esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Finite Element Formulation
### Formulation at the Element Level  
The inertia matrix of an element is given by the following symmetric matrix:  
\[M^{ij}=\left[\begin{array}{cc}m_{rr}{}^{ij}&m_{rr}{}^{ij}\\ sym&m_{ff}{}^{ij}\end{array}\right]=\left[\begin{array}{cc}m_{r_{o}{}^{i}}&m_{ r_{o}{}^{ij}}&m_{r_{o}{}^{ij}}&j\\ m_{\theta\theta}{}^{ij}&m_{\theta f}{}^{ij}\\ sym&m_{ff}{}^{ij}\end{array}\right] \tag{118}\]where:  
\[m_{r_{\sigma}r_{\sigma}}{}^{ij}=\left[\begin{array}{cc}m^{ij}&0\\ 0&m^{ij}\end{array}\right]\text{for planar case.} \tag{119}\]  
\[m_{r_{\sigma}0}{}^{ij}=R_{\theta}{}^{i}{}^{\mathcal{C}}{}^{ij}\overline{S}{}^{ ij}\overline{C}{}^{ij}e^{ij} \tag{120}\]  
\[m_{r_{\theta}}{}^{ij}=R^{i}{}^{\mathcal{C}}{}^{ij}\overline{S}{}^{ij} \overline{C}{}^{ij} \tag{121}\]  
\[m_{f}{}^{ij}=\overline{C}{}^{ij}\overline{C}{}_{f}{}^{ij}\overline{C}{}^{ij} \tag{122}\]  
\[m_{\theta}{}^{ij}=e^{ijT}\overline{C}{}^{ij}\overline{C}{}^{ij} \tag{123}\]  
\[m_{\theta}{}^{ij}=e^{ijT}\overline{C}{}^{ij}\overline{C}{}^{ij} \tag{124}\]  
For a 2D beam element with length \(l^{ij}\), the matrices \(S_{ff}\), \(\overline{S}{}^{ij}\) and \(\overline{S}{}^{ij}\) are given by:  
\[S_{ff}{}^{ij}=m^{ij}\left[\begin{array}{cccc}1/3&&&sym\\ 0&13/35&&\\ 0&\frac{11}{210}l^{ij}&\frac{(l^{ij})^{2}}{105}&\\ 1/6&0&0&1/3\\ 0&9/70&\frac{13}{420}l^{ij}&0&13/35\\ 0&-\frac{13}{420}l^{ij}&-\frac{(l^{ij})^{2}}{140}&0&-\frac{11}{210}l^{ij}& \frac{(l^{ij})^{2}}{105}\end{array}\right] \tag{125}\]  
\[\overline{S}{}^{ij}=\frac{m^{ij}}{12}\left[\begin{array}{cccc}6&0&0&6&0&0\\ 0&6&l^{ij}&0&6&-l^{ij}\end{array}\right] \tag{126}\]  
\[\overline{S}{}^{ij}=\frac{m^{ij}}{60}\left[\begin{array}{cccc}0&21&3l^{ij}&0 &9&-2l^{ij}\\ -21&0&0&-9&0&0\\ -3l^{ij}&0&0&-2l^{ij}&0&0\\ 0&9&2l^{ij}&0&21&-3l^{ij}\\ -9&0&0&-21&0&0\\ 2l^{ij}&0&0&3l^{ij}&0&0\end{array}\right] \tag{127}\]  
The stiffness matrix of an element is:  
\[K^{ij}=\left[\begin{array}{cccc}0&0&0\\ 0&0\\ sym&K_{ff}{}^{ij}\end{array}\right] \tag{128}\]  
where:  
\[K_{ff}{}^{ij}=\overline{C}{}^{ij}\overline{K}_{ff}{}^{ij}\overline{C}{}^{ij} \tag{129}\]  
\[\overline{K}_{ff}{}^{ij}=\frac{E^{ij}l^{ij}}{l^{ij}}\left[\begin{array}{cccc }\frac{A}{l^{ij}}&&&sym\\ 0&\frac{12}{(l^{ij})^{2}}&&&\\ 0&6/l^{ij}&4&&\\ -\frac{A}{l^{ij}}&0&0&\frac{A}{l^{ij}}&\\ 0&-\frac{12}{(l^{ij})^{2}}&-6/l^{ij}&0&\frac{12}{(l^{ij})^{2}}\\ 0&6/l^{ij}&2&0&-6/l^{ij}&4\end{array}\right] \tag{130}\]
# Two NEH Heuristic Improvements for Flowshop Scheduling Problem with Makespan Criterion
### NEH Heuristic Analysis  
The pseudo-code of the NEH method is recalled in Algorithm 1, highlighting the first sort and the second point where a choice is required to be made. Consequently, the improvement ideas proposed in this paper will be better illustrated. Figure 2 gives an example of the application of the original NEH heuristic using a five jobs/three machines example. It presents, step by step, how the first sort is used in the heuristic and how the second part of the heuristic inserts jobs in successive partial sequences.  
```
Rank jobs in decreasing order of total processing time // (First point sort) Remove first job of ranked list and insert it as first element of current partial sequence \(k=1\) Do{ Take the first job of ranked list Insert it in all of \(k\)+1 possible places of current partial sequence Evaluate all of \(k\)+1 resulting partial sequences Keep the best sequence as new current partial sequence // (Second point choice) \(k=k\)+1 While(k\(\leq\) number of jobs to schedule)
```  
**Algorithm 1**(NEH heuristic)  
The two points of choice in the NEH heuristic have been clearly highlighted within Algorithm 1 and Figure 2. At the first point, jobs are ranked in decreasing order of processing time, and at the  
Figure 2: Illustration of the two points of choice in the [PERSON], Enscore, and Ham (NEH) heuristic in a five jobs, three machines problem.  
second point, only the best sequences are kept depending on their partial makespan. For the first point, a sorting routine is necessary. Then, depending on which sorting routine is used, the results of the NEH heuristic can differ. For the second point, a choice may have to be made between two equivalent partial schedules; again, the choices can lead to different results. Indeed, they do differ. This difference explains why a large number of studies, in which NEH heuristic results are presented with the well-known Taillard flowshop benchmarks, are different [18; 19; 20; 21]. This is also why observing the different possible orders of [PERSON]'s benchmarks is of interest. The two improvement methods proposed in this paper are direct consequences of this observation. The first improvement consists of testing the equivalent possibilities in the first sort, and the second consists of testing some equivalent possibilities for the second choice.
## 3 Results and Discussion  
### Exploratory Data Analysis  
The acquired XRF spectra of the samples are presented in Figure 1. It can be seen that the visual appearance of the ore and steel samples spectra varies significantly due to the significant differences in chemical composition. In order to visualize all the samples simultaneously in a concise manner, we have applied PCA to the spectral data. The intensities obtained in light and heavy channels for each sample were merged prior to the analysis. The resulting PCA score plot is given in Figure 2. Two distinct clusters formed by the ore and steel samples correspondingly can be observed in this plot. This sample separation confirms the substantial difference in the spectra and the chemical composition and consequently in the expected matrix effects in the spectra.  
Figure 1: XRF spectra: (**a**)—light channel for steel samples, (**b**)—heavy channel for steel samples, (**c**)—light channel for ore samples, (**d**)—heavy channel for ore samples. Insets in plots show zoom-in into low-energy ranges of the spectra.  
Figure 2: PCA score plot for ore and steel samples.  
Therefore, the calibration models built separately for ores and for steels will not be capable of accurate prediction in a cross manner: the calibration model for steels will not be able to handle ore samples properly, and vice versa. This is well illustrated by Figure 3, where \"measured vs predicted\" plots for the corresponding IC-PLS models are presented. Quantification of Al and Mn is considered as an example. Unacceptably poor accuracy of the predictions can be seen in all four plots. The cross-validated models built separately for the ores and for the steels for the quantification of Al and Mn are shown in Figure S1 (Supplementary Materials). Similar results were obtained for Si and Ti.  
In attempting to construct a universal calibration model embracing both ore and steel samples, we have applied several quantitative modeling methods: FP, IC-PLS, and KRLS.
## 4 Experimental Validation
### Results  
The classification results using the ItalianPVS dataset are presented in Figures 4 and 5 (confusion matrix). The results show that the network model achieved good results with an F1-score of 0.8974, and Area Under Curve (AUC) of 0.9433.  
The classification results using the Lithuanian voice dataset are presented in Figures 6 and 7 (confusion matrix).  
Figure 8a display the alluvial plot for the Italian dataset, while Figure 8b display the aluavian plot for the Lithuanian dataset. The classifier has achieved worse results on the Lithuanian voice dataset as it is a smaller dataset, achieving F1-score of 0.7778, and Area Under Curve (AUC) of 0.8179. Perhaps, better results could have been achieved if more voice data would be available for network, training, which is supported by the shape of PR and ROC curves, which are not perfect.  
The sensitivity-specificity plots for both datasets are presented in Figure 9a,b, respectively. They show that the best performance of the classifier model is achieved, when the cutoff value is equal to 0.1 for the ItalianPVS dataset, and 0.8 for the Lithuanian voice dataset.  
Figure 8: Alluvial plot of the classification results: (**a**) ItalianPVS dataset, and (**b**) Lithuanian voice dataset.  
Figure 6: Precision-recall curve and receiver operating curve (Lithuanian voice dataset).  
Figure 7: Confusion matrix (Lithuanian voice dataset).  
The time performance of the proposed deep learning model on both datasets is compared in Figure 10. An excellent time performance of processing one voice record in just 4 ms was demonstrated, which experimentally validated the characteristics presented in Table 2. The results show that the proposed model can be used for real-time PD screening.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total params: & 11,692,525 \\ Trainable params: & 11,692,525 \\ Non-trainable params: & 0 \\ Model size (params + buffers): & 44.65 Mb \\ Framework and CUDA overhead: & 1942.21 Mb \\ Total RAM usage: & 1986.86 Mb \\ Floating Point Operations on forward: & 1.70 GFLOPs \\ Multiply-Accumulations on forward: & 850.23 MMACs \\ Direct memory accesses on forward: & 863.85 MDMAs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size of the U-lossian model.  
Figure 10: Time performance of the proposed network model on the ItalianPVS and Lithuanian voice datasets.  
Figure 9: Sensitivity-specificity plot of (**a**) the ItalianPVS dataset and (**b**) the Lithuanian voice dataset.  
The performance results of both datasets are presented in Table 3 and summarized visually in Figure 11. These results show lower performance for the Lithuanian voice dataset with a higher variability (i.e., wider confidence range), which can be explained by the smaller size of the Lithuanian voice dataset.
## 2 Background
### AR Placement Methods: Related Works
However, an accuracy of 5 cm horizontal and 10 cm vertical might not be accurate enough to apply AR technologies over short distances or to identify small features on-site. Ref. [57] presented improvement approaches to increase the accuracy of camera pose estimation for accurate placement. Since the inertial sensors in today's smartphones are so full of noise, the accuracy of an inertial tracker is often lower than that of a visual tracker. As a result, combining the two results usually only yields a minor improvement in accuracy. As a goal, the authors proposed two different models of visual measurements for use in Kalman-based filters that also integrate inertial/magnetic measurements to estimate tracking and placement by a handheld IMU/camera sensor unit. Ref. [58] worked on BIM-AR in site inspection using smart glasses. Although the developed BIM-AR system is capable of achieving 1 cm accuracy, this research argues that a more reliable and stable tracking system that could consistently maintain sub-1 cm accuracy would support the adoption of the BIM-AR system. Ref. [7] used the HoloLens (head-mounted MR device) to develop an AR-based inspection tool that place BIM models onto a physical construction site. The authors used an AR-based interface, allowing inspectors to check off construction elements within a virtual holographic checklist. This study developed a construction inspection system by combining BIM and Mixed Reality (MR), which allows the user of the HoloLens to display the BIM model on the construction project site with the exact size. Ref. [5] examined the various placement and tracking technologies for AR and concluded that GPS/GIS and fiducial markers are the most often used methods thanks to their low cost and ease of use. However, environmental conditions, such as temperature and sunshine, affect their performance. As a result, they emphasized the need to improve the accuracy and manage the occlusion of AR devices used on construction sites. According to the state of the art, there have been several research studies addressing the issue of placing 3D models using AR. However, there is currently limited research studying and assessing the various methods and solutions for this placement for indoor and outdoor environments. In this paper, we propose a quantitative and qualitative assessment of several AR placement methods of a 3D model. Our contribution also provides a discussion about the requirements in terms of placement accuracy and stability in some use cases of the AECO domain and so analyzing the fitness of use of each studied placement method.
## 4 The Application of AWS Machine Learning (AML) for the Surface Finishing Process
### Research Motivation, Experimental Setup, and Data Acquisition
The chamfer length measurement process using the gap gun is shown in Figure 6.  
In this study, one complete cycle of the deburring process on one side of the work coupon (replica boss hole) is referred to as one \"pass\". During one pass, three sets of vibration signals are generated from the X, Y, and Z directions of the 3-axis accelerometer. A detailed visualization of the three directions can be seen in Figure 6. To remove the burrs that are produced from the previous manufacturing process and to develop a chamfer on the boss-hole edges, five passes of the deburring process and data acquisition are conducted. A more detailed visualisation of the vibration signals obtained from the deburring process is presented in Reference [11]. A feature from the combined wavelet decomposition and Welch's spectrum estimation is calculated and applied to obtain the raw vibration signal. This feature is selected based on a previous study [12]. The feature extraction results of each pass and the associated chamfer length for each pass are presented in Tables 2 and 3. Figure 5: The data acquisition of the deburring process. Figure 6: The accelerometer setup and chamfer length measurement process. Six work coupons were used in the experiment. Three were used for the machine learning training process and the remaining three were used for testing the process. For demo simplification of the AML prediction, we calculated the mean value of the features and the associated chamfer length from the three work coupons for both the training and testing processes. We then normalize the feature into the form of the baseline. The objective was to obtain a better visualisation of the decreasing trend of the feature values associated with the increasing number of passes. It can be seen from Tables 2 and 3 that the decrease on the features (X-, Y-, and Z-axis) corresponds to the increasing number of passes and the chamfer length.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.2 802.1X Authentication  
Authentication is a very important part in this architecture. IEEE 802.11u-aware IEEE 802.1X authentication can be useful in the underlying scenario. IEEE 802.11u can support network discovery and selection by using GAS, ANQP, and quality of service (QoS) map distribution facilities. Upon a client-side X.509 certification, EAP-TLS can be used to allow such clients to become securely connected with the server side (AAA server). On the other hand, IEEE 802.1X authentication needs the components, supplicant (STA-a IoT device), authenticator (acts as bridge between the supplicant and access point), and authentication server (AAA server), which is a trusted server. The authenticator server informs the authenticator about the possibility that the supplicant is allowed to connect the network. Such an AAA server can run protocols such as the EAP or remote authentication dial-in user service (RADIUS) in their local machines. Sometimes, it can be integrated with the authenticator device itself. The whole process of authentication involves four procedures, such as, (i) initialization: at this stage, the supplicant is allowed for 801.1X traffic and other data such as the internet protocol, and the allied TCP and UDP services are dropped; (ii) initiation: in this stage, the authentication process begins where the authenticator transmits the EAP-Request/Identity frame to the local network segment at the 01:80:C2:00:00:03 address, and, upon receipt of such a frame, the supplicant responds with an EAP-Response frame to the authenticator with its own user identity, and the authenticator then transmits this frame in the form of a RADIUS Access Request packet to the AAA server; (iii) EAP negotiation: in this phase, the AAA servers send a RADIUS Access-Challenge packet to the authenticator with an embedded EAP method, and the same is forwarded to the supplement by the authenticator, then the supplement can start with the mentioned EAP method, or it can respond with a non-acknowledgement frame to the authenticator; (iv) authentication: this is the final phase where both the AAA server and supplement agree on a given EAP method, wherein the EAP Request and EAP Response packets are transmitted between the supplement and AAA server via the authenticator, and this process continues until the AAA server responds with an EAP-Success message inside the RADIUS Access-Accept packet to the supplement, otherwise, a RADIUS Access-Reject message is sent to the supplement. Finally, upon the successful completion of the authentication process, the supplement sets the post to an authorized state and normal data traffic is allowed, and upon logging off by the supplement, the authenticator sets the post to an unauthorized state.
## 4 Discussion
### Clinical Implications and Future Perspectives  
The results of the present study suggest relevant clinical implications. The aim of this study was to investigate the incidence and risk factors of temporomandibular and cervical complications after endodontic procedures.  
Root canal therapy is a common treatment that has shown great benefits in relieving symptoms and restoring function to patients. However, this study showed that very prolonged procedures can cause TMJ and cervical symptoms in patients. A decrease in mouth opening, PPTs, and cervical mobility, and an increase in MTrPs were demonstrated, with higher risk in patients with previous mouth opening limitation and TMDs. These results should be considered in the preventive and post-interventional management of these patients.  
Several treatment methods have been proposed to treat the musculoskeletal symptoms of TMDs, including manual therapy, exercise, education, or dry needling [12]. However, there is limited evidence concerning definitive management of mouth opening restrictions and cervical dysfunction [47; 48], so prevention should be the primary focus. Earlier recognition of these patients could help to develop preventive interventions. Care should be taken in the use of mechanical stress for the TMJ and cervical spine during some dental procedures. Different devices could be used to stabilize the mandible, such as a bite block [46].  
The development of a prediction model could be useful to adopt preventive measures in patients with a high risk of temporomandibular complications after endodontic procedures. The results of this study should be validated, and can be used to develop a preventive program for temporomandibular and cervical complications after endodontic procedures. Exploring the combination of multidisciplinary treatment between dentists and physiotherapists could be a potential solution to decrease post-intervention pain or even find alternatives for conducting physiotherapy treatment during the same session.
## 3 Results and Discussion
### Thermal Stability under Vacuum Annealing
Crystallization of the Ta-Zr-Si-B-C coating was observed at a temperature of 600 \({}^{\circ}\)C, with the formation of a nanocomposite structure with h-TaSi\({}_{2}\) and c-TaC crystallites \(\sim\)10-20 nm in size and an amorphous interlayer based on a-SiC with dissolved zirconium and boron, whereas the Ta-Zr-Si-B-N coating crystallizes at a temperature of 1000 \({}^{\circ}\)C, with the formation of a nanocomposite structure with an h-Ta\({}_{5}\)Si\({}_{3}\) and c-TaN crystallite \(\sim\)6-10 nm in size, surrounded by an amorphous matrix a-Si(Zr,B)N. It can be concluded that the nitrogen-containing coating has a higher thermal stability compared to the carbon-containing sample.
# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 4 Proposed Algorithm for Feature Selection
### Computational Complexity Analysis  
The computational complexity of an algorithm is an important measure to evaluate its running time, which is usually expressed by the big O notation. The computational complexity of the algorithm depends on the number of individuals (\(SN\)), the dimension of the problem (\(D\)) and the number of iterations (\(NMAX\)). The time complexity of the basic ABC, BABCGWO and BABCWOA is discussed here.  
For basic ABC:  
1. In the initialization stage of the algorithm, the time complexity is \(O(SN*D)\);
2. The time complexity of each iteration in the updating phase of the employed bee, the onlooker bee and the scout bee is \(O(SN*D)+O(SN*D)+O(SN*D)\cong O(SN*D)\);
3. The time complexity in the process of calculating individual fitness is \(O(SN)\). For BABCGWO:  
1. During initialization, the time complexity is \(O(SN*D)\);
2. \(O(SN*D)+O(SN*D)\cong O(SN*D)\) is required for each iteration in the evolution of the employed bee phase and grey wolf phase;
3. The time complexity of calculating the fitness is \(O(SN)\). For BABCWOA:  
1. The time complexity of the initialization step is \(O(SN*D)\);
2. The time complexity of each iteration in the updating process of employed bees and whales is \(O(SN*D)+O(SN*D)\cong O(SN*D)\);
3. \(O(SN)\) is consumed by evaluating the fitness of each individual.  
According to the above analysis, it can be concluded that the basic ABC algorithm, BABCGWO algorithm and BABCWOA algorithm have the same computational complexity, and the total computational complexity is \(O(SN*D*NMAX)\) after many cycles. In Section 5.2, we will conduct an experimental analysis on the specific execution time of each algorithm.
## 5 Case Study
### Example Results
#### 5.2.2 Scenario 2: Energy Utilization Efficiency (EUE)  
Due to the high cost of ES, it is necessary to improve the EUE of ES equipment. This section aims to maximize EUE and optimize it using PSO and MAPSO respectively. Similarly, the linear  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Population Size** & **Iterations** & **UBIW** & **LBIW** & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & **Size of Global Environment** \\ \hline PSO & 16 & 50 & 0.9 & 0.2 & 2.0 & 2.0 & \\ MAPSO & 16 & 50 & 0.9 & 0.2 & 2.0 & 2.0 & 8\({}^{\prime}\)8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO).  
Figure 3: Comparison of convergence with EE objective.  
decreasing inertia weight is chosen here, where the parameters of PSO and MAPSO are the same as Section 5.2.1. However, the maximum iterations are changed to 50 on the basis of the tested computing results. The CPU time for solving this issue is calculated 20 times as well using the identical laptop in Section 5.2.1. The average solving time of MAPSO is 0.046338 s and 0.026641 s of PSO.  
The convergence comparison of PSO and MAPSO with the target of EUE is shown in Figure 4, and the result comparison is presented in Table 6.  
It is known from Figure 4 that MAPSO reaches the optimal solution in about 5 steps, and PSO runs in approximately 5 steps at the same time. From Table 6, the minimum, mean energy capacity of PSO is a little larger than that of MAPSO, while the maximum value obtained by the two algorithms is 4.6000 \(\times\)\(10^{6}\) MW\(\cdot\)h. As far as the results of Figure 4 and Table 6 are concerned, the advantages of MAPSO are not so obvious. This is because the objective function of EUE is relatively simple, and the superiorities of MAPSO are not well reflected.