esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Correspondence Analysis
#### 4.3.2 Innovation Theory - Based Analysis  
Figure 9 shows the results of the correspondence analysis based on innovation theory. Figures 7 and 9 appear similar, however while Figure 7 samples the text of all firms in a given year, Figure 9 uses only the text of firms in each adopter category. Accordingly, four categories are proposed: earlier adopters, early majorities, late majorities, and laggards. Earlier adopters recognized AI as a related technology, such as \"big data\", \"IT\", \"cloud\", and \"robotics\". The early majorities focused on the future prospects of \"investment\", \"growth\", \"aggressiveness\", and \"change\". Meanwhile, the late majorities preferred to focus on \"management\", \"groups\", \"human resources\", \"operations\", and \"efficiency\", which are management functions. The laggards in 2022 mostly mentioned accounting terms, such as \"revenue\", \"segment\", and \"sales\", in addition to the marketing-related terms \"customer\", \"build\", and \"solution\". In other words, the focus of participants shifted to technology awareness (earlier adopters), future investment (early majorities), group strategies and applications (late majorities), and marketing and accounting (laggards). This flow is similar to the stages of AI perception depicted in Figure 8.  
Note that there is no evidence that the latter category referred to or updated the interpretation of the former category; however, we argue that the interpretation of AI changed as a new category with a new interpretation of AI entered the market. Moreover, new interpretations could have attracted the next category.
## 4 Numerical Example
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of** & **Number of Valid** & **Ratio of Number of Valid** \\  & **Waked SMs** & **Waked SMs** & **to the Number of Waked SMs** \\ \hline DIDM method & 81 & 22 & 25\% \\ BDIIM method & 49 & 19 & 38\% \\ Improvement time & / & / & 1.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data of the SM performance of nodes in WC. The sending/receiving message number is also considered to compare the WC performance of the two methods (DIDM and BDIIM), as shown in Figure 13. The detailed calculation data are listed in Table 7. The sending messages number achieved via the proposed BDIIM method is lower than that achieved via the previous DIDM method. According to the given ratio of receiving messages to sending messages, representing the efficiency of the WC, the improvement in time of the BDIIM method is 1.59 s when compared with the DIDM method. The target position estimation error and total energy cost of WSN are also simulated and compared with the previously mentioned DIDM method. Here, the target position estimation error denotes the distance between the actual target position and the estimate position (the centroid of the valid waked sensor nodes). As for the total energy cost, we consider that the energy consumed by the \"idle\" sensor node in each time interval is one unit, and one sensor node sending/receiving a message is also considered one unit. Due to the fact that the calculation operation costs much less energy compared with the operation of sending/receiving messages, the calculation cost is not considered. In Figure 14, the total energy cost across the whole process via the proposed BDIIM method is always less than that obtained via the DIDM method. Finally, the target position estimation error is basically consistent during the first 30 s, while the error via the proposed BDIIM method is slightly less than that obtained via the DIDM method after 30 s. Finally, the total energy cost and position estimation error of the BDIIM method, as listed in Table 8, are reduced to 50.8% and 78.9% compared with the DIDM method, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Number of** & **Average Number of** & **Ratio of the Receiving** \\  & **Sending Messages** & **Receiving Messages** & **to the Sending** \\ \hline DIDM method & 52 & 145 & 2.79 \\ BDIIM method & 33 & 147 & 4.45 \\ Improvement time & / & / & 1.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The numbers of sending and receiving messages nodes in the WC. Figure 13: The WC performance of the sending and receiving messages numbers: (**a**) Number of sending messages. (**b**) Number of receiving messages. (**—**: BDIIM¿ -: DIDM). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Total Energy Cost** & **Target Position Estimation Error (after 10 s)** \\ \hline DIDM method & 297 & 19.5 \\ BDIIM method & 151 & 15.4 \\ Reduction percentage & 50.8\% & 78.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Energy cost and position estimation error of the nodes in WC.
## 1 Introduction
The lath phase coarsens around M\({}_{23}\)C\({}_{6}\) (M = chromium, iron, tungsten and molybdenum) carbides along the PAGBs, reducing their pinning effect and hence reducing the strengthening mechanism [19]. Heat treatment of P91 steel produces a tempered lath martensite structure with M\({}_{23}\)C\({}_{6}\) and MX (M = Niobium, Vanadium and X-carbon) precipitates [2]. During normalisation, the homogenisation of the microstructure occurs, causing an untempered lath microstructure with a small number of precipitates and a high dislocation density. Tempering causes the precipitation of the M\({}_{23}\)C\({}_{6}\) carbides along the grain boundaries and MX particles in the matrix [19]. The high dislocation density and the M\({}_{23}\)C\({}_{6}\) precipitates are responsible for the pinning of lath boundaries, while MX precipitates prevent dislocation movement [2,17]. The heat treatment process, therefore, restores the effects of the creep process and provides a stable microstructure for the steel to achieve high creep strength [20]. Rejuvenation heat treatment, as mentioned earlier, provides an economic benefit in terms of the expensive process of replacing parts and time and energy savings [9]. In addition, an appropriately designed refurbishment process can significantly improve the lifetime service of components or a production plant. To sufficiently develop a heat treatment process, it is imperative to understand the response of heat treatment on creep-exhausted steel to thermomechanical processing parameters. This study provides a basis for understanding the metal flow pattern during hot deformation. A comparative study using constitutive models developed from the hot deformation process of a virgin (Steel A) and creep-exhausted steel (Steel B) is used for this analysis [21]. Phenomenological constitutive models such as Arrhenius equations are widely used to study the metal flow behaviour of various metals and alloys [22]. Some examples of such materials studied using this model include low-carbon steel [23,24], P92 steel [25], nickel-based super alloys [26], alloys of aluminium [23], and magnesium [27]. The accuracy and reliability of this model in predicting the flow stress forms the basis of studying the material process behaviour [28]. These models also act as inputs for finite element method codes. These computer codes assist in the simulation and the optimisation of thermomechanical process parameters. The simulation results depend on the accuracy and dependability of the constitutive models [21,28]. The objective of this present study was to investigate the flow stress behaviour of steel A and steel B using an Arrhenius constitutive model. The steel B was heat treated by normalisation followed by air cooling, then tempering processes. Isothermal hot deformation tests were then conducted at various strain rates and temperatures to study the flow stress behaviour for the two steels. Experimental flow stress values were used to develop constitutive equations based on the Arrhenius-type equation. These equations were validated using statistical parameters.
# Low-Pass Filtering Method for Poisson Data Time Series
## 2 Method  
### Quasi-Gaussian Digital Low-Pass Filter  
#### 2.1.1 Statement of the Problem  
One-dimensional FIR filter synthesized here is built according to the following difference equation:  
\[X(T_{0}n)=\sum_{s=0}^{s_{0}}a_{s}Y(T_{0}(n-s)),n=1,2,\ldots, \tag{2}\]  
where \(r_{0}=s_{0}+1\) is the FIR filter dimension, \(a^{T}=(a_{0},a_{1},\ldots,a_{s_{0}})\) is a weight factors vector, \(X(T_{0}n)\) is the output time series, \(Y(T_{0}n)\) is the FIR filter input--the hourly normalized Poisson data time series from MH according to (2), which begins from the values \(Y(T_{0}(1-s_{0}))\), \(Y(T_{0}(1-s_{0}+1))\), \(Y(T_{0}(1-s_{0}+2)),\ldots\). Transfer function (TF) \(H(j\omega T_{0},a)\) for filter (2) is defined as follows:  
\[H(j\omega T_{0},a)=\sum_{s=0}^{s_{0}}a_{s}e^{-j2\pi\omega T_{0}s}. \tag{3}\]  
Here \(\omega\) is the TF frequency parameter. For (3), a normalized sequence is introduced, \(w,\omega T_{0}=w\pi,0\leq w\leq 1.0\), and its discrete values are calculated: \(w_{l}\)  
\[dw=1.0/L_{0},\ w_{l}=dw(l-1),\ l=1,\ldots,L,L=L_{0}+1. \tag{4}\]  
The frequency response (FR) \(H(w_{l},a)=|H(jw_{l},a)|\), considering (3), is the following:  
\[\begin{split} H(w_{l},a)^{2}=H_{1}^{2}(w_{l},a)+H_{2}^{2}(w_{l}, a),\\ H_{1}(w_{l},a)=\sum_{s=0}^{s_{0}}a_{s}\cos(2\pi w_{l}s),\\ H_{2}(w_{l},a)=\sum_{s=0}^{s_{0}}a_{s}\sin(2\pi w_{l}s)\end{split} \tag{5}\]  
for discrete normalized frequencies \(w_{l},\ l=1,\ldots,L\) according to (4). The cutoff frequency \(w_{c}\) for FR (5) is found based on the equality \(|H(jw_{A},a)|^{2}=0.5\).  
For a low-frequency FIR filter synthesis, the FR of the prototype filter is used, based on a Gaussian function \(H_{0g}(w,w_{c0})\)  
\[H_{0g}(w,w_{c0})=\exp(-(w/w_{c0})^{2}). \tag{6}\]
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Data Preprocessing
 \tag{8}\]  
In practice, \(\mathbf{R}_{0}\) can be estimated from \({}_{L}\) snapshot data:  
\[ \begin{split}\mathbf{R}_{0}&=\frac{1}{J}\sum \limits_{j=1}^{J}\big{(}\mathbf{T}\big{(}f_{j}\big{)}\mathbf{\hat{R}}_{j} \mathbf{T}^{\mathrm{H}}\big{(}f_{j}\big{)}\big{)}\\ &=\frac{1}{J}\sum\limits_{j=1}^{J}\sum\limits_{l=1}^{L}\big{(} \mathbf{T}\big{(}f_{j}\big{)}\mathbf{x}_{l}\big{(}f_{j}\big{)}\mathbf{x}_{l}^{ \mathrm{H}}\big{(}f_{j}\big{)}\mathbf{T}^{\mathrm{H}}\big{(}f_{j}\big{)} \big{)}\end{split}  \tag{9}\]  
where \(\mathbf{\hat{R}}\) denotes the estimated values of \(\mathbf{{}_{R}}\). The more accurate the estimation of \(\mathbf{{}_{R}}\), the higher the training quality of the proposed model framework. After the focusing operation, the data dimensionality of the wideband array signals can be reduced by a factor of \(J\). Particularly for broadband or ultra-wideband signals, the proposed algorithm effectively reduces the storage requirements and computational complexity. By analyzing the structure of the covariance matrix \(\hat{\mathbf{R}}_{0}\), it is known that it is a conjugate symmetric matrix, and its diagonal elements are independent of the signal incidence angle. Therefore, the upper triangular elements of the output covariance matrix are extracted as input features for the model, which can be formulated as  
\[\eta=\left[r_{12},\cdots,r_{1M},r_{23},\cdots,r_{2M},\cdots,r_{(M-1)M}\right]^ {\mathrm{T}} \tag{10}\]  
where \(r_{ij}=\hat{\mathbf{R}}_{0}(i,j)\) is the \(i\)-th row and \(j\)-th column element of \(\hat{\mathbf{R}}_{0}\). In Equation (10), the complex-valued vector \(\eta\) is decomposed into its real and imaginary components and converted into a real-valued vector for subsequent processing. After normalization, the input feature vector \(\mathbf{z}_{0}\) is derived as  
\[\mathbf{z}_{0}=\frac{\left[\Re(\eta^{\mathrm{T}}),\Im(\eta^{\mathrm{T}}) \right]^{\mathrm{T}}}{\left\|\eta\right\|_{2}} \tag{11}\]  
where \(\left\|\cdot\right\|_{2}\) denotes the \(l_{2}\) norm of the vector, \(\Re(\cdot)\) is the operation of taking the real part, and \(\Im(\cdot)\) is the operation of taking the imaginary part. The dimension of the input feature vector \(\mathbf{z}_{0}\) is \(M(M-1)\), which remains constant at all times.
# A Structural Analysis of AI Implementation Challenges in Healthcare
## 2 Literature Review
### Clinical Implementations  
Clinical application is a significant hurdle in the introduction of AI into healthcare. Despite promising advances in AI technology, successfully integrating these technologies into established healthcare procedures involves overcoming several challenges [4]. Clinicians may struggle to comprehend and trust AI-generated outputs, limiting their use in decision-making processes. Furthermore, integrating AI systems with electronic health records [12] and other aspects of healthcare infrastructure may be difficult, frequently necessitating extensive customization and interoperability concerns. Keeping AI systems compliant with regulatory norms, protecting patient privacy [4], and addressing ethical concerns complicate the deployment process.
## 3 Secchi Disk Depth Estimation Method
### \(Z_{sd}\) Estimation Based on \(K_{d}\) and \(R_{rs}\)  
The classic underwater visibility theory interprets that the \(Z_{sd}\) is inversely proportional to the sum of the diffuse attenuation and beam attenuation coefficients (\(c\), m\({}^{-1}\)) [36]. As \(c\) is generally 2-5-fold greater than \(K_{d}\), this theory contradicts the fact that no universal connections exist between \(Z_{sd}\) and \(c\), which is corroborated by human experience with respect to the Secchi disk depth. To solve these problems, [PERSON] et al. [16] developed a new underwater visibility theory, arguing that \(Z_{sd}\) is only inversely proportional to \(K_{d}\) and can be expressed as follows:  
\[Z_{sd}=\frac{1}{2.5 min(K_{d}^{tr})}ln(\frac{|0.14-R_{rs}^{tr}|}{0.013}), \tag{5}\]  
where \(K_{d}^{tr}\) denotes the diffuse attenuation coefficient of the water body over the visible spectral range (410-665 nm), and \(R_{rs}^{tr}\) represents the corresponding remote-sensing reflectance at this wavelength. Specifically, we must retrieve the \(K_{d}\) at 443, 488, 532, 555, and 665 nm.
###### Abstract  
In this paper, a new octagonal close ring resonator (OCRR)-based dumbbell-shaped tuning fork perfect metamaterial absorber for C- and Ku-band applications is presented. This design is a new combination of an octagonal ring close ring resonator with two dumbbell-shaped tuning forks metal strips integrated on epoxy resin dielectric substrate. The proposed perfect metamaterial absorber (PMA) is assessed by finite-integration technique (FIT)-based electromagnetic simulator-Computer simulation technology (CST) software. The anticipated assembly reveals dual resonance frequencies of 6.45 GHz and 14.89 GHz at 99.15% and 99.76% absorption, respectively, for TE incidence. The projected design is augmented through various types of parametric studies, such as design optimization, the effect of the octagonal ring resonator width, and varying the split gap of the double tuning fork. The numerical results are also investigated and verified using the equivalent circuit model, another electromagnetic simulator high frequency structural simulator (HFSS), and different array combinations that showed very negligible disparity. The TE polarization wave is applied to analyze the absorption separately and oblique incidence angle showing polarization insensitivity up to 30\({}^{\circ}\) and wide incident angle up to 60\({}^{\circ}\). The presented metamaterial absorber is suitable for satellite communication bands, stealth-coating technology, and defense and security applications.  
osajournal Article  
1  
Medical Design of the OCRR  
1 Space Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangaan Malaysia, Bangi 43600, Malaysia; [EMAIL_ADDRESS] (M.S.U.A.); [EMAIL_ADDRESS] (M.B.H.); armaneee@3769 mail.com (A.M.S.)  
2022 by the authors. Liconse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
reflect metamaterial absorber; octagonal close ring resonator; dual band applications; polarization insensitivity
## 3 Methods
### Silhouette Coefficient Method  
As an unsupervised learning algorithm, the K-prototypes algorithm requires an appropriate method to determine the number of clusters, that is, the value of \(K\), which directly impacts the effectiveness of the clustering results. The elbow method [59] and silhouette coefficient [60] are mostly used to calculate the value of \(K\). The elbow method is based on observing the trend of the sum of squared errors (SSE) across different \(K\) values to identify the optimal \(K\) value. However, the elbow method may be somewhat subjective in selecting the optimal \(K\) value, as the determination of the elbow point depends on the judgment of the observer [61]. In some instances, the decreasing trend of SSE may not be obvious,making it difficult to identify the elbow point. In contrast, the silhouette coefficient is generally considered to be more objective and accurate since it considers both cohesion and separation factors, measuring how similar a sample point is to other points in its cluster and how well it separates from different clusters [62]. Consequently, this study adopts the silhouette coefficient method to determine the optimal number of clusters K for the K-prototypes algorithm. The steps for calculating the silhouette coefficient are as follows:  
Step 1: Select a point denoted as \(x_{i}^{(m)}\) from cluster m. Within the same cluster, calculate the distances between point \(x_{i}^{(m)}\) and all other points \(x_{j}^{(m)}\) to identify the average of these distances within the cluster, which is \(a(i)\), reflecting the intra-cluster cohesion of the m cluster, as shown in Equation (4), where \(n_{m}\) is the total number of samples in cluster \(m\).  
\[a(i)=\frac{1}{n_{m}}\sum_{j=1}^{n_{m}}\left(x_{i}^{(m)}-x_{j}^{(m)}\right)^{2} \tag{4}\]  
Step 2: Let \(x_{j}^{(c)}\) be the point in cluster \(c,j=1,2,\ldots,n_{c}\). Calculate the average distance between \(x_{i}^{(m)}\) and \(x_{j}^{(c)}\), and then identify the minimum average distance \(b(i)\) by traversing all other clusters, as outlined in Equation (5). This reflects the separation between clusters, where \(K\) is the number of clusters.  
\[b(i)=min\left(\frac{1}{n_{c}}\sum_{j=1}^{n_{c}}\left(x_{i}^{(m)}-x_{j}^{(c)} \right)^{2},c=1,2,\ldots,m-1,m+1,\ldots K\right) \tag{5}\]  
Step 3: The silhouette coefficient \(s(i)\) is determined by both inter-cluster dispersion and intra-cluster cohesion, as shown in Equation (6).  
\[s(i)=\frac{b(i)-a(i)}{max(a(i),b(i))} \tag{6}\]  
Step 4: Calculate the average silhouette coefficient of all sample data to obtain the overall silhouette coefficient. The value of the silhouette coefficient should range from 0 to 1. As it approaches 1, the clustering results are more in line with the actual situation.
# Global Resource Scheduling for Distributed Edge Computing
## 4 Algorithm Design
### Improvement of Selection Probability  
To solve the problem that the ACO can easily fall into the local optimum and converge to a local solution quickly, we propose the idea of selective mutation. Set a variation index (VI). Random variation will occur when the probability of selecting the next node is more significant than VI. She mutates the selection probability, which is half of the original probability, and the selection probability of other nodes should also be changed accordingly. The probability formula for selecting the next node is:  
\[p_{ij-new}^{k}(t)=\left\{\begin{array}{cl}p_{ij}^{k}(t)\times\frac{1}{1+\bar{t }}&p_{ij}^{k}(t)>\text{VI}\\ p_{ij}^{k}(t)&\text{otherwise}\end{array}\right. \tag{14}\]  
where \(p_{ij}^{k}(t)\) refers to the probability of selecting the next node, while \(p_{ij-new}^{k}(t)\) refers to the probability of selecting the next node after selection and variation. \(\delta\) indicates the probability decay rate of the next node. \(t\) represents the current number of iterations. After selecting one node changes, the probability of selecting other nodes will be normalized accordingly; that is, the probability distribution of the final roulette selection can be obtained.  
According to the Formula (14), when the value of \(t\) is minimal, that is, in the first few iterations, the value of \(p_{ij}^{k}(t)\) has a significant attenuation. With the increasing value of \(t\), the probability of selecting the next node will be closer to the original unchanged probability. The advantage of this is that when the number of iterations is small, it can better weaken the probability of selecting the next node so that the algorithm has better breadth and randomness when searching for nodes and avoid quickly entering the optimal local value. With the increase in the number of iterations, the probability of modifying the selection of the next node becomes smaller and smaller and converges faster. It can ensure the breadth of ants searching for nodes and accelerate the speed of searching for the global approximate optimal solution.  
This approach is not entirely blind and random to find new results, which cannot be called a random search. Because this selection mutation operation only weakens the probability that the path with too much influence is selected, rather than discarding this probability, it makes up the defect that the algorithm converges to the optimal local solution too quickly.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Data Preparation
#### 4.1.1 Data Collection  
Data collection for intrusion detection systems (IDSs) involves consolidating raw data from diverse sources, including network traffic and system logs. Integration of incident information and external threat feeds enhances IDS capabilities. Despite challenges in real-world adoption, systematic dataset generation addresses testing limitations. Datasets like DARPA, KDD Cup 1999, NSL-KDD, ISCX IDS 2012, UNSW-NB15, CIC-IDS2017, and CSECIC-IDS2018 support research, facilitating intrusion detection system evaluation. In this research, raw data \"rDATA\" is derived from the CIC-IDS2017 and CSECIC-IDS2018 datasets, chosen for their contemporaneity and representation of real-world data.