esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 3 Distributed Kernel Extreme Learning Machines
### DS Evidence Theory Fusion  
A.P. [PERSON] used upper and lower limit probability to solve the multi-value mapping problem, marking the formal birth of DS evidence theory, and [PERSON] further developed the evidence theory, introduced the concept of trust function, and formed a set of mathematical methods of \"evidence\" and \"combination\" to deal with uncertain reasoning [36]. DS theory is a generalization of Bayesian inference method, which is mainly carried out by using Bayesian conditional probability in probability theory. It can represent \"uncertainty\" well and is widely used to deal with uncertain data.  
In this subsection, DS evidence theory is introduced to design the classification fusion device of DKELMs algorithm. It is assumed that number of categories is \(k\), and the \(i_{th}\) category is represented as \(c_{i}\). The identification framework is \(U=\left\{c_{i}\right\}_{1}^{k}\), and the mass function \(m:2^{U}\rightarrow[0,1]\) satisfies two equations: \(m(\emptyset)=0\) and \(\sum_{C\subset U}m(C)=1\). The mass function \(m(C)\) is the basic probability assignment of \(C\), and denotes the degree of confidence.  
For the classification problem, the output of each subnetwork can be transferred into the mass function by the softmax function, and the mass function outputs of \(l\) subnetworks can be expressed as \(m_{1},m_{2},\cdots,m_{l}\) respectively. For each proper subset \(C\) of \(U\), the Dempster combination rules are  
\[m_{1}\oplus m_{2}\oplus\cdots\oplus m_{l}(C)=\frac{1}{\Omega}\sum_{C_{1} \bigcap C_{2}\bigcap\cdots\bigcap C_{l}=C}m_{1}(C_{1})m_{2}(C_{2})\cdots m_{ l}(C_{l}) \tag{18}\]  
\[\begin{split}\Omega&=\sum_{C_{1}\bigcap C_{2} \bigcap\cdots\bigcap C_{l}\
eq\emptyset}m_{1}(C_{1})m_{2}(C_{2})\cdots m_{l} (C_{l})\\ &=1-\sum_{C_{1}\bigcap C_{2}\bigcap\cdots\bigcap C_{l}=Q}m_{1}( C_{1})m_{2}(C_{2})\cdots m_{l}(C_{l})\end{split} \tag{19}\]  
where \(\Omega\) is the normalization factor, and \(1-\Omega\) reflects the degree of conflicts from different subnetworks. Let the proper subset \(C\) be a category\(\left\{c_{i}\right\}\), and then the probability that a sample having input feature \(\mathbf{x}\) belongs to this category should be  
\[P(\mathbf{x}\in c_{i})=m_{1}\oplus m_{2}\oplus\cdots\oplus m_{l}(C) \tag{20}\]  
By synthesizing the output results of multiple subnetworks, the output after fusion is more accurate than that of the subnetworks, and not lower than the accuracy of the original KELM network. In addition, as the scale of each subnetwork is effectively controlled, the sparsity is improved and the overfitting problem is weakened to some extent. Furthermore, the decrease of computation results in higher learning speed.
## 3 Results and Discussion
### Catalyst Characterisation
#### 3.1.2 XPS Results  
X-ray photoelectron spectroscopy (XPS) was performed on the material, obtaining information on the surface properties of the PdZn catalyst. From the XPS survey spectra, both metals were detected. The Pd/Zn atomic ratio detected by XPS is higher than the nominal one but similar to the EDX atomic ratio (Table 1), which indicates an enrichment of Pd on both surface and bulk.  
In the high-resolution spectra of the Pd 3d region (Figure 3), both metallic Pd and Pd oxide were detected with relative amounts of 9% and 91%, respectively. The novel catalyst shown in this study possesses a high Pd(II) content with respect to the monometallic 1% Pd/HHT, which has a Pd(0) content of 52% [4], suggesting an oxidation effect due to the presence of Zn. By analysing the high-resolution spectra of the Zn 2p region, only ZnO was detected.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**Survey**} & \multicolumn{2}{c}{**Pd 3d**} & \multicolumn{2}{c}{**Zn 2p**} & \multicolumn{2}{c}{**Pd/Znnom**} & \multicolumn{2}{c}{**Pd/ZnXPS**} & \multicolumn{2}{c}{**Pd/ZnEDX**} \\ \hline  & **C 1s** & **O 1s** & **Pd 3d** & **Zn 2p** & **Pd(0)** & **Pd(II)** & **Zn(II)** & & & \\ \hline Pd\({}_{6}\)Zn\({}_{4}\)/HHT & B.E. (eV) & 284.5 & 532.6 & 337.6 & 1021.6 & 336.0 & 337.6 & 1022.1 & & & \\ \% At. & 95.6 & 3.6 & 0.6 & 0.1 & 9 & 91 & 100 & 1.5 & 6.0 & 6.1 \\ \hline \end{tabular}
\end{table}
Table 1: Results of survey spectra.  
Figure 2: High Resolution STEM images (**A**), digital diffraction patter of a selected area (**B**) and simulated diffraction pattern of Pd among the <1 0 1> axis zone.
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## 3 Configurable Algorithm
### Reconfigurable Mesh-Ring Algorithm  
The RMR algorithm introduces a peak-search method to locate the optimum placement of the available mesh links. The peak-search method contains three functional blocks, namely the topology formation block, the network capacity block, and the optimum decision block. To determine the optimal Mesh-Ring configuration for the three configuration cases of various sizes of networks, a peak-search method is presented. This scheme is a systematic approach, and is implemented by three functional blocks: the topology formation block generates the mesh-ring topology, the network capacity block computes the throughput performance, and the optimum decision block introduces a decision-making criterion to determine the optimum connected bridge node of each mesh link.  
Figure 5 shows a block diagram of the peak-search method. Initially, parameter \(I\) is set as one and \(1\leq I\leq ml\) for the RMR formation, where \(ml\) is the maximum available mesh links. In the network capacity block, the average throughput performance is defined to reflect the scatternet capacity of total Mesh-Ring topology by summing up the piconet throughput on the ring. Finally, the total throughput is calculated and compared with its previous connected bridge in the optimum decision block. This algorithm is iterated until the optimum connected bridge of each mesh link \(I\) is determined.  
To realize the peak-search method, a novel algorithm is implemented in the coordinator node of the RMR network for both topology formation and maintenance phases. At the beginning, the maximum number of mesh link \(ml\) is introduced as the counter of the mesh link. At the same time, the coordinator node collects sufficient information, including each average piconet throughput in the ring subnet, to calculate and compare the average mesh-ring throughput for the optimum decision block to make a decision.  
From this point on, the designated root M1 in the ring-subnet periodically executes the peak-search method to determine the optimum connected bridge for each link \(I\) when the size of subnets grows or decreases. When the topology variation exceeds a threshold and the optimum \(I\) is not located in each time interval \(t\), the designated master increases the connecting sequence of bridges by one to locate the mesh link \(I\) and continuously computes the average throughput in the ring subnet. This procedure is repeated until the optimum connected bridge for each \(I\) is determined.
## 5 Conclusions  
The following three question were addressed in this systematic review:  
* The first question that this systematic review aims to answer is whether there is sufficient evidence to justify the use of photodynamic therapy as an adjunct to conventional nonsurgical periodontal therapy using manual or mechanical instruments. A PDT emerged as a beneficial treatment option for periodontitis. The results of many studies indicated that aPDT combined with SRP has a slight advantage in the treatment of periodontitis. Despite a wide range of heterogeneity, all the included studies indicate that aPDT has the potential to be an effective adjuvant in the treatment of periodontitis. Scaling and root planing (SRP) remains essential for the promotion of a healthy subgingival environment.
* The second question was whether photodynamic therapy (aPDT) provides additional effects to traditional treatment with manual and mechanical instruments when comparing the patient's clinical parameters. The analysis of the studies suggests a short-term benefit of PDT in addition to SRP in terms of clinical outcome variables; however, the evidence regarding its long-term efficacy is still insufficient, and no significant effect was confirmed in terms of CAL gain at 3 months.
* The third question concerned was whether in patients with untreated periodontitis the application of photodynamic therapy (aPDT) provides additional effects to the traditional treatment with manual and mechanical instruments when comparing the microbiological parameters of the patient. The bactericidal effect of aPDT as an adjunctive treatment to SRP against periodontitis in periodontal disorders remains questionable.  
Although the short-term clinical benefit of aPDT as an adjunct to SRP was confirmed in this systematic review and meta-analysis, some questions still remain to be answered. Future RCTs should focus on the difference between split-mouth and parallel designs in their protocols. Furthermore, the optimal combination of photosensitizers and laser parameters should be determined with high-quality studies. Clinicians are looking for an efficient and safe treatment modality, both as an alternative and in addition to standard therapies. Photodynamic therapy, as evidenced by the results of this meta-analysis, has the potential to improve some clinical parameters in the short term.  
Conceptualization, A.P., A.G. and M.A.; methodology, A.P., A.G. and M.A.; software, A.P., A.G. and M.A.; validation, A.B. and S.G.; formal analysis, A.P., A.G. and M.A.; investigation, A.P., A.G. and M.A.; resources, A.P.; data curation, A.G. and M.A.; writing--original draft preparation, A.P., A.G. and M.A.; writing--review and editing, S.G.; visualization, M.P. and A.S.; supervision, A.B.; project administration, A.S.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are available at the corresponding authors upon reasonable request.
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 4 Results
### Result of Model Comparison  
In order to show the effectiveness of our proposed approach, we compare the LSTM prediction model with the random forest algorithm, ARIMA, and ANN. We set 1000 as the number of epochs, 0.01 as the learning rate, five as the batch size, and Softmax as the activation for the LSTM model. For the random forest model, a 1000 n_estimator, TRUE oob_score, and 42 random_state are given. The 1000 number of epochs, 0.01 learning rate, five batch size, and Softmax activation are set for the ANN model. As mentioned above, we use 29,365,980 PPD records, which consist of 50% training set, 25% validation set, and 25% testing set. First, we split the dataset into training, test, and validation data. Furthermore, the training data is divided into subsets that use the different folds as validation data. We also nominate several values for each hyperparameter of each model. Then, we find the best parameters using training subsets and hyperparameter options grid search. For testing, we utilized the grid search method.  
Detailed testing results of the models are presented in Table 8. As shown in this table, among the three models, the LSTM model achieved the highest accuracy. The random forest achieves a performance closest to that of our proposed approach, but its ability to estimate and predict algorithms is still considerably lower than that of the proposed method.  
We applied the methods proposed above to perform a five-step prediction of PQP for a specific facility every 30 min. Therefore, since this problem is fundamentally a classification problem, it can be observed that the performance of the random forest method, which is used as a representative classification method in machine learning, is relatively higher than that of the ARIMA method, which is used as a prediction method according to time. If big data used for the experiment in this paper is used, the accuracy of the deep learning method will be higher than that of the machine learning method. Therefore, the LSTM method is widely used to know the classification results over time in deep learning techniques. As the PQP prediction problem presented in this paper is of the same type, it can be observed that the experimental results using LSTM are relatively high compared to the ANN technique applied as a deep learning technique.
## 4 Experiments and Results
### Experimental Setup and Participants  
In this study, we employed a HoloLens 2 HMD connected via cable to a PC running Windows 11, equipped with an Intel i5-10200H CPU, 16 GB of RAM, and an NVIDIA RTX 1650 GPU. Recruitment for the experiment was conducted through the campus network, resulting in 16 student volunteers. Among them, 12 were male and 4 were female. All participants were computer science majors and right-handed. The participants' ages ranged from 21 to 25 years (M = 23.9, SD = 1.1). All had normal vision and were able to perceive objects and text cues displayed by the device.  
To assess participants' proficiency with immersive devices, we administered a pre-study questionnaire that categorized proficiency into four levels: never used, beginner, intermediate, and expert. The survey results revealed that 3 participants had never used immersive devices, 7 were beginners, 4 were intermediate, and 2 were experts in terms of proficiency with any VR/AR/MR device. In addition, 6 participants had never used  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**History Tools**} & \multicolumn{6}{c}{**Features**} \\ \cline{2-7}  & **Submit** & **Switch** & **Preview** & **Zoom** & **Move Freely** & **Interaction** \\ \hline HybridFilm & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ PyMOL Scenes & ✓ & ✓ & ✓ & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of HybridFilm and PyMOL Scenes.  
Figure 11: PyMOL Scenes functional interface.  
gesture-recognition devices, 4 were beginners, 5 were intermediate, and 1 was an expert in this area. The participants were assigned numbers from P1 to P16.
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 2 Methodology
### Defect Localization
#### 2.2.2 Segmentation  
The threshold segmentation is performed after the heat map of the input image obtained from Grad-CAM to locate the defect regions. Let \(f(m,n)\) represent the binarized image for the heat map, and \(f(m,n)\) is as shown in  
\[f(m,n)=\left\{\begin{array}{ll}255,&\text{if }f_{hm}(m,n)\geq\sigma\\ 0,&\text{otherwise}\end{array}\right. \tag{6}\]  
where \(f_{hm}(m,n)\) indicates the heat map after graying, and \(\sigma\) is the threshold, respectively. In \(f(m,n)\), pixels whose gray value is 255 indicate the defect region, and pixels whose gray value is 0 present the non-defective area. In order to get better localization results, it is significantly important to choose the threshold segmentation method for \(\sigma\). Experiments show that different types of defects and defects distribution in the entire image can result in different methods of threshold segmentation. For images with defects of limited distribution and signal type, a simple fixed threshold segmentation can be used to obtain a better result. For images with defects of scattered distributions and variable types, the adaptive Otsu [25] algorithm can obtain satisfactory results.
## 2 Regional Structure  
From north to south, the Fenwei graben system consists of the Datong Basin, the Xinding Basin, the Taiyuan Basin, the Linfen Basin, the Yuncheng Basin, the Weihe Basin, and several other large fault basins (Figure 1). The Yuncheng Basin is located in the southwestern part of Shanxi Province and has a total area of 4885 km\({}^{2}\).  
The Yuncheng Basin is a Cenozoic faulted basin superimposed on the multi-cyclic superimposed Ordos Basin. The sedimentary strata have been deformed by multiple tectonic movements and have experienced multiple tectonic periods, such as the Yanshanian and Himalayan movements. A representative large rift basin formed later [27]. The basic framework of the Zhongtiao mountain uplift area and the Emei platform uplift area in the basin was laid by the strong tectonic movement in the Yanshanian period, and it was mainly affected by the Himalayan movement during the Paleogene. Large-scale fault block tectonic movement occurred in this area. The tectonic setting of the basin has changed from a compressive and twisting thrust fault to a tension and twisting normal fault, and the embryonic form of the basin has been modified since then. The main structural deformations preserved today include a fault system in the Himalayan strike-slip extensional background [2].  
The main forms of tectonic movement in the Yuncheng Basin are faults. The Basin was controlled by the Yanshan movement in the Mesozoic era, the Himalayan movement in the Cenozoic era, and especially by tectonic movement since the Late Cenozoic era, and therefore fault structures have been well-developed in the basin. The main active fault in the basin is the large fault on the northwest side of Zhongtiao mountain, which is also the main controlling boundary fault in the Yuncheng Basin [25]. There are eight active faults in the basin, with NE, NEE, and NNE strikes. They enclose the boundaries of the Yuncheng Basin and the secondary structural units inside the basin, and they mutually restrict and control the structural framework of the basin [27]. The Yuncheng Basin is bordered by the Emei Platform to the west, and the Zhongtiao mountains to the southeast. The NE-trending Mingtiaogang in the middle of the basin divides the Yuncheng Basin into the Sushui river plain and the Qinglong river plain. The basin is an asymmetrical sag basin that is deep in the south and shallow in the north.  
The basin contains a large amount of Cenozoic strata, accounting for about 80% of the total area, and the thickness of the strata increases from the northeast to the southwest. The thickness of the Cenozoic strata in the basin is generally greater than 1000 m, and the Quaternary sediments are also more than 300 m thick. This loose, thick sedimentary layer provided the material basis for the extensive development of earth fissures.  
In general, factors such as the uplift of the horst in the Yuncheng basin, the faulted basement, the over-exploitation of groundwater, and the loose, thick sedimentary layer have laid the foundation for the development of earth fissures in the basin.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 4 Joint Simulation of the Calibration Process
### Time Domain Simulation  
The joint simulation in the time domain needs to apply the time-varying calibrating sound pressure, as shown in Figure 6, to the microphone diaphragm. The sound pressure has not only an amplitude attenuation but also a phase lead against the piston excitation. Since the phase sensitivity is the time difference between the calibrating sound pressure and the microphone voltage, the initial phase of the sound pressure needs to be wiped off. Therefore, the corrected waveform of the sound pressure is fitted to the spline curve and then added to the microphone diaphragm to conduct the simulation. The flow chart of the time domain joint simulation is shown in Figure 13.  
Figure 11: (a) Calibration in which the outer end of the vent is inserted in the calibrating sound field. (b) Calibration in which the outer end of vent is in the outside atmosphere.  
Figure 12: Multi-physical joint simulation of the microphone primary calibration.  
Figure 13: Flow chart of the time domain simulation.  
Taking the calibration process at 0.1 Hz as an example, the second order Fourier series is used to fit the waveform of the calibrating sound pressure, as shown in Figure 14.  
The fitting formula is given by:  
\[p=a_{0}+a_{1}\cos(\omega t)+b_{1}\sin(\omega t)+a_{2}\cos(2\omega t)+b_{2}\sin(2 \omega t), \tag{5}\]  
where \(a_{0}=-7.069,a_{1}=-1.353,a_{2}=11.58,b_{1}=-3.654,b_{2}=147.3\), and \(\omega=0.3156\) rad/s.  
An independent pistonphone simulation at 0.1 Hz is first conducted to obtain the waveform of the piston displacement and the calibrating sound pressure, as shown in Figure 15a. Secondly, the fitting waveform of the calibrating sound pressure is added to the microphone diaphragm and microphone simulations in both the vent in field and vent out field states are conducted. The diaphragm deformation of the microphone in the two venting states is shown in Figure 15b.  
By calculating the time difference of the second peak of the voltage output with that of the calibrating sound pressure, the phase sensitivities of the microphone for the vent in field and vent out field calibrations are calculated as 1.2\({}^{\circ}\) and 81.4\({}^{\circ}\), respectively. It is clear that the microphone phase sensitivities in the two venting states are quite different at low frequencies.  
It can be seen that the diaphragm deformation of the vent in the field calibration shows a more severe attenuation and phase lead than the vent out field calibration at low frequencies. That is because, for the vent in field calibration, the vent end is in the pistonphone chamber and bears the calibrating sound pressure with only a certain delay due to the vent resistance, so that the microphone output decreases a lot and the 81.4\({}^{\circ}\) phase lead occurs.
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 1 Introduction
Finally, the feature map tensors are linearized and converted into the entity embedding space, and an inner product is calculated with the tail entity embedding to evaluate the correct triplet. The following are the contributions of this paper:  
* A new link prediction method called CANet for learning the embedding representation of KBs is proposed. As a multi-layer network method, CANet takes advantage of the parameter-efficient and fast operators of CNNs, thereby generating additional expressive features and addressing the overfitting problem for large KBs. * An interaction operation is adopted to generate multiple triplet-specific embeddings for relations and entities. Thus, for various triplet predictions, the operation can simulate the selection of different information. Furthermore, multiple interaction embeddings provide rich representations and generalization capabilities. * To increase the representational capacity of CANet, an attention function is inserted into the convolutional operation. This functionality enables the network to achieve recalibration features adaptively. By learning to use global content, CANet can selectively accentuate features while suppressing pointless ones. Figure 1: Illustration of a knowledge base. To test the efficiency of our proposed approach, five real-world datasets were used in our experiments. In comparison to several previous approaches, the results show that CANet achieves state-of-the-art efficiency for general assessment metrics. The following is how the remainder of the article is structured: In Section 2, we checked and discussed the related works. In Section 3, we defined the research problem systematically, and the CANet is described in detail. The experimental setup and results of the proposed method are detailed in Sections 4 and 5. Finally, the conclusions and suggestions for future research are presented in Section 6.