esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Classification of Metro Vehicle Wheelset Health States Based on Hierarchical Clustering
#### 3.4.1 SHI Construction Based on Health Class Centers  
If a wheel has failed during the maintenance cycle, it indicates that there are Run to Failure (Rtf) data for that wheel. Therefore, for training samples containing Rtf data, it is essential that both failure data and healthy data are present across all classes generated by HC.  
This study proposes using the geometric center \(C_{h}\) of the healthy class as the baseline for calculating the health factor.  
Figure 2: Maximum number of clusters setting.  
The geometric center \(C_{h}\) of the healthy class is represented as a \(1\times m\) vector, where \(m\) denotes the dimensionality of the data used for the clustering analysis. Assuming the total number of samples is \(n\), the data for the i-th sample, after dimensionality reduction, is represented as an \(l_{i}\times m\) matrix denoted by \(R\), with \(l_{i}\) indicating the lifespan of sample \(i\). Based on the distance between \(R\) and \(C_{h}\), a composite health factor \(H_{i}(l_{i})\) can be generated, where \(i=1,2,\ldots,n,\ l_{i}=1,2,\ldots,L_{i}\). The calculation formula employs the Euclidean distance:  
\[H_{i}(l_{i})=\sqrt{\big{(}R_{l_{i}}-C_{h}\big{)}\big{(}R_{l_{i}}-C_{h}\big{)} ^{\prime}} \tag{14}\]  
Qualitatively, a larger value of the SHI indicates a greater distance from the geometric center of the healthy class, suggesting that the health status of the wheelset is deteriorating.
## 4 Implementation Results and Discussion
### TI Implementations  
If first-order security is sufficient in an application, TI is a valid choice for a masking scheme. Regarding area, our results for the KSA adder are similar to the figures from [2]. The clock frequency that was estimated by the synthesis tool differs significantly from our results, although the authors performed their benchmark on an FPGA from the same family as we did. Different speed grades of the devices and different constraining of the synthesis might explain this discrepancy. Note that our implementation only uses 31 bit of randomness as the carry output is not computed. The SA design utilizes significantly less logic than the KSA, which can be attributed to the lower number of PG blocks. The number of flip-flops is the same as it is dominated by the number of stages in a pipelined architecture. When masking using TI, the BKA utilizes even less logic than the SA design but the higher number of stages lead to a higher flip-flop requirement. In a scenario where logic is expensive and high throughput is demanded but memory and latency are less important, the BKA is a suitable design.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Design** & **LUTs** & **Flip-Flops** & **Freq. (MHz)** & **Latency** & **Rand. (Bit)** \\ \hline TI KSA [2] & 937 & 1330 & 62 & 6 & 32 \\ TI KSA & 873 & 1416 & 228 & 6 & 31 \\
1 st-order HPC2-KSA & 2936 & 3981 & 176 & 12 & 249 \\
2 nd-order HPC2-KSA & 3915 & 8001 & 148 & 12 & 747 \\ \hline TI SA & 579 & 1416 & 174 & 6 & 41 \\
1 st-order HPC2-SA & 1801 & 3166 & 153 & 12 & 119 \\
2 nd-order HPC2-SA & 1994 & 5979 & 128 & 12 & 357 \\ \hline TI BKA & 487 & 2352 & 280 & 9 & 31 \\
1 st-order HPC2-BKA & 1588 & 4317 & 173 & 18 & 74 \\
2 nd-order HPC2-BKA & 1666 & 7122 & 158 & 18 & 222 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Implementation results for different 32-bit adder designs.
## 2 Materials and Methods
### Testing Design  
Conducting full-factor tests is challenging due to the weak influence of low-sensitivity factors and their interaction on \(R_{\rm n}\). Based on this, sensitivity analyses of \(R_{\rm n}\) for seven typical factors were conducted using the control variable method, requiring 28 test groups. The main and secondary controlling factors were identified, and each independent analysis was performed for the main controlling factors. The interaction analyses were then conducted, and orthogonal theory was applied to design the experiments [33; 34; 35], using the L16(4\({}^{\rm d}\)) orthogonal design table shown in Table 3. In total, 37 experimental groups were conducted, excluding 7 replicate sets. To ensure comparability, each experimental group started from the same reference point and start mode. Additionally, any damage to the falling rock specimen or slope surface material, along with changes in test factors and levels, was promptly addressed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & \multicolumn{4}{c}{**Actual Levels ***} \\ \hline Shape factor of falling rock (\(\eta\)) & 1 & 1.167 & 1.242 & 1.390 \\ Material factor (\(A_{\rm slope}\)), slope surface & 0.1295 & 0.1996 & 0.3454 & 0.4340 \\ Initial velocity (\(v\)/m\(\cdot\)s\({}^{-1}\)) & 0 & 2 & 4 & 6 \\ Slope angle (\(\theta\)/rad) & 0.5236 & 0.7853 & 1.0472 & 1.3090 \\ Material factor (\(A_{\rm rock}\)), rock type & 0.7149 & 0.7979 & 0.8695 & 1.0279 \\ Falling height (\(h\)/m) & 0.625 & 1.25 & 1.875 & 2.5 \\ Falling rock mass (\(m\)/kg) & 0.44 & 0.65 & 0.93 & 1.27 \\ \hline \hline \end{tabular}  
* Shape factor of falling rock (\(\eta\)): the index of the falling rock, with higher values corresponding to more angular rocks and lower values to rounder rocks. Material factor (\(A_{\rm slope}\)): reflects the rigidity and deformability of the slope surface material. Higher values represent softer, energy-absorbing materials like turf, while lower values correspond to rigid surfaces like concrete. Initial velocity (\(v\)): the speed at which the rock is released, representing kinetic energy at the moment of impact. Slope angle (\(\theta\)): the angle of the slope with respect to the horizontal, affecting the impact velocity and directional forces. Material factor (\(A_{\rm rock}\)): characterizes the rigidity and hardness of the falling rock. Higher values represent denser and harder rocks. Falling height (\(h\)): the vertical distance from which the rock is released, affecting the initial impact velocity. Falling rock mass (\(m\)): the mass of the falling rock, influencing the magnitude of the forces involved in the collision.  
\end{table} 
Table 2: Seven influencing factors and their actual levels used in this study to model rockfall behavior. The actual levels represent the quantitative settings of each factor, indicating the range of values tested in the experiment. These levels were chosen to evaluate the effects of individual and interacting factors on \(R_{\rm n}\).
## 4 Role of UAV in Precision Pest Management
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Crop Name**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{3-6}  & & **Type of UAV** & **Camera** & **No. of Rotors** & **Pest Name** & **Observations** \\ \hline [PERSON] et al., 2018 [42] & Cotton & Multi-Rotor & Multispectral & 4 & Two-spotted spidermite & Damage assessments \\ \hline [PERSON] et al., 2018 [43] & Sugar Beet & Multi-Rotor & Hyperspectral & 4 & \begin{tabular}{c} Beet \\ Cyst Nematode \\ \end{tabular} & Visual Images \\ \hline [PERSON] et al., 2017. [44] & Potato & Multi-Rotor & Multispectral & 6 & Colorado potato beetle & Damage assessments \\ \hline [PERSON] et al., 2017 [45] & Sorghum & Fixed Wing & Multispectral & 1 & Sugarcane aphid & Arthropod counts \\ \hline [PERSON] et al., 2016a. [46] & Canola & Multi-Rotor & Multispectral & 8 & Green peachapidid & \begin{tabular}{c} Arthropod counts, soil and plant tissue \\ nutrient analyses \\ \end{tabular} \\ \hline [PERSON] et al., 2016 [47] & Onion & Fixed Wing & Multispectral & 1 & Thrips & NA \\ \hline [PERSON] et al., 2021 [19] & Wheat & Multi-Rotor & \
\begin{tabular}{c} RGB, \\ Multispectral \\ \end{tabular} & 4 & Fall armyworm &
\begin{tabular}{c} Outbreak reported by \\ grower \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Remote sensing data is used for studying the infestations of pests and insects in agricultural fields efficiently. In winter wheat (Triticumaestivium) fields in Kansas, USA, the association between Hessian fly (Mayetiola destructor) infestation and normalized difference vegetation index (NDVI) is evaluated using aircraft data and multispectral satellite. In each field, Hessian fly infestation was surveyed with multiple sampling points in a uniform grid fashion. The results have proven an increase in pest infestation with decreased NDVI in both aircraft and satellite data. NDVI satellite data performed better than NDVI aircraft data in pest infestation fields. The results show that remote sensing technology data can be used for monitoring the health of wheat plants and areas of poor growth [50]. Infestations of pests and insects in the agriculture field are not uniform and can proliferate in intensity and size. Remote sensing with multispectral data is used for assessing the sorghum fields for the infestations by sugarcane aphids. The difference in the normalized differenced vegetation index (NDVI) with bi-temporal images and analysis of changes in the image captured is efficient for assessing the infestation of temporal changes in the sorghum field by the sugarcane aphids. Experimentation on comparing changes in the field and distribution categories concerning normalized differenced vegetation index (NDVI) image classification from the sorghum field with infested sugarcane aphid, an essential technique for assessing the infestations of temporal changes by sugarcane aphids
## 4 Results and Findings
### Top Ten Most-Cited Publications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rank** & **Title** & **Authors** & **Research Focus** & **Journal** & **Citations** \\ \hline \multirow{4}{*}{1} & “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions” & [50] & Deep Learning techniques, CNN Architecture, Major concerns and future trends in DL & \multirow{4}{*}{_Journal of Big Data_} & \multirow{4}{*}{3744} \\  & “Machine Learning: Algorithms, Real-World Applications and Research Directions” & [51] & Overview of ML Techniques, ML applications and future scope & \multirow{4}{*}{_SN Computer Science_} & \multirow{4}{*}{2331} \\  & “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions” & [52] & DL techniques, applications and threats & \multirow{4}{*}{_SN Computer Science_} & \multirow{4}{*}{1205} \\  & “A state-of-the-art survey on deep learning theory and architectures” & [53] & DL algorithms applications and future research directions & \multirow{4}{*}{_SN Computer Science_} & \multirow{4}{*}{1172} \\  & “ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns” & & & \\  & “Wild patterns: Ten years & & & \\  & 6 after the rise of adversarial machine learning.” & [55] & Advancements and Concerns & \multirow{4}{*}{_Electronics (Switzerland)_} & \multirow{4}{*}{1172} \\  & “Machine Learning and Deep Learning Methods for Cybersecurity” & [56] & Evaluation of ML and DL in cybersecurity & \multirow{4}{*}{_Healthcare (Switzerland)_} & \multirow{4}{*}{1110} \\  & “Distributed attack detection & & & \\  & scheme using deep learning approach for Internet of Things” & & & \\  & “Blockchain for AI: Review and open research challenges” & [58] & Blockchain integration with AI & \multirow{4}{*}{_Pattern Recognition_} & \multirow{4}{*}{875} \\  & “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches” & [59] & ML Techniques for Cyber Attacks and Anomaly Detection & \multirow{4}{*}{_IEEE Access_} & \multirow{4}{*}{677} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top ten most-cited publications in ML/DL by search results.
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
## 1 Introduction
The difference between the known and the proposed approach is as follows:  
* first, we must ensure the transfer of all data to all distrustful nodes (in known versions, data are not delivered to them);
* second, if there are no paths to transmit packets to some nodes, without the participation of distrustful nodes, they will still be used for transmission;
* third, the number of distrustful nodes, if they must be used, should be minimized, as well as the total number of all intermediate nodes. In this work, we propose an approach in which potentially threatening nodes (distrustful) will not be included in the routing table as intermediate nodes, if possible. If it is impossible to have a path without distrustful nodes, their number in the route should be as minimal as possible. To some extent, it can be considered that node trust is the second additional criterion used by us when building routing tables. It should be noted that the value of this criterion (trustworthy or untrustworthy) may change over time. Thus, the object of this study is the process of routing in telecommunication networks. The subject of this research is algorithms used to obtain the shortest path tree, when the routing table is constructed. This research aims to propose paths for the transmission of packets that will contain the minimum possible number of distrustful nodes. The main contributions of this paper can be summarized as follows:  
* we substantiated the need to consider an additional condition when forming routing tables in a network with distrustful nodes, for example, in the case of temporarily occupied territories;
* we modified [PERSON]'s algorithm for obtaining the shortest path tree, based on the criterion of the minimum number of hops, which will provide the shortest path without intermediate distrustful nodes, if such a path exists;
* we also show that our algorithm will find a path with the minimum possible number of distrustful nodes, for the case when there is no shortest path without using such nodes.
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 5 Conclusions  
In response to the need for equitable resource distribution among nodes in an RF network over IBOC FM, cooperative game theory with transferable utility is proposed as an optimization strategy. The goal is to maximize the allocated bandwidth for each node based on the service class, incorporating the Shapley value in a saturated channel state. This approach optimizes resource allocation by recognizing cooperation among nodes and ensuring adequate Quality of Service (QoS) levels. In the proposed scenario, the cooperative game is framed as a bankruptcy game, characterized by a ternary structure (_N_, \(d\), _C_). Here, \(N\) = {1,2,..., _n_} denotes the set of players, \(d\) = {_d_1,\(d\)2,..., _dn_} with _di_ \(\geq\) 0 for all \(i\)\(\in\)\(N\) represents the vector of demands, and \(C\) signifies the net value to be distributed among the elements of \(N\). This formulation allows for the establishment of game imputations, leading to the estimation of the resulting payment vector using four proposed techniques. The outcomes of employing the Shapley value demonstrated outstanding results in equitable resource allocation, closely aligning with the specified service class requirements, with a confidence level of 95%. Furthermore, it was observed that the total sum of bandwidth assigned to each player aligns precisely with the total bandwidth available in the RF channel. This reinforces the effectiveness of the Shapley value as an optimal strategy for resource allocation in cooperative RF network scenarios.  
M.R.M.: Conceptualization, methodology, software, validation, investigation, project administration, and funding acquisition; J.C.V.F.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, and supervision; J.C.V.: validation, investigation, data curation, and supervision; M.C.R.: resources, visualization, supervision, project administration, and funding acquisition; A.A.D.T.: Conceptualization, methodology, validation, and investigation; W.A.C.C.: Conceptualization, validation, resources, and data curation. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the financial support provided by UNAD (ECPITEXT102022--CRC) and Micenicias during the development of this study. The authors want to acknowledge Micenicias and CRC through \"Conv. 908-2021- nuevo conocimiento, desarrollo tecnologico e innovacion para el fortalecimiento de los sectores de TIC, postal y de contenidos audiovisuales\" for technical and financial support of project 80740-032-2022.  
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.
## 4 Torsional Vibration Control and Analysis of a Diesel Generator Set
### Results Obtained from the Gradient Flow Method  
In this case, it is expected to change the original 10 th natural frequency (267.20 Hz) and 11 th natural frequency (267.77 Hz) to the corresponding desired frequencies of 262.50 Hz and 263.00 Hz, respectively. From the results given in Table 9, the 10 th natural frequency and 11 th natural frequency are shifted to 262.50 Hz and 262.99 Hz, respectively, and all the relative errors between the modified frequencies and the desired frequencies are equal to zero. In addition, according to the actual engineering requirements, it is impractical or unrealistic to change the parameters of diesel engines and generators after their designs have been finished or real machines have been produced. For this reason, it is assumed in this example that the relevant inertia (\(J_{3}\), \(J_{4}\), \(J_{5}\), \(J_{6}\), \(J_{8}\)) and stiffness (\(K_{34}\), \(K_{45}\), \(K_{56}\), \(K_{78}\)) parameters of the diesel engine and generator remain unchanged. From Table 10, it can be shown that the maximum changes of inertia and stiffness are 0.02 (kg\(\cdot\)m\({}^{2}\)) and 0.04 (10\({}^{5}\) N/m), respectively. These two small changes have almost no impact on the vibration characteristics; thus, they can be neglected in practice. Through the above analysis, it can be concluded that the torsional vibration of the shafting associated with the close frequencies is effectively controlled by shifting the relevant frequencies under the constraints of practical engineering operability. This example provides a useful scheme for the torsional vibration control of complex branched shafting.  
Figure 12: Frequency response function with desired frequencies modification.
## 4 Results and Discussions
### Scenario II: Identification of Gap Size  
This section provides the investigation results of Scenario II, where gap size is identified from acoustic process emissions. This scenario aims to illustrate whether the information in a segment suffices to determine the severity of error in a joint, dealing with more detailed information than Scenario I. Since it involves more classes than Scenario I, visualizing the class separability is not possible. Consequently, the informativeness of signal segments is assessed using both classification accuracy and relative recall presented in Sections 4.3.1 and 4.3.2, respectively.
## 4 Results and Discussion
### Case Study for DMSA in Static Scheduling
#### 4.1.2 Algorithm Parameters Initializing  
On the data preprocessing stage, the satellites and targets information in the scenario is imported into STK in batches by MATLAB, and the visible window information of payload and target is obtained through the visible analysis and calculation of STK. On this basis, the adaptive PSO algorithm is then called for task scheduling. The main parameters of the PSO algorithm are set as follows: particle population set to 50, PSO maximum iteration number set to 100, acceleration coefficients \(c_{1}=c_{2}=1.5\), inertia weighted parameter \(\lambda_{max}=0.9\),  
Figure 5: Test scenario example.  
\(\lambda_{min}=0.4\). The indication of early termination is that best fitness value of population has not been improved for 15 consecutive generations. The length of memory table \(L\) is set as 5.
## 4 Comparison Studies with LASSO and PCR Methods
### Principal Component Regression Method  
The eigenvalues are calculated and arranged in order from large to small, and the corresponding eigenvectors are arranged accordingly. The principal component contribution rate is computed based on the eigenvalue results, as shown in Figure 16.  
According to the results of Figure 16, principal component 1 and principal component 2 account for more than 95% of all components, which satisfies the requirement of principal component analysis of greater than a 85% contribution rate. Figure 17 shows the strain distribution of the training and testing dataset after projection in principal component 1 and principal component 2, and their distribution is used to determine whether the reduction is reasonable.  
Figure 16: Principal component contribution rate of first two components.  
Figure 17: Projection of training and test datasets in principal components 1 and 2.  
From Figure 17, it manifests that the projection distribution of all training sets and test sets on principal component 1 and principal component 2 is scattered, and there is no obvious correlation between them. After the reduction from 13 dimensions to principal component 1 and principal component 2, the training sets and test sets can be clearly distinguished from each other.  
The error comparison of the No. 1 loading actuator is shown in Figure 18.  
The predicted errors of the No. 2 loading actuator are presented in Figure 19.  
Figure 19: Expected and predicted load errors of No. 2 load actuator.  
Figure 18: Expected and predicted load errors of No. 1 load actuator.  
From the results of Figures 18 and 19, the prediction error of principal component regression is less than 5%, and the accuracy is clearly improved when compared with two different monitoring points based on LASSO regression. However, the PCR method requires the information of all 13 monitoring points. Thus, it is not suitable for strain gauge location optimization.
## 5 Simulation
### Varying Packet Generation Rate
#### 5.2.1 Average End to End Delay  
As shown in Figure 14, WEAMR with hotline-assisted approach outperforms both AODV and AOMDV as connections are kept constant and the packet generation rate is increased. The average end to end delay increases with the increase in packet generation rate, however the average delay for AODV and AOMDV is much higher because these protocols work in hop by hop fashion for packet delivery and hotline infrastructure is not used. With very high packet rates performance for AODV and AOMDV reduces as they do not have any mechanism to mitigate congestion at high loads, whereas WEAMR shows lower end to end delay due to the fact that hotline-assisted routing can handle more simultaneous connections and reduce the number of intermediate hops.  
Figure 14: Average end to end delay with increasing number of packets.  
Figure 15: Average packet loss in end to end communication with increasing number of packets.