esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Network Model  
After comprehensive analysis and study of the characteristics of the ECG signals, this paper designed the multi-channel CNN-LSTM model structure as shown in Figure 2. The input layer was the extracted heartbeat signals after preprocessing, and the adjacent five heartbeats (e.g., 3 s of ECG data) were selected and input into five identical CNN channels to obtain the feature map, which was concatenated into the LSTM network to acquire the temporal characteristics between signals. Finally, the temporal characteristics were output to the fully-connected layer for classification. The network structure had 16 layers including nine convolutional layers, two max-pooling layers, one global average pooling layer, one dropout layer, one LSTM layer, one flatten layer, and one fully-connected layer.  
1. Convolutional layers: since the input ECG signals were one-dimensional signals, the selected numbers of filters of the one-dimensional convolutional layer were 4, 8, and 16, respectively; the convolution kernel size was 5 and the stride was 1; the specific parameter setting process is described in Section 4. Meanwhile, batch normalization was utilized to ensure that the input distribution of each layer of the neural network was the same, and ReLU function was applied as the activation function. Compared with the sigmoid function and tanh function, the ReLU function converges faster and alleviates the over-fitting problem. ReLU activation function can be expressed as Equation (10): \[f(x)=\left\{\begin{array}{l}0,x<0\\ x,x\geq 0\end{array}\right.\] (10)
2. Pooling layers: this paper used the max-pooling (kernel size was 5) in the fourth and eighth layers of the network, decreased the dimensionality of the calculated characteristic parameters, and retained the significant features to accelerate the calculation. The tenth layer used the global average pooling to reduce the extraction of potential features and obtain the calculation result of the convolutional network.
3. Dropout layer: the dropout layer was applied between the global average pooling layer and the LSTM layer to achieve stronger generalization capability by randomly invalidating some network nodes.
4. LSTM layer: after the convolutional network as described above, due to the strong temporal correlation of the ECG signals, we connected a layer of the LSTM network to obtain the temporal characteristics in the output features of the convolutional network.
5. Flatten layer: we converted the multi-dimensional output of the LSTM network into a one-dimensional output.  
Figure 2: The network structure diagram.  
(6) Fully-connected layer: the features after all processes were input to the fully-connected layer for classification, and the classifier was Softmax.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
###### Abstract  
As the idea of a new wireless communication standard (5G) started to circulate around the world, there was much speculation regarding its performance, making it necessary to carry out further research by keeping in view the challenges presented by it. 5G is considered a multi-system support network due to its ability to provide benefits to vertical industries. Due to the wide range of devices and applications, it is essential to provide support for massively interconnected devices. Network slicing has emerged as the key technology to meet the requirements of the communications network. In this paper, we present a review of the latest achievements of 5G network slicing by comparing the architecture of The Next Generation Mobile Network Alliance's (NGMN's) and 5G-PPP, using the enabling technologies software-defined networking (SDN) and network function virtualization (NFV). We then review and discuss machine learning (ML) techniques and their integration with network slicing for beyond 5G networks and elaborate on how ML techniques can be useful for mobility prediction and resource management. Lastly, we propose the use case of network slicing based on ML techniques in a smart seaport environment, which will help to manage the resources more efficiently.  
5G; NGMN; 5G-PPP; SDN; NFV; NS; ML; RRM
# MAC Address Anonymization for Crowd Counting
## 2 Results
### Requirement 1: Impossibility to Recover the Original SA from SA Identifiers  
Cryptographic hash functions such as SHA-256 cannot be directly reversed; in practice, reversing consists of trying inputs until finding one whose hash is the output to be reversed. It is possible for an attacker to know the input MAC address of a particular entry in the list of anonymized PRs; for example, an attacker may go near sensors and send fake PRs with precise timing patterns that make it easy to identify them. In this case, brute forcing the pepper entails testing many of the 256-bit sequences that exist (on average, half of them should be tested). Attackers usually perform this operation using graphical processing units (GPUs), field-programmable gate arrays (FPGAs), or, if they have large resources, application-specific integrated circuits (ASICs). Let us examine if this attack is feasible with GPUs.  
For example, 1 million Nvidia RTX 2080 SUPER Founders Edition graphics cards can compute roughly 5700 SHA-256 terahashes per second [17]; this implies that testing all 256-bit peppers (approximately \(1.16\times 10^{65}\) terahashes) takes \(2.04\times 10^{61}\) s, i.e., \(6.47\times 10^{53}\) years. Should one of the two 128-bit peppers be known to an attacker, testing all 128-bit sequences still takes roughly \(1.90\times 10^{15}\) years. We point out that relying on a regular SHA-256 hash function without peppers is not safe (see [9; 12] and ([11], Section VI)) as the entropy of MAC addresses is too low to resist brute force attacks. We also highlight that using computationally intensive hashes like _bcrypt_[18] and _Argon2_[19] would imply unreasonable computational requirements for sensors (see also ([9], Section 5)).
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 5 Nomological Deductive Reasoning (NDR): A Proposed Solution
### NDR Framework Architecture and Operational Integration with Machine Learning
#### 5.2.4 Inference Flow and Operationalization  
The following step-by-step flow describes the full inference pipeline from the raw input to the final recommendation:  
1. **Input**: For example, in a bank loan application scenario, applicant data are provided as structured records and constitute the system input.
2. **ML Feature Extraction**: A trained model (e.g., Random Forest) identifies the most influential features. These features are passed as symbolic assertions onto the knowledge layer.
3. **Symbolic Assertion Mapping**: Features are translated into logical predicates (e.g., SavingsLow(Applicant123)).
4. **Deductive Inference**: The reasoning engine applies the rules using forward chaining (modus ponens) to derive intermediate-risk indicators and final recommendations.
5. **Explanation Generation**: Activated rules are converted into natural-language templates.
6. **Output**: A classification decision (e.g., \"High Credit Risk\") with justification is presented to the user.  
The flowchart of the process is presented in Figure 8.  
Figure 8: Flowchart diagram of NDR-based explanations.
# An Analysis of Differential Evolution Population Size
## 8 Summary Discussion and Implications
### Implications for Practitioners
For instance, [PERSON] and [PERSON] [97] used empirical data and applied linear regression to formulate an equation for determining the optimal population size, and polynomial regression for other DE control parameters in a real-world problem. Additionally, the study by [PERSON] et al. [114] utilized an artificial neural network (ANN) to establish connections between DE performance and control parameter values, extracting the optimal values for control parameters, including the population size. While these studies provide advanced approaches, the current study offers a more comprehensive experimental analysis. Practitioners can gain further insight by combining the findings of this study with advanced approaches to achieve insightful population size tuning for the DE algorithm. Future research could also explore the integration of these advanced approaches to enhance DE performance across diverse problem domains. 10. **Reassess setting the initial population size for DE strategies with adaptive population**: Practitioners are advised to pay special attention when designing and configuring DE strategies with adaptive populations. The experimental results presented in this study suggest that increasing the population size beyond a threshold limit hampers DE performance. Moreover, the interactions between DE control parameters highlight the need for careful tuning, particularly the interaction between \(NP\) and \(CR\) for small population sizes.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification
### Experimental Verification
Subsequently, an infrared camera operating in the 8-14 \(\upmu\)m range is used to capture images, and the resulting temperature data are shown in Figure 23. It can be observed that compared to FSR, IRSL-FSR significantly reduces the structure's infrared emissivity. The infrared emissivity can be simulated by Equation (22), where \(T_{r}\), \(T_{a}\) and \(T_{0}\), respectively, represent the temperature displayed in the IR image, the ambient temperature (25 \({}^{\circ}\)C), and the actual temperature (40 \({}^{\circ}\)C). \[e_{IRSL}=\frac{T_{r}^{4}-T_{a}^{4}}{T_{0}^{4}-T_{a}^{4}} \tag{22}\]  
According to Figure 23 and Equation (22), the calculated emissivity of IRSL-FSR from testing is 0.26, which differs slightly from the calculated result of 0.23 in Section 2.3. This is due to uneven heating and the resistance welding of FSR. The calculated emissivity of FSR is 0.62; thus, the test results have validated that the design in this paper can effectively reduce the infrared emissivity of the structure. Finally, a comparison between our designed structure and existing research findings was conducted, as is shown in Table 1. It was discovered that our design slightly improves the absorption bandwidth while greatly expanding the transmission bandwidth, all while maintaining the original low infrared emissivity. Additionally, even with the  
Figure 23: The IR imaging results at 40\({}^{\circ}\)C  
Figure 22: S-parameters comparison between measured and simulated FSR (**a**) and IRSL-FSR (**b**). inclusion of IRSL, the structure designed in this paper still maintains a relatively minimal relative thickness.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 6 Results
### Resource Mapping  
Table 5 shows that providing examples in the prompt increases the resource mapping accuracy, especially with basic and CoT prompting techniques. We also observe more diversity in the results with few-shot prompting settings, particularly at lower temperatures. Notably, in some situations (e.g., Mistral with few-shot prompting), very high accuracy results are observed with the CoT prompting technique (above 7.5). Additionally, Gemini and Mistral tend to have similar resource mapping accuracy across all prompt techniques and temperature levels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Hallucination**} \\ \hline \multirow{2}{*}{**Clinic**} & \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Prompt Tech.**} & \multicolumn{2}{c}{**Zero-Shot**} & \multicolumn{2}{c}{**Few-Shot**} \\ \cline{3-6}  & & & **Low** & **High** & **Low** & **High** \\ \hline \multirow{4}{*}{Skin Care} & \multirow{2}{*}{Gemini} & Basic & 9.45 (0.93) & 9.82 (0.6) & 7.45 (2.5) & 5.55 (2.34) \\ \cline{3-6}  & & Two-Step & 3.64 (0.92) & 3.55 (0.82) & 3.36 (1.21) & 4.18 (1.66) \\ \cline{3-6}  & & CoT & 6.73 (3.2) & 6.55 (3.05) & 5.27 (2.57) & 4.27 (1.68) \\ \cline{2-6}  & \multirow{2}{*}{Mistral} & Basic & \(\times\) & 10.0 (0.0) & 6.25 (2.89) & 3.73 (1.27) \\ \cline{3-6}  & & Two-Step & 4.0 (1.48) & 4.7 (2.16) & 5.88 (3.33) & 4.11 (1.05) \\ \cline{3-6}  & & CoT & 7.36 (3.38) & 8.27 (2.65) & 3.73 (1.35) & 3.64 (1.36) \\ \hline \multirow{4}{*}{Mammography} & \multirow{2}{*}{Gemini} & Basic & 9.78 (0.67) & 9.78 (0.67) & 5.56 (2.24) & 6.44 (2.51) \\ \cline{3-6}  & & Two-Step & 3.44 (1.24) & 3.67 (1.12) & 3.33 (1.41) & 3.11 (1.27) \\ \cline{3-6}  & & CoT & 7.44 (3.54) & 7.22 (2.82) & 4.33 (1.41) & 5.11 (2.03) \\ \cline{2-6}  & \multirow{2}{*}{Mistral} & Basic & \(\times\) & \(\times\) & 5.86 (2.93) & 4.56 (2.3) \\ \cline{3-6}  & & Two-Step & 3.78 (1.56) & 4.56 (1.74) & 4.86 (3.61) & 4.33 (1.66) \\ \cline{3-6}  & & CoT & 6.78 (3.15) & 6.1 (3.48) & 4.25 (1.04) & 3.89 (1.45) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average and standard deviation of hallucination rates for different clinics, models, prompt techniques, and example presence at various temperatures. Color coding reflects value ranges: 1–2.5 (green), 2.5–5 (olive green), 5–7.5 (orange), and 7.5–10 (red). Standard deviations are shown in parentheses. Lower values are ideal and \(\times\) indicates no valid conversion.  
These observations underscore the importance of careful prompt engineering and temperature control in optimizing the conversion of unstructured clinical data to structured FHIR formats. By understanding these factors, we can better design prompts and adjust parameters to enhance the accuracy and reliability of the converted data.
## 2 Survey for Outdoor Agricultural UAV Applications
### Irrigation  
Sensitive irrigation applications are an area of concern for the whole world in terms of water consumption. The fact that 70% of the water consumed worldwide is used to irrigate crops highlights the importance of precision irrigation [40]. A UAV equipped with spectral and thermal cameras can identify places where water is lacking and intelligently deliver water to these areas. Image processing and artificial intelligence algorithms with different imaging techniques ensure that water is used at the required place and in sufficient amounts. Soil morphology obtained by UAV enables these applications, and water waste is prevented [3]. Irrigation applications can be carried out similarly to spraying applications by loading water instead of pesticide. In future smart farming applications, it is anticipated that a collaborative irrigation system with UAVs, unmanned ground vehicles (UGVs) or herd robots will be implemented [24]. Figure 3 is given as an example of a study aimed at irrigation with a UAV. [PERSON] et al. [41] used thermal cameras for early detection of water stress in crops and detected areas with water deficiency with the adaptive crop water stress index (CWSI) method.
# Microscopic Multiple Fatigue Crack Simulation and Macroscopic Damage Evolution of Concrete Beam
## 2 Microscopic Multiple Crack Simulation and Fatigue Crack Growth Law
### Modeling of Multiple Crack Growth  
When a number of microcracks start to propagate simultaneously, induced by external fatigue load, the morphology of crack linking with each other may be very complex. However, it could be decomposed into three basic modes. As shown in Figure 3a, the two endpoints of Crack 1 and 2 meet together such that the two cracks merge into a single crack. Figure 3b shows Crack 4 is growing intersecting with Crack 3. In this situation, stress singularity at the intersected endpoint of Crack 4 vanishes--this crack tip will stop growing. Therefore, the situation of Figure 3c will not happen in the present work, though it could be simulated as well.  
In the present study, a program has been developed comprehensively to achieve all crack growing situations, including detecting and treating crack merging and intersecting, multiple crack growth, and detecting boundary edges [32]. Presented in the highlighted area of Figure 2 is a relatively complicated situation of crack growth and multiple crack linking. It could be seen that the method could successfully simulate the concurrent growth of microcracks, crack coalescence, the formulation and growth of macroracks, and the final rupture. Details of the approach can be found in [31; 32].  
Figure 1: Schematic of single crack growth in concrete. (**a**) Schematic of concrete material; (**b**) growth of single microcrack.  
Figure 3: Basic modes of crack linking. (**a**) meeting; (**b**) merging; (**c**) intersecting.  
Figure 2: Multiple microcrack growth and linking: an example.  
On the basis of the formulated multiple crack simulation, we are mainly focused on the fatigue crack growth in concrete materials.
## 3 Cross-Sectional Failure Area of the Fatigue Wire  
When a pair of cylinders with parallel axes make contact and are subjected to pressure, the line contact changes to surface contact, and its contact surface is a narrow rectangle [23]. In the test between the wire and the groove in this paper, the groove can be regarded as a cylinder with an infinite radius. Therefore, the wear marks of the fatigue wire are theoretically narrow rectangles. As shown in Figure 2, the cross-section of the wear zone  
Figure 1: Structural diagram of the test rig.  
is approximately arched; the wear surface is a narrow rectangle. The wear depth and the cross-sectional failure area of the wear zone are calculated directly from the geometric relationship between the wear mark width and the wire diameter (Figure 2c). The shaded area is the failure area of the worn wire. \(L\) = AB, which is the width of the wear mark, \(r\) = OA = OB, which is the wire radius; \(\theta\) is the angle between OB and OC. From the geometric relations, \(\theta\ =\ \arcsin\frac{L}{2r}\), failure area \(A^{\prime\prime}\ =\ r\theta^{2}-\frac{1}{2}L\cos\theta\). As shown in Tables 1 and 2, as the number of fatigues increases, so does the fatigue wire wear failure area. Furthermore, as the contact load and fatigue load peak increase, so does the wear failure area. This is due to the fact that as the contact load increases, so does the contact stress between the contact surfaces. The axial stress of the fatigue wire increases with increasing fatigue load. The plastic deformation of the material becomes more severe. Meanwhile, the dissolution of the material is aggravated by the action of the corrosion solution. Therefore, the cross-sectional failure area of the fatigue wire increases, and the wear is intensified.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{2}{c}{**N = 1 \(\times\) 10\({}^{5}\)**} & \multicolumn{2}{c}{**N = 2 \(\times\) 10\({}^{5}\)**} & \multicolumn{2}{c}{**N = 3 \(\times\) 10\({}^{5}\)**} & \multicolumn{2}{c}{**N = 4 \(\times\) 10\({}^{5}\)**} & \multicolumn{2}{c}{**N = 5 \(\times\) 10\({}^{5}\)**} \\ \hline \multirow{3}{*}{Contact load (N)} & 60 & 0.0023 & 0.0037 & 0.006 & 0.0086 & 0.0124 \\  & 80 & 0.0027 & 0.0045 & 0.0068 & 0.0101 & 0.0142 \\  & 100 & 0.0033 & 0.0053 & 0.0085 & 0.0127 & 0.0173 \\ \hline \multirow{3}{*}{Fatigue load peak (N)} & 1100 & 0.0023 & 0.0037 & 0.006 & 0.0086 & 0.0124 \\  & 1200 & 0.0025 & 0.0044 & 0.0074 & 0.012 & 0.0182 \\ \cline{1-1}  & 1300 & 0.0027 & 0.0052 & 0.0088 & 0.0139 & 0.0198 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-sectional failure area of fatigue wire (pair: groove) under different contact and fatigue loads.  
Figure 2: Cross-section diagram (**a**), wear mark profile (**b**), and schematic diagram of failure area (**c**) of the worn steel wire.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 5 Results of Attack-Specific Two-Class Models
### Exposure to Unseen Data
#### 5.2.2 Layer-7 Denial of Service  
CSE-CIC-IDS2018 contains two days of denial of service attacks. The first of which has malicious packets generated by tools such as slowloris or HULK that abuse web servers by exhausting their resources. The second day contains traffic exclusively from exploiting the Heartbleed vulnerability on the affected implementation of OpenSSL (1.0.1-1.0.1f). CIC-IDS2017 bundles both types of attacks in a single day, using the same tooling. Because the attacks exist in one day in the 2017-version, the attack types got squashed into binary classification. It is a good use-case to test whether these attacks should be treated as the same category or not.  
All tree-based methods overfit heavily, as they did on the brute force traffic. Good performance is only ever recorded for models that had very little data available to train on. Making matters worse is the inconsistency with which these results occur. In numerical terms, recall-precision pairs above 60% are very rare for any of the pretrained models. Once more, the worst results are obtained on models that had normalized features.  
Nearest-neighbors has balanced accuracy scores consistently falling in the 70-80% range. Changes in scaling, training volume or feature reduction do not significantly alter this result. It is not good enough to be considered. Nearest-centroids separates the classes worse, indicated by the balanced accuracy of 50-60%. The only results that are better than chance were observed when using normalized features. Higher training volume or less feature reduction, do not affect the results.  
The logistic regression models trained on min-max-scaled features follow the pattern that the section introduction put forward. Great generalization performance, with a stable, straightforward relationship between training volume and classification metrics (Figure 4). Those metrics are a stable 97.5% recall, paired with 70-75% precision yielding a total of 97.9% balanced accuracy (5 features removed). This amount of class separation is enough to recommend the classifier as a genuine method to classify unseen layer-7 DoS traffic. A linear support vector classifier or rbf-kernel SVM (with the same parameters) have nearly identical results. All models in the other category perform poorly if the features were standardized.