esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Derivation of PIC Model
### Contact Pressure Caused by Wire Twisting
#### 2.2.2 Non-Diagonal Contact Points in Different Layers  
If the contact point between wires is not on the diagonal and is situated between different layers, as shown in Figure 4(b.1-b.3), the inward transmission path of contact pressure becomes more intricate, necessitating individual consideration for each wire's position. In such cases, the angle between the upper-layer wire and the central wire must be taken into account, and a reduction in the twisting force should be applied before superposition.  
\[q_{1(i,j)}^{\langle k\rangle}=\sum_{l=1}^{n}q_{(i+l,j)}\beta_{(i+l,j)} \tag{4}\]  
For example, in the case of Figure 4(b.2), the calculation of the contact pressure at point \(\langle 1\rangle\) on the wire \((i,1)\) involves the superposition of all the contact pressures from the upper-layer wires. This can be expressed as follows:  
\[q_{1(i,1)}^{\langle 1\rangle}=q_{(i+1,1)}\beta_{\alpha 2(i+1,1)}+q_{(i+2,1)} \beta_{\alpha 2(i+2,2)}+q_{(i+3,3)}\beta_{\alpha 2(i+3,3)} \tag{5}\]  
where \(\beta_{\alpha 2}\) is the reduction coefficient in the direction of the angle \(\alpha_{2}\), and it is calculated by the following formula:  
\[\beta_{\alpha 1}=\frac{\sin\alpha_{2}}{\sin 12\
icefrac{{\cdot}}{{ 2}}} \tag{6}\] \[\beta_{\alpha 2}=\frac{\sin\alpha_{1}}{\sin 12\
icefrac{{\cdot}}{{ 2}}}\]  
For contact points between wires on different layers, since the direction of the twisting force is generally not along a straight line with the contact point, it is necessary to consider allocating the twisting force to different contact points based on the angle during superposition.  
As Figure 6 shows, the superposition of twisting forces can be demonstrated using wire (5,2), (4,2) and (3,2) as an example. For the outermost wire (5,2), the contact pressure mainly depends on its own twisting force \(q_{(5,2)}^{tw}\) and reduction coefficient \(\beta_{\alpha}\). And for the inner wires (4,2) and (3,2), their contact pressure is related to the contact pressure transmitted by the upper wire and its own twisting force at the same time.  
Figure 5: Superposition of contact pressure at diagonal contact points: (**a**) contact force and twisting force in the (5,1) wire, and \(q_{(5,1)}^{tw}\) is the twisting force in (5,1) wire, \(q_{1(5,1)}^{\langle 4\rangle}\) is the contact pressure at the contact point \(\langle 4\rangle\) in (5,1) wire, (**b**) contact force and twisting force in (4,1) wire, (**c**) contact force and twisting force in (3,1) wire. The red arrow indicates the contact force and the black arrow indicates the twisting force of each wire.
## 3 The Proposed SP-IoUAV Model
### Privacy-Preserving Ensemble
#### 3.2.1 Federated Learning  
Machine learning techniques such as federated learning enable models to be trained on different devices or nodes without transferring raw data. After combining all UAV models, data privacy is protected because the actual data are not shared after they are combined. Each UAV stores its own local data and uses these to train its own model.  
Suppose \(D=D_{1},D_{2},\ldots,D_{n}\) represents local datasets from \(n\) UAVs, where \(\theta\) represents the global model parameters, and \(F(\theta)\) represents the global loss function. Based on its local data \(D_{i}\), each UAV \(i\) trains its local model to minimize its own local loss function \(f_{i}(\theta)\). The local models are then sent to a central server where they are combined to update the global model parameters as follows:  
\[\theta_{\text{global}}\leftarrow\text{Aggregate}\Big{(}\theta_{\text{global}}, \theta_{1},\theta_{2},\ldots,\theta_{n}\Big{)} \tag{1}\]  
As part of the aggregation process, a weighted average of local model parameters is calculated according to the size of each UAV's dataset. Upon updating the global model, the UAVs are then sent back to iterate until convergence is achieved.  
Algorithm 1 describes clearly how our proposed federated learning model works.  
```
Input: Global model parameters \(\theta_{\text{global}}\) Output: Updated global model parameters \(\theta_{\text{global}}\)
1 Initialize global model parameters \(\theta_{\text{global}}\);
2 foreach\(t=1,2,\ldots,T\)do
3 foreach\(UAV\)\(i=1,2,\ldots,n\)do
4 Receive global model parameters \(\theta_{\text{global}}\);
5 Train local model using data \(D_{i}\) and update local parameters: \(\theta_{i}\leftarrow\text{argmin}_{\theta}f_{i}(\theta)\);
6 Send local model parameters \(\theta_{i}\) to the central server;
7
8 end for
9 Aggregate local model parameters using Equation (1);
10 end for
```  
**Algorithm 1** Federated Learning Algorithm  
Several UAVs are involved in the algorithm, which operates in rounds. Global model parameters are initialized and shared with UAVs for local model training. UAVs update the global model using their local models, protecting data privacy. As UAV privacy is protected, federated learning improves intrusion detection and prevention.
## 4 QoS-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETs
### QoS-Aware Multimetric Dynamic Source Routing
Finally, we design a proper tuning function to dynamically update the period \(T_{routing}\) to refresh the set of available paths from which the best path is selected:  
\[T_{routing}^{i+1}=\alpha\cdot NState^{i}+\beta \tag{4}\]  
To obtain the previous equation, a high number of simulations were conducted under a wide range of network conditions where the network performance was _good_, _normal_ or _bad_. For the scenario under consideration used in the present work, the obtained values were \(\alpha=10\) and \(\beta=3\). The idea behind this equation is that when the global network state is in good conditions (i.e., \(NState^{i}\) is high) the current forwarding paths can be used longer (i.e., \(T_{routing}\) increases), whereas when the network state is in bad conditions (i.e., \(NState^{i}\) is low) the forwarding scheme should be updated sooner (i.e., \(T_{routing}\) decreases).
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 5 Explainability in Federated Intrusion Detection
### Explainability as a Trust and Auditing Layer  
The integration of explainability also enhances the trust management layer within FL. By correlating model updates with their explainability profiles, it becomes possible to  
* Identify malicious clients that submit untrustworthy gradients (e.g., poisoned updates with incoherent feature attributions);
* Support reputation scoring in a federated context (clients with consistent, interpretable updates are rated higher);
* Enable regulatory audits and provide post-incident forensics (why was a critical device flagged? What patterns triggered it?);
* Improve the transparency of blockchain-logged updates with attached attribution summaries.  
Accountability becomes not just a usability feature, but a structural component of federated trust and security.  
To highlight the operational and security benefits of integrating explainability into FL systems, Table 3 compares traditional FL models with explainability-enhanced counterparts across several criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **FL Without Explainability** & **FL with Explainability** \\ \hline Transparency & Low & High (via SHAP/LIME, etc.) \\ Model Trustworthiness & Limited & Improved \\ Compliance (e.g., GDPR) & Non-compliant (no rationale) & Yes (interpretability enabled) \\ Resource Overhead & Lower & Moderate (client-side XAI) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of FL systems without explainability integration.  
Figure 10: Explainability integrated into the FL pipeline.  
As shown above, incorporating explainability into FL significantly improves the trustworthiness, compliance readiness, and operational auditability of the system. While it introduces a modest computational overhead, these trade-offs are acceptable in high-stakes environments where understanding model behavior is critical for decision making, incident response, and legal accountability.
## 4 Performance Evaluation  
### Analysis of High-Temperature Performance  
Eight sets of gradation were used to shape the rutting plate, and they were tested through a rutting test at 60 \({}^{\circ}\)C. The rutting test was conducted with reference to the \"Highway Engineering Asphalt and Asphalt Mixture Test Procedures\" (JTG E20-2011). The results are shown in Figure 7. The dynamic stability and the relative deformation rate are shown in Figure 7a, and the ratio \(K\) of the dynamic stability to the relative deformation rate is shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Substitution Ratios**} & \multicolumn{8}{c}{**Mass Percentage (\%) through Sieve Pore (mm)**} \\ \cline{2-10}  & **16** & **13.2** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline
0 & 100.00 & 88.94 & 62.90 & 32.80  
As can be seen from Figure 7a, the dynamic stability (DS) first increased and then decreased as the substitution ratio increased and reached its maximum when the substitution ratio was 1.5. In the process of loading the asphalt mixture, no matter which structure was used, the load transfer path would always choose the large particle size [40]. When the substitution ratio was 1.5, the coarse aggregate was relatively large, the fine aggregate was enough to fill the skeleton, and a good embedded structure could be formed. Thus, the mixture exhibited better high-temperature performance. Since the CRT had higher elasticity, the detected value of the deformation rat depth may have been higher, so it is reasonable to assume that the actual high-temperature stability was better than the experimental value.  
It can be seen from Figure 7a that the relative deformation rate and the trend of the DS were roughly opposite. Therefore, it was considered that the DS could not evaluate the influence of the CRT on the high-temperature performance of the asphalt mixture. The deformation rate was used as an auxiliary evaluation. To synthesize the two indicators, the high-temperature performance of the asphalt mixture was evaluated by the ratio \(K\) of the DS to the relative deformation rate. It can be seen  
Figure 7: High-temperature test results for different mixture types. (**a**) Dynamic stability (DS) and relative deformation rate; (**b**) \(K\).  
in Figure 7b that when the substitution ratio was 1.5 and 0.5, the performance was relatively good. From the perspective of comparative gradation, the gradation of 1.18 mm was higher than that of the 2.36-mm gradation.
# Evaluation on Improvement Zone of Foundation after Dynamic Compaction
## 3 Parametric Studies
### Effect of Drop Number  
The numerical simulation of DC is performed with tamping distance \(S=6.0\) m, drop energy \(MH=4000\) kN\(\cdot\)m, tamper radius \(R=1.2\) m, and blows of 10 at each impact point to investigate the effect of the drop number on the soil improvement. As shown in Figure 7a, with the increase in drop number, it is observed that there is a gradually increasing trend in the improvement zone of DC. Whereas the incremental values generally decrease until about 8-10 blows, when the drop number becomes essentially insignificant for single-point tamping. This implies that there is a \"saturation\" stage in DC. The reason is that the soil is relatively loose in the early few blows, which is beneficial for absorbing drop energy. However, as the soil is compacted gradually, the improvement effect begins to be weakened. Figure 7b shows a similar tendency for multipoint tamping. However, the zone below point 1 in the right part has not changed during the impact at point 2, indicating that the impact at point 2 has almost no effect on the \(I_{\rm r}\) beneath point 1 with \(S=6.0\) m.  
Figure 8a shows that the value of \(I_{\rm r}\) commonly increases as the drop number increases, but the incremental value of \(I_{\rm r}\) generally decreases. The value of \(I_{\rm r}\) at the first six blows increases remarkably, whereas the subsequent blows do not induce an obvious change of \(I_{\rm r}\), which is consistent with the observations of other researchers [17; 18]. Figure 8b shows that the value of \(I_{\rm r}\) below point 3 always increases with the increasing drop number. However, the final value of \(I_{\rm r}\) below point 3 (especially for the upper layer) is still lower than that of point 1, indicating that a relatively weak zone exists between impact points. This finding is
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Optimization of Data and Its Operations
#### 4.1.3 Convolution Optimization
At the same time, it is worth noting that just because of the action mechanism of Im2 col convolution optimization algorithm, the number of elements after Im2 col expansion will be more than the number of elements of the original block. Therefore, optimizing the convolution operation using Im2 col consumes more memory. Therefore, when the Im2 col convolution optimization algorithm is used, memory optimization technology is often used together. In terms of FPGA-based neural network Im2 col convolution optimization, in 2017, [PERSON] et al. [92] used the Im2 col algorithm to optimize the convolution algorithm and then converted the optimized convolution algorithm into a matrix operation, so as to improve the operation speed. In 2020, [PERSON] et al. [93] combined the quantization method with the Im2 col algorithm for visual tasks and designed a dedicated data-stream convolution acceleration under the heterogeneous CPU-FPGA platform PYNQ, including the changed data-stream order and Im2 col for convolution. Compared with ARM CPU, 120\(\times\) acceleration was achieved on FPGA operating at 250 MHz. In 2021, [PERSON] et al. [94] applied the Im2 col algorithm to the FPGA accelerator of homomorphic encryption of private data in order to optimize convolution, which greatly reduced the delay of the convolution algorithm. In October of the same year, [PERSON] et al. [95] combined the Im2 col algorithm with pipeline design and applied it to the lightweight object detection network YOLOV3-Tiny, achieving 24.32 GOPS throughput and 3.36 W power consumption on the Zedboard FPGA platform. In 2022, [PERSON] et al. [96] proposed a smart data stream transformation method (SDST), which is similar to but different from the Im2 col algorithm. Different from the Im2 col algorithm, with the improvement of the peak performance of the GEMM kernel, the overhead caused by the Im2 col algorithm, which converts convolution to matrix multiplication, becomes obvious. SDST divides the input data into conflict-free streams on the basis of the local property of data redundancy, which reduces the external storage of data and keeps the continuity of data. A prototype system based on SDST was implemented on Xilinx ZC706 APSoC (all programmable system-on-chip), and the actual performance of accelerated CNN was evaluated on it. The results showed that SDST was able to significantly reduce the overhead associated with the explicit data conversion of convolutional inputs at each layer. In March of the same year, [PERSON] et al. [97] combined a fixed-point quantization technique with the Im2 col algorithm in order to optimize convolution, reduce temporary memory size and storage delay, perform the application of digital classification of the same CNN on acceleration hardware in the ARM processor and FPGA, and observe the delay time. The results show that the digital classification application executed in the FPGA was 30 times faster than that executed in the ARM processor.
# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
## 4 Experimental Results
### Experiment No. 3: Performances of ML Models Using the \(\chi^{2}\) Feature Selection Module  
In this study, we evaluated the performances of the state-of-the-art models of ML, i.e., NB, KNN, RF, DT, LR, and SVM with different kernels using the \(\chi^{2}\) feature selection module along with the newly proposed model (\(\chi^{2}\_\)RF). A grid search strategy was used to optimize the hyperparameters of the specified ML models. For a fair comparison, we selected balanced classes in the dataset generated by the SMOTE approach. Table 6 shows the results of each ML model along with the newly proposed model (\(\chi^{2}\_\)RF) with selected features (SF) from the dataset and performance evaluation metrics, such as accuracy on training data (ACC\_train), accuracy on test data (ACC\_test), sensitivity, specificity, and the Matthews correlation coefficient (MCC).  
Figure 4: Confusion matrix of the proposed model \(\chi^{2}\_\)RF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SF** & **Acc\_Train** & **Acc\_Test** & **Sensitivity** & **Specificity** & **F1\_Score** & **MCC** \\ \hline
02 & 77.04 & 81.10 & 81.08 & 75.25 & 81.00 & 0.2355 \\
04 & 79.98 & 81.98 & 83.65 & 57.14 & 82.00 & 0.2531 \\
06 & 78.98 & 82.10 & 83.20 & 60.00 & 82.00 & 0.2495 \\
08 & 84.24 & 83.78 & 90.91 & 56.52 & 82.00 & 0.4908 \\
10 & 88.32 & 86.15 & 91.11 & 61.90 & 86.00 & 0.5301 \\
13 & 88.00 & 84.84 & 90.11 & 60.00 & 85.00 & 0.4917 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the proposed model (\(\chi^{2}\_\)RF) on imbalanced data.  
The proposed model (\(\chi^{2}\)-RF) achieved the highest test accuracy of 94.59% compared to the other ML models using the same feature selection module (\(\chi^{2}\)). In addition, we validated the performances of the ML models using the ROC curve. Figure 5 shows the performances of the ML models based on the AUC criterion using the balanced dataset and the \(\chi^{2}\) feature selection module. From Figure 5, it can be seen that SVM achieves the highest AUC of 91.70% compared to the other ML models.
## 4 Experimental results
### Communication costs
network load is therefore of \(7\), i.e., \(6\) receptions and one transmission, to be compared with the highest network load of \(103\) for the root node in the default scheme. This results in a reduction of about \(85\%\) of the network load. Extracting more than \(15\) components leads however the highest network load to be higher than in the default scheme, as the sensor node aggregating the packets from its 6 children will sustain a network load of \(105\) packets per epoch. The overall network load generated by the aggregation of \(k\) components is in the same manner \(k\) times the load generated by of the aggregation of one component. For a communication radius of \(10\) meters, this overall load was of \(103\) packets as reported in Fig. 9, left, to be compared with an overall load of \(466\) packets for the default scheme. The overall load generated by the aggregation process is therefore higher than the load of the default scheme from \(5\) components. This is due to the fact that aggregation, while decreasing the load of the most solicited node, increases evenly the load of all other nodes.
## 3 Methodology
### Optimization Algorithm: Bayesian Optimization  
The TPE is a Bayesian optimization approach proposed by [PERSON] et al. [75]. TPEs are better than other optimization algorithms because (1) they handle high-dimensional hyperparameter search spaces; (2) they converge faster than most optimization algorithms such as meta-heuristic algorithms, grid search, and random search; (3) they are efficient for training computationally expensive machine learning algorithms; and (4) they are easy to implement with different libraries in python. To that end, three predictive models are constructed and cross-validated five times during training to assess the different models' generalization capabilities. The TPE utilizes the Gaussian mixture model to find the best hyperparameter impacting the models [76; 77]. The TPE runs on two main ideas: (1) using Parzen estimators to model the best hyperparameters for the model and (2) using a tree-like data structure known as the posterior-inference graph to optimize the algorithm runtime. Let \(X\) be a tree-structured search space and \(f:X\rightarrow\mathbb{R}\) be an objective function, then the minimization problem is defined as \(\gamma^{*}\in\underset{\gamma\in X}{\text{argmin}}f(\gamma)\). Suppose a set of observations is \(T=\left\{\left(\gamma^{(1)},y^{(1)}\right),\ldots,\left(\gamma^{(n)},y^{(n)}\right)\right\}\), then the TPE replicates \(p(\gamma|y)\) for every parameter \(\gamma(\in X_{i})\) using two probability density functions as follows:  
\[p(\gamma|y)=\begin{cases}l(\gamma)&if\ y<\ y^{*}\\ g(\gamma)&if\ y^{*}\leq y\end{cases} \tag{11}\]  
where \(p(\gamma|y)\) is the conditional probability of the hyperparameter \(\gamma\) and the model loss \(y;l(\gamma)\) establishes the subset of the observed probability density values that are less than \(y^{*}\), and \(g(\gamma)\) establishes the subset of the remaining observed probability density values; the value \(y^{*}\) is chosen to be a quantile \(\lambda\in(0,1)\) of the observed \(y\) values that satisfy \(p(y<y^{*})=\lambda\).  
To choose a particular candidate for evaluation, the TPE employs the expected improvement (\(EI\)) for \(y^{*}\) and the parameter \(\gamma\) as the acquisition function.  
\[\begin{array}{l}EI_{y^{*}}(\gamma)=\int_{-\infty}^{y^{*}}\max(y^{*}-y,0)p(y| \gamma)dy\\ =\int_{-\infty}^{y^{*}}(y^{*}-y)p(y|\gamma)dy\\ \alpha\left(\lambda+\frac{g(\gamma)}{l(\gamma)}(1-\lambda)\right)^{-1}\end{array} \tag{12}\]  
To gain new information with the maximum \(EI\), \(g(\gamma)/l(\gamma)\) is minimized. To keep minimizing \(g(\gamma)/l(\gamma)\), the process is repeated where \(\gamma^{*}\) is returned in the function and fitted again until the hyperparameter optimization is complete. It can be concluded that the probability of \(l(\gamma)\) is as high as possible, while the probability of \(g(\gamma)\) is as low as possible when the improvement is maximized [78].