esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
## 3 Methodology and Materials
### Features and Hyperparameters
Figure 5a,b show respectively the trend of GLCM-2 DDE and GLCM-Entropy (i.e., Shannon Entropy) for the Port Scan attack. The blue plot shows the trend of the specific feature while the bar graph (purple bars superimposed on the plot) identifies the windows where the attack  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature Id \(F_{ID}\)** & **Feature Name and Parameters** \\ \hline
1,17,33,49 & Contrast [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Not Symmetric \\
9,25,41,57 & Contrast [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Symmetric \\
2,18,34,50 & Energy [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Not Symmetric \\
10,26,42,58 & Energy [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Symmetric \\
3,19,35,51 & Homogeneity [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Not Symmetric \\  & metric \\
11,27,43,59 & Homogeneity [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Symmetric \\
4,20,36,52 & Correlation [0 \(G_{D}\)], [\(-G_{D}\) 0], [\(-G_{D}\) - \(G_{D}\)], [\(-G_{D}\) \(G_{D}\)] Not Symmetric \\
12,28,44,60  
is implemented and labelled. It can be seen in Figure 4(a) that the values of GLCM-2 DDE (called simply 2 DDE in the rest of this paper) are notably higher in correspondence to the Port Scan attack than the normal legitimate traffic. This difference is less evident for the GLCM-Entropy feature. These differences in values are the reason why the performance of GLCM-2 DDE is higher than the GLCM-Entropy when ML is applied. We highlight that Figure 5 and the previous paragraph are only used for informational purposes to provide to the reader with a visual recognition of the difference of the trends in the data set once two different entropy measures are applied to the network flows data. Figure 5 is not used to select features for the classification phase because the SFS is used for this purpose as described in Section 4.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Challenges in Industrial Environments  
#### 3.3.1 WSN in Industrial Settings  
The industrial environment has always been harsh to wireless channels. The effect of this phenomenon is visible in the erroneous packet transfer in IWSN. To solve this effect, Reference [97] developed a novel Mixed Integer Programming (MIP) Framework that improves the quality of packet transfer for more battery sustainability in IWSN. The influence on the communication channel of WSN depends on the environment in which they are deployed. This applies to industrial environments as well, and the extent of influence on the channel also depends on the type of industrial setup. In [98], some challenges, such as security, routing of data, etc., were studied in addition to application types of WSN. Reference [99] discussed most of the factors that should be considered when planning WSN deployment for targeted usage. These factors include the communication range of nodes, sensing range, deployment strategy, etc. Reference [45] primarily focused on the challenge of coverage in WSNs. Among the three types of coverage (barrier, point, and area) presented, the authors discussed in detail the coverage area type. The details of which are on typical research work, classified area coverage based on the type of node and deployment, coverage detection, sensing, and communication range.
## 2 Materials and Methods
### Single and Mixed Gas Permeation Measurement  
Membrane characterization utilized a homemade single gas permeation setup. Various gases, including He, H\({}_{2}\), CO\({}_{2}\), N\({}_{2}\), CH\({}_{4}\), and SF\({}_{6}\), were measured in dead-end mode using the pressure rising method. The kinetic molecular diameter of gases can be observed in Figure S1 [30; 31; 32]. In this study, single gas permeation measurements were performed three times for each gas, with repeated cycles of separate vacuum, gas flow, and average values being reported. Details of the single gas permeation measurement set up are described in Figure S2. CO\({}_{2}\)/CH\({}_{4}\) mixtures were separated using mass flow controllers that controlled flow rates. An overpressure of 0.005 MPa was maintained on the permeate side at a pressure of 0.105 MPa (absolute) and no sweep gas was introduced. Permeate flow was measured using a soap bubble counter, while feed gas flow was controlled by a mass flow controller. A gas chromatographic method was used to analyze feed, retenate, and permeate compositions. Selectivity, a unitless factor, reflects the ability of the membrane to separate components A and B in a gas mixture. If the partial pressure of the gas mixture (comprising components A and B) introduced on the feed side (high-concentration) of the membrane is denoted as \(X_{A}\) and \(X_{B}\), and the partial pressure on the permeate side (low-concentration side) is \(Y_{A}\) and \(Y_{B}\), the separation factor \(\alpha\) is defined as follows [9]:  
\[\alpha=\frac{Y_{A}/Y_{B}}{X_{A}/X_{B}}=\frac{Y_{A}X_{B}}{Y_{B}X_{A}} \tag{1}\]  
The permeance \(P_{A}\) of component A in a gas mixture is defined as the amount \(n_{A}\) of gas A that permeates through a specific membrane area \(A\) over a specific time \(t\), driven by the partial pressure difference \(p_{A}\) of gas A across the membrane. The permeance is given by the following:  
\[ Permeance\ P_{A}=\frac{n_{A}}{Atp_{A}} \tag{2}\]and is expressed as [mol/m\({}^{2}\)sPa] in SI units [17].
## 2 Method Description
### The Local Search Procedure  
The local search procedure initiates from the vector \(\overrightarrow{w}\) that is produced for any given chromosome \(g\) using the procedure described in Section 2.1. The procedure minimizes the error of Equation (1) with respect to vector \(\overrightarrow{w}\). The minimization is performed within a value interval created around the initial point \(\overrightarrow{w}\) keeping the network structure intact. The main steps of this procedure are given below:  
1. **Set**\(d=(n+2)H\). This value denotes the total number of parameters for \(N\big{(}\overrightarrow{x},\overrightarrow{w}\big{)}\).
2. **For**\(i=1,\ldots,d\)**do** 1. **Set**\(L_{i}=-F\times|w_{i}|\), the left bound of the minimization for the parameter \(i\). 2. **Set**\(R_{i}=F\times|w_{i}|\), the right bound of the minimization for the parameter \(i\).
3. **EndFor** 4. **Minimize** the error function of Equation (1) for vector \(\overrightarrow{w}\) inside the bounding box \(\left[\overrightarrow{L},\overrightarrow{R}\right]\) using a local optimization procedure \(\mathcal{L}(w)\). The BFGS method as modified by [PERSON] (2018) was incorporated in the conducted experiments.  
As an example, consider a dataset with \(n=2\) and the following constructed neural network:  
\[N\big{(}\overrightarrow{x},\overrightarrow{w}\big{)}=2\sigma(1.5x_{2}-2.0)+2.5\sigma(-1.2x_{1}+3.7) \tag{5}\]  
Figure 2: An example of the method of one-point crossover used in the grammatical evolution procedure.  
where the number of nodes is \(H=2\). In this case, the vector \(\overrightarrow{w}\) has the elements \(\overrightarrow{w}=(2,0,1.5,-2,2.5,-1.2,0,3.7)\). If the parameter \(F\) has the value \(F=2\), then the bound vectors \(\overrightarrow{L}\) and \(\overrightarrow{R}\) are defined as  
\[\overrightarrow{L} = (-4,0,-3,-4,-5,-2.4,0,7.4)\] \[\overrightarrow{R} = (4,0,3,4,5,2.4,0,7.4)\]  
Hence, the minimization method could not change the elements \(w_{2}\) and \(w_{7}\), and as a consequence, the architecture of the network remains intact.
# Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor
## 2 Materials and Methods
### Analysis Method  
The results for the HMT, including the axle torque, rotational speed, and power transmission efficiency, were compared and analyzed using a bench test and simulation. The power transmission efficiency of the HMT was calculated with Equation (6) using the torque and rotational speed of the input and output shaft [34].  
\[\eta=\frac{T_{out}\times n_{out}}{T_{in}\times n_{in}}\times 100 \tag{6}\]  
where \(\eta\) is the power transmission efficiency (%), \(T_{in}\) is the input torque (Nm), \(n_{in}\) is the input rotational speed (r/min), \(T_{out}\) is the output torque (Nm), and \(n_{out}\) is the output rotational speed (r/min).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Item**} & \multicolumn{2}{c}{**Specification**} \\ \hline Input motor & Max. Power (kW) & 1000 \\  & Max. torque (Nm) & 2600 \\ \hline Dynamometer & Max. Power (kW) & 1000 \\  & Max. torque (Nm) & 2600 \\ \hline Torque and speed sensor & Range & 0–5000 Nm, 0–5000 r/min \\  & Accuracy & \(\pm\)0.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the test bench.  
Figure 5: Diagram of the test bench for the performance evaluation of the HMT such as axle torque and rotational speed, power transmission efficiency.  
Figure 6: Photo of the test bench for the performance evaluation of the HMT used in this study.  
In order to validate the simulation model for the HMT, the t-test and linear regression analysis were performed through the statistical software IBM SPSS Statistics (SPSS 24, IBM Corp., Armonk, USA), and the correlation of the data between measured and simulated was compared and analyzed. The t-test was conducted for the axle torque, rotational speed, and power transmission between the measured and simulated results. Finally, the error and accuracy of the simulation model for the power transmission efficiency of HMT were analyzed through the coefficient of determination, root-mean-square error (RMSE), and relative deviation (RD), derived by the linear regression, to see if the power transmission efficiency under each gear stage showed high accuracy in comparison with the measured values. Generally, we judged that a model with an R-squared of more than 0.9 was reliable [35]. The R-squared, RMSE, and RD through regression analysis were calculated through Equations (7)-(9) [36; 37; 38].  
\[R^{2}=\frac{\sum_{i}(\hat{y}_{i}-\overline{y})^{2}}{\sum_{i}(y_{i}-\overline{y })^{2}}=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\overline{y})^ {2}} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i}(y_{i}-\hat{y}_{i})^{2}} \tag{8}\]  
where \(R^{2}\) is the coefficient of determination of the regression equation, \(RMSE\) is the root-mean-square error, y is the measured value, \(\hat{y}\) is the simulated value, and \(N\) is the number of pairs of values.  
\[RD=\frac{RMSE}{\eta_{Mean}}\times 100 \tag{9}\]  
where RD is the relative deviation (%), and \(\eta_{Mean}\) is the average of power transmission efficiency.
## 4 Use Cases  
To evaluate the effectiveness of the proposed optimization algorithm applied to distribution systems through reconfiguration and flexible buses, these use cases analyze how the optimization of a distribution system can lead to significant improvements. The selected distribution systems are the Kumamoto and IEEE-33 systems.
## 1 Introduction  
A _Mobile Ad hoc NETwork_ (MANET) is a set of mobile nodes which communicate between themselves through wireless links. In contrast with conventional networks, a MANET does not need any previous infrastructure, since nodes rely on each other to operate themselves, forming what is called multi-hop communication.  
Such networks have more problems and disadvantages than a conventional network. The topology of mobile networks may change quickly and in an unpredictable way. Moreover, variations in the capacity of nodes and links, frequent transmission errors and a lack of security may occur. Finally, the limited resources of the nodes must be taken into account given that an _ad hoc_ network will normally be formed by battery operated devices.  
To communicate with each other [1] the _ad hoc_ nodes need to configure their interfaces with local addresses which are valid within the _ad hoc_ network. The _ad hoc_ nodes may also need to set global routing addresses to communicate with other devices on the Internet. From the perspective of the IP layer, an _ad hoc_ network presents itself as a multi-hop level 3 network constituted by a collection of links.  
This paper is organized into six sections, including this introduction. Section 2 tackles the problematic implications of the design of auto configuration protocols for mobile _ad hoc_ networks. Section 3 discusses the inapplicability of the standard solutions. In Section 4, a classification of auto-configuration protocols for mobile _ad hoc_ networks is carried out, with the most representative being mentioned, and special emphasis being made on a protocol proposed by the author: D2 HCP. Then, in Section 5, a comparative study of the above-mentioned protocols is performed. Finally, Section 6 of the paper draws together the conclusions of this piece of work.
## 2 Cuboid Multi-Mode Multi-Port Antennas  
M\({}^{3}\)PAs [17] are designed based on the theory of characteristic modes. According to this theory, the surface current on a conducting body can be decomposed into orthogonal components, called characteristic modes. Each set of these characteristic modes corresponds to a far-field pattern. Depending on the chosen M\({}^{3}\)PA design, multiple antenna ports are realized on a single antenna structure. Each of these ports excites a number of characteristic modes that result in electric far-field radiation. The resulting far-field radiation patterns are accordingly orthogonal and can be radiated simultaneously [22].  
An innovative M\({}^{3}\)PA design suitable for aerial DF applications was recently introduced in [40]. It has a cuboid-shaped structure and provides three uncorrelated ports that can be driven individually. A photo of the antenna used in subsequent simulation and experimental results is shown in Figure 1. The operating frequency is 1060 MHz. This is the central frequency between 1030 MHZ and 1090 MHZ, the operating frequencies of ACAS and Mode S interrogators [5]. The design introduced in [40] followed a systematic procedure for analyzing and developing the investigated M\({}^{3}\)PA in order to fulfill DF requirements in aerial applications. As suggested in [36], the employed antennas in ACAS Xu should be vertically polarized. Furthermore, omnidirectional as well as directional radiation patterns are desired for the transmission of interrogations and reception of replies. Accordingly, the implemented M\({}^{3}\)PA features one omnidirectional and two directional radiation patterns of the \(\theta\)-component. Figure 2a illustrates the position of the cuboid M\({}^{3}\)PA in the considered 3D Cartesian space, along with the definition of azimuth \(\phi\) and co-elevation \(\theta\) angles. Figure 2b-d show the measured realized gain along the \(\theta\)-component of the three ports of the investigated M\({}^{3}\)PA in the far field. The gain along the co-polarized \(\theta\)-component dominates the radiation pattern of the considered M\({}^{3}\)PA. Therefore, the gain along the cross-polarized \(\phi\)-component is negligible and hence not shown here.  
Figure 1: The antenna proposed in [40]. This antenna is used throughout this paper.
## 3 Proposed DRFENet Construction
### Dense Enhancement Block  
Skip connections simplify the transmission of high-level information to enhance noise expression, improving image denoising [38]. We establish the connection between different layers and fully use the feature map to alleviate the problem of gradient disappearance. Meanwhile, this connection mode makes the transmission of features more effective, making the network easier to train. Aiming further to enhance the feature expression ability of the network, we optimized the dense block of Densenet for the image-denoising task [34].  
This structure can reuse the output of each convolution layer to extract more information, but this is also its disadvantage. In the original dense block, all the features output by the convolutional layer are reused in the subsequent convolutional layer. The deep layer receives extensive useless feature information that contributes to parameter redundancy.  
We create a new block that passes information from the first three convolutional layers to the next three layers in batches. In other words, the first three convolutional layers will share characteristic parameters with the third one that follows it. We call this strategy interval transmission. This feature reuse strategy reduces the parameter redundancy, and the deep layer distils fit feature information. DEB reduces the feature graph immediately after the skip connection so that the output feature graph of each convolution layer is maintained at 64 to avoid feature map redundancy. The last three layers of the network implicitly depth-monitor their predecessors.  
The DEB Is shown in Figure 2. Connecting lines in the figure represent a skip connection. The DEB is a six-layer CBR structure. The CBR in the sixth layer can alleviate the problem of gradient explosion and gradient extinction by transmitting the output information of the first three layers to the second three layers according to interval transmission. The DEB can be expressed in Equation (1). The details of the DEB are expressed in Equation (2).  
\[O_{DEB}=F_{DEB}(X_{N}), \tag{1}\]  
\[O_{DEB,CBR1}=F_{CBR}(X_{N})\]  
\[O_{DEB,CBR2}=F_{CBR}(O_{DEB,CBR1})\]  
\[O_{DEB,CBR3}=F_{CBR}(O_{DEB,CBR2})\]  
\[O_{DEB,CBR4}=F_{CBR}(Cat(O_{DEB,CBR1},O_{DEB,CBR3}))\]  
\[O_{DEB,CBR5}=F_{CBR}(Cat(O_{DEB,CBR2},O_{DEB,CBR4}))\]  
\[O_{DEB}=F_{CBR}(Cat(O_{DEB,CBR3},O_{DEB,CBR5}))\]  
where \(F_{DEB}\), \(O_{DEB}\), \(F_{CBR}\), \(Cat\), and \(O_{DEB,CBR(1,2,3,4,5)}\) denote the function of the DEB, the output of the DEB, the function of CBR, the skip connection, and the CBR output for each layer, respectively.  
Figure 2: DEB in DRFENet.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 2 Literature Review
### VR in Maintenance and Lifecycle Support  
Early work by [PERSON] and [PERSON] [39] introduced VR tools designed specifically for maintenance and assembly training, laying the foundation for immersive procedural learning and diagnostics. This continuity between design-time simulation and real-world system upkeep highlights VR's holistic role across the system lifecycle. [PERSON] et al. [40] further expanded on this by developing VR-based verification systems for complex jet turbine maintenance, showcasing the technology's ability to manage high-risk procedures precisely. More recently, [PERSON] et al. [41] emphasized the importance of conducting maintainability analysis early in the design process, thereby closing the loop that links design considerations directly to long-term operational efficiency and reliability. In parallel, VR supports critical functions in engineering, such as risk management and hazard evaluation.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 3 Route Nonuniformity Path Modelling
### How to Measure Route Feature Values for RA Calculation
#### 3.2.3 Algorithm for Calculating RA
In contrast, calculations of conventional metrics, such as packet loss rate and throughput, require that the source node receive feedback information from the destination node.
## 5 Model Studies
### Verification of the New Algorithm  
To verify the new algorithm, an isotropic horizontal layered geoelectrical model, as shown in Figure 8, was considered first. In this model, air and seawater were 1000 m thick. A 100 m thin-layer reservoir was embedded in the sediment at 1500 m to 1600 m depth. The EM field response was based on the model presented in Table 2. An \(x\)-oriented 1 Hz frequency current source was placed at the location \((0,\ 0,\ 950\ \mathrm{m})\). The receiving stations were located on the seafloor \((z=1000\ \mathrm{m})\). The modeling domain was \(3\ \mathrm{km}\ \times 3\ \mathrm{km}\) square and \(4\ \mathrm{km}\) in the vertical direction \((-1\ \mathrm{km}\leq z\leq 3\ \mathrm{km})\). A non-uniform rectangular grid was used to discretize \(\Omega\). We refined the grid near the current source, receiving stations, and target layer. The analytical response was calculated using the numerical integration of the Hankel transform [25; 26; 27; 28; 29].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Conductivity (s/m)** \\ \hline Air & \(10^{-6}\) \\ Sea Water & \(3.30\) \\ Sediment & \(1.00\) \\ Reservoir & \(0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The conductivities of an isotropic horizontally layered geoelectric model.  
Figure 7: Interpolation errors and convergence rates for the two basis functions approximated by EFEM. H is the length of edge of a element.  
Figures 9 and 10 show the difference in the \(x\)-component of the electric field by applying conventional serendipity (SC)-and Lagrange (LC)-type second-order hexahedral elements, and the proposed elements (new serendipity (SN) and Lagrange element (LN)). These new quadratic edge elements were at the same level of accuracy as conventional quadratic elements, both higher than that of linear edge elements.  
Figure 8: Horizontally layered geoelectric model with rectangular mesh; the red star denotes the electric dipole source. The blue, green, red and gray areas represent the air layer, sea water, oil and gas, and bedrock, respectively.  
Figure 10: A comparison of the \(x\)-component of the secondary electric field by using different FEM methods (Lagrange-type second-order element) with the analytical solution.  
Figure 9: A comparison of the \(x\)-component of the electric field using different FEM methods (serendipity-type second-order element) with the analytical solution.  
Table 3 lists the CPU times for different elements. Less time was required when using new quadratic elements than when using conventional quadratic elements. Therefore, one can say that the computation is reduced at no cost to accuracy.