esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection
### The CBA-CLSVE Framework for Intrusion Detection
#### 4.2.2 Fitness Function Definition  
The Detection Rate (_DR_) and False Positive Rate (_FPR_), also called the False Alarm Rate (_FAR_), are the most commonly used indicators to evaluate the performance of IDSs. The optimization problem proposed in this paper is a multi-objective optimization problem that involves the two measures mentioned above. Multi-objective optimization can be transformed into single-objective optimization by designing a weighted fitness function. An ideal intrusion-detection model will have a higher Detection Rate and a lower False Positive Rate. Hence, a weighted fitness function _Fit_, shown below, can be proposed:  
\[\textit{Maximize Fit}=w_{1}\times DR+w_{2}\times(1-\textit{FPR}) \tag{15}\]  
where \(w_{1}\) and \(w_{2}\) represents the weights for the Detection Rate and the False Positive Rate. A higher fitness _Fit_ means better intrusion-detection performance.  
The Class-Level Soft-Voting Ensemble is shown in Algorithm 1. The proposed Class-Level Soft-Voting Ensemble scheme based on the Chaos Bat Algorithm for intrusion detection can be described in Algorithm 2.  
```
Input: the parameters used for the base learners, training dataset with labels, the class-level weight Output: the confusion matrix or other evaluation indicators.
1. for each base learner do
2. probabilityMatrix = learner. predict_proba(train_data)
3. for each row do
4. probabilityMatrix[row] = weight \(\times\) probabilityMatrix[row]
5. endfor
6. endfor
7. predict_label = argmax\(\sum_{t=1}^{T}\) probabilityMatrix
8. get the confusion matrix = confusion_matrix(train_label, predict_label) or other indicators
```  
**Algorithm 1**The Class-Level Soft-Voting Ensemble  
```
Input: the parameters used for the base learners and the BA, training and testing dataset with labels Output: the final result of the proposed model.
1. The SelectKBest function is used to select appropriated features
2. Training:
3. Initialize the bat's population using the chaos and opposition-based learning strategy
4. Compute the initial fitness of each bat to find the best search agent X*
5. Initialize the value of the chaotic map x0 randomly
6. for each iteration do
7. Update the chaotic variable using the sinusoidal map
8. for each individual do
9. Update the positions and other important metrics of the individual
10. Update the fitness = ensemble_model (train_data, train_label, the position)
11. Find the best individual based on their fitness values
12. endfor
13. endfor
14. return the optimal position, that is, the class-level weights to create the ensemble
15. Testing:
16. testing accuracy, confuse matrix = ensemble_model (test_data, test_label, the optimal weight)
```  
**Algorithm 2**CBA-CLSVE
# A Polarization Independent Quasi-TEM Metamaterial Absorber for X and Ku Band Sensing Applications
## 3 Absorption and Pressure Sensor Results and Discussion
### Quasi-TEM Polarization Independence Regarding Maxwell Equations  
For microstrip lines or patches, the TEM properties have some slightly propagation differences. As we know, TEM has transverse characteristics of EM wave, but as for a general microstrip, the EM wave propagates through the air above the top pattern and the dielectric substrate. As a result, in two different media having different resistivity the wave propagates at different speeds in both the regions.  
Figure 5: Surface current distribution of proposed MM absorber (**a**) 13.78 GHz, (**b**) 15.3 GHz.  
This is referred to as quasi-TEM mode. Conventionally, the EM wave propagation as depicted in Equation (7) can be represented in regarding voltage and current like:  
\[\left.\begin{array}{l}\frac{d^{2}V}{dz^{2}}-\gamma^{2}=0\\ \frac{d^{2}I}{dz^{2}}-\gamma^{2}=0\end{array}\right\} \tag{10}\]  
where, \(\gamma\) is dependent on \(\varepsilon\), \(\mu\), \(\omega\), and \(\sigma\) as shown in Equation (6). On the contrary, the propagation constant \(\gamma=\sqrt{Z^{\prime}Y^{\prime}}\) as the wave propagation is characterized by a traveling wave. Here, \(Z^{\prime}\) and \(Y^{\prime}\) are the transmission line normalized impedance and admittance of the line per unit length, respectively. Now, if we observe the surface current distribution in Figure 5, the inner and the outer resonator have contours in adjacent fields. That is why Equations (6) and (10) have a slight variation in expression which can be approximated as [42]  
\[\frac{dE_{x}}{dz}=-Z^{\prime}H_{y}=-j\omega\mu H_{y} \tag{11a}\] \[\frac{dH_{y}}{dz}=-Y^{\prime}E_{x}=-j\omega\varepsilon E_{x} \tag{11b}\]  
so, the value of impedance and admittance can be further modified using the circuit diagram shown in Figure 1a and if the transmission line is not so long, propagation of a wave can be treated as quasi-TEM. In Figure 6 the absorption of this quasi-TEM propagation of proposed MM absorber is shown.
## 3 JPRL-Based Joint Resource Allocation Approach
### Bandwidth Allocation of MADDQN  
Bandwidth allocation is a non-convex problem with discrete space; there are finite choices. The size of the action space grows exponentially with the number of users. Therefore, a MADDQN algorithm with centralized training was presented to achieve sufficient exploration of the actions, which had good performance in large-scale discrete action spaces.  
A MADDQN model consists of a target Q network and an evaluated Q network, which creates a copy of neural network for the two networks, respectively. For multiple agents, an arbitrary agent taking actions to improve its performance could lead to the degradation of the overall performance as the agents are interacting with each other. Therefore, the effect of mutual synergy between agents cannot be ignored. A centralized training architecture, to this end, denotes a joint action-state function \(Q_{\text{sum}}^{b}\) that composes the action-state functions from different agents to promote cooperation between agents. The concrete formula is defined as  
\[Q_{\text{sum}}^{b}\left(\mathcal{S}_{t},\mathcal{A}_{t}^{b}\right)=\sum_{k=1} ^{K}Q_{k}^{b}(s_{k}(t),a_{k}^{b}(t)\mid\omega), \tag{9}\]  
where \(\omega\) is the parameter of the evaluated Q network. \(Q_{k}^{b}\) is the \(k\)-th user's action-state function based on its own state. In the training phase, the joint action-state function is used for back propagation to promote cooperation, and a mini-batch sample is randomly sampled from the replay memory \(\mathcal{D}1\) that stores the states, actions, next states, and rewards  
Figure 4: Flow chart of the JPRL method.  
of all the agents (note that all the agents have the same reward value) to minimize the loss function, which is written as  
\[L=\underset{\left(\mathcal{S}_{t},\mathcal{A}_{t}^{b},\mathcal{S}_{t+1},R_{t} \right)\sim\mathcal{D}1}{\mathbb{E}}[\left(Y_{t}-Q_{\text{sum}}^{b}\left( \mathcal{S}_{t},\mathcal{A}_{t}^{b}\mid\omega\right)\right)^{2}], \tag{10}\]  
where \(\mathbb{E}[\bullet]\) denotes the mathematical expectation and  
\[Y_{t}=R_{t}+\gamma_{1}\underset{\mathcal{A}}{\text{argmax}}Q_{\text{sum}}^{b} \left(\mathcal{S}_{t+1},\mathcal{A}_{t+1}^{b}\mid\omega\right); \tag{11}\]  
\(\gamma_{1}\) is the discount ratio. For each agent, the soft updating is given by  
\[\omega^{\prime}\longleftarrow\eta\omega+(1-\eta)\omega^{\prime},\eta\in(0, 1), \tag{12}\]  
where \(\omega^{\prime}\) are the parameters (including the weights and biases) of target Q network.  
In the multi-cell multi-user system, the MADDQN model of agent \(k\) chooses the bandwidth assignment action according to its own state \(s_{k}(t)\) in step \(t\). Note that the agents can share their past training process (state, the influence based on training). Then, all the agents are centralized trained to minimize the loss value by \(Q_{\text{sum}}^{b}\).
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 2 Materials and Methods  
### Data Collection  
The study area is the Cap-Bon region, located in North East Tunisia (36\({}^{\circ}\)27\({}^{\prime}\) N, 10\({}^{\circ}\)44\({}^{\prime}\) E) (Figure 1). The climate of the study area is Mediterranean semi-arid with an average annual precipitation of 600 mm and annual reference evapotranspiration estimated using FAO-56 Pemman-Monteith exceeding 1100 mm. This semi-arid region of Tunisia covers the administrative region called the Governorate of Nabeul. It occupies 2.825 km\({}^{2}\), and it is characterized by a great variety of landscapes.  
Figure 1: Location map of Cap-Bon region.  
In the current study, a valid and adequate daily data series of maximum (Tmax) and minimum (Tmin) air temperature and precipitation (P) were collected from the National Meteorological Institute (INM) for the period of 1982-2020. The consistency of the databases was checked, and a graphical analysis of the daily time series was performed. Despite the overall quality of the used data being fairly good, there were some missing data. These gaps were filled with the corresponding average value of the other years. Then, the datasets were aggregated into monthly seasonal and annual time scales and investigated for homogeneity and trend detection analysis.
# Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment
## Methods
### Clustering Stability and Determining the Preferred Number of Clusters: The Stopping Criterion  
The clustering process is completed using the parabolic approximation-estimation test described above, which estimates the jumps of a monotonically increasing sequence of \"trend set\" values. The magnitude of the significant jump sufficient to stop the process depends on the sensitivity of the stopping criterion, which is set using the non-negative coefficient \(q\)[15; 16]. The higher the value of \(q\), the lower the criterion's sensitivity for stopping the clustering process. The stopping criterion has the highest sensitivity at \(q=0\), in this case, as a result of clustering, the most significant number of clusters will be obtained. By increasing \(q\), the stopping criterion's sensitivity can be reduced so that the process continues until all \(m\) vectors are combined into one cluster. In this case, intervals of stable clustering \(Q_{i}=[\alpha_{i},\beta_{i}]\) will occur on which for \(\forall\ q\ \mid\ a_{i}\leq q\leq\beta_{i}\) the same clustering results will be obtained.  
Cluster analysis, in a sense, has a high degree of subjectivity. Therefore, the interpretation of its results largely depends on the researcher. So far, no rigorous definition of \"sustainable/stable clustering\" has been introduced; the scholars only speak of an intuitive concept. They argue that \"clustering stability\" shows how different the resulting partitions into equivalence classes become after using the clustering algorithms for the same data many times. A slight discrepancy between the results is interpreted as high stability [25].  
In our case, a quantitative measure of stability of clustering can be considered the value of the interval of variation of the coefficient \(q\), within which the same result is obtained for the set \(X\). Here, we note again that the \"chain effect\" arises at the final stage of the clustering process when already-formed clusters are added one after another to some other cluster. In this case, the correct choice of sensitivity threshold for the stopping criterion on the account of the non-negative coefficient \(q\) is essential. In the general case, the sequence of intervals of stable clustering, for various values of the coefficient \(q\), is denoted by: \(Q_{1}\), \(Q_{2}\),..., \(Q_{e-2}\), \(Q_{e-1}\), \(Q_{e}\), where \(Q_{i}\) (\(1\leq i\leq e-1\)) is the interval of stable clustering, and \(Q_{e}\) is the set of values of the coefficient \(q\), in which all \(m\) points are combined into one cluster.  
Clustering with Markov stopping time allows for automation of the procedure for determining the number of clusters in the text corpus. Based on the analysis of numerical experiments and general considerations, the following hypothesis was formulated earlier: \"Preferred a number of clusters is formed at \(q\in Q_{e-2}\)\" [16]. The main motive for formulating this hypothesis was that a chain effect is manifested in the interval of stable clustering \(Q_{e-1}\), at which already formed clusters are combined.
## 4 Training and Detection Using Deep Learning
### Training Model
#### 4.3.1 Loss Function in Training  
The loss function used in training comprises classification, detection, and segmentation losses (Equation (4)). The cross entropy of the real and predicted values of the object was used to calculate classification loss. Further, the real and predicted values of the four vertex coordinates of the rectangular box were used to calculate detection loss using the smooth-L1 formula. Whereas, the binary cross entropy of the mask's real and predicted values was used to calculate the segmentation loss. Furthermore, in each training, each loss value was summed to calculate the final loss value. The unit of the function loss is non-dimensional. In addition, the training quality of the model is determined by observing the decline of the loss curves later.  
\[L=L_{class}+L_{Bbox}+L_{mask} \tag{4}\]  
where \(L\) is training loss, \(L_{Bbox}\) is the loss of detection loss, \(L_{class}\) is classification loss, and \(L_{mask}\) is segmentation loss.
## 2 Materials and Methods
### Prediction and Optimisation Models  
#### 2.3.1 Rsm  
RSM is a fundamental regression modelling technique that employs regression and probabilistic studies to delineate an approximate and explicit functional relationship between input random variables and output responses [47]. Minimal computing work and implementing a randomised and unbiased experimental design can significantly diminish superfluous experimental labour, conserving both the time and financial resources associated with the experiment. Figure 2 illustrates the schematic diagram of the RSM model, whereas Equation (17) delineates the functional relationship between input parameters and output variables in RSM.  
\[y=\alpha_{0}+\sum_{i=1}^{N}\alpha_{i}x_{i}+\sum_{i=1}^{N}\alpha_{ii}x_{i}^{2}+ \sum_{i<j}\sum\alpha_{ijx_{i}x_{j}} \tag{17}\]  
In this context, \(y\) represents the response variable, \(x_{i}\) signifies the levels of the input parameter, \(\alpha_{0}\) indicates the bias, \(\alpha_{i}\) represents the linear effect, \(\alpha_{ii}\) defines the squared effect of the components, and \(\alpha_{ij}\) refers to the interaction effect between the influences.  
This work employed the BBD method of RSM for the experimental design. BBD is a commonly used experimental design method in response surface methodology. It is particularly suited to situations where there is a need to explore how three or more factors affect one or more response variables. BBD uses three levels of each factor (usually denoted \(-1\), \(0\), \(1\)), which allows it to fit a quadratic model and thus capture nonlinear effects. BBD conducts the experiments at the centre level of each pair of factors, varying only two factors at a time while keeping the other factors at their centre level. This design avoids trials under extreme conditions and helps reduce costs and time. The experimental data were  
Figure 2: Schematic representation of the RSM model.  
processed and analysed using Design-Expert software version 11.1.0.1, and the fit of the RSM model to the data was evaluated by analysis of variance (ANOVA) to ascertain the extent of influence of the independent factors on the dependent variable.
## 2 Problem Formulation
### Group-Oriented Structured Clustering Problem  
Clustering is essentially a swarm partition problem. On the one hand, too many clusters will increase the communication delay and cost between different clusters of the UAV swarm. On the other hand, UAVs performing the same task frequently exchange information and should be divided into one cluster. In addition, considering the mobility of UAVs, gathering UAVs with high communication link correlation into one cluster can make the clustering structure more stable. Based on the above analysis, we define the group-oriented structured clustering problem of the UAV swarm as follows.  
**Definition 1** (Group-oriented structured clustering problem).: _Given the UAV swarm \(\mathcal{N}\) with task group \(\mathcal{G}\), finding the clustering structure that divides \(N\) UAVs of the swarm into \(K\) disjoint clusters so that the following goals can be realized under the premise of satisfying the constraints in Section 2.1: Goal 1: The number of UAVs in one cluster should achieve as high a maximum as possible. Goal 2: UAVs in the same task group should be in one cluster. Goal 3: The clustering structure should have high total intra-cluster communication link correlation._  
**Remark 1**.: _The total inter-cluster communication link correlation is the smallest, and the corresponding total intra-cluster communication link correlation is the largest._  
Figure 2: The diagram of the relative movement between two UAVs.
## 5 Potential Challenges
### Challenges in Resource Allocation  
Creating a system with optimal resource allocation is difficult in a real-time scenario. The DL models should be trained using data relevant to users in a dynamic context. It is important to consider the computing expense to train neural networks in relation to network convergence, neurons, learning rate, and the number of hidden layers. The additional difficulties in the DL models may include fitting problems, e.g., underfitting and overfitting.
## 3 Results and Discussion
### Reaction Products
[54] observed the carbonation rate to decline with rising activator concentration in calcium-high alkali-activated systems (such as metakaolin + slag) due to the more intense carbonate salt precipitation generated by the interaction between the highly alkaline solution and the CO\({}_{2}\) in the air. Carbonate salt precipitation would lower the pore volume in the matrix, hindering additional CO\({}_{2}\) ingress in the sample and carbonation progression [54]. As noted earlier, further to the XRD and SEM data, the 6 and 8 M NaOH-activated pastes, rather than developing high calcite contents, precipitated sodium and sodium-calcium carbonates such as natron and para-alumohydrocalcite/hemicrabouluminate. Briefly, the rise in the degree of reaction with higher activator concentration, proven by the appearance of progressively more compact matrices in the pastes studied (Figure 5), would explain the higher compressive strength values in the 6 and 8 M than in the 4 M NaOH-activated pastes. The higher two and 28 days strength in the pastes studied here, than by other authors, was associated with a higher degree of reaction over time and the formation of gels with greater silicon and calcium contents. There are different factors that affect the development of mechanical strength such as: total porosity, size and porous distribution, degree of reaction, type and morphology of the reaction product, structure and composition of cementitious gels, etc. All these aspects are important and depend on the reactivity of the precursor and the alkaline solution used and the curing procedure. In the literature, there are already works that use molecular dynamics simulations to evaluate the fracture toughness of C-S-H [55] techniques. In this work, we have focused on the chemical aspects (instead of the physical characteristics). In addition, the high SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio and the co-precipitation of C-A-S-H and (N,C)-A-S-H gels observed in all the pastes studied might explain the high strength of our materials (higher than the ones reported for studies using common low-calcium clays) [5; 22; 23; 24; 25].
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Real Data Experiments  
#### 4.2.1 Experiment Settings  
The method is further evaluated by a real camera and flash LiDAR system as shown in Figure 2b. The camera is fixed on a precise rotation stage, and the flash LiDAR is fixed on a precise motorized linear stage. The parameters of flash LiDAR are provided by the manufacturers, as shown in Table 2. The horizontal resolution of flash LiDAR is \(50^{\circ}/320=0.156^{\circ}\), and the vertical resolution is \(40^{\circ}/240=0.167^{\circ}\). We can acquire \(320\times 240=76,800\) points. The resolution of camera is \(1280\times 1024\) and the size of cell is \(20\) um \(\times\)\(20\) um. The triangular pyramid used in this experiment is about \(1\) m in base and about \(0.4\) m in height, whose size need not be known. The size of the chessboard cell is \(0.05\) m \(\times\)\(0.05\) m.
# IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
## 6 Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rounds** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Accuracy & 98.26\% & 99.04\% & 99.75\% & 99.95\% & 99.99\% & 100.0\% & 100.0\% & 100.0\% \\ Precision & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ Recall & 1.26\% & 45.42\% & 85.74\% & 97.00\% & 99.39\% & 99.87\% & 99.98\% & 99.99\% \\ Accuracy’ & 98.23\% & 98.19\% & 98.18\% & 98.19\% & 98.18\% & 98.18\% & 98.19\% & 98.19\% \\ Times (s) & 10.49 & 17.21 & 23.90 & 29.75 & 35.29 & 40.62 & 45.71 & 51.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluate the impact of parameter _Rounds_ on experimental results (\(AQ_{10}\), \(Ratio=30\%\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dimension (n)** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Accuracy & 98.65\% & 98.83\% & 99.12\% & 99.39\% & 99.60\% & 99.75\% & 99.85\% & 99.91\% \\ Precision & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ Recall & 94.62\% & 92.53\% & 90.61\% & 88.80\% & 87.24\% & 85.74\% & 84.30\% & 82.99\% \\ Accuracy’ & 64.65\% & 80.15\% & 89.04\% & 93.99\% & 96.70\% & 98.18\% & 99.01\% & 99.46\% \\ Times (s) & 4.20 & 5.88 & 8.04 & 11.04 & 16.02 & 23.90 & 37.74 & 63.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of the algorithm IFDA under \(AQ_{nt}\) (rounds = 3, Ratio = 80%). Finally, the simulation results shown in Table 5 evaluate the impact of the probability of intermittent faulty nodes exhibiting failures on the experimental results. The data from Table 5 are illustrated in Figure 4(b). In this experiment, the dimension of the augmented cube is set to 10, and the number of testing rounds between nodes is set to 3. Because of the relatively large base of fault-free nodes in the system, the parameters Accuracy and Accuracy' exhibit minimal changes. Meanwhile, the value of Precision remains consistently at 100%. As the ratio parameter increases, intermittent faulty nodes have a higher probability of exhibiting failure states, leading to a gradual increase in the Recall parameter. The experimental results demonstrate that the probability of intermittent faulty nodes exhibiting failures significantly influences the diagnostic outcomes of the algorithm.