esa-sceva/satcom-chunk-collection huggingface dataset:
# Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
## 5 Discussion
The application of fuzzy logic and machine learning techniques for the proposed future IBN is depicted in Figure 34.  
Thus, in the future, network automation based on controllers and machine learning algorithms, supported by SDN, and an even more comprehensive vision of IBN can improve the effectiveness of digital businesses. In the future, IBN information technology will allow automation of the management of all network domains, including campuses, branches, WAN, Internet of Things, 5G, and big data, providing a significantly new level of automation, increasing the efficiency of service, speed of innovation launch, and reliability of network infrastructure. Figure 33: Decision points to change the network configuration. For effective implementation of centralized network management, it is necessary to take into account some interesting aspects that should be included at the beginning of planning in order not to make the network dangerous or inaccessible, such as:  
* The availability of a controller is the main aspect to consider. A strong relationship between network devices and the controller can be a problem when rules need to be changed. Moreover, if networks are designed with only one centralized controller in mind, it can be a \"single point of failure\". A distributed approach can be implemented to ensure availability and prevent potential unwanted failures. In addition, a dedicated redundancy or backup solution can be used to ensure reliability. * The security is important as well. A controller is a component with critical knowledge of the network and this dimension subjects the controller to possible attacks and threats. In addition, the channels between the controller and the switches can be vulnerable. According to the OpenFlow specification, it is possible to use secure communication via the TLS protocol, but its use depends on the network design. * Scalability of the network also depends on the controller, which can potentially become a bottleneck. If too many packets are delivered to the controller, the network may experience performance problems. This means that it is important to take into account the distribution of the control plane to avoid these unwanted problems. We believe that the future Intent-Based Networking using IBN/SDN controllers can be a critical step in the evolution of networking technologies, helping companies optimize network operations and increase availability. Obviously, the transition to a new technology will take time, but we can expect that the concept of IBN will gradually replace the traditional approach and will be implemented in an increasing number of corporate networks.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Implementation of Explainable AI Techniques: SHAP, LIME, and DiCE
#### 5.4.1 SHAP-Global Behavior Analysis  
The SHAP global plot identifies the features with the greatest influence on classifying an instance as either an attack or normal, providing insights into the model's overall behavior. Figure 8 visualizes the impact of each feature on the model's predictions across all instances, with blue representing Class 1 (attack) and red representing Class 0 (normal). The balanced distribution of blue and red lines for each feature indicates that the model considers both attack and normal instances, showing that its decision-making process is influenced by patterns in both classes.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture
### Amazon's System  
In 2019, Amazon announced their LEO mega constellation of satellites Kuiper, having their filing in an approved state [33]. The set-up of this constellation will be made in 5 phases, launching 3236 satellites. They have not yet launched any satellites, planning their first launch for the end of 2022.  
The elevation of the antennas, which sizes from 1 m to 2.4 m, will be no less than 20 degrees, being unable to access Kuiper's network below these angles. Each satellite will have access to two different gateway earth stations, reducing interferences and achieving the throughput of the system. These gateways will use the K-band in both uplink (27.5-30 GHz) and downlink (17.7-20.2 GHz) [33].
## 4 Methodology and Experimental Results
### Results for Classification Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flesch–Kincaid + Auto. Read. + Linear Formula + Diff. Words + Polly. Count** & & & & \\ \hline
**Classifier** & **CCI** & **ICI** & **Pre** & **Re** & **F1** \\ \hline NB & 57.9 & 42.1 & 0.588 & 0.579 & 0.568 \\ NB-K & 59.675 & 40.325 & 0.597 & 0.597 & 0.597 \\ \hline NB-D & **59.7** & **40.3** & **0.597** & **0.597** & **0.597** \\ \hline J48 & 58.15 & 41.85 & 0.582 & 0.582 & 0.581 \\ \hline J48-C 0.05 & 58.8875 & 41.1125 & 0.589 & 0.589 & 0.588 \\ \hline J48 -U & 57.425 & 42.575 & 0.575 & 0.574 & 0.573 \\ \hline Ilk & 41.375 & 51.662 & 0.42 & 0.41 & 0.41 \\ \hline Ilk-K 13 & 54.75 & 45.25 & 0.548 & 0.548 & 0.547 \\ \hline Ilk-K 71 & 58.725 & 41.275 & 0.590 & 0.587 & 0.584 \\ \hline Ilk-K 551 & 59.975 & 40.025 & 0.604 & 0.600 & 0.596 \\ \hline Ilk-K 3501 & 59.5875 & 40.4125 & 0.596 & 0.596 & 0.596 \\ \hline SMO & 58.8625 & 41.1375 & 0.592 & 0.589 & 0.584 \\ \hline Random Forest & 44.125 & 58.875 & 0.441 & 0.441 & 0.441 \\ \hline Random Forest-P 25 & 49.95 & 50.05 & 0.499 & 0.500 & 0.499 \\ \hline Random Forest-P 1 & 58.8625 & 41.1375 & 0.589 & 0.589 & 0.588 \\ \hline Random Forest-P 1 -10,000 & 59.525 & 40.475 & 0.597 & 0.595 & 0.594 \\ \hline Decision Table & 59.1 & 40.9 & 0.591 & 0.591 & 0.591 \\ \hline Simple Logistic & 59.5125 & 40.4875 & 0.596 & 0.595 & 0.594 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from WEKA using 10-fold cross-validation and Flesch–Kincaid + Automated Readability Index + Linear Write Formula + Difficult Words + Polysyllable Count. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Information Gain Ranking Filter Results**} \\ \hline
**Entropy** & **Feature Number** & **Feature** \\ \hline
0.03611 & 6 & Automated Readability Index \\ \hline
0.03583 & 4 & Gunning Fog \\ \hline
0.03571 & 9 & Spache Readability \\ \hline
0.03486 & 2 & Flesch–Kincaid Grade Level \\ \hline
0.03247 & 18 & School Grade \\ \hline
0.03237 & 8 & Linear Write Formula \\ \hline
0.02712 & 1 & [PERSON] Reading Base \\ \hline
0.02334 & 10 & McAlpine EFLAW \\ \hline
0.02191 & 17 & Difficult Words \\ \hline
0.02144 & 5 & Coleman–Liau Index \\ \hline
0.01825 & 14 & Polysyllable Count \\ \hline
0.01714 & 7 & Dale–Chall Readability Score \\ \hline
0.01597 & 11 & Reading Time \\ \hline
0.01572 & 12 & Syllable Count \\ \hline
0.0103 & 15 & Word Count \\ \hline
0.00822 & 16 & Mini-Word Count \\ \hline
0.00272 & 13 & Monosyllable Count \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feature ranking results after ranking the information gain algorithm through the WEKA environment. The final order of the selected attributes is 6, 4, 9, 2, 18, 8, 1, 10, 17, 5, 14, 7, 11, 12, 15, 16, and 13.
## 6 Conclusions  
In this paper, we prove that the secret key can be selected from the error distribution \(D_{g}\) with small samples and variable parameters. A public key encryption scheme with less noise is proposed. In addition, a new key switching technology is proposed. The noise generated by the key switching is controlled by \"modulus scaling\", which does not depend on the circular security assumption and generates a smaller evaluation key. It can effectively solve the problem of key switching on RLWE, sacrificing security for efficiency. The result shows that the evaluation key size of our scheme is less than that of the MB18 scheme. We construct a shorter key batch FHE scheme for RLWE with key switching technology and packing technology using the polynomial CRT, and give the noise analysis and security proof in detail. Finally, the parameter setting process of the scheme and the parameter comparison of the FV12 and the MB18 schemes are given. The result shows that the parameter size of our scheme is better than that of the MB18 scheme. When the security is improved, the evaluation key size of our scheme is slightly larger than that of the FV12 scheme.  
Conceptualization: Y.C. and R.H.; methodology, Y.C.; validation, Y.C., R.H. and B.Y.; formal analysis, B.Y.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation Project of China under Grant No. 62062009 and the Guangxi Innovation-Driven Development Project under Grant Nos. AA17204058-17 and AA18118047-7.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{L}\) & \(\mathbf{0}\) & \(\mathbf{1}\) & \(\mathbf{5}\) & \(\mathbf{10}\) & \(\mathbf{15}\) \\ \hline \(d\) & 1024 & 2048 & 8192 & 16,384 & 32,768 \\ \(\log q\) & 11 & 27 & 91 & 183 & 225 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of degree \(d\) and modulus \(q\) at the different circuit depths.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme** & **Plaintexts** & **Ciphertext** & **Public Key** & **Secret Key** & **Evaluation Key** \\ \hline
**FV12** & \(\text{O}(1)\) & \(2 dlogq\) & \(2 dlogq\) & \((d+1)logq\) & \(2l\cdot d\cdot logq\) \\
**MB18** & \(\text{O}(n)\) & \(2d\log q\) & \(2d\log q\) & \((L+1)(d+1)\log q\) & \(2l\cdot d\cdot L\cdot\log q\) \\
**Our scheme** & \(\text{O}(n)\) & \(2d\log q\) & \(2d\log q\) & \((L+1)(d+1)\) & \(6 dL\cdot\log q\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A scheme parameter size comparison.
## 3 Results and Discussion
### Basic Chemical Composition, and Physicochemical and Microbiological Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Product (P)**} & \multicolumn{3}{c}{**Ripening Period (Week) (RP)**} & \multicolumn{3}{c}{**Interaction**} \\ \cline{2-9}
**Characteristics** & **Pul** & **ZS** & **0** & **2** & **4** & **Source of Variance** \\ \cline{2-9}  & **M** & **M** & **M** & **M** & **M** & **P** & **RP** & **P** \(\times\) RP \\ \hline Water (\%) & 72.15a & 71.02b & 74.29a & 72.19b & 69.12c & * & * & * \\ Protein (\%) & 18.83a & 20.63b & 18.19a & 18.79a & 20.85b & * & * & ns \\ Fat (\%) & 4.99a & 4.53b & 3.43a & 4.77b & 6.44c & * & * & * \\ Ash (\%) & 3.02a & 4.04b & 1.31a & 3.96b & 4.55c & * & * & * \\ Salt (\%) & 1.89a & 1.70b & 0.42a & 2.47b & 2.64c & * & * & * \\ pH & 5.52a & 5.69b & 5.69a & 5.68a & 5.33b & * & * & * \\ a\({}_{\rm w}\) & 0.97a & 0.96b & 0.98a & 0.96b & 0.95c & * & * & * \\ \hline \end{tabular}  
* Pul—Pulawska; ZS—Zlotnicka Spotted; M—mean;
* F values of interaction significant at \(p<0.05\), \(n=9\); ns—not significant; a, b, c—values in rows with different letters differ significantly at \(p<0.05\), \(n=9\).  
\end{table} 
Table 1: The least-squares means from two-factor analysis of variance of the basic chemical composition and physicochemical characteristics in relation to the type of dry-cured ham and the ripening period.
## 2 Materials and Methods
### Research Methods
#### 2.2.2 Information Acquisition of the Walnut Planting Area
In addition to the spectral features, textural features and terrain features are added to build an image feature set, in order to improve the classification accuracy of ground objects in Ganquan Town and better extract the walnut planting area. This research selects DEM data drawn by SRTM (shuttle radar topography mission) as data for building the topographic features of the research area, with a spatial resolution of 30 m. Different ground objects have different textural information, and textural features reflect the distribution characteristics of image pixels. In summary, classification and recognition can be conducted according to the textural information of the research object [27]. The methods commonly used to extract texture features are as follows: gray-level co-occurrence matrix, local binary patterns, Gabor transform feature extraction, etc. This study builds textural features with a gray-level co-occurrence matrix (GLCM), and the calculation function of the gray-level co-occurrence matrix [28] is provided by the GEE cloud platform. To avoid redundancy, this study selects four types of texture information with low correlations for calculations [29; 30]:  
Figure 3: Time series NDVI changes in various ground objects within the research area. Figure 2: Monitoring process of walnut growth. angular second moment (ASM), inverse difference moment (IDM), contrast ratio (CON), and correlation (CORR). The calculation formula is as follows:  
\[ASM=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}M(i,j)^{2} \tag{2}\]  
\[IDM=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}\frac{M(i,j)}{1+\left(i-j\right)^{2}} \tag{3}\]  
\[CON=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}M(i,j)(i-j)^{2} \tag{4}\]  
\[CORR=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}\frac{\left(i-\mu\right)\left(j-\mu \right)M(i,j)^{2}}{\varepsilon} \tag{5}\]  
\[\mu=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}M(i,j) \tag{6}\]  
\[\varepsilon=\sum_{i=0}^{k-1}\sum_{j=0}^{k-1}M(i,j)\times\left(i-\mu\right)^{2} \tag{7}\]  
where \(k\) is the gray level of the original image, \(\mathrm{M}\left(i,j\right)\) is the gray-level co-occurrence matrix generated based on the original image, \(\mu\) is the mean value, and \(\varepsilon\) is the variance. With respect to the selection of classification methods, based on the GEE cloud platform, this study classifies remote sensing images in the research area with the random forest algorithm, and extracts the planting area of walnuts in the study area from 2017 to 2021. There are multiple decision trees in the random forest algorithm (RF), and each decision tree is a classifier. When a sample is inputted, any tree in the random forest will generate a corresponding classification result; the random forest algorithm collects the classification results of all subtrees and takes the category with the most covered trees as the final outputted result [14].
## 3 The Obtained Data and Their Discussion
### Mechanisms of Pressing and Sintering Processes  
For the pressing process, the addition of B4C nanoparticles increases the density after compacting because of the condensation process during the compacting process, as shown in Figure 4a. Afterward, all nanoparticles will close together owing to the increase in pressing force. For the sintering process, necking will be created between all nanoparticles of raw powders and the additive nanoparticles owing to the welding between them, which then causes shrinkage of the samples and increases the density of the sintered samples [38]. The temperature of the sintering process will create strong bindings at the interfaces between aluminum particles and the additive nanoparticles and then enhance the mechanical characteristics such as young modulus, ultimate strength, and micro-hardness. Therefore, binding forces have an extremely negative effect on the wear behavior of nanocomposites, and then increase the dislocations by interlocking and forming loops close to the reinforcing nanoparticles, which inhibit the movement of the dislocations [33]. The increment in the concentrations of B4C reinforcing nanoparticles will increase the density of manufactured nanocomposites, as illustrated in Figure 4a. This leads to an increase in the wettability at the interfaces between the B4C nanoparticles and Al particles. Moreover, much porosity can be formed at the interfacial regions of TiC nanoparticles and Al particles, decreasing the density of Al/TiC nanocomposites, as shown in Figure 4b. On the other hand, the porosity decreases with increasing B4C and increases  
Figure 3: (a) XRD for Al/B4C Nanocomposite. (b) XRD for Al/TiC Nanocomposite.  
with increasing TiC, and then the porosity of Al/B4C nanocomposites is lower than for Al/TiC, as demonstrated in Figure 5.
## 1 Introduction  
One of the main functions of the human intestinal microbiota is to ferment indigestible dietary fiber in the large intestine. The products of this fermentation process are short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate [1]. SCFAs can either be excreted in the feces or taken up by the gut epithelium to participate in a variety of physiologic processes. Uptake of SCFAs involves poorly selective anion-transporting proteins expressed by the gut epithelium, whose purpose is to maximize the amount of SCFAs absorbed from the lumen [2]. Butyrate, in particular, serves as the primary energy source for colonocytes [3]. Other molecules enter the portal circulation where they can be metabolized by the liver, or released into the systemic circulation where they can bind to SCFA receptors in the vascular epithelium or afferent arterioles and alter cardiometabolic health [4].  
Several lines of evidence suggest that SCFAs may be beneficial for cardiometabolic health. In vitro experiments have demonstrated that butyrate plays a key role in the maintenance of gut-barrier function by preserving luminal anaerbiosis through the stabilization of specific transcription factors, assembly of tight junction proteins, and mucin secretion [5; 6; 7]. By strengthening the intestinal barrier, butyrate may block the translocation of lipopolysaccharide-acrylamide (LPS)--a potent inflammatory molecule produced in the cell membrane of Gram-negative bacteria--which has been associated with metabolic endotoxemia, inflammation, insulin resistance, adiposity, and hepatic fat [8]. SCFAs may also be involved in appetite regulation [9], and play a role in gut and peripheral immune responses [10]; inducing the differentiation of T-regulatory cells [11].  
Yet not all evidence supports a beneficial role for SCFAs. An estimated 5-10% of daily calories come from the oxidation of SCFAs, and their metabolites can be utilized for de novo lipid and glucose synthesis [12]. Correspondingly, murine models and epidemiologic studies have consistently shown that higher fecal SCFA concentrations are positively associated with body weight [13; 14; 15], and increase with calorie-rich diets [16]. Conversely, lower fecal SCFA levels have been associated with leanness [15]. Moreover, metagenomic studies of obesity have shown that the obese human gut microbiome is enriched for pathways involved in microbial processing of carbohydrates (e.g., phosphotransferase systems), as well as in genes related to SCFA production (e.g., acetyl/propionyl-CoA carboxylase and acetyl-CoA carboxylase) [17].  
In summary, in vitro experiments have demonstrated beneficial physiologic effects of SCFAs, but human studies suggest that fecal SCFA concentrations are associated with excess adiposity. Given this incongruent evidence, we aimed to examine associations of fecal microbiota and SCFA concentrations with gut permeability, adiposity, and cardiometabolic health outcomes in a community-based sample of male and female adults.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework
### Reducing the Model Size
However, the time required increases rapidly with the problem size, as expected, reaching about 140 s when the number of constraints is about 20,000, which is obtained when \(N=80\), \(T=3\), \(F=5\), \(S^{\text{max}}=4\), and \(B^{\text{max}}=160\), where \(S^{\text{max}}\) is the maximum number of node antennas. Although the use of the \(Q_{lf}\) value can reduce the problem size to some extent, the high complexity of some variables and constraints still limits the scalability of the model. To alleviate this, we may need to further restrict the range of variables, or to relax some restrictions. For example, constraint (33) is originally imposed because the MU-MIMO receiver can theoretically handle interfering streams coming into the same channel, but for this transmission to actually be successful, a process for obtaining the channel information for the interfering nodes is required, which creates an excessive overhead [30]. Hence, rather than just focusing on maximizing the DoF, the allocation of potential interference streams to different channels would be more practical and plausible. Thus far, we have assumed that a single entity (e.g., central controller) solves the whole optimization problem, which might be infeasible for large-scale networks, but this assumption could be relaxed in several ways. One approach is to use local optimization with multiple dedicated nodes. We can further control the complexity by limiting the number of nodes or flows that each of them must serve. In this case, it is difficult to obtain global optimization, but this approach may be more appropriate to deal with dynamic network changes. Another method is to replace it with heuristic algorithms that separate coupled optimization elements so that a near-optimal solution can be obtained with low complexity, as adopted in many studies. We leave further study of this aspect to future work. ```
1:\(Q_{lf}\leftarrow\emptyset\)
2:\(G\leftarrow(N,L)\)
3:for\(f\in F\)do
4:\(V\gets f^{\text{dst}},f^{\text{src}}\)\(\triangleright\)\(V\) keeps track of the visited nodes. 5:\(P\leftarrow\emptyset\)\(\triangleright\)\(P\) is the path traced so far
6: DFS\((f,G,V,P,Q_{lf})\)
7:endfor
8:return \(Q_{lf}\)
9:function DFS\((f,G,V,P,Q_{lf})\)
10:\(i\gets V(end)\)\(\triangleright\)\(V(end)\) is the last visited node, while \(V(1)\) is the destination of \(f\). 11:for\(l\in L_{l}^{\text{dst}}\)do
12:if\(l^{\text{dst}}==V(1)\)then
13:\(Q_{lf}\gets 1\)
14:\(ret\gets 1\)
15:else
16:if\(l^{\text{dst}}\
otin V\) && \(|P|<h\)then\(\triangleright\)\(h\) is the maximum number of hops. 17:\(V\gets V+l^{\text{dst}}\)
18:\(P\gets P+l\)
19:\((ret^{tmp},Q_{lf})\leftarrow\text{DFS}(f,G,V,P,Q_{lf})\)
20:\(Q_{lf}\gets ret^{tmp}\)
21:\(ret\gets ret^{tmp}||ret\)
22:\(V\gets V-l^{\text{dst}}\)
23:\(P\gets P-l\)
24:endif
25:endif
26:endfor
27:return \(ret\), \(Q_{lf}\)
28:endfunction
```  
**Algorithm 1**\(Q_{lf}\) Computation.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 1 Introduction
### Outline and Notations  
Section 2 presents the main concepts of an RS network, including the UL channel estimation and the payload transmission and reception. Section 3 formalizes the UE power allocation problem, which is modeled as a resource allocation optimization problem where both the previously mentioned metrics are combined into a bi-objective approach. A detailed description of the DE MH and the MO methods are also presented. Section 4 includes the simulation results and critical analysis. Finally, Section 5 draws the main conclusions.  
Throughout this paper the following notation will be employed: bold lettering (e.g., \(\mathbf{s}_{n}\)) is used to denote a vector of samples at the time domain, while non-bold lettering (e.g., \(s_{n}\)) are used to denoted the samples of those vectors, respectively. \(\lceil.\rceil\) is the ceiling-taking operator and the superscripts \(\mathbf{A}^{T}\), \(\mathbf{A}^{*}\) and \(\mathbf{A}^{H}\) denote transpose, complex-conjugate, and Hermitian of matrix \(\mathbf{A}\), respectively. Additionally, \(\text{tr}(\mathbf{A})\) and \(\text{diag}(\mathbf{A})\) denote the traceand diagonal elements of \(\mathbf{A}\). \(\mathbf{I}_{n}\) is a \(n\times n\) identity matrix and \(\mathbb{E}\{.\}\) is the expected value operation. Finally, \(\mathcal{N}_{\mathbb{C}}(0,\mathbf{R})\) is a circularly symmetric complex Gaussian distribution with correlation matrix \(\mathbf{R}\), \(\mathcal{U}(a,b)\) represents the standard uniform distribution in the interval \([a,b]\) and \(\mathcal{N}(\gamma,\alpha)\) represents normal distribution with \(\gamma\) mean and standard deviation (std) \(\alpha\).