esa-sceva/satcom-chunk-collection huggingface dataset:
# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 5 Conclusions  
This paper proposed the Harris Hawks sparse auto-encoder network (HHSAE-ASR) framework for automatic speech recognition. Initially, the human voice signal is collected and analyzed by using the spectrum decomposition approach. Here, spectrum deviations and fluctuations are analyzed to replace the noise signal with the average spectrum phase value. Then, different features are extracted from the signal by decomposing the signals into four levels. The decomposed signals are further investigated to get the Mel-frequency coefficient features, which are more useful to create the acoustic, lexicon, and language model. The extracted features are applied to the Markov model-based convolution network to train the network for resolving the loud and noisy environment speech signal analysis. During this process, the network is fine-tuned, and the parameters are updated according to the Harris Hawk prey searching behavior with certain updating conditions. This process reduces misclassification error rate problems and maintains the robustness and availability of the system. Thus, the system ensures a 99.18% accuracy, which outperforms the existing algorithms.  
Natural language recognition is a challenging task, as different dialects, speeds, and traditions vary in actual applications. In the future, a relevant feature selection process will be incorporated to improve the overall effectiveness of the system. By using Mel-frequency cepstral coefficients to express the characteristics, the correctness of the classification could  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Participants** & **MFCC (\%)** & **MSE (\%)** & **HHSAE-ASR (\%)** \\ \hline
10 & 47.2 & 50.4 & 71.4 \\
20 & 48.4 & 53.6 & 74.7 \\
30 & 49.5 & 56.8 & 76.3 \\
40 & 52.7 & 58.5 & 77.7 \\
50 & 54.8 & 60.7 & 81.9 \\
60 & 55.9 & 62.2 & 83.1 \\
70 & 59.4 & 61.3 & 85.2 \\
80 & 60.6 & 65.6 & 88.5 \\
90 & 63.8 & 68.8 & 90.6 \\
100 & 66.2 & 69.2 & 91.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data accessing analysis.  
improve. It will be useful to integrate deep learning algorithms into the classifier design instead of traditional methods.  
Conceptualization, A.R.; Data curation, M.H.A.; Formal analysis, M.M.J., S.K.A., M.J.A., D.V.-A., R.D. and S.A.B.; Funding acquisition, D.V.-A.; Investigation, M.H.A., M.M.J. and S.K.A.; Methodology, A.R.; Software, M.H.A.; Validation, S.K.A., A.R., M.J.A., R.D. and S.A.B.; Writing--original draft, M.H.A., M.M.J., S.K.A., A.R., M.J.A. and S.A.B.; Writing--review & editing, D.V.-A. and R.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Datasets are available at [[https://github.com/jim-schwoebel/voice_datasets](https://github.com/jim-schwoebel/voice_datasets)]([https://github.com/jim-schwoebel/voice_datasets](https://github.com/jim-schwoebel/voice_datasets)) (accessed on 18 April 2019).  
The authors declare no conflict of interest.
# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 3 Social Link
### Link Factors  
#### 3.1.1 Reputation Degree  
Ontology is essentially a conceptual framework that gives a set of terms to identify a set of concepts. In addition, domain ontology is a professional ontology that describes the concepts and the relationship between concepts in a particular field. In our previous study [2], we have proposed a user personalization model based on ontology technology. Therefore, for a specific user, by analyzing and refining his/her historical operation records, we can mine their interesting points which performance as local nodes of domain ontology. In other words, each personalization model is a sub-tree of an ontology tree.  
For instance, if the numbers of ontology concepts called by the user \(u_{a}\) can be gained based on his/her operation history and the distribution of the numbers can be shown as Figure 1, in which the number above each box represents the number of calls to the concept in the box, it can be concluded that the total number of ontology concepts called by \(u_{a}\) is 75, then, \(u_{a}\)'s interestingness in each concept can be calculated, for example, the interestingness in concept \"_CompanyBond_\" is 12/75, which is 0.16. It is worth noting that although the number of concept \"_InvestmentFund_\" called is zero, since it is the parent concept of concept \"_OpenEndFund_\", concept \"_ExchangeTradeFund_\" and concept \"_IndexFund_\", the interestingness in it is actually 0.34.  
Replacing the numbers of concept called in Figure 1 with the calculated interestingness, the user personalization model of \(u_{a}\) can be obtained, which is shown as Figure 2. Compared with Figure 1, it has the same tree structure, and the main difference is that there is a value above each concept box.  
Figure 1: The distribution of the numbers of ontology concepts called by \(u_{a}\).  
Based on the proposed user personalization model, we define a user's Reputation Degree (\(RD\)) is the proportion of the quantity of the concepts in his/her personalization model to the quantity of the whole concepts in the corresponding ontology.  
\[RD_{m}=\frac{N_{m}}{N_{o}} \tag{1}\]  
where, \(RD_{m}\) is the Reputation Degree of user \(u_{m}\); \(N_{m}\) means the quantity of the concepts in the personalization model of \(u_{m}\); \(N_{o}\) is the quantity of the whole concepts in the corresponding ontology. For example, if the personalization model of user \(u_{a}\) is shown as Figure 1, and the quantity of the whole concepts in the Finance Ontology is 100, that is, \(N_{a}\) = 15, and \(N_{0}\) = 100, then \(RD_{a}\) = 0.15.
## 4 Application
### Pressure  
The MP-SAWS demonstrates a high sensitivity to variations in stress. In medical applications, pressure detection enables heart rate monitoring. The MP-SAWS can contribute to the study of cardiovascular diseases and the evaluation of heart failure through heart rate detection [63]. Leveraging the quantitative research capability of the MP-SAWS in pressure analysis, it can combine hemodynamics to evaluate coronary heart disease under minimally invasive conditions, providing more detailed cardiac information [64].  
When measuring pressure through finite element simulations, it is necessary to consider the surface deformation caused by external forces, as it may affect the resonant frequency of the sensor. During stress analysis, the environmental temperature is set at 293.15 K, which corresponds to typical room temperature. The measurement environment is free of external magnetic fields. The stress analysis of the MP-SAWS finite element model is depicted in Figure 6a, illustrating the curve showing the relationship between external stress and the resulting variation in the MP-SAWS characteristic frequency. Figure 6b represents the admittance curve before and after applying stress, demonstrating the deviation of the characteristic frequency under stress. Based on the simulation results, the pressure sensitivity of the MP-SAWS is 10.426 kHz/MPa. This level of response is sufficiently accurate to meet the measurement requirements.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 3 Related Works
### Problem Formulation
#### 3.1.3 Dependency on Link-State Routing Protocols  
Another important fact is that several analyzed FRR mechanisms require topological network information from a link-state routing protocol database to calculate an alternative path [26]. This feature limits the application of FRR mechanisms only to networks where a primary link-state routing protocol is deployed. Currently, most of the existing FRR mechanisms are dependent on information from link-state routing protocols.
# The Cybersecurity Focus Area Maturity (CYSFAM) Model
## 6 Evaluation
### Case Study and Results  
The following requirements concerning the case study company were identified (inspired by [63]):  
* The case study company is sufficiently large; there is a grounded possibility that the concepts found in the literature are part of the case study company's routine.
* The case study company is active in a \"security-sensitive\" domain; the case study company manages and/or governs systems usually containing data that are of value.
* The case study company can provide the resources (time, money) and the conditions (culture) which are required to carry out the research.  
The case study was conducted at a Western European bank, which almost perfectly met the conditions described above.  
CYSFAM is considered to be successful if the following points can be verified (partly inspired by [15]:  
* CYSFAM assessment can be performed within a 4 h timeframe provided that the conditions are suitable (timely communication with experts where needed is guaranteed).
* The questions make sense--at least to information and cybersecurity domain experts. This is the most significant target audience for CYSFAM, notwithstanding that other information technology professionals could consider CYSFAM beneficial.
* The case study organization recognizes itself in the assessment results.  
Considering the verification points described above, it can be concluded that the assessment of CYSFAM during the case study was proven to be successful. The organisation's experts were able to answer all the questions within four hours. The end-result of the assessment reflects the cybersecurity maturity stage that the organization is currently in, i.e., effort has been put into improving cybersecurity; however, there is a lot of work to be done, whereas the optimal stage of maturity is not in sight yet. The assessment results are depicted in Figure 5. As can be seen from this figure, the cells shaded present the capabilities achieved by the organisation.  
The evaluation results are further discussed in the next section.  
Figure 5: The outcome of the CYSFAM assessment within the case study organization. The letters (A–E) represent the capabilities; shaded areas show the maximum capability achieved for the corresponding focus area.
## 5 Discussion
### PBC Dataset Results  
The results obtained from the PBC datasets demonstrated a stark contrast in the performance of the pre-trained ImageNet ILSVRC models and the Google ViT. The Google ViT consistently outperformed all ImageNet models across all three PBC datasets (DS-1, DS-2, and DS-3) in terms of both validation accuracy (VA) and validation loss (VL). It achieved a remarkable 100% validation accuracy and a validation loss close to zero. This exceptional performance suggests that the Google ViT is well-suited for handling imbalanced datasets with high-quality, detailed images.  
Furthermore, when we analyzed the accuracy difference (AD) values, it was evident that the Google ViT maintained a constant 0% AD across all three PBC datasets. In contrast, the ImageNet models exhibited positive AD values, indicating overfitting issues, especially when working with the DS-1 dataset. This overfitting was likely due to the high variances caused by the presence of limited data in DS-1. The Google ViT's consistent performance regardless of dataset size suggested its robustness in dealing with small and medium-sized datasets.  
The LD (loss difference) values further emphasized the Google ViT's superiority. While the ImageNet models displayed varying degrees of loss difference as the dataset size increased, the Google ViT consistently maintained minimal loss differences, once again highlighting its stability.  
Overall, the performance of the Google ViT on the PBC datasets indicated its effectiveness in handling large, imbalanced, and high-quality image datasets while mitigating overfitting issues.
## 4 Traditional Algorithm Analysis  
### A* Algorithm  
To handle path planning problems in a campus environment, the graph search algorithm was analyzed. The A* algorithm, which is representative among graph search algorithms, is a general method of finding the shortest path between a starting point and arrival point with guaranteed real-time performance. The principle of how to get the shortest path depended on the occupancy grid map and the distance cost. Each of the nodes had one state, namely 'Opened', 'Closed', or 'Non'. At first, only the start node's state is 'Opened'. Another node is the state of 'Non', and based on the distance cost that is calculated by the Euclidean distance between the start node, the current node, and the arrival node, the low-cost node among the 'Opened' options is selected. By using this principle, the A* algorithm finds the shortest path; as shown in Figure 3a.  
Figure 1: Wrong and correct case of local path planning; (**a**) the case in which the local route penetrates into the side of the road and (**b**) the case where the local route is generated correctly, following the reference path.  
Figure 2: Problem of traditional local path planning; (**a**) the case in which the turning ratios of the vehicles are not considered and (**b**) the case in which the turning ratios of the vehicles are considered.  
However, the traditional A* algorithm is not suitable for a real vehicle environment. The path's route points are generated at intervals of 45 degrees since these points are proliferated on a discrete occupancy grid map (Figure 3a). It is therefore unsuitable for autonomous driving vehicles since the path angle of A* could be outside the allowable steering angle of 30 degrees. To solve these problems, Hybrid A* [18; 19; 20] was proposed, which was applied in the DARPA Urban Challenge [21], a competitive contest of autonomous driving vehicles. This algorithm has the same principle as A* except for the propagation type. A* use a discrete propagation method while Hybrid A* uses a continuous propagation method (Figure 3b). Since Hybrid A* uses continuous propagation, it can consider vehicle kinematic models. Therefore, the path angle generated from Hybrid A* satisfies the allowable steering angle of vehicles.  
However, it is difficult for the Hybrid A* algorithm to consider the constraints. For example, in the vehicle road environment, a mobile robot should observe lane information; the A* algorithm, however, could not obey these constraints.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 3 Method: LEACH-MTC Based on Moving Target State Prediction
### Network Monitoring Accuracy  
It is assumed that the position of the moving target is \(T(x_{T}(k),y_{T}(k))\) at time \(k\), every working node \(s_{i}\) inside the elliptical area has its own measurement \(Z_{s_{i}}(x_{T}(k),y_{T}(k))\) for the moving target. As the node measurements are subject to environmental interference during the monitoring process, and as the distance \(d_{Ti}(k)\) between the working node and the moving target increases, the interference effect becomes more serious. Therefore, the measurement of working node \(s_{i}\) can be expressed as follows:  
\[Z_{s_{i}}(x_{Ti}(k),y_{Ti}(k)) =T(x_{T}(k),y_{T}(k))+f(\epsilon,d_{Ti}(k))+V(k) \tag{28}\] \[d_{Ti}(k) =\sqrt{\left(x_{i}-x_{T}(k)\right)^{2}+\left(y_{i}-y_{T}(k) \right)^{2}} \tag{29}\]  
where the position of the working node \(s_{i}\) is \((x_{i},y_{i})\) which is fixed deployment. \(f(\cdot)\) is a function of the working node monitoring accuracy and distance; here, \(\epsilon\) is a weighting factor. \(V(k)\) is Gaussian white noise.  
In order to ensure high monitoring accuracy, it is necessary to enable multiple nodes to work simultaneously. Thus, more state information of the moving target can be obtained. However, in order to select the appropriate number of working nodes to reduce network resource, the network monitoring accuracy can be developed by the difference between the actual and the expected value of the selected working node. Besides, \(\Delta Z_{T}(k)\) represents the measurement error of the working node, and it is as follows:  
\[\Delta Z_{T}(k)=\frac{\left\|\frac{1}{m}\sum\limits_{i=1}^{m}Z_{s_{i}}(x_{Ti}(k ),y_{Ti}(k))-T(x_{T}(k),y_{T}(k))\right\|_{2}}{\left\|T(x_{T}(k),y_{T}(k)) \right\|_{2}}\times 100\% \tag{30}\]  
where \(m\) represents the number of working nodes at time \(k\).  
According to the size of the network monitoring area and the monitoring environment, it can be set that the network monitoring accuracy error can meet the monitoring accuracy requirements as long as it satisfies \(\Delta Z_{T}(k)\leq\Phi\%\); here, \(\Phi\%\) is the threshold of accuracy.
## 4 Discussion
The conductivity value found in this study is comparable with those reported in other literature, ranging from 5 to 10 S/m [5]. Muscles and high-density tissues have relatively high conductivity and permittivity, while adipose tissue has comparatively low permittivity and conductivity values [5]. Additionally, silicone possesses favorable dielectric properties such as high dielectric breakdown strength and high resistivity [11]. For most dielectric materials, ionic conductivity plays a significant role at frequencies less than 200 MHz. Basically, insulators have a lower dielectric constant \(\epsilon^{\prime}r\); thus, in this study, PDMS was a better insulator compared to SE. The attenuation properties of our standard phantom were studied to ensure that it could mimic human tissue. Therefore, Phy-X/ZeXTRA software was used to calculate \(Z_{\text{eff}}\)[22]. The effective atomic number is constant in the intermediate-energy region, whereas noticeable variation was observed for low- (<20 keV) as well as high-energy regions [23]. Overall, the effective atomic number of PDMS is around 4.00 to 5.00 in a higher-energy range (\(\geq\)1.00 \(\times\) 10\({}^{-1}\) MeV) and comparable with the effective atomic number of the kidney found in literature [18]. This \(Z_{\text{eff}}\) is lower at the intermediate energy range due to the dominance of pair production and scattering effects. This study only focused on the chemical formula of PDMS since PDMS is found to be the material of choice to be used as the kidney phantom due to its effective atomic number, high elastic modulus, and comparable real part of the complex dielectric constant \(\epsilon^{\prime}r\), as reported by other researchers [4,17]. Overall, we proved that a minimal difference in curing agent not exceeding 20% (curing agent to base ratio) between SE samples in this study does not alter the elastic modulus substantially. In contrast, PDMS is more sensitive to the amount of cross-linker compared to SE; the amount of cross-linker varies by 5%, 10%, and 16%, yet the elastic modulus changes substantially. Hence, the 5% cross-linker reduces the elastic modulus by half and deteriorates the strength of the sample regardless of thickness. The dielectric properties can also be affected by the amount of cross-linker. As the level of curing increases, the maximum dielectric loss shifts toward lower frequency, while the change in the dielectric constant at \(\alpha\) static with \(\alpha\) infinity diminishes [21]. This behavior represents the gradual transition from the rubbery to the glassy state of the polymer with an increase in cross-link density.
## 3 The Present: 2010-2015
### Applications of UPS and UBPS
#### 3.3.2 Delamination  
As the patterns in a H-UPS image are governed by the stimulation conditions for Lamb waves, these patterns must shift position in the presence of a delamination. This is easily understood considering that a delamination divides the original laminate in two sub-laminates, resulting in different boundary conditions. Different boundary conditions invoke different Lamb wave stimulation conditions, and as such yield different H-UPS images [15; 30]. This is demonstrated in Figure 10 for a cross-ply [0\({}^{\circ}\),90\({}^{\circ}\)]s C/E laminate (thickness \(d\) = 1.1 mm) provided with water-filled delaminations (with nominal thickness \(D\) = 50 \(\upmu\)m) at different depth positions. Note that such types of delaminations are in general difficult to assess in thin composites using conventional normal incident ultrasonic techniques. Figure 10 clearly shows that the depth position of the delamination is well represented in the H-UPS images. Figure 10d further indicates that the H-UPS can even detect multiple overlapping water-filled delaminations.  
Figure 9: P-UPS images of [\(-\)45\({}^{\circ}\),\(+\)45\({}^{\circ}\)]s C/E laminate: before (**a**) and after (**b**) applying the shear load. Microscopic image of the fracture area when shear loading a [\(-\)45\({}^{\circ}\),\(+\)45\({}^{\circ}\)]s C/E laminate until failure (**c**).  
Excellent agreement is obtained between the results of experiment and simulation. Further, our numerical computations indicate the ability of the H-UPS to characterize delamination parameters such as depth position and delamination thickness \(D\). Currently, an inversion model is being implemented to extract these delamination parameters in an automated way.
## 4 Discussion
### Relationship between Sediment Transport and Ocean Dynamics  
Waves are the main driving force for sediment transport in coastal waters. In the process of wave propagation to the coast, waves will break and form a coastal flow when the wave orthogonal and the coastline have a certain intersection angle, resulting in coastal sediment transport [34]. In the northern part of Bohai Bay, waves usually propagate from S-SE and the strongest waves mainly propagate from ENE-NE (Figure 1), resulting in westward transport of coastal materials from the modern Luanhe River and the ancient Luanhe River delta. In the southern part of Bohai Bay, waves usually propagate from NE-SE and the strongest waves mainly propagate from the NE (Figure 1), resulting in westward transport of coastal material from the Yellow River estuary and delta. Since the wave direction is almost perpendicular to the shoreline (Figure 1), waves disperse in the western part of Bohai Bay, leading to insignificant sediment transport along the coast (Figure 2).  
Another important role of waves is to re-suspend sediment from the sea floor, as sediment can move with seawater only once it enters the water column. Sediment transport pattern in the Bohai Sea is seasonally variable and has been described as \"stored in summer and transported in winter\" [37]. Early studies have shown that the amount of suspended sediment along the Yellow River Delta is tens or even hundreds of times higher in winter than in summer, as strong winds and waves occur much more often in winter than in summer [14; 38]. The sediment is re-suspended under wave disturbance and is constantly carried away by tidal currents into Bohai Bay when seawater flows to the northwest and into Laizhou Bay when seawater flows to the southeast.  
Tidal currents also play an important role in sediment transport. Both in the northern [39] and southern [40] parts of Bohai Bay, the residual flow is westward, which leads to the transport of discharged material from the Yellow River estuary and the Luanhe River estuary to the western part of Bohai Bay. Figure 3 clearly indicates that the sediment in the wide area from the Yellow River estuary westward to the west coast of the bay has great similarity with the Yellow River source, and the sediment in the wide area from the Luanhe River estuary westward to the west coast of the bay has great similarity with the Luanhe River source. In addition, the circulation in Bohai Bay is in approximately the same direction as the residual flow (Figure 1), resulting in an environment conducive to sediment sinking formed by the circulation centers in the middle of the northwest of Bohai Bay and the middle of the southwest of Bohai Bay.