the grouped data after excluding outliers from among the grouped data. Excluding the sustained strength of the concrete variable, originally treated as the seventh input variable in the work of [PERSON] et al., resulted in improving the determination coefficient (\(R^{2}\)) values. Moreover, the gradient boosting model showed a high \(R^{2}\) value at 0.753, indicating a high accuracy in predicting outcomes. Further analysis using data excluding outliers shows that the \(R^{2}\) value increased to 0.803. Moreover, the average data excluding outliers provided the best \(R^{2}\) value at 0.915. Finally, a permutation feature importance (PFI) analysis was carried out to determine the strength of the relationship between the feature and the target value for the gradient boosting model. The analysis results showed that the maximum stress level (\(S_{\text{max}}\)) and loading frequency (\(f\)) were the most significant input variables, followed by compressive strength (\(f^{\prime}_{c}\)) and maximum to minimum stress ratio (\(R\)). Shape and height to width ratio (\(h/w\)) were the features with a non-significant influence on the model. This trend was previously confirmed by a Pearson and Spearman correlation analysis.  
fatigue; machine learning; stress level; frequency; compressive strength; specimen shape; outliers; correlation
###### Abstract  
Since several Internet of Things (IoT) applications have been widely deployed on unstable wireless networks, such as the Delay Tolerant Network (DTN), data communication efficiency in DTN remains a challenge for IoT applications. Vehicular Delay Tolerant Network (VDTN) has become one of DTN's potential applications, in which the network experiences connectivity interruption due to the lack of an end-to-end relay route. VDTNs focus on node cooperation to achieve this goal. As a result, it is essential to ensure that almost all network nodes adopt the protocol to preserve network performance. This is a challenging task since nodes may diverge from the basic protocol to optimize their effectiveness. This article presents an Efficient Monitoring System (EMS) to detect and respond to just selfish nodes to minimize their entire network and data communication efficacy. The scheme is based on a network-wide cooperative sharing of node reputation. It is also necessary to increase overall network efficiency by tracking selfish nodes. The NS-2 simulator is used to run this experimental setup. Simulation results indicate that the proposed scheme performs better in terms of probability of package delivery, package delivery delay, energy consumption, and amount of packet drops. For 80% selfish nodes in the network, the packet delivery of EMS is 37% and 31% better than SOS and IPS. Similarly, the average delivery delay of EMS is 22% and 18% lower than SOS and IPS when 80% selfish nodes are incorporated in the network.  
Internet of Things; VDTNs; selfish nodes; monitoring system; cooperation 2022
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 3 Proposed Transformer Estimation Algorithm
### Training Process  
The proposed Transformer estimation algorithm training process can be described as follows: The inputs of the training data are the results of the conventional channel estimation algorithm, and the labels are actual channel responses. Because the channel response is a complex form, our training data splices the real and imaginary parts together. The channel responses from the previous \(L\) moments are used to predict the output of the next moment as follows:  
\[h_{t}=f_{Transformer}(h_{t-L+1},\cdots,h_{t-1}) \tag{18}\]  
where the timestep, \(L\), can be determined by using the partial autocorrelation coefficient (PACF). The PACF is the linear correlation of the sequence, \(\{h_{t}\}\), with the sequence \(\{h_{t-k}\}\), with lag of order, \(k\), and removes the linear dependence of \(\left\{h_{t-1},h_{t-2},\cdots,h_{t-(k-1)}\right\}\). In Figure 4, the magnitude of the correlation coefficients of the PACF is compared between the real parts and imaginary parts at different lags of order, \(k\). The black solid line and the red dashed line in the Figure 4 represent the lags of order and the correlation coefficient when the data is approximately uncorrelated, respectively. It is clear to see that the PACF value drops below 0.1 when lags of order \(k=18\), so we can assume that the correlation is almost nonexistent. It is worth mentioning that choosing a lag that is too short may result in incomplete learning of the temporal correlation within the sequence. Conversely, an excessively large lag could introduce extraneous noise during training, thereby impacting its effectiveness.  
In the training phase, the mean square error (MSE) is selected as the loss function [28]:  
\[J_{MSE}=\frac{1}{L}\sum_{i=1}^{N}{(f_{Transformer}(h_{t-L+1},\cdots,h_{t-1})-H _{label})^{2}} \tag{19}\]  
where \(H_{label}\) is the actual value and \(N\) is the total number of samples in the training set. The adaptive moment estimation (ADAM) algorithm is applied to update our dataset adaptively [29]. The sizes of our training set and test set are 8000 and 2000, respectively. The learning rate, batch size, and epochs are adjusted to 0.001, 128, and 150.
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
#### 4.2.3 Traffic Balancing Dynamic Routings
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Structure** & **Reference** & **Proposed Algorithm/Scheme** & **Main Contributions** \\ \hline \multirow{6}{*}{GEO-LEO} & [96] & A balanced traffic routing scheme & Delay-sensitive traffic can be prioritized for transmission, the maximum flow is only through the GEO satellite \\ \cline{2-4}  & [103] & A parametric adaptive multi-attribute decision making access and switching algorithm & Load traffic balancing can be achieved by adjusting dynamic parameters \\ \cline{2-4}  & [105] & A routing method based on game theory and distributed cache negotiation & A new competition model for LEO satellite traffic is proposed to convert non-convex issues into convex optimal solutions \\ \hline \multirow{2}{*}{[108]} & [108] & An improved Markov-model-based E2E traffic forecasting method & Adopted the idea of gradual convergence \\ \cline{2-4}  & [109] & A traffic scheduling algorithm based on multi-agent reinforcement learning & SDN and RL are utilized, utilized neural network modeling, deployed SDN controllers on GEO satellites \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The summary of the works related to the traffic-balancing dynamic routings.