esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The first studies on the use of piezoelectric actuators to induce vibrations on an elastic structure were carried out by [PERSON] and [PERSON] [23], who proposed a predictive one-dimensional model describing the shear transmission at the ice-structure interface. Afterwards, further studies and theoretical models were proposed in the literature [24]. To date, to the authors' best knowledge, no papers exist in the literature dealing with the development of efficient and reliable models of strain actuation on curved relatively soft beams by piezoelectric ceramic transducers, while also taking into account the adhesive layer, for the prediction of the forces and deformations transmitted to curved aerodynamic surfaces. These aspects could be of certain interest for many applications, including for the ice protection of windshields [25], piping [26], or wing parts (even if in this case one talks generally of actuation on harder materials). In the case of piezoelectric ceramic actuators that are used as de-icing systems for curved-wing structures (e.g., the leading edge), the matter gets more complicated, and further studies are needed due to the curved shape of the surface. Therefore, the scope of the research activity is to study, develop, and propose an efficient model to fill the gap of knowledge in terms of actuation by piezoelectric patches bonded on relatively soft curved structures. This paper aims to describe numerical and experimental investigations on the use of Lamb waves for transmitting forces and deformations to curved beams through piezoelectric ceramic transducers bonded on the surface. For this purpose, the theory of strain actuation on curved beams was proposed as an extension of the [PERSON] theoretical models. Then, finite element models were developed for predictive analyses. Static and dynamic investigations were performed to estimate the effective shear at the interface and the resulting forces and deformations transmitted to the curved beam. This allowed for assessing the influence of the curvature of the beam on both the forces and the strain transmitted to the beam, while also providing the tools to properly set the position of the piezo device on the curved structure. To better orient the reader, a block diagram was prepared to illustrate the structure of the work and highlight the main phases (Figure 1).
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Delay Variance Attacks
#### 5.2.1 Packet Delivery Ratio (PDR)  
An illustration of how delay variance attacks affect the PDR is provided in Figure 13. Similar to periodic drop attacks, with slightly higher values in delay variance attacks the PDR decreases whenever the number of malicious nodes grows. For three malicious nodes, the proposed protocol performs at 94.62%, higher than [37], 2017 (about 80.2%), and [20], 2018 (about 90.38%). When the number of attacker nodes is exaggerated to reach nine, the PDR of [37], 2017, reduces significantly to 58.42%, while [20], 2018, has a PDR of 71.93%, and our proposed protocol maintains the best PDR of 79.53%. The outcomes indicate that SecE-V2X performs significantly better in terms of the PDR than [37], 2017, and [20], 2018, demonstrating that our recently designed protocol works effectively even in the presence of Jellyfish delay variance attacks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Attackers** & **Metrics** & **SecE-V2X** & **[20], 2018** & **[37], 2017** \\ \hline \multirow{4}{*}{0} & PDR (\%) & 95.34 & 93.80 & 91.60 \\  & EED (ms) & 6.80 & 7.11 & 8.64 \\  & Throughput (packets/s) & 638.15 & 596.33 & 534.55 \\ \hline \multirow{4}{*}{3} & PDR (\%) & 94.62 & 90.38 & 80.20 \\  & EED (ms) & 7.92 & 9.34 & 13.84 \\  & Throughput (packets/s) & 617.42 & 562.19 & 455.41 \\ \hline \multirow{4}{*}{6} & PDR (\%) & 91.33 & 81.65 & 69.47 \\  & EED (ms) & 9.11 & 13.33 & 17.59 \\  & Throughput (packets/s) & 560.75 & 483.62 & 383.34 \\ \hline \multirow{4}{*}{9} & PDR (\%) & 79.53 & 71.93 & 58.42 \\  & EED (ms) & 11.28 & 15.09 & 23.14 \\ \cline{1-1}  & Throughput (packets/s) & 448.76 & 400.86 & 298.36 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance values comparison under Jellyfish delay variance attacks.  
Figure 12: Nodes’ data throughput under Jellyfish periodic drop attacks [20; 37].
## 5 Conclusions  
This study investigated how the changes in the shape of the edge on a ceramic friction material can affect frictional behavior of a ceramic sliding plate interface of bearing supports. The results of actual test and FEA were compared, and the following conclusions can be drawn.  
1. The horizontal motion of the ceramic friction material allowed it to penetrate the underlying stainless-steel sliding plate because of the higher hardness of the ceramic compared to that of the stainless-steel. Compared to the actual test, the finite element model under-evaluated the horizontal load by less than 4.3% and overestimated the relative slip at the maximum horizontal load. Such slight discrepancy could be attributed to the fact that the finite element model was constructed in idle conditions and adopted elastic conditions for the ceramic. On the other hand, the manufacturing tolerance of the actual specimen caused a large slip to occur when the horizontal load was applied. Nevertheless, the developed model provided sufficient accuracy for the evaluation of the frictional behavior.
2. The stress and deformation of the ceramic friction material increased with larger vertical loads, but the increments varied depending on the improved edges of the ceramic friction material. The improved shapes adopting chamber and round edges achieved a low increment in stress and deformation, whereas the tapered edge provided stress and deformation increments proportional to the increase in the vertical load. The size effect for the camber and round edges was relatively low with a difference in stress and deformation below 10%.
3. All C, R, and T types have the effect of improving friction behavior, but in the case of type T, there is a risk of damage if a load larger than the design load occurs. The C and R types show similar behaviors, but it can be seen that it is advantageous to use type C, which is easy to process, as a friction material in terms of productivity.  
Software, formal analysis, data curation, and writing--original draft preparation, O.H.; Conceptualization, supervision and methodology, J.-W.K.; validation, investigation, and resources, J.-W.L.; visualization, project administration, and funding acquisition, W.-J.H. All authors have read and agreed to the published version of the manuscript.  
Research for this paper was carried out under the KICT Research Program (project no. 20220327-001, Development of sliding pads for bridge bearings with improved durability using ceramic) funded by the Ministry of Science and ICT.  
Not applicable.  
The authors declare no conflict of interest.
# Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud
###### Abstract  
The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.  
OpenStack cloud; KVM; Docker; MPI; performance; discrete element method software +
Footnote †: journal: Applied Computer Science  
1
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 5 Experiments and Results
### Hardware-in-the-Loop Simulation
#### 5.2.1 HIL Simulation Setup
Similarly, the periods from start point A to turning point B, from turning point B to turning point C, and from turning point B to end point D correspond to the 'high-speed' stage, 'low-speed' stage, and 'accelerating' stage, respectively.
## 3 Experiment Design and Methodology
### Solution of Bi-Directional Prediction Model  
Based on the cHGWOSCA-SVR algorithm, a bi-directional prediction model for hot pressing production parameters and quality of high-performance bamboo-based fiber composites is divided into a forward prediction model and a reverse prediction model. Forward prediction refers to predicting the quality parameters of high-performance bamboo-based fiber composites by using the production parameters as input data. Reverse prediction refers to predicting the production parameters in the hot pressing process using the proposed sheet quality as input data.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 4 Case Study
### ROP Prediction Experiments  
In this section, two sets of controlled experiments were designed. The first set of experiments utilized the original data from wells A1 to A3 as the training dataset and the original data from well A4 as the test dataset to assess the performance of the conventional model. The second set of experiments employed the corrected data from wells A1 to A3 as the training dataset and the corrected data from well A4 as the test dataset to evaluate the performance of the model proposed in this paper. The results of these two sets of experiments were compared to determine whether considering the friction torque improves the accuracy of the mechanical drilling rate prediction model.  
Four different prediction methods, namely support vector regression (SVR), random forest (RF), artificial neural network (ANN), and decision tree (DT), were employed for conventional data-driven ROP prediction as well as data knowledge double-driven ROP prediction in horizontal wells. The performance of the proposed method was evaluated using the model predictions and the measured ROP data, as depicted in Figures 12 and 13. The results presented in Figures 12 and 13, along with the data summarized in Table 2,  
Figure 11: Horizontal projection map of a multi-well pad targeting area.  
clearly demonstrate that the proposed data knowledge double-driven approach exhibits an superior capability to capture changes in ROP trends.  
In conclusion, the proposed data knowledge double-driven ROP prediction method outperforms the conventional data-driven ROP prediction method. The application of this method in the Sichuan region has yielded promising results, further confirming the validity and reliability of the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Proposed Method** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **Conventional Method** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** \\ \hline RF & 0.96 & 1.08 & 0.65 & RF & 0.65 & 1.87 & 1.31 \\ ANN & 0.95 & 1.12 & 0.71 & ANN & 0.55 & 2.1 & 1.53 \\ DT & 0.93 & 1.21 & 0.75 & DT & 0.52 & 2.18 & 1.53 \\ SVR & 0.94 & 1.2 & 0.68 & SVR & 0.6 & 1.98 & 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation results of the two methods.  
Figure 12: ROP prediction results of the proposed model and measurement.
# Product Innovation Design Based on Deep Learning and Kansei Engineering
## 3 Empirical Study
### Semantic Space Spanning  
Questionnaires were constructed by combining the 100 samples and 30 Kansei words. We invited 15 women (5 designers and 10 consumers) to evaluate the coat images on a 7-point Likert scale (7 corresponding to \"strongly agree\" that the image-word is very close to the image, and 1 corresponding to \"strongly disagree\"). Their ages ranged from 18 to 45. Through the questionnaire, we obtained dataset 1 with a dimension of \(15\times 30\times 100\), where 15 indicates the number of women who completed questionnaires, 30 indicates the number of Kansei words, and 100 indicates the number of sample images. We also calculated the average of 100 images (based on dataset 1) to obtain dataset 2 (15 \(\times\) 30). Factor analysis is a statistical method used to convert many observable variables into few latent factors. That is to say, several related variables are classified into the same class, and each class becomes a factor. The cumulative percentage of the factors is considered to determine the number of factors. Generally, the number of factors should account for more than 60% of the total variance [28]. We can use factor analysis to convert 30 Kansei words into few latent factors. In MATLAB (MathWorks, Natick, MA, USA), firstly, we converted dataset 2 into a matrix of 15 \(\times\) 30, then the principal component method was used as an extraction technique and the rotation of varimax was adopted as an orthogonal rotation method [29]. Finally, the results shown in Table 1 were obtained. As shown in Table 1, Factor 1, Factor 2, Factor 3, and Factor 4 account respectively for 17%, 17%, 15%, and 15% of the variance. The cumulative percentage of variance is 64%, which is more than 60%. Clearly, it is appropriate to divide 30 adjectives into four main factors. The four factors are named by considering the Kansei words' loading coefficient: Factor 1 describes the degree of professional or leisure style. It is defined based on the occasion of usage, comprising these adjectives: \"Relaxed\", \"Natural\", \"Peaceful\", \"Formal\", \"Strict\" and \"Capable\". Factor 2 describes the degree of vogue or classic style. It is defined based on the fashion degree, comprising these words: \"Modern\", \"Fashionable\", \"Particular\", \"Classical\", \"Traditional\" and \"Conservative\". Factor 3 describes the degree of grand or youth style. It is defined based on the age of users, comprising these words: \"Mature\", \"Steady\", \"Sweet\", \"Young\" and \"Energetic\". Factor 4 describes the degree of simple or delicate style. It is defined from the structure of the coat, comprising these words: \"Simple\", \"Plain\", \"Delicate\" and \"Luxurious\". Through factor analysis, we obtain eight kinds of coat style.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Source Domain Refinement (SDR)
#### 3.3.2 Outlier Removal Scheme  
In order to increase the classification accuracy and reduce overfitting, we presented an outlier removal technique. Any distant observation from the other observations can be considered as an outlier [32]. Assuming that total \"N\" RSS vectors are obtained in step 1 and the total number of sub-areas is \"A\", in order to distinguish the outliers from other fingerprints in each area, we calculate the mean vector \(\delta^{a}\) of each area \"a\", where \(a\in A\). The Euclidean distance \(\gamma_{i}^{a}\) of each fingerprint \(x_{i}^{a}\) from its \(a^{th}\) area's mean vector \(\delta^{a}\) is  
\[\gamma_{i}^{a}=Euclidean(x_{i}^{a}-\delta^{a}). \tag{2}\]  
Here, all the RSS vectors showing the value of Euclidean distance above a certain threshold, \(Th_{a}\), would be considered as an outlier. The value of \(Th_{a}\) is chosen arbitrarily.
# Sphere Fitting with Applications to Machine Tracking
## 2 Methods
### Median-Sphere
#### 2.1.1. Notation  
For a center (point) \(x\in\mathbb{R}^{d}\) and radius \(r\geq 0\), we define sphere of radius \(r\) around \(x\) by  
\[S(x,r)=\{p\in\mathbb{R}^{d}\mid\|p-x\|=r\}.\]  
We denote by \(\mathbb{S}^{d-1}\) the union over every sphere in \(\mathbb{R}^{d}\).  
Let \(P=\{p_{1},p_{2}\dots p_{n}\}\subseteq\mathbb{R}^{d}\) be a set of \(n\) points. For every \(S\in\mathbb{S}^{d-1}\), and \(p\in P\), we define distance between \(p\) and \(S\) by  
\[\operatorname{dist}(p,S)=\min_{q\in S}\|q-p\|\,.\]  
For every \(S\in\mathbb{S}^{d-1}\) and a weight function \(u:P\to[0,\infty)\), we denote the weighted sum of distances to the sphere by  
\[\operatorname{cost}(P,u,S)=\sum_{p\in P}u(p)\operatorname{dist}(p,S).\]  
Hence, smaller cost implies a better fit of the sphere to the points. For an unweighed set \(P\), we use a default function \(u:P\to\{1\}\) that maps a weight of \(1\) to every point in \(P\).
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
## 1 Introduction
In the proposed dynamic game, the UAV can optimally control its resources for energy transfer, and the wireless nodes can optimally control their resources for information transmission;
* Finally, based on the dynamic game, we have proposed two objective functions for the UAV, which are both formulated as profit maximization functions. The objective functions will be distinguished for different wireless nodes based on the energy consumption assumptions. The remainder of the paper is organized as follows: Section 2 summarizes the related work in WPT and UAV assisted WPT. The system model and problem formulation are given in Section 3. The analysis of the proposed game model is shown in Section 4. The numerical simulations are given in Section 5, and finally, we conclude the work in Section 6.
## 4 Analysis of Classification Results
### Influence of WT on DBN Classification  
The detecting signals were interfered with by white background noise, causing changes in the raw waveform. To illustrate the effectiveness of employing wavelet transform denoising for DBN classification in this study, noise reduction was applied to partial discharge and corona discharge signals, effectively filtering out white noise and spikes, as shown in Figure 10. When observing the amplified signal after noise removal, it was found that the signal appeared as a smooth curve, with signal amplitudes closely matching the measured discharge signals.  
The signals before and after denoising were used to create datasets with noise and noise-free data. Half of the dataset was utilized for model training, while the other half was employed to validate the classification performance. The precision of DBN and WT-DBN models underwent validation 100 times each. The results are presented in the form of a histogram as depicted in Figure 11.  
Figure 10: Comparison of denoising result before and after wavelet transformation: (**a**) undenoised partial discharge signal; (**b**) undenoised corona discharge signal; (**c**) wavelet denoising for partial discharge signal; (**d**) wavelet denoising for corona discharge signal.  
As shown in Figure 10(a), the accuracy of the DBN and WT-DBN models predominantly falls within the range of [95.3333%, 98.3333%]. Compared to DBN, the accuracy of WT-DBN is mainly distributed in the range of [98%, 99.3333%], indicating an improvement in classification accuracy with WT-DBN. Figure 10(b) illustrates that the introduction of WT shifts the density curve of accuracy distribution towards higher accuracy, further confirming that wavelet denoising enhances the overall classification accuracy of DBN. Analysis of Figure 10 reveals that due to waveform amplitude differences, white noise affects signals with high amplitudes less than those with low amplitudes. This impact reduces the similarity among signals of the same type, blurring the differences in low-amplitude signals of different types, making it challenging for DBN to extract signal features. The elimination of white noise interference from the raw signals by wavelet transform increases the deep belief network's performance to recognize partial discharge and corona discharge.
## 5 Calibration Experiment and Algorithm Comparison
### Calibration Accuracy Verification Test  
The calibration accuracy verification test, as shown in Figure 12, involves placing the ruby probe at the center of the feature hole of the workpiece calibrated by the vision system. Using the robot's Search L instruction, the robot performs a linear motion along the programmed path. The robot stops when the probe touches the sidewall of the circular hole, and the probe immediately records the current coordinate data from the robot controller at the moment of contact.  
Afterward, the robot returns to the center of the hole and proceeds to the next measurement in the programmed direction. After completing at least three measurements, the positions of the three points on the feature hole are obtained: Point 1 (x\({}_{1}\), y\({}_{1}\)), Point 2 (x\({}_{2}\), y\({}_{2}\)), and Point 3 (x\({}_{3}\), y\({}_{3}\)). Once the measurement of a feature hole is completed using the search program, the three-point method is used to determine the center of the circle based on the three measured points. The precise coordinates of the circle's center are calculated, completing the calibration of the feature hole.  
Figure 11: The LEONI 6D TCP calibration to check the accuracy of TCP parameters.  
The radius of the circular hole on the workpiece is 10 mm. The measured radius obtained through calculation are as follows: Circle 1: 10.1159 mm, Circle 2: 10.1848 mm, and Circle 3 10.1042 mm. The absolute measurement error is within 0.2 mm. The primary sources of measurement error include the robot's intrinsic absolute positioning error and the signal delay when the robot stops upon receiving a signal. The ABB robot used in this experiment underwent absolute accuracy calibration, ensuring that the robot's absolute positioning accuracy is controlled within 0.2 mm. As a result, the precision of workpiece calibration using the contact probe reached the limit of the robot's calibration accuracy. This method effectively achieved the desired calibration outcome.
## 3 Results and Discussion
### Coating Formation Stages
#### 3.5.2 Carbonatation  
The second stage of the coating formation is the formation of calcium carbonate, according to reaction (3)  
\[\text{Ca}^{2+}(\text{Mg}^{2+})+\text{HCO}_{3}{}^{-}\leftrightarrow\text{Ca}^ {2+}\text{(Mg}^{2+})+\text{CO}_{3}{}^{2-}+\text{H}^{+}\leftrightarrow\text{Ca (Mg)CO}_{3}\downarrow+\text{H}^{+} \tag{3}\]  
Precipitation of carbonates from carbonated aqueous solution is strongly dependent on factors such as pH, temperature and solution composition. Calcium carbonate solubility increases with CO2 content in the solution, so precipitation of carbonate groups continues while CO2(g) is released into the atmosphere from the carbonating solution. The release of hydroxyls at the Mg surface due to the exchange of OH\({}^{-}\) by CO3\({}^{2-}\) also shifts the equilibrium in reaction (3) towards the precipitation at the surface of Ca(Mg)CO3 magnesian calcite. We can resume this stage as precipitation of calcium carbonate or carbonatation. Carbonatation takes place by multiple nucleations of ACC nanoparticles and lateral growthonto the Mg surface until the formation of the ACC calcium carbonate layer is observed by SEM and AFM. This coating stage leads to the complete coverage of the Mg surface with ACC. The presence of Mg\({}^{2+}\) greatly increases ACC stability [42], facilitating the deposition of ACC clusters.