esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The models were chosen for their distinct characteristics, which set them apart from one another: RF is based on numerous decision trees for classification and other objectives, and it reduces the tendency of decision trees to overfit. GB boosts the performance of weaker models by creating an ensemble, using simple decision trees as weak models. This machine learning model frequently performs better than RF. KNN is used for classification or regression, where the output is determined by the most common choice or the average values of the nearest neighbors. This study is significant that it utilized only reproducible radiomic features from two different ROI shapes, polygon and square. Since US visualizes two-dimensional structure vertical to the surface of the transducer, the orientation of the transducer affects US images [30], which in turn causes ROI selection within images to influence radiomic feature values [1; 31]. This study sought to reduce shape effects in radiomic analysis by selecting features with an ICC over 0.8 across two ROI shapes. The previous literature has shown that parotid gland function differs according to age and sex [25]. From a cellular aspect, gene expressions have also been found to differ in older parotid glands [26]. This study is meaningful because it examines this divergence in parotid gland imaging biomarkers. Given the exploration of the biologic significance of radiomics, it can be speculated that age- and sex-related variation in radiomic features exists within normally functioning organs. Although such biologic differences have been widely studied, no previous research has scrutinized the differences in radiomic features. In US of the normal population, harmonization of radiomic features in submandibular glands was investigated across various units, rather than categorizing through radiomic features as in the current study [17]. In addition to US, the fat fraction of the parotid gland was analyzed in magnetic resonance imaging with the IDEAL-IQ technique, comparing the control to the sialadenitis group [32]. The fat fractions in the sialadenitis group were lower (\(p<0.001\)) than those in the control group across all body mass index categories. This study has some limitations. Although a relatively large number (91 images) of parotid gland US images were used [5; 17], a greater number of images would likely produce more robust results. Furthermore, only two ROI shapes were used for reproducible feature selection. Nonetheless, to the best of my knowledge, the present study is the first study to reduce shape effects in the radiomic analysis of US images. Lastly, images from only one US unit were included; including images from more units would likely yield more solid results. Future studies with more images from various US units and further reduced shape effects are needed for more generalizable conclusions.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 2 System Description  
### Possible Problem Statement  
When a large-scale wind farm is connected to the main power grid, a wind farm management system (WFMS) is used to provide a regulating process by focusing on a particular objective. The WFMS is based on the SCADA network, designed to interact with connected units, including turbines. Figure 1 represents the flow of information that needs to be considered in the WFMS. By applying the acquired data, generated reference values are allocated to the whole generation system. However, since the signals flow through a communication network, there is the potential for information disruption within the update process. The aim of this paper is to provide an appropriate reactive power order modification to avoid potential disruptions. The required response actions in WFMS, in terms of order generation, are as follows:  
* Unexpected device malfunction and maintenance
* Order changes, and low voltage conditions due to faults
* Tripping, due to over voltage conditions.  
As a reactive power order is formed to respond to voltage variations at the grid side, an automatic response mechanism is required, taking into account the above considerations. With this mechanism, the previous allocation plan can be operated within the designated compensation range during communication failure.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 5 Simulation Results  
In this section, a comprehensive and detailed analysis of the proposed neighborhood-enabled TDMA and existing approaches (such as a hybrid-approach TDMA [35], an opportunistic TDMA [36], a prioritized TDMA [28], a cooperative TDMA [37] and a cooperativeMAC [38]) is presented. These algorithms were implemented in OMNET++, which is an open source simulation software, and evaluated based on numerous performance metrics, such as average throughput, slot waiting time interval, empty slot utilization, and APDR. A random deployed methodology was adopted to generate the IoT network where an embedded delay is assumed in the transceiver modules of every device \(C_{i}\). Additionally, various other parameters that were used in the simulation setup are shown in Table 1.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 2 Problem Formulation
### SR-WLS Algorithm
\[\widehat{b}=\frac{\sum_{i=1}^{N}\left(P_{0}-P_{i}-10\gamma\log_{10}\frac{\| \hat{\mathbf{x}}-\mathbf{a}_{i}\|}{d_{0}}\right)}{N},\]  
\[\widehat{\beta}=\frac{\sum_{i=1}^{N}\left(d_{i}-\|\hat{\mathbf{x}}-\mathbf{a}_{i}\| \right)}{N}.\]  
The alternating procedure is given in a flow chart, presented in Figure 1. For more details, the reader is referred to [21]. As it can be seen from Figure 1, SR-WLS is composed of two main phases: solving the localization problem and updating the NLOS bias estimates. In the first iteration, all links are treated as LOS, i.e., \(\widehat{b}=\widehat{\beta}=0\) is set. With the use of these estimates, the localization problem is solved to acquire an estimation of the target location. Then, by exploiting this estimate, an update of the NLOS bias estimates is performed, and the localization problem is solved again by employing the updated estimates. This alternating procedure is executed \(T_{\text{max}}\) times [21]. Figure 1: Flow chart diagram of the SR-WLS algorithm in [21].
# Differentially Private XGBoost Algorithm for Traceability of Rice Varieties
## 3 Methodology
### XGBoost Algorithm  
Suppose \(D=\{(x_{i},y_{i})\},i=1,2,\cdots,N\) is the training set, where \(x_{i}\in\mathbb{R}^{d}\) represent the features and \(y_{i}\) represent the labels. \(d\) represents the dimension of the feature. We develop a new XGBoost algorithm named DPXGB to obtain \(\epsilon\)-differential privacy guarantee. To achieve this goal, we need to define the basic model of the boosting method. The strong classifier model composed of K weak classifiers can be described by the following expression  
\[\hat{y}_{i}=\phi(x_{i})=\sum_{k=1}^{K}f_{k}(x_{i}),f_{k}\in \mathcal{F}. \tag{5}\]  
where \(\mathcal{F}=\{f(x)=w_{q(x)}\}(q:\mathbb{R}^{d}\to T,w\in\mathbb{R}^{ T})\) is the hypothesis space of the weak classifiers. \(q\) represents the structure of each weak classifier. \(T\) is the number of leaf nodes in a decision tree or regression tree.  
\[\mathcal{L}=\sum_{i=1}^{N}l(\hat{y}_{i},y_{i})+\sum_{k=1}^{K}\Omega(f_{k}). \tag{6}\]  
where \(\Omega(f)=\gamma T+\frac{1}{2}\lambda\|w\|^{2}\) is a regular term that limits the complexity of the model, thus ensuring that the model will not be over-fitting. By using Taylor expansion to expand to the second order, Equation (6) can be approximately expressed by the following form  
\[\mathcal{L}\simeq\sum_{i=1}^{N}[l(y_{i},\hat{y}_{i}^{(t-1)})+g_{i}f_{t}(x_{i} )+\frac{1}{2}h_{i}f_{t}^{2}(x_{i})]+\sum_{k=1}^{K}\Omega(f_{k}). \tag{7}\]  
where \(g_{i}=\mathcal{L}^{\prime}(y_{i},\hat{y}_{i}^{(t-1)})\) and \(h_{i}=\mathcal{L}^{\prime\prime}(y_{i},\hat{y}_{i}^{(t-1)})\) represent the first-order and second-order derivatives of loss function.  
Since \(l(y_{i},\hat{y}_{i}^{(t-1)})\) is obtained at the \(t-1\)-th iteration, which is a certain value at the \(t\)-th iteration, we can omit this constant term.Let \(I_{j}=\{i|q(x_{i})=j\}\) be the set of all samples in the \(j\)-th leaf node. Thus, we can obtain the expression of the loss function as follow  
\[\mathcal{L}^{(t)}=\sum_{j=1}^{T}\left[\left(\sum_{i\in I_{j}}g_{i}\right)w_{j }+\frac{1}{2}\left(\sum_{i\in I_{j}}h_{j}+\lambda\right)w_{j}^{2}\right]+ \gamma T. \tag{8}\]  
To obtain the optimal leaf weight \(w^{*}\), we take the derivative of the Equation (8) and let it be equal to zero. Then we have  
\[\begin{cases}W_{j}^{*}=-\frac{G_{j}}{H_{j}+\lambda},\\ \\ Obj^{(t)*}=\min Obj^{(t)}\approx\gamma T-\frac{1}{2}\sum_{j=1}^{T}\frac{G_{j }^{2}}{H_{j}+\lambda}.\end{cases} \tag{9}\]  
where \(G\) and \(H\) are the cumulative values of the first derivative and the second derivative on the \(j\)-th leaf node, respectively. We can also express them mathematically as \(G_{j}=\sum_{i\in I_{j}}g_{i}\), \(H_{j}=\sum_{i\in I_{j}}h_{j}\). \(Obj^{(t)*}\) represents the optimal objective function at the \(t\)-th iteration, which is obtained by substituting the optimal weight into the objective function.
## 5 Conclusions  
In this study, we compare the loss tangent values and field attenuation ratios of four types of water media in the frequency range commonly adopted in CPT systems and evaluate at what operating frequency and media spacing each media can have a positive effect on the CPT system. The current density in Ampere's law can be divided into conduction current components and displacement current components, and depending on which current is dominant, the media can be classified as good conductors or good dielectrics. A medium defined as a good conductor may not be suitable for use in a CPT system because the field intensity is attenuated according to the propagation distance even if the medium has a high permittivity. As a result, most water types except distilled water can be considered as dielectrics with high losses in the MHz band, which can negatively affect the transmission distance and transmission efficiency.  
Figure 10: Comparison of maximum \(S_{21}\) and \(C_{m}\) (at \(d=10\) mm to 200 mm).  
In addition, we designed an underwater stacked CPT coupler and analyzed its transmission characteristics inside a medium (tap water). As a result, the underwater stacked CPT coupler has a large resistance compared to the air coupler due to the influence of water conduction resistance, which significantly reduces the performance of the system. Underwater stacked CPT couplers are disadvantageous for power transmission compared to underwater parallel CPT couplers under the same conditions due to the reduced mutual coupling between the transmitter and receiver due to high cross-coupling and the influence of water conduction resistance. In this study, the water conduction resistance \(R_{w}\) is expressed as a series-connected element of \(C_{1}\) and \(C_{2}\) in PEC, which can be defined because most of the components of \(C_{1}\) and \(C_{2}\) are \(C_{12}\) and \(C_{34}\). However, since the capacitances of \(C_{1}\) and \(C_{2}\) also include mutual coupling components between the transmitter and receiver, \(R_{w}\) is a parameter that is determined more complexly. A clear definition of \(R_{w}\) in water media is left for future work. The results of this study may help evaluate media with the potential to improve the performance of underwater CPT systems and suggest why stacked CPT couplers used in air may be unsuitable for use underwater.  
Data curation, K.B.; validation, K.B. and S.P.; investigation, S.P.; formal analysis, K.B.; visualization, K.B.; software, K.B.; writing--original draft preparation, K.B.; writing--review and editing, S.P.; supervision, S.P. All authors have read and agreed to the published version of the manuscript.  
This results was supported by the \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004). This work was supported by the Soonchunhyang University Research Fund (No.20230654).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 6 Results and Discussion
### Comparison with Other Optimization Algorithms  
This section provides a comparison between the best approach BHHO\({}_{TV4}\) and other well-known metaheuristic methods (BGSA, BPSO, BBA, BSSA, and BWOA). The comparison is made based on different criteria, including average classification accuracy, number of selected features, and fitness values. As per results in Table 7, it can be observed that BHHO\({}_{TV4}\) outperforms other algorithms for 11 out of 18 datasets in terms of accuracy rates. It reached the maximum accuracy averages on five datasets. We see that BHHO\({}_{TV4}\), BPSO, and BSSA reached maximum accuracy for the Zoo dataset. In addition, compared to BHHO\({}_{TV4}\), it can be seen that BPSO obtained better results on Exactly2, Vote, and WaveformEW datasets. As per F-test results, we observe the BHHO\({}_{TV4}\) is ranked one, followed by BPSO, BSSA, BWOA, BGSA, and BBA methods. To see whether the differences between obtained results from BHHO\({}_{TV4}\) and other algorithms are statistically significant or not, a two-tailed Wilcoxon statistical test with 5% significance was used. Table 8 presents the p-values of the Wilcoxon test in terms of classification accuracy. It is clear that there are meaningful differences in terms of accuracy averages between BHHO\({}_{TV4}\) and its competitors in most of the cases. In terms of the least number of selected features, as stated in Table 9, it is observed that BHHO\({}_{TV4}\) obtained the best averages on 13 out of 18 datasets while BPSO outperforms all other algorithms on three datasets. As per F-test results, we can see that the BHHO\({}_{TV4}\) is ranked as the best one, followed by BPSO, and BBA methods, respectively. Inspecting the results of the \(p\)-value in Table 10, it is evident that the insignificant differences in terms of the lowest number of selected features between BHHO\({}_{TV4}\) and other peers are limited. Fitness rates are shown in Table 11, and it can be noticed that BHHO\({}_{TV4}\) reached the lowest fitness values compared with other algorithms on 11 out of 18 datasets. We can also see that BPSO is the best in four cases. Again, according to F-test results as in Table 11, it is clear that the BHHO\({}_{TV4}\) is ranked as the best, followed by the BPSO method. In addition, Table 12 shows the \(p\)-values of the Wilcoxon test in terms of best fitness rates. It can be observed that the differences between BHHO\({}_{TV4}\) and others are not statistically significant in only four cases. The convergence behaviors of BHHO\({}_{TV4}\) and other algorithms were also investigated to assess their ability to make an adequate balance between exploration and exploitation by avoiding local optima and early convergence. The convergence behaviors of BHHO\({}_{TV4}\) on 12 datasets compared to other optimizers are demonstrated in Figures 4 and 5. In all tested cases, the superiority of BHHO\({}_{TV4}\) can be seen in converging faster than other competitors towards the optimal solution.
## 4 Results and Discussion
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation
#### 4.1.9 Does the Company Perform Data Analysis during the Production Process?  
In order to evaluate the sophistication of management of the production process, this question aimed to analyze the appetite of companies to perform data analysis during the production process. As shown in Table 5, item 9, 81% of the companies stated that they performed data analysis during the production process.  
1.10 Should the Results of These Analyzes Be Cross-Checked with the Traceability of Production Information?  
Following questions to assess sophistication, companies were asked whether they cross-referenced the analysis of collected data results during the production process with the traceability of production information. In this case, 57% of companies claimed to carry out this task, as shown in Table 5, item 10.
## 5 Results for Part 2
### Knowledge Retention  
We measured the retention of knowledge among the two groups; those who played and those who read the notes. We used the t-test used to compare the mean difference of the performance of each group across all questions in the pre-test experiment and two weeks later and found that it has great statistical significance. The mean marks of the game players and notes reader groups are 88.2% and 43.1%, respectively (t = 8.7, \(p\)-Value \(<\) 0.0001) in the pre-test experiment and 82.1% and 37.99% (t = 6.9, \(p\)-Value \(<\) 0.0001) two weeks later, as indicated in Figure 14. However, the performance of the participants who played the game did not differ significantly between the pre-test (88.2%) and two weeks later (82.1%) (t = 0.0056, df = 21.228, \(p\)-Value = 0.8998). Moreover, reading notes has not improved the knowledge of the participants in the control group; rather they continue to have lower performance in pre-test (43.1%) and two weeks later (37.9%). The statistical results depicted that there were no significant differences in their knowledge through reading notes from the two experiments (t = 0.012, df = 41.89, \(p\)-Value = 0.9999). The performance of the two groups has been seen to drop slightly in almost equal dimensions between the first experimental results and two weeks later. Those who played the game dropped from 88.2% to 82.1%, and those who read the notes dropped from 43.1% to 37.9%. Despite the slight decrease in performance of both groups, the group that played the game maintained at least 80% of their knowledge from the previous experiment, which is still reasonable knowledge retention since they only played the game a few times. On the contrary, the group that read the notes exhibited a continuous drop in performance. Therefore, the game enabled the participants to retain their knowledge of how to protect themselves from phishing at a higher rate than notes reading; however, the rate would have been higher if participants had been given more time to play the game.  
Figure 13: Teenagers’ phishing knowledge performance after playing the game and reading notes.
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 4 Experimental Results
### Experimental Results and Analysis  
In this section, we present the PSNR results of our proposed two-stage denoising method. To validate the effectiveness of our approach, we conducted experiments on both synthetic and real-world noisy images. Initially, we performed denoising experiments on synthetic images from the SET12, BSD68, and Urban100 datasets and compared them with other supervised and unsupervised denoising methods. Tables 5-7 provide a detailed comparison of various denoising methods on each noisy image from the SET12 dataset, while Table 8 provides an overall performance comparison of various denoising methods on the SET12, BSD68 and Urban100 datasets at different noise levels. The best results are highlighted in bold. It can be observed from Tables 5-8 that the proposed method outperforms the second-ranked algorithm by a margin of 0.4 dB on the SET12 dataset, 0.14 dB on the BSD68 dataset, and 0.30 dB on the Urban100 dataset, respectively, in terms of the PSNR evaluation metric. This indicates that our proposed method achieves superior results compared to mainstream supervised learning approaches such as FFDNet, DAGL, DreamNet, DRUNet, and SwinIR, in addition to unsupervised learning methods such as DIP and N2V. Furthermore, to assess the efficacy of our proposed methodology in real-world noisy images, we conducted experiments on a randomly selected subset of 10 images from the SIDD dataset and compared the denoising outcomes with state-of-the-art algorithms including BM3D, FFDNet, SwinIR, DRUNet, DRCNN, IRCNN, and DIP. Given that real-world noisy images presents greater complexity than synthetic ones, conventional approaches exhibit comparatively inferior performance when confronted with real-world noise images. Table 9 displays the average PSNR value of the competing methods, with the best result highlighted in bold. Based on experimental results, our proposed method achieves a superior performance by 0.7 dB compared to the second-ranked denoiser, which demonstrates the distinct advantages of our approach for addressing real-world denoising problems. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number of Blocks** & **1 Block** & **2 Blocks** & **3 Blocks** & **4 Blocks** & **5 Blocks** \\ \hline Size & 256 \(\times\) 256 & 512 \(\times\) 512 & 256 \(\times\) 256 & 512 \(\times\) 512 & 256 \(\times\) 256 & 512 \(\times\) 512 & 256 \(\times\) 256 & 512 \(\times\) 512 \\ Time (s) & 30.94 & 110.33 & 60.24 & 212.45 & 89.54 & 317.49 & 119.52 & 424.34 & 147.92 & 524.55 \\ PSNR & 30.98 & 33.07 & 31.05 & 33.12 & 31.18 & 33.13 & 31.17 & 33.20 & 31.16 & 33.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The execution time required for denoising images across diverse resolutions using different numbers of Transformer blocks.
# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 5 Case Study
### The Industrial Asset Stream  
#### 5.2.1 Critical Components Identification  
In the Industrial Asset Stream, the analysis initially focused on studying one of the company's assets to identify the most critical components and make decisions about the methods and countermeasures to adopt to avoid failures.  
The analysis was carried out applying the FMECA methodology [50], which allowed one to decompose the asset into smaller components and associate an RPN with each one, thus defining the criticality of each one. The RPN is usually computed through the multiplication of three values:  
* Severity (S) evaluates the consequences of a failure in terms of costs or time, or other indicators depending on the company's interest.
* Occurrence (O) evaluates the probability of occurrence of a failure.
* Detectability (D) expresses the possibility of detecting a failure happening to the object of analysis.  
All these values may be computed based on the experience of the experts or historical data collected by the company. Beta was already using FMECA to identify criticalities in its assets, but only in the design phase. The novelty brought by the D3M framework consisted of using a Dynamic FMECA [51] and periodically updating the RPNs based on the operational and maintenance data of the asset. Indeed, the traditional FMECA, created during the asset design, did not consider the possible misuses of the customer that could lead to unexpected failures. Using the Dynamic FMECA, the company adapted maintenance policies (advised or sold to the customer) to the actual use of the asset, increasing their effectiveness.