esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Effect of the Focus Position of Laser Beam  
In order to find out the difference in laser cutting with respect to the kerf width, the focus position of the laser beam (\(\rm F_{b}\)) was set to \(-20\) mm and \(-30\) mm. The other cutting conditions are shown in Table 4.  
The underwater laser-cutting results are shown in Figure 8 and Table 5. When \(\rm F_{b}=-30\) mm, the top kerf width (\(\rm W_{TK}\)) was increased by about 65% compared to \(\rm F_{b}=-20\) mm. The biggest change was the striation line length on the cut surface. Comparing Figure 8a,b, the upper and middle striation line lengths were long and the lower striation line length was short when \(\rm W_{TK}\) = 2 mm compared to when \(\rm W_{TK}\) = 1.3 mm. In the case of the wide kerf width, the flow momentum of the assist gas was maintained through the cutting section, and it blew away the molten metal. Additionally, in the case of \(\rm F_{b}=-20\) mm, a wide area of the melt and re-solidification area was observed as shown in the cross-sectional views presented in Figure 8a,c. This is because the molten metal was not well-removed from the cut surface and remained inside the cut surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Laser Power** & **Feed Rate** & **Focus Position of** & **Assist Gas** & **Stand-Of Distance** & \\ **(kW)** & **(\(\rm V_{c}\), mm/min)** & **Beam (\(\rm F_{b}\), mm)** & **Pressure (\(\rm P_{g}\), bar)** & **(\(\rm D_{n}\), mm)** & **Nozzle Type** \\ \hline
9 & 30 & \(-20\), \(-30\) & 8, 10 & 1 & Conical–flat \\ \hline \hline \end{tabular}
\end{table}
Table 4: Laser-cutting process parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nozzle Type** & \begin{tabular}{c} **Top Kerf Width** \\ **(\(\rm W_{TK}\), mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Bottom Kerf Width** \\ **(\(\rm W_{BK}\), mm)** \\ \end{tabular} &
\begin{tabular}{c} **Striation Line Length (mm)** \\ \end{tabular} \\ \hline Conical & 1.3 & 2.5 & 13.3 & 11.6 & 24.1 \\ Conical–flat & 1.3 & 3.1 & 7.5 & 13.6 & 28.9 \\ Supersonic & 1.9 & 1.6 & 22.3 & 10.4 & 17.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Striation line length and kerf width depending on the nozzle type.  
Figure 7: Underwater laser-cutting results depending on the nozzle types: (**a**) conical, (**b**) conical–flat, and (**c**) supersonic.  
To reduce the generation of aerosols, the assist gas pressure (P\({}_{\rm g}\)) was set to 8 bar and its cut surfaces are shown in Figure 8c,d. Comparing Figure 8a,d, even though the assist gas pressure was P\({}_{\rm g}\) = 8 bar, the top and the mid striation lines were longer than in the case of P\({}_{\rm g}\) = 10 bar and F\({}_{\rm b}\) = \(-\)20 mm.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 3 Problem Statement  
* The main challenge in energy-efficient routing is to have data transmission without connectivity by exploiting stringent controlling procedures.
* Usually, sensor nodes depend on battery energy, which gets exhausted at a quicker rate due to calculation and transmission processes [34].
* The single-hop transmission between the CH and BS creates a high energy consumption over the network [35]. Therefore, data reliability must be preserved during the data packets' transmission.
* In WSN, the progressing of multiple subjects and node energy become serious issues because of inadequate power delivered to the nodes [36].  
Solution:  
To obtain a faster and efficient solution for the clustering and routing problem, a meta-heuristic approach or classical approach is exceedingly required. Therefore, this research paper developed CAR-MOSOA-based clustering and routing algorithms for WSN-IoT with the consideration of energy consumption of the sensor nodes for prolonging network lifetime. In this CAR-MOSOA, optimal CHs are selected from the clustering by considering distinct fitness functions such as network coverage, communication cost, residual energy, and node degree. This optimal CH selection is used to achieve load balancing between the nodes and to minimize the energy consumption. Moreover, the routing over the network is accomplished using the queue length, link quality, communication cost, and residual energy. Here, the queue length considered in the MOSOA leads us to achieve congestion-aware routing over the WSN-IoT.
## 2 Overview and Outline of Machine Learning Analysis  
### Application of Machine Learning Methods to Solid-Liquid Phase Fraction Analysis  
In this study, to learn the spectra for LBE solids with various crystalline textures produced by solidification, we assembled training data from the solidified LBE data itself at the time of the experiment. In addition, the neutron transmission spectrum handled here was composed of a large number of data points by the neutron time-of-flight (TOF) method, and the computational cost was too large to be applied to ML in its original form. Therefore, we first reduced the dimensionality of the spectral data through unsupervised machine learning and then used the reduced dimensionality data to discriminate the spectral shape by supervised machine learning in the second stage. The procedure for the analysis was as follows:1. Measure the solidification process of liquid LBE with NTSL.
2. Obtain the solid-phase spectrum and liquid-phase spectrum of LBE from the imaging data. For the solid-phase spectrum, after the whole sample has solidified, divide the solid zone into several parts and obtain multiple spectra with various crystalline textures from each part. For the liquid-phase spectrum, the neutron data for the entire melted liquid zone are integrated to obtain a liquid-state spectrum.
2. Create a training dataset with different crystalline textures and solid-liquid fractions by adding the liquid-phase spectrum to each solid-phase spectrum acquired in step (2a) in fractions from 0 to 100%.
3. Reduce the dimensionality of the training data in step (2b) using unsupervised machine learning, then build an ML model using the training dataset through supervised machine learning.
4. Reduce the dimensionality of the experimental spectra of step (1) using unsupervised ML.
5. Apply the supervised machine learning model built in step (2c) to the dimensionality-reduced experimental spectra in step (3) and obtain the solid-liquid phase fraction. Visualize the obtained phase fraction at each pixel.  
In this study, we built ML programs with python and scikit-learn libraries, which are commonly used.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## SEIRS-D Agent-Based Model
### Coefficients
#### 3.2.2 Transmission Coefficient  
This coefficient refers to the probability that an infected sensor will transmit malware to its susceptible neighbouring sensors. The propagation of malware on a network depends fundamentally on the ability to transfer malware from one node to another. The computational characteristics of each sensor determine this capacity. This coefficient is useful for studying the conditions that must be met for malware to spread.  
The transmission coefficient of \(i\)-th sensor agent at the step of time \(t\) is represented mathematically by \(b[i,t]\) where \(1\leq i\leq n\) and \(n\) is the total number of sensors. As a consequence, the probability that an infected \(i\)-th sensor agent will transmit malware at time \(t+1\) is given by the following coefficient:  
\[b[i,t]=\prod_{1\leq j\leq 3}X_{j}\left(\widetilde{k}_{j}\right),\quad 0\leq b[ i,t]\leq 1,\]  
where \(\widetilde{k}_{j}\) is a vector representing the features on which depends the variable \(0\leq X_{j}\leq 1\). The factors \(X_{1},X_{2}\) and \(X_{3}\) determine the probability of transmission. In this case, \(X_{4}\), \(X_{5}\) y \(X_{6}\) have not been considered because the phenomenon of interest, human action and devices do not directly influence the transmission of malware from one sensor to another, as these factors are neither physical nor logical part of the network.  
The risk of transmission can be determined using the Equation (2); For example, a network with high computational capacity nodes, a low-security level, mesh topology that allows all nodes to communicate with each other and also uses the EECR protocol, may have a higher risk that the sensors will be able to transmit malware to other sensors if the following conditions are fulfilled:  
* The variable \(X_{1}\left(\widetilde{k}_{1}\right)\) depends on \(\widetilde{k}_{1}=\left(\alpha_{1}=2,\alpha_{2}=3,\alpha_{3}=4,\alpha_{4}=5, \alpha_{5}=7\right)\) such that \[X_{1}\left(\widetilde{k}_{1}\right)=P_{2,2}\cdot P_{3,5}\cdot P_{4,2}\cdot P_ {5,1}\cdot P_{7,1}\] (8) since values \(q=2,5,2,1\) and \(1\) are considered for \(k=2,3,4,5,7\) respectively.
* The variable \(X_{2}\left(\widetilde{k}_{2}\right)\) depends on \(\widetilde{k}_{2}=\left(\alpha_{1}=9,\alpha_{2}=10\right)\) such that \[X_{2}\left(\widetilde{k}_{2}\right)=P_{9,1}\cdot P_{10,3}\] (9) since values \(q=1\) and \(3\) are considered for \(k=9,10\) respectively.
* The variable \(X_{3}\left(\widetilde{k}_{3}\right)\) depends on \(\widetilde{k}_{3}=\left(\alpha_{1}=12\right)\) such that \[X_{3}\left(\widetilde{k}_{3}\right)=P_{12,2}\] (10) since the value \(q=2\) is considered for \(k=12\).
###### Abstract  
Many studies in the literature have presented multiple remote sensing techniques for defect inspection of paintings. At present, however, papers on defect inspection and restoration of oriental architectural arts--such as door god paintings--are still rare. If an aged and damaged door god painting needs a restoration, then following the style and treatment skill of the original artist as much as possible is important for the restoration. Unfortunately, it is usually difficult to access the original artists for some of the aged door god paintings. This paper considers the texture features of auspicious patterns of armors on warrior door gods as useful information to recognize styles of door god paintings by unknown artists. First, a two-level two-dimensional discrete wavelet transform coupled with co-occurrence matrix calculation was adopted to analyze the texture features, based on the descriptors of angular second moment (ASM), entropy (ENT), contrast (CON), homogeneity (HOM), dissimilarity (DIS), correlation (COR), and cluster tendency (CLU), in the four orientations of 0\({}^{\circ}\) (horizontal), 45\({}^{\circ}\) (vertical), and 90\({}^{\circ}\) and 135\({}^{\circ}\) (double diagonal). Second, a two-tailed \(t\)-test based on the analyzed texture features was introduced into the hypothesis testing for demonstrating the master and apprentice relationships between the surveyed artists, and for recognizing the door god painting styles of unknown artists as well. The experimental results show that the proposed method effectively describes the texture features of the auspicious patterns of the surveyed door god paintings, and is able to determine the useful co-occurrence features for recognizing unknown artists' painting styles.  
door god paintings; texture features; discrete wavelet transform; two-tailed \(t\)-test; painting style recognition +
Footnote †: journal: Applied Sciences
## 2 The Vision of Future Manufacturing Industry Networks
### Enabling Techniques for M2M-Based Collaborative Automations
#### 2.2.1 Communication Level Techniques  
With respect to communication level, many optional protocols, middleware, and application program interface (API) libraries are promising for M2M messaging. Although they are based on different techniques and for different application scenarios, the purpose is to gain flexibility in interactions between connected machines.  
The main competitive candidates invented for different requirements and use scenarios are summarised in Table 1[4; 7]. Some are expert in handling lightweight messaging under varying levels of latency due to bandwidth constraints or unreliable connections, such as Message Queue Telemetry Transport (MQTT), and some are suitable for resource limited internet devices like wireless sensor networks (WSN) nodes in IoT applications, such as Constrained Application Protocol (CoAP). Others focus on data format, message orientation, queuing, routing, reliability, and security, such as Advanced Message Queuing Protocol (AMQP). There are highly capable techniques for supporting service discovery across network domains, such as Extensible Messaging and Presence Protocol (XMPP), which are well suited for cloud computing where virtual machines and networks would present obstacles. In addition, some are developed to simplify complex network programming for big data applications, like Data Distribution Service (DDS). There are also techniques providing easy connection, presence, and flexible development of application level functions like ZeroMQ (ZMQ). Since each solution has its strengths and particular suited fields, the selection of the alternative techniques should be based on the requirements of specific applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
In the M2M messaging technologies, the information-centric networking (ICN) is an innovate networking paradigm in the IoT domain, which consists of the retrieval of content by names, regardless of origin server location, applications and distribution channels thus enables in-network caching and content-based security [8; 9]. ICN is a new networking paradigm, which provides mechanisms that allow information to be exchanged using flexible semantics based information identifiers, rather than relying on end point, location dependent identifiers. Typical applications of ICN are information-centric IoT management architecture [10], ICN-based IoT architecture [11], and ICN-based M2M communications [12]. Although ICN is still in its early stage, it has attracted much research interest and is considered to be a promising networking technique to revolutionise IoT applications and reshape their future.
## 2 Results and Discussion
### ParDE-AbrB-ParE Composite  
In the canonical type II TA system, the toxin ParE is associated with the antitoxin ParD [79]. ParE is a subfamily of the RelE/ParE toxin superfamily, and it could either have a bacteriostatic or a bactericidal effect, as reviewed in [79; 80]. The \"mix and match\" phenomenon mentioned above applies here as well; in addition to the ParD antitoxin, the ParE toxin may associate with other antioxins [68; 79; 81]. For instance, the ParE toxin may associate with the SpoVT-AbrB-type DNA-binding domain (SpoVT stands for Stage V sporulation protein T), as indicated by the NCBI CDD tool. The SpoVT-AbrB domain belongs to the MazE antitoxin superfamily [79; 82]. According to the TA domain description, SpoVT-AbrB domain-containing antitaxins are described as AbrB antioxins [69].  
The number of reported AbrB antiotoxins is continuously increasing [82]. Although AbrB-ParE has not been reported as a TA composite, we believe we can present it as a ParDE TA family based on the TA family classification by [PERSON] et al., 2013 [69]. According to this classification, a TA pair composed of AbrB-RelE represents RelBE, and since RelE belongs to the same protein superfamily of ParE toxin [83], the association of AbrB with ParE is not unexpected and the TA family could then be named as ParDE. Moreover, in this classification, the TA pair AbrB-Doc represents the Phd-Doc TA family, AbrB-MazF represents the MazEF TA family, and AbrB-PIN represents the VapBC TA family [69]. Our analysis indicated that three of the _Geobacillus_ strains have ParDE TA families harboring AbrB-ParE TA composites, as described below.
## 4 Experiments and Analysis
### Evaluation of Performance Indicators  
In this study, we utilized the Mean Absolute Error (MAE) and Mean Square Error (MSE) as evaluation indices to assess the experimental outcomes. The MSE is particularly adept at reflecting the model's performance, especially when the error distribution is approximately normal, offering a robust performance evaluation. Furthermore, employing MAE as a loss function can enhance the model's generalization capabilities in scenarios characterized by substantial outliers or noise within the data. The computational formulas for MAE and MSE are defined as follows in Equations (14) and (15):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\abs{\hat{y}_{i}-y_{i}} \tag{14}\]  
The Mean Absolute Error (MAE) is the average of the absolute differences between the target and predicted values.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{15}\]  
The Mean Square Error (MSE) is the most commonly utilized regression loss function, calculated by summing the squares of the discrepancies between predicted and actual values. Taken together, MAE (Mean Absolute Error) and MSE (Mean Squared Error) serve as evaluation metrics; smaller values of these metrics indicate superior model performance. Although MAPE provides an intuitive understanding of percentage error in some cases,it is usually limited in practical applications owing to its treatment of zero values and over-sensitivity to small values, so we chose to use MAE and MSE, which are more stable and versatile.
## 2 Prerequisites  
_Math Operators._ At first, a short introduction into the mathematical part of the work is presented; this is, mainly based on [PERSON] [19] who introduced the theory of admissible perturbations of an operator. The admissible perturbation operator was also studied in [20].  
_Demicontractive operators._ As already stated in our previous work [18], a _demicontractive operator_ (\(T\)) is defined by \(C\), a subset of \(\mathbb{R}\) (domains and co-domains). For an existing _contraction coefficient_ (\(k<1\)), each fixed point (\(p\)) of the demicontractive operator and all numbers (\(x\in C\)) the inequality (1) is true.  
\[\|Tx-p\|^{2}\leq\|x-p\|^{2}+k\|x-Tx\|^{2}. \tag{1}\]  
For a nonempty set (\(X\)) and an admissible mapping, (\(G:X\times X\to X\)), the following statements are true: for all \(x\in X\) G(x, x) = x and \(G(x,y)=x\) implies \(y=x\)[19].  
The admissible perturbation of the operator \(f\) (\(f:X\to X\)) [19] is the admissible mapping \(f_{G}:X\to X\) (\(f_{G}(x):=G(x,f(x))\)).  
_Krasnoselskij operator._ The _Krasnoselskij algorithm_[19], corresponding to an admissible mapping (\(G:X\times X\to X\)) of an nonlinear operator (\(f:X\to X\)), is defined as an iterative algorithm \(\{x_{n}\}_{n\in\mathbb{N}}\) with \(x_{0}\in X\) and \(x_{n+1}=G(x_{n},f(x_{n}))\), where \(n\geq 0\).  
For further details and examples, see [18; 20; 21].  
_The \(\chi\) operator._ The \(\chi\) operator \(\chi:\mathbb{R}\times\mathbb{R}\longrightarrow[0,1)\) is defined as:  
\[\chi(x,y)=\frac{x^{2}\cdot y^{2}}{(1+x^{2})\cdot(1+y^{2})}, \tag{2}\]  
where \(y:\mathbb{R}\rightarrow\mathbb{R}\), \(y(x)=\frac{2}{3}x\sin\frac{1}{x}\), if \(x\
eq 0\).  
_Particular Math Functions._ The operators further used as \(f(\cdot)\) in the considered methods (see (8)) are as follows:  
\[\text{Sin:}\hskip 56.905512 ptf(x)=\sin\!\left(\frac{\pi x}{2\lambda}\right) \text{, if }0\leq x\leq\lambda; \tag{3}\]  
\[\text{KH:}\hskip 56.905512 ptf(x)=(1-\lambda)\cdot x+\lambda\cdot\frac{2}{3}x \sin\frac{1}{x}\text{ if }x\
eq 0; \tag{4}\]  
\[\text{Chi:}\hskip 56.905512 ptf(x)=(1-\chi(x,y(x)))\cdot x+\chi(x,y(x))\cdot y(x) \text{ if }x\
eq 0; \tag{5}\]  
The functions (3)-(5) are zero in all other possible cases. The \(\lambda\) parameter from Equations (3) and (4) adjusts the operators used as test functions in [22]. In [23], the authors used two admissible perturbation operators for computing the heuristic value required within the ACO algorithm.  
In the present article, where admissible perturbations of demicontractive mappings are utilized as test functions, and the \(PSL\) vector is utilized for each ant, a sensitivity to the artificial pheromone is introduced using a specific coefficient influenced by the image's intensity values for the edge detection problem.  
The ants have different roles in edge extraction: some agents are explorers and others are exploiters; these roles are exchanged as the \(PSL\) vector updates during processes. The obtained results for CT and X-ray medical images and the comparison among the results using the proposed operators are made in Section 4.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 7 Simulation Analysis
### Effects of Various \(\left|U_{p}\right|\) on Utility of the Proposed Scheme
If the value of \(P_{th}^{accept}\) is set too large, the system will not be able to better play its advantage because the number of potential service requesters satisfying the condition \(P_{pi,l}^{accept}>P_{th}^{accept}\) is reduced. If the value of \(P_{th}^{accept}\) is set too small, many potential service requesters may be disturbed  
Figure 4: The relationship between utility and different communication environment: (**a**) Utility versus \(\lambda\); (**b**) Utility versus \(P_{th}^{accept}\); (**c**) Utility versus \(|F|\). by the video push service, and then the QoE of users will be reduced, so in all experiments, the value of \(P_{th}^{accept}\) defaults to 0.5. The relationship between utility value and the number of videos \(|F|\) is shown in Figure 4c. According to Equation (6), as the number of videos increases, the popularity of individual video turns small, further reducing the willingness of potential service requesters to accept the pushed video, and thus the utility value become lower. Through the above experiments and analysis, the D2D transmission performances of systems with different numbers of potential service requesters in different communication environments are clarified. In general, the parameter setting has a great influence on the system performance and the more potential requesters, the better the utility value for the system.
## 3 EPU Parameters Identification Procedure
### EPU Static Maps  
Static maps represent processed data and are the first step in characterization. The identified parameters (contained within arrays) are a function of the control PWM signal. Figures 10-12 show static maps of aerodynamic thrust force and drag torque with respect to input PWM signal, while Figures 13-15 show static maps of electric current and electric power with respect to input PWM signal, for three series of measurements (three different EPU setups).  
For the first setup, 5030 and 7024 propellers were tested, where the first two numbers of the designation define the propeller diameter (5\({}^{\prime\prime}\) and 7\({}^{\prime\prime}\)), and the other two numbers the propeller pitch angle (3\({}^{\prime\prime}\) and 2.4\({}^{\prime\prime}\)). For the second setup, propellers with 9\({}^{\prime\prime}\) to 10\({}^{\prime\prime}\) diameter and various geometric characteristics were selected and for the third setup, propellers with 15\({}^{\prime\prime}\) to 17\({}^{\prime\prime}\) diameter in combination with 4S and 6S LiPo batteries were selected. Generally, it can be seen that EPU setups with lower \(K_{\rm v}\), paired with larger propeller diameters, achieve higher aerodynamic forces and torques.  
Figure 8: Rotor RPM for setup with MN 2214 BLDC motor: (**a**) raw measurement; (**b**) RPM static maps.  
Figure 10: Static maps—1806 BLDC series: (**a**) thrust force; (**b**) drag torque.  
Figure 9: Rotor RPM for setup with MN 4014 BLDC motor: (**a**) raw measurement; (**b**) RPM static maps.  
Consequently, as the EPU drag torque increases, the power consumption increases. Electrical consumption is extremely important from the aspect of designing a multirotor UAV with a specific purpose (flight tasks). The first two series of measurements were performed using a 12 V power supply equivalent to a 3S LiPo battery (Figures 13 and 14). The third series (Figure 15) was done with 4S and 6S batteries as a power source. The experimental measurement of electrical quantities was additionally verified using a multifunction logger. Higher power EPUs generally operate at higher voltages.  
Figure 11: Static maps—MN 2214 BLDC motor: (**a**) thrust force; (**b**) drag torque.  
Figure 12: Static maps—MN 4014 BLDC motor: (**a**) thrust force; (**b**) drag torque.  
Figure 13: Static maps—1806 BLDC series: (**a**) electric current; (**b**) electric power.