esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 5 Conclusions  
A deep learning system is used to determine whether or not 5G will be successful by utilising channel data, context metrics, cell metrics, and throughput statistics, as well as an analysis of their application. Utilized in this scenario were the deep learning models known as deep reinforcement learning (DR), LSTM, and convolutional neural networks (CNN). Deep learning has not been utilised in the majority of the recent research that has been conducted on the subject of the implementation of 5G; rather, the emphasis has been placed on technical assessments. Those businesses that use deep learning are more likely to emphasise the need to estimate the size of the 5G mobile industry and evaluate the accuracy of such estimates. The results of the predictions showed that LSTM models provide the impression of being very accurate but generate data of poor quality. It was proved, through the use of the metric known as root-mean-squared error (RMSE), that the outputs of both the DR model and the CNN model are aesthetically pleasing. The novelty of this research lies in the fact that it places an emphasis on the utilisation of channel metrics, context metrics, cell metrics, and throughput data in order to concentrate on generating predictions regarding the development of 5G networks themselves as well as the generation of the factors that will determine the adoption of 5G. The limitation of this study dwells in the dataset itself, which represents one of the most significant aspects of this research. The dataset was for a 5G production dataset that had been obtained from a large Irish mobile service provider. In future research, dataset issues ought to be taken into consideration when deciding whether or not to make use of other datasets from other countries in order to gain some additional insights from those other countries. The deep learning algorithm model that was utilised presents yet another limitation to the findings. Despite the fact that it is effective at mapping inputs to outputs, it is not very good at comprehending the context of the data it is managing. A future research project ought to consider several architectural models that take the context into account.
# Design and Construction of Oblique Prestressed Concrete Payement: A Case Study in China
## 4 Design of Demonstrative Concrete Pavement Section
### Stress Analysis  
#### 4.3.1 Stress Analysis on Concrete  
Concrete slab dimensions were selected to be \(L\) = 100 m, \(w\) = 4.5, \(t\) = 20 cm, the tensile strength of concrete \(f_{r}\) = 5 MPa, elastic modulus \(E_{c}\) = 25,500 MPa, coefficient of reliability \(\gamma\) = 1.08, [PERSON]'s ratio \(\
u_{c}\) = 0.15, expansion coefficient \(\alpha_{c}\) = 1 \(\times\) 10\({}^{-5}\)/\({}^{\circ}\)C, and temperature gradient \(\Delta T\) = 0.9 \({}^{\circ}\)C/cm.  
According to the fracture criteria,  
\[r=0.537h\big{(}\frac{E_{c}}{E_{t}}\big{)}^{\frac{1}{3}}=0.537\times 0.2\times \big{(}\frac{30,000}{165}\big{)}^{\frac{1}{3}}=0.61\]  
\[\sigma_{L}=0.077r^{0.60}\;\mathrm{h}^{-2}=0.077\times 0.61^{0.60}\times 0.2^{-2}=1.43\;\mathrm{MPa}\]  
\[\sigma_{L,r}=k_{f}k_{c}\sigma_{L}=1.69\times 1.2\times 1.43=2.90\;\mathrm{MPa}\]  
\[\sigma_{\Delta T}=\frac{E_{c}\alpha_{c}\Delta T}{2(1-v_{c})}=\frac{25,500\times 1 \times 10^{-5}\times 0.9\times 20}{2\times(1-0.15)}=2.7\;\mathrm{MPa}\]  
\[\sigma_{F}=\mu\rho\chi=0.8\times 0.024\times 50=0.96\;\mathrm{MPa}\]  
Then \(\sigma_{py}\geq\gamma(\sigma_{L,r}+\sigma_{\Delta Tr})+\sigma_{F}-f_{r}=1.08 \times(3.78+2.7)+0.96-5=2.96\;\mathrm{MPa}\)  
This means that the prestress applied on the concrete slab should be higher than 2.96 MPa to prevent fracture distress.  
According to the fatigue criteria,  
\[N=\frac{N_{e}}{t}=\frac{100\times 10^{4}}{30}=3.3\times 10^{4}\]  
\[\rho_{\sigma}=0.972-0.08281 lgN=0.972-0.08281 lg\;\Big{(}3.3\times 10^{4}\Big{)}=0.6\]  
Then, \(\sigma_{py}\geq\frac{\gamma(\sigma_{L,r}+\sigma_{\Delta T})+\sigma_{F}}{1+ \rho_{d}}-\frac{\rho_{d}}{1+\rho_{d}}f_{r}=\frac{1.08\times(3.78+2.7)+0.96}{1 +0.6}-\frac{0.6}{1+0.6}\times 5=3.10\;\mathrm{MPa}\)  
This means that the effective prestress applied on the concrete slab should be higher than 3.1 MPa to prevent fatigue failure. Therefore, the prestress level should be higher than 3.10 MPa taking into account both the fracture and fatigue criteria.
## 5 Experimental Results and Analysis
### Discussion
Figure 10: Line graph displaying the average “create time”, with respect to payload size, for the MAM protocol and non-protocol methods. the next channel key is incorporated with the current message. Our technique, on the other hand, makes use of MAM's restricted mode, which, as mentioned in Section 2.2, allows an individual to revoke access from previous subscribers by making the addresses of future transactions unknown to them. This can be achieved only if the user changes the authorization key and, hence, access by undesired subscribers is revoked. Data can be kept private inside the transactions, due to the access controls that the MAM channel modes offer and are, thus, contradictory to the feature of transparency in distributed ledgers. If an environmentalist prefers not to have that amount of control over their data, they can generate and store authorization keys themselves, or they can delegate that power to environmental professionals with higher authority. The United States Environmental Protection Agency (US EPA) or another federal agency must store these authorization keys. Environmental officers will be allowed to access data if an environmentalist is unable to recollect them, give a log of their authorization keys, or, if the officer is needed to take immediate action, to manage an environmental threat. Even though we demonstrated how MAM could be used to enable secure environmental sensor data exchange, we built our framework to be flexible enough to accommodate any open environmental data exchange standards. Furthermore, data can be transmitted using MAM from any endpoint with an internet connection, such as an environmentalist's computer, a server at a government institution like the US EPA, a mobile device, or a Bluetooth low-energy sensor. Our proposed system can be effortlessly linked with any professional in the digital environmental ecosystem due to the accessibility of encrypted data through open APIs. This, we believe, will facilitate acceptance, and open the door to new uses that go beyond environmental data collected without the oversight of environmental experts.
# Singular Integral Solutions of Analytical Surface Wave Model with Internal Crack
## 5 Conclusions  
The presented study mainly focuses on singular integral solutions to investigate the existing analytical model and to provide a new analytical solution. The study also presents an efficient dynamic FE analysis with an optimized model configuration for wave separation. The simulation that uniquely offers the wave scattering phenomena is designed to obtain the displacement value under the same given conditions as with the analytical model. The data from the simulation results are used to demonstrate the viability of the wave scattering model. The obtained analytical solutions with displacement variation curves are investigated by defining the Pearson's correlation of the FE results. Finally, the result shows the significantly correlated displacement variation of the proposed model with the FE results. The obtained results are discussed in terms of the curved shape and the correlation coefficient between the analytical model and FE simulation. The following conclusions are drawn based on the analytical solutions and FE simulation results and discussion presented in the study.  
* The computation time of the analytical model is much shorter than the FE simulation. In the study, 177 FE models are created and simulated in order to provide an adequate resolution of the displacement variation curves. Each simulation computing time is about 20 minutes, and post-processing is required, while the numerical solution for analytical model takes a few seconds without designing and post-processing time.
* Regardless of crack depth and size, a similar amount of wave scattering energy occurs in the same \(a/b\) ratio.
* The smaller crack sized model has a peak value at the relatively higher \(k_{R}a\). Since the lower frequency waves can pass through the smaller length of the crack, it shows lesser wave scattering in the lower \(k_{R}a\). Therefore, the peak displacement variation is formed in the higher \(k_{R}a\) in the case of the smaller crack model.
* The Gauss-Legendre quadrature integration method shows the highest correlation coefficient values, with an averaged value of 0.94 compared to all considered models, between its analytical model and the FE simulation.
* The analytical model can provide an efficient and reliable approach to solving the parametric relationship of wave scattering. Further studies are needed to investigate the limits of applicability of the developed analytical formulation, such as nonlinearity.  
**Author Contributions:** S.K. and S.H. designed the study; S.K. and Y.C.W. carried out numerical simulation, analyzed the collected. S.H. acquired the funding, supervised the study, reviewed and improved the manuscript draft. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the U.S. Department of Transportation, Tran-SET, Grant Number 19 SAUTA04 and 19 SAUTA03.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract
Although friction-stir welding can be a compelling alternative with lower welding temperature, the tremendous equipment requirement and expensive cost limit the use of this technology [22; 23; 24; 25]. To overcome these shortcomings, activated flux-tungsten inert gas welding (A-TIG) is introduced as one of the novel variants of the TIG welding process by using the mixture of inorganic material known as flux [26; 27; 28; 29]. The A-TIG welding process fully abolishes the requirement of filler wire, minimizes welding time and improves the depth of penetration, and thus was considered a better way of steel welding in industry. Previous studies reported the mechanism of increase in depth of penetration, microstructure and mechanical properties of A-TIG welding developed using microscale component fluxes [30; 31; 32; 33]. Different kinds of oxide fluxes have been used and studied, such as SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), CaO, Fe\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), ZnO, MnO\({}_{2}\) and CrO\({}_{3}\), etc. Among those kinds of oxides, although nearly all of them increase the penetration of the welding, the effects of their addition on the penetration are quite different. When the target steel plate is ferritic, the increase in weld penetration and the decrease in bead width are significant with the use of the activating fluxes Fe\({}_{2}\)O\({}_{3}\), ZnO, MnO\({}_{2}\) and CrO\({}_{3}\)[34; 35]; when welding austenitic steel, the results of adding SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) as fluxes are more obvious [36; 37]. Recently, various innovations in traditional welding and joining were reported by integrating nanotechnology [38; 39]. There is limited study on nanomaterials as flux, except a recent report using nanoscale TiO\({}_{2}\) flux in enhancing penetration of TIG welding [40]. However, the effect of nanowire fluxes on weld penetration, microstructure, and mechanical properties of A-TIG welding on HMS is scarce. Since adding Fe\({}_{2}\)O\({}_{3}\) as flux resulted in the increase of weld depth remarkably both on austenitic and ferritic steels, it is worth exploring the effect of nanoscale Fe\({}_{2}\)O\({}_{3}\) flux on ATIG welding for various engineering materials. In the present work, attempts were devoted to developing nanowire flux with Fe\({}_{2}\)O\({}_{3}\) nanowires for improved welding of A-TIG on high manganese steel and comparison with microscale Fe\({}_{2}\)O\({}_{3}\) fluxes. These fluxes were easily applied to the steel coupons with spray coating. The microstructural features, hardness, tensile properties, and angular distortion of the weldment were investigated in detail. Based on these investigations, the nanomaterial-enhanced welding mechanism was discussed.
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 4 Results
### Analysis on Aspects of Calling Apps
#### 4.3.3 Sentiments for Individual Apps  
This section contains the aspect-based analysis of each calling app. It also categorizes the app concerning different features according to people's sentiments. In addition, the shortcomings of the apps are highlighted for improvement.  
1. Per Topic Sentiments for IMO It can be observed in Figure 9 that, among all the topics, IMO sustained more positive responses than negative or neutral ones against its app, account, calls, messages, video, and working. However, updates encountered a low number of positive sentiments.  
Figure 8: Overall statistics of the calling apps.  
Figure 7: All topic sentiments.  
2. Per Topic Sentiments for Skype In the same way, it can be observed from Figure 10 that, among all the topics, Skype acquired a good number of positive responses against its app, call, messages, video, and working, whereas more issues were reported against accounts and updates of Skype.
3. Per Topic Sentiments for the Telegram App Figure 11 shows the sentiment analysis on the calling app Telegram. We found that Telegram obtained the maximum number of positive responses against its app, call, messages, video, and working, while lacking a high number of positive sentiments against accounts and updates.  
Figure 11: Sentiments on telegram.  
Figure 10: Sentiments related to Skype.  
Figure 9: Sentiments regarding the IMO app.  
4. Per Topic Sentiments for WeChat Likewise, the sentiment analysis on WeChat is represented in Figure 12. The graphical representation reveals that WeChat obtained a good number of positive responses for apps, calls, messages, updates, videos, and working features, while a lower number of positive sentiments were observed for accounts and updates. The distribution of the sentiments reveals that WeChat, as a whole, did not obtain good reviews, as the number of negative sentiments for the account was substantially large.
5. Per Topic Sentiments for WhatsApp Figure 13 presents the distribution of positive, negative, and neutral sentiments regarding different features. The stats represent that WhatsApp obtained a good number of positive responses on apps, calls, messages, videos, and working. For the updates feature, on the other hand, the ratio of negative sentiments was high indicating an user unsatisfactory attitude regarding the updates offered by WhatsApp.
# New Possibilities of Using DEMATEL and ERPN in the New PFMEA Hybrid Model
## 3 Results and Discussion
### Hybrid Model PFMEA-DEMATEL-ERPN
#### 3.5.3 Evaluation of Models  
In Table 13, we can see the results obtained after application in the transfer molding process.  
We converted the monitored criteria into a graphical representation for better visualization (Figure 7).  
Based on Figure 7, we can conclude that our prediction of the estimation of the model's functionality was correct. We recorded the lowest number of defective products with the DEMATEL model. The categorization of occurrence O was at the same level for all proposed models. Improvements occurred only in failure modes to which measures were specifically  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**PFMEA**} & \multicolumn{3}{c}{**Conventional**} & \multicolumn{3}{c}{**DEMATEL**} & \multicolumn{3}{c}{**ERPN**} \\  & & \multicolumn{3}{c}{**Initial State**} & \multicolumn{3}{c}{**PFMEA**} & \multicolumn{3}{c}{**} & \multicolumn{3}{c}{**} & \multicolumn{3}{c}{**} \\ \hline  & n & \(\mathbf{n_{n}}\) & **O** & **RPN** & **KI** & \(\mathbf{n_{n}}\) & **O** & **RPN** & \(\mathbf{n_{n}}\) & **O** & **RPN** & \(\mathbf{n_{n}}\) & **O** & **RPN** \\ \hline FM1 & 18,100 & 362 * & 10 & 80 & 9–11 & - & - & - & - & - & - & - & - & - \\ FM2 & 20  
applied. We recorded the lowest average RPN value for the ERPN model. However, it should be emphasized that criteria O and RPN do not have such a telling value and are not as specific as the number of defective products.  
The conventional PFMEA reduced the number of defective products the least.  
It is important to emphasize that with the DEMATEL and ERPN models, we have a clearly defined prioritization for the possible further proposal of measures. PFMEA groups failure modes with the same RPN and O, which is one of the most common limitations of PFMEA.
## 1 Introduction
For the UEM designs, it has been experimentally established that when the width of the developed excavation is two times larger than the width of the actuator, the bucket filling factor is 0.7, and the energy consumption for the lateral motion of the rotor in the face (the place where the soil is directly developed) reaches 20-30% of the power of its drive. In general, the energy consumption of UEM soil development is 10-15% higher than that of a conventional trencher, which is quite natural [15]. The reasons for this are the imperfection of the kinematics of the cyclic translational and rotational motion of the actuator in the digging mode, soil spills occurring in front of the actuator during its lateral  
Figure 1: Machinery for digging trenches of variable width: 1—basic tractor; 2—rotary soil evacuator; 3—chain soil development actuator; \(H\)—digging depth; \(B_{min}\)—the minimum excavation width; \(B_{max}\)—the maximum excavation width; \(\beta\)—the angle of rotation of the actuator. motion, pressing this soil to the excavation walls at the end of each half-cycle. It should be noted that the energy consumption of soil development by UEM rotor actuators is lower compared to the soil development by chain and bar or chain and bucket actuators. The statements determine the direction, purpose, and objectives of the necessary research to create UEM. To solve this problem, scientific work has been performed on the creation of universal continuous rotor earthmoving machinery [15; 16]. The purpose of the study is to develop the method for determining the stability of the course of the universal earthmoving machinery operating under cyclic lateral loading. The objectives of the study are:  
* To reveal the physical essence of the process of interaction of the UEM rotary actuator with the soil in the process of digging under the condition of the translational and rotational supply of the actuator;
* To develop a mathematical model of the power load of the UEM operating equipment and the machinery in the process of digging the soil under the condition of the two-hinged, two-link hitch of the actuator at the aft of the machine and on its basis the method of the machinery course stability determination as a decisive factor in ensuring its operability. In order to achieve the objectives, the article includes the following sections:  
* Section 1 introduces the subject and provides an overview of the literature;
* Section 2 presents the description of the used methodology, determining the course stability and the experimental setup used;
* Section 3 presents the results of the experimental studies;
* Section 4 summarizes the work;
* Section 5 describes further research.
## 4 Selection of FEC Codes for the Railway Application
### POLAR Codes
#### 4.1.2 Decoding  
Although belief propagation can be used to decode POLAR codes, the usual decoding approach is successive cancellation (SC). The SC decoder may be found immediately from the encoder, where the probabilistic nodes \(f\) and \(g\) represent the XOR and connection nodes, respectively. The \(f\) and \(g\) nodes in the LLR domain conduct the following calculations for input Log-Likelihood Ratios \(\alpha\) and \(b\)  
\[f(\alpha,b)=\log(\frac{e^{\alpha+b}+1}{e^{\alpha}+e^{b}}) \tag{4}\]  
\[g(\alpha,b,s)=(-1)^{s}\alpha+b \tag{5}\]  
with \(s\) being the partial sum, namely the sum of the prior bits that have been decoded and they are taking part in the current \(g\) node. The \(f\) node can be susceptible to an application of approximation. The LLRs are propagated from right to left as we can see in [43]. By passing the LLRs through the proper \(f\) nodes, the first bit, \(u_{1}\), can be directly decoded. After it has been decoded, the \(u_{2}\) can be decoded using a \(g\) node, which requires the associated partial sum. Because just \(u_{1}\) is involved, the partial sum is equal to \(u_{1}\). If \(u_{1}\) is frozen, the decoder already knows what it is and can set it to zero. The channel LLRs and \(u_{1}\) are employed to decode \(u_{2}\), resulting in increased \(u_{2}\) decoding reliability. The decoding procedure will continue until all of the nodes have been processed.  
An improvement of the performance can be undertaken by using a List-SC decoder. That is, for every decoded bit, the two cases of the decoding to be 0 or 1 are taken. This is accomplished by separating the current decoding path into two other paths, which represent one of the cases. The number of cases across the decoding tree is bounded by the size of the list. After the decoding process is over, the path with the smallest path metric is chosen. More improvement strategies can be encapsulated by the employment of a Cyclic Redudancy Check (CRC) on the paths that remain, with the one satisfying it, to be the right one.
## 3 Results
### PERFORM Test Results  
#### 3.3.1 Case Study I: PERFORM Results  
The MF relationships between the input and output variables can be visualized as a 3D plot (Figure 14). This view shows the ideal parameters value, designated in yellow on the color scale and displayed as the maximum value on the z-axis. The objective for the vehicle in this case study, reflected in the rule base and defined numerically in the MFs, is to maintain a safe distance, determined as 2 m, while minimizing the total distance to the goal (i.e., desired waypoint). As shown, the yellow portion of the plot corresponds to these goals spanning the area defined by a 2 m CPA and the region extending from 0 m to roughly 24 m of total distance traveled.  
Figure 12: Membership functions for Case Study III to observe path percent error and average vehicle speed.  
Figure 13: Membership functions for the performance score output.  
Figure 15 provides a side-by-side comparison between the PFM/A* path planning method (left) and that of the PRM method (right). Table 7 summarizes the simulated performance parameter values. An additional route is given in Figure 16 for additional context to demonstrate a path that would generate a poor score. This route (referred to as a \"Generic Planner\") is significantly more circuitous than either of the PFM/A* and PRM methods and also traverses much closer to the obstacle than is desired.  
Performance score results from applying the FIS to the input values in Table 7 are shown in Table 8. The resulting output fuzzy set overlaid by the defuzzified outputs given in Table 8 are shown in Figures 17 and 18 for both 3-MF and 5-MF based systems for the PRM and PFM/A* planners, respectively. Visual observation shows that the PFM/A* route has the best performance given the mission goals. As expected, the generic planner scores  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Parameter** & **Total Distance (m)** & **CPA (m)** \\ \hline PFM/A* & 19.28 & 1.50 \\ \hline PRM & 21.52 & 2.48 \\ \hline Generic Path & 31.92 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of simulated performance values for Case Study I.  
Figure 14: 3D plot MFs relating input variables (total distance and CPA) with corresponding output variables (performance scores).  
Figure 15: PFM/A* generated path (**left**), PRM generated path (**right**).