esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Max-Plus Algebra-Based Wavelet Transforms (MP Wavelets)  
The max-plus algebra [36], works on the set of real numbers \(\mathbb{R}\). This set is expanded by adding an element \(\varepsilon\) such that \(\mathbb{R}_{\varepsilon}=\mathbb{R}\cup\varepsilon\), where \(\varepsilon\) is equal to negative infinity \(-\infty\). The max-plus algebra uses two operations, namely, max-plus addition represented by \(\oplus\) and max-plus multiplication represented by \(\otimes\). When performing max-plus addition on any two elements \(r\) and \(r^{\prime}\) in \(\mathbb{R}_{\varepsilon}\), the result is the maximum of \(r\) and \(r^{\prime}\). On the other hand, max-plus multiplication of \(r\) and \(r^{\prime}\) involves adding the two elements together.  
MP wavelets [23] use the max-plus algebra as the fundamental mathematical basis in their wavelet transform. Initially, the input signal is decomposed into several wavelets through a wavelet filter bank, where each wavelet indicates a distinct frequency constituent of the input signal. Then, the wavelet coefficients are subjected to mathematical operations using max-plus algebra.
## 3 Parameter Selection Experiment
### Selection of Feature Parameters  
#### 3.3.1 First Step Selection  
Firstly, the above six AE parameters are standardized to eliminate the influence of different dimensions on the threshold setting, where the \(h_{max}\) is set as 100, and the \(h_{min}\) is set as 0. Then, all of the values of the parameters for each sample are averaged, and the results are shown in Figure 5. Finally, the tolerance between groups is calculated, and the number of elements smaller than the tolerance threshold in the tolerance vector is obtained. Compare this number with the number threshold; the feature parameters that are greater than the number threshold are discarded. The main parameters in the first step selection are shown in Table 1.  
During the optimization of thresholds, we found that the big \(p_{thr}\) and the small \(r_{thr}\) values can lead to the incorrect deletion of effective parameters, which causes an insufficient input in moisture identification. Similarly, the small \(p_{thr}\) and the big \(r_{thr}\) can lead to the incorrect reservation of invalid parameters, which causes redundant inputs and overfitting. Neither of these cases is conducive to accurate moisture identification. In this experiment, the \(p_{thr}\) and \(r_{thr}\) values were determined to 0.09 and 3, respectively, after the iterations. Table 1 shows that the value of \(r\) in Arrival Time was four, which is bigger than \(r_{thr}\); thus, Arrival Time was discarded in first step selection.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Characteristic**} & \multicolumn{1}{c}{\(\boldsymbol{a}_{j}^{(q)}|_{max}\)} & \(\boldsymbol{a}_{j}^{(q)}|_{min}\) & \(\boldsymbol{a_{1}}\) & \(\boldsymbol{a_{2}}\) & \(\boldsymbol{a_{3}}\) & \(\boldsymbol{a_{4}}\) & \(\boldsymbol{a_{5}}\) & \(\boldsymbol{P_{1}}\) & \(\boldsymbol{P_{2}}\) & \(\boldsymbol{P_{3}}\) & \(\boldsymbol{P_{4}}\) & \(\boldsymbol{r}\) \\ \hline Energy & 50.97 DB & 40.09 DB & 0.95 & 0.79 & 0.60 & 0.38 & 0.16 & 0.16 & 0.19 & 0.22 & 0.22 & 0 \\ Duration & 0.536 ms & 0 ms & 0.91 & 0.51 & 0.25 & 0.15 & 0.05 & 0.40 & 0.26 & 0.10 & 0.10 & 0 \\ Counts Number & 21 & 2 & 0.70 & 0.49 & 0.35 & 0.20 & 0.05 & 0.21 & 0.14 & 0.15 & 0.15 & 0 \\ Rise Time & 0.1683 ms & 0.087 ms & 0.79 & 0.19 & 0.16 & 0.20 & 0.02 & 0.60 & 0.03 & 0.04 & 0.18 & 2 \\ Arrival Time & 0.0901 ms & 0.083 ms & 0.65 & 0.71 & 0.75 & 0.74 & 0.66 & 0.06 & 0.04 & 0.01 & 0.08 & 4 \\ Peak Amplitude & 30.98 DB & 24.70 DB & 0.77 & 0.70 & 0.41 & 0.34 & 0.24 & 0.07 & 0.29 & 0.07 & 0.10 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result of parameters in the first step.  
Figure 5: Mean value of parameters with different moisture contents.
## 5 Evaluation and Performance
### Results and Discussion
#### 5.2.3 Packet Drop Ratio  
Figure 4 depicts the numerical results of the RHCER in comparison to other alternatives in terms of packet drop ratio. The RHCER significantly improved the packet drop rates compared to earlier solutions. The existing method ignored the importance of the transmission link element, resulting in higher drop rates. Our suggested architecture measures signal intensity as well as the remaining energy of nodes throughout data forwarding, resulting in efficient routing performance. Furthermore, the suggested framework enhances the packet delivery ratio by analyzing the multiple criteria-making process and avoiding crowded data routing nodes. Furthermore, the suggested framework includes the distance to and from the BS factor.
## 3 Results and Discussion
Specific EMI SE values of the samples in different frequency bands are obtained and summarized in Table 1. As the frequency gets higher, the difference in shielding effectiveness exhibited by mesh coatings with different thicknesses gradually decreases, indicating that the variation in mesh thickness has little effect on the shielding effectiveness at high frequencies. In contrast to some transparent frequency selective surfaces (FSSs) based on periodic metal mesh, which can provide passbands for partial frequency bands and achieve high shielding effectiveness in narrower frequency bands [44, 45, 46], the metal mesh design in this work mainly emphasizes its stable shielding characteristics within a wide frequency band. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Thickness of Cr/Au**} & \multicolumn{5}{c}{**Average SE (dB)**} \\ \cline{2-7}  & **1.7–2.6** & **2.6–4** & **4–6** & **6–8** & **8–12** & **12–18** \\
**of Cr/Au** & **(GHz)** & **(GHz)** & **(GHz)** & **(GHz)** & **(GHz)** & **(GHz)** \\ \hline
35 nm/285 nm & 26.1 & 23.7 & 21.9 & 24.7 & 25.2 & 20.9 \\
30 nm/260 nm & 25.9 & 21.3 & 20.3 & 23.1 & 23.9 & 20.9 \\
30 nm/230 nm & 25.7 & 20.6 & 19.2 & 22.2 & 23.5 & 20.4 \\
25 nm/205 nm & 22.7 & 21.1 & 19.1 & 22.7 & 23.3 & 20.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of average SE of meshes with varying thickness in each frequency band. Figure 5: EMI shielding performance of metallic meshes with different thickness in varying frequency band. (**a**) 1.7–2.6 GHz. (**b**) 2.6–4 GHz. (**c**) 4–6 GHz. (**d**) 6–8 GHz. (**e**) 8–12 GHz. (**f**) 12–18 GHz. To highlight this feature, a comparison of the variations in SE at different frequencies for representative reported shielding materials based on meshes is shown in Table 2. The mesh shape includes squares [24; 26; 42], hexagons [9], and irregular shapes [23], and the materials cover different metals and conductive oxides [47]. Although some works only provide the shielding performance of the material in one frequency band, one can see that the mesh-based transparent electromagnetic shielding materials generally exhibit significant shielding effectiveness fluctuations over a wide frequency band, especially the difference between high and low frequency bands. In [24], for example, the variation in shielding effectiveness between 4 GHz and 12 GHz for square copper meshes reaches 10 dB. In comparison, the difference in shielding effectiveness between these two frequency points is only 0.6 dB in our design. While the square Ti/Au/Ag mesh presented in [48] is thinner, the variation in shielding effectiveness at 12 GHz and 18 GHz is \(\sim\)6 dB, which is still greater than that of this work. The overall variation in shielding effectiveness over a wide frequency band is small. More importantly, the thickness of the mesh is much thinner, which offers great processing convenience and cost advantages.
## 5 Data Collected and Statistical Analysis
### Water-to-Binder Ratio  
According to the previous studies (Table 5), a statistical study of the water-to-cement ratio (\(w/c\)) for cement mortar reveals a range that extends from 0.3 to 0.74%, with values falling somewhere in the middle. The average is 0.467%, which corresponds to a standard deviation of 0.11 percent, and the coefficient of variation (COV) is around 23.9%. Figure 6 demonstrates that between 0.4 and 0.6% of the entire volume of cement mortar accounts for more than 72 percent of the overall volume. In addition, an examination of the water-to-binder ratio (\(w/b\)) data for lime-modified cement mortars, which was also obtained from the literature (Table 5), reveals that the ratio may vary anywhere from 0.3 to 0.52%, with an average of 0.44%. The coefficient of variation is 17.65%, while the standard deviation is 0.072%.  
Figure 5: Splitting strength for various mixes at age of 28 days.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Country** & **Lime, L (\%)** & _w/c_ & **Curing Time, t** & **Compressive** & **Flexural** & **Strength, \(\sigma_{t}\)** & **Test(s)** \\ \hline
[41] & Canada & 0–10 & 0.33 & 1, 3, 7, and 28 & 29–64 & - & Compressive strength \\ \hline
[42] & Turkey & 0 and 30 & 0.5 & 2, 7, and 28 & 16–60 & 4–11 & Compressive and flexural strengths \\ \hline
[43] & China & 0 and 30 & 0.5 & 3 and 28 & 17–60 & - & Compressive strength \\ \hline
[44] & Ethiopia & 0–35 & 0.5 & 2 and 28 & 6.7–62 & - & Compressive strength \\ \hline
[45] & Italy & 0–20 & 0.6 & 3, 7, and 28 & 3–33 & 2-12 & Compressive and flexural strengths \\ \hline
[46] & China & 0 and 30 & 0.43 & 3, 7, and 28 & 46–65 & - & Compressive strength \\ \hline
[47] & Turkey & 0–15 & 0.5 & 2, 7, and 28 & 9–55 & - & Compressive strength \\ \hline
[48] & China & 0–45 & 0.3–0.5 & 7 and 28 & 18–65 & 4–12 & Compressive and flexural strengths \\ \hline
[49] & Croatia & 0–15 & 0.5 & 3, 7, and 28 & 15–40 & - & Compressive strength \\ \hline
[50] & China & 0–30 & 0.74 & 7 and 28 & 5–32 & - & Compressive strength \\ \hline
[51] & Norway & 0–35 & 0.5 & 1 and 28 & 14–47.5 & 3.4–7.9 & Compressive and flexural strengths \\ \hline
[52] & China & 0–30 & 0.5 & 3, 7, and 28 & 18–54 & 4–11 & Compressive and flexural strengths \\ \hline \multirow{2}{*}{Present study} & \multirow{2}{*}{Iraq} & \multirow{2}{*}{0–50} & \multirow{2}{*}{0.45} & \multirow{2}{*}{7, 28, and 90} & \multirow{2}{*}{12.8–41.1} & \multirow{2}{*}{1.04–4.4} & Compressive, flexural splitting, and direct tensile strength \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: A summary of cement–lime mortar’s mechanical properties from several studies.
# Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase
## 4 Performance Evaluation Framework
### Decomposed Model  
As shown in Section 4.2, the Markov chain \(R_{n}\) that describes the number of active EDs in the RA phase is not ergodic. As a result, the stationary distribution of \(R_{n}\) does not exist, making the numerical analysis impossible. To avoid this, we first propose artificially limiting the maximum states of the system at the RA and DT phases using large values \(R\) and \(D\). These values should be empirically chosen such that packet losses are negligible. Second, we decompose the two-stage service model into separate systems associated with the RA and DT phases. For the considered system, it is feasible because the number of active EDs in the RA phase, \(R_{n}\), is independent of the number of EDs in the DT phase, \(D_{n}\). The resulting Markov chains capturing the dynamics of the RA and DT phases are one-dimensional, making them simpler to analyze.
## 1 Introduction  
In networks which are formed by wireless devices we often lack a global entity to organize the network traffic. This is especially the case in ad hoc networks. So in this setting the devices need to form structures (e.g., communication backbones) by passing information from one to the other. As such networks are often much too large to be entirely known by a single node, we are interested in local algorithms. These are algorithms in which the status of a vertex \(v\) (i.e., whether or not a vertex is part of the dominating set, independent set, etc.) depends only on the vertices which are a constant number of edges (hops) away from \(v\). We require this constant to be independent of the size of the network.  
We model the wireless network with Unit Disk Graphs (UDGs). These are undirected graphs in the plane in which two vertices are connected by an edge if and only if their Euclidean distance is at most one unit. Unit disk graphs are widely used for modeling wireless networks. We assume that each node has knowledge of its geographic position in the plane (location awareness). As positioning systems like GPS become more and more common this assumption seems practical.  
In wireless networks, dominating sets play an important role for topology control. In such networks, nodes are often organized in clusters where one node is declared to be the cluster-head. This vertex is responsible for ensuring the communication of the nodes in the cluster with other nodes in the network. So the cluster-heads form a dominating set in the underlying network graph. In order to be able to send messages from one cluster to another, one needs the set of dominators to be connected. This leads to a connected dominating set. For organizing the network as efficiently as possible we are looking for small (connected) dominating sets. In order to give the cluster-heads more control, it can be advantageous if they are not adjacent to each other [1]. So in this case they form a maximal independent set. Another important problem in graphs is the minimum vertex cover problem.  
Even though several algorithms have been designed to solve these tasks, very little is known about lower bounds for approximation ratios of local algorithms. Such bounds would allow us to understand the prospects and limits of local algorithms. In particular, we want to comprehend the restrictions imposed on algorithms when a certain locality distance is required. In this paper we address this issue.
## 6 Experiments and Results
### Experiments and Results Using Lexicase Selection  
All settings were kept consistent with the TS method, and the first experiment was conducted with a population size of 1000 across 30 generations, repeated over 30 runs. This resulted in a training and test success rate of 09/30, taking an execution time of 57 min 42 s, as shown in Table 2. Since this is not an acceptable success rate, we increased the number of generations to 100. This adjustment led to a success rate of 17/30, with an execution time of 178 min and 41 s, which is approximately three times higher than the TS method under the same generation setting, as seen in Table 2. Despite this improvement, the graph  
Figure 6: Error bar graphs representing the mean of the maximum fitness values of the individuals across experiments run with the mentioned population size and number of generations using TS.  
still displayed large error bars and a lack of saturation, leading to a further increase in the number of generations to 500. This change resulted in a success rate of 29/30, taking an execution time of 867 min and 56 s, again significantly outperforming the TS method under identical hyperparameter settings. No further experiments were conducted because this success rate was nearly perfect. The resulting graph showed the mean of the maximum fitness value approaching saturation, with consistently narrowing error bars.  
It can be seen that in evolving FSMs, where multiple diverse and sometimes conflicting TCs must be satisfied, LS outperforms TS by preserving individuals that solve specific difficult cases. Unlike TS, which favours generalist individuals with good average performance and risks losing useful specialists early, LS evaluates performance case-by-case, maintaining a richer diversity of FSM behaviours. This diversity is crucial for complex FSM evolution, where different transitions and outputs may be optimised at different stages. As a result, LS enables better exploration and often leads to higher-quality FSM solutions.  
Incorporating both LS and TS, the proposed method is highly scalable to larger and more complex FSMs. However, scaling may require greater computational and evolutionary resources. To adapt this methodology for larger FSMs, modifications to the grammar, training dataset, and testbench would be necessary. For example, expanding the current FSM to support more than two products would require a complete restructuring of the dataset and FSM, alongside corresponding updates to the grammar and testbench. Future work will focus on scaling this methodology to enable the evolution of more complex and larger FSMs.
# Chapter Detection in Robotic Milling Using Entropy Features
## 2 Theoretical Basis and Framework for the Chatter Detection System
### Weighted Refined Composite Multiscale Dispersion Entropy (wRCMDE)  
#### 2.2.1 Dispersion Entropy (DisEnt)  
Considering the given time series \(\{u(i),\,i=1,\,2,\,3,\ldots,\,L\}\) with length, \(L\), _DisEnt_ can be calculated as follows:  
Step 1: Each element, \(u(i)\), is mapped into \(c\) classes. To be more specific, \(u(i)\) is first normalized to \(\{y(i),\,i=1,\,2,\,3,\ldots,\,L\},y(i)\in[0,\,1]\) using the normal cumulative distribution function, and \(y(i)\) is transformed into an integer from \(1\) to \(c\) using:  
\[z_{i}^{c}=round(c\times y(i)+0.5) \tag{5}\]  
Step 2: Defining embedding vectors as:  
\[\mathbf{z}_{i}^{m,c}=\Big{\{}z_{i}^{c},\ z_{i+\tau}^{c},\ \ldots,\ z_{i+(m-1)\tau}^{c} \Big{\}}i=1,\ 2,\ \ldots,L-(m-1)\tau \tag{6}\]  
where \(m\) is the embedding dimension and \(\tau\) is the time delay. Each embedding vector corresponds to a dispersion pattern, \(\pi_{v_{0}v_{1}\ldots v_{m-1}}\), where \(v_{0}=z_{i}^{c},v_{1}=z_{i+\tau}^{c}\ \cdots\,\)\(v_{m-1}=z_{i+(m-1)\tau}^{c}\). The total number of possible dispersion patterns is \(c^{m}\).  
Step 3: The relative probability of \(\pi_{v_{0}v_{1}\ldots v_{m-1}}\) is calculated by:  
\[p\big{(}\pi_{v_{0}v_{1}\ldots v_{m-1}}\big{)}=\frac{\{i|i\leq L-(m-1)\tau, \mathbf{z}_{i}^{m,c}\text{ has type }\pi_{v_{0}v_{1}\ldots v_{m-1}}\}}{L-(m-1)\tau} \tag{7}\]Step 4: _DisEn_ can be calculated by:  
\[DisEn(u,m,c,\tau)=-\sum\limits_{\pi=1}^{c^{m}}p(\pi_{v_{0}v_{1}\ldots v_{m-1}}) \ln(p(\pi_{v_{0}v_{1}\ldots v_{m-1}})) \tag{8}\]  
_DisEn_ is a statistical measurement that reflects the complexity of the time series. In general, the more complexity a time series has, the larger the _DisEn_ value.
## 3 Results
### Analysis of Classification Results
#### 3.2.3 Recognition Rate of Different Feature Combinations  
From Table 1 and Figure 4, it can be seen that each spore has its own color, shape, and texture features. In order to analyze the effects of the color, shape, and texture features on the recognition rate of spores, the classification results for the different feature combinations under the SVM model are shown in Figure 6.  
We can see from Figure 6 that the recognition rates for spores based on color; shape; texture; color and shape; color and texture; shape and texture; and fusion features were 45.86%, 79.62%, 71.45%, 82.84%, 75.36%, 86.18%, and 94.36%, respectively. The shape feature had the greatest influence on the recognition of spores. The color feature had the least influence on the recognition of spores. However, the recognition rates for fusion features were higher than those for other feature groups at 48.5, 14.74, 22.91, 11.52, 19, and 8.18 percentage points higher than for color; shape; texture; color and shape; color and texture; and shape and texture feature groups, respectively.  
Figure 6: Classification results for different feature combinations under the SVM model.
## 3 Materials and Methods
### Data Sampling  
A dataset with unequal class distribution is technically defined as imbalanced data [46]. There are several strategies for imbalanced data problems. The sampling-based approach is one of the most effective methods. The artificial sampling-based approach is divided into three categories: over sampling, under sampling, and hybrid sampling.  
The focus is on increasing the number of minority class examples to enhance classification performance, without regard for the majority class. Random Over Sampling (ROS) is based on the logic of increasing the size of the dataset by replicating the original samples [47]. The Synthetic Minority Over-sampling Technique (SMOTE) generates samples based on the distance between each data (usually using Euclidean distance) and the nearest neighbors of the minority class, ensuring that the generated samples differ from the original minority class [48]. The Adaptive Synthetic Sampling Approach (ADASYN) is based on the adaptive generation of minority data samples based on their distribution [49].  
Under sampling aims to balance the majority and minority classes by reducing the size of the dominant classes. Random Under Sampling (RUS) aims to achieve balanced data samples by randomly discarding some samples from the majority class [50]. The Edited Nearest Neighbors (ENN) methodology employs \(k\)-nearest neighbors to identify the neighbors of the targeted class samples. It removes the observations if any or most of its neighbors belong to a different class [51]. Tomek's links form a Tomek link when two samples from different classes are the nearest neighbors. The underlying principle is that Tomek's links are noisy or difficult-to-classify observations, which do not aid the algorithm in establishing an appropriate separation boundary [52].  
[PERSON]'s extended algorithm for addressing data imbalances is known as SMOTE (Synthetic Minority Over-sampling Technique). Essentially, the SMOTE algorithm creates new samples by randomly interpolating linearly between a small number of samples and the samples that are nearby counterparts. In order to improve the unbalanced dataset's classification effect, a specified number of false minority samples are created, increasing the data imbalance ratio. The following outlines the precise SMOTE method [48].  
**Step 1.** For each minority sample \(x_{i}(i=1,2,3,\ldots,n)\), calculate the distance to other samples in the minority sample according to certain rules to obtain its \(k\) nearest neighbors.  
**Step 2.** According to the over-sampling magnification, the random \(m\) nearest neighbors of each sample \(x_{i}\), as a subset of the \(k\) nearest neighbors set, are selected and denoted as \(x_{ij}(i=1,2,3,\ldots,m)\), then an artificially constructed minority sample \(p_{ij}\) is calculated by Equation (2).  
\[p_{ij}=x_{i}+rand(0,1)x\left(x_{ij}-x_{i}\right) \tag{2}\]  
where \(rand(0,1)\) is a random number uniformly distributed within the range of [0, 1]. The operation of formula 2 is stopped until the fused data reaches a certain imbalance ratio.