esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Model Evaluation
#### 4.3.2 Evaluation Results at 26 GHz Shifts
Such optimization of our approach, including optimization of machine learning models for augmentation, will be the focus of our further studies. To further minimize disruption related to data acquisition, one of the potential approaches could be the case where each ROADM vendor provides pre-captured constellation data or a mathematical model of precise frequency response when they sell the system.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison
### Communication Overhead  
The communication overhead in our analysis only considers the payload related to key distribution and update, and it does not include the IP packet encapsulation of the network layer.  
The length of AES-based session key is set to 16 bytes. The bytes of IP message encapsulation are not included in the calculation of the traffic generated during key distribution and update. For the static scenario, in stage 1, the effective communication load between the cluster head and the base station is 32 bytes. In stage 2, the effective communication load between the sensor node and the base station is 64 bytes. Therefore, the communication load consumed by a cluster for a complete key distribution process is 96 bytes. In the key update phase, the effective communication load between the cluster head node and the base station and the sensor nodes is 64 bytes in total, of which the load of broadcasting messages to the sensors in the cluster makes up 32 bytes. As for the dynamic scenario, the communication overhead of the CH and the sensor are the same as that of the static scenario.  
As the frequency of session key update increases, the bandwidth occupied by key distribution also increases. This outcome means there is a tradeoff between security and communication load in wireless sensor networks.
# A Disc-Type High Speed Rotary Ultrasonic Motor with Internal Contact Tenth
## 4. Stator Vibration Measurement  
A prototype with optimal dimensions was made and assembled. The resonant frequency of the stator was measured by precision impedance analyzer Agilent 4294A. The result of the measurement is shown in Figure 9. In the graph, the orange line is \(|Z|-f\) curve and the blue one is \(|\theta|-f\) curve, where \(|Z|\) is the impedance of the stator and \(\theta\) is the phase shift. The frequency changes from 80 kHz to 120 kHz. The lowest point of the \(|Z|-f\) curve means that the stator has the lowest impedance at the corresponding frequency, which is 98.128 kHz. Therefore, the resonant frequency measured by the impedance analyzer is 98.128 kHz. The difference between the measured and simulated frequencies is approximately 0.98%.  
Figure 8. Phases of the stator operation.  
Figure 9. Impedance-frequency characteristics of the stator.  
The vibration mode of the stator was measured by 2D laser Doppler vibrometer, as Figure 10d is shown. The result of the measurement is shown in Figure 10a. It shows that B\({}_{03}\) vibration mode is dominant in the vibration shape of the stator, which meet a good agreement with FEM results. Figure 10c shows the resonant frequency measured by laser vibrometer, which is 98.875 kHz. The difference between the measured and simulated frequencies is 0.76%. The error between resonant frequency obtained by impedance analyzer and vibrometer is caused by differences in clamping during measurements. Amplitudes in axial direction of the three contact teeth were measured separately, using the trimorph stator disc. The tip of each of the contact teeth was taken to be measured, and the results are shown in Figure 10b. The axial vibration amplitudes of the three points are 14.59 nm, 62.62 nm and 27.24 nm, respectively, at a voltage of 40 V\({}_{\text{pp}}\), which confirms that the B\({}_{03}\) mode was excited under the test condition and no out-of-plane mode interference was found.
## 6 Performance Evaluation on Simulator
### Autonomous Systems for Evaluation
#### 6.2.2 Localization Module  
Simultaneous localization and mapping (SLAM) [29], which utilizes a point clouds matching methods based on 3D LiDAR, was used. The GNSS was used to locate the initial position of the micro-EV based on SLAM. The way SLAM estimates localization is by assuming that the point clouds from LiDAR have accurate distance information. By projecting the point clouds acquired from real-time LiDAR on the stored reference map constructed using point clouds, accurate localization information is available. To build a point cloud reference map in an outdoor environment, the Graph SLAM [30] and G-ICP [30] methods are applied. After that, an interactive algorithm [30] is applied to increase the accuracy of the point cloud reference map. The resulting reference map is shown in Figure 8. The output data of this SLAM consisted of x, y orientation. In this study, when the micro-EV was driving, the localization accuracy was lower than 10 cm.  
Figure 6: Image similarity comparison between the real and virtual scenes. (**a**) Real scene and (**b**) virtual scene.  
Figure 7: Block diagram of the software algorithm architecture.
# Achieving Patency in Straight Canals Obturated with AH Plus Bioceramic Sealer: An Ex Vivo Study
## 4 Discussion
The lower calcium silicate content in AH Plus Bioceramic may contribute to its high solubility and lower compression modulus and strength, making it much easier to remove [39,40]. SEM/EDS analysis provided detailed information on the elemental composition and the microstructural characteristics of the sealer surface after exposure to the different solutions. The objective was to visualize surface changes, such as erosion patterns and structural breakdown, potentially induced by the tested agents. Although the microstructural alterations observed did not directly correlate with the lack of statistically significant differences in regaining patency, this analysis was conducted in an effort to identify possible explanations for the outcomes. [PERSON] and [PERSON] [22] observed the presence of pores and dissolution of sealer material in response to acid exposure, which aligns with our findings. Specifically, exposure to 17% EDTA caused cracks in Total Fill samples, a feature also observed in this study with AH Plus Bioceramic sealer. Cracks were evident in SEM images after exposure to 17% EDTA, as well as to 10% CA and 10% FA (Figure 4). In the study by [PERSON] and [PERSON] [41], cracks observed in aged samples were attributed to an artifact resulting from the dehydration process required for coating and imaging. The authors [41] also emphasized the full disintegration of AH Plus Bioceramic when in contact with the storage solution over a period of 7 and 28 days. [PERSON], et al. [42] reported that AH Plus Bioceramic exhibited early-stage porosity, indicating displacement of particles from the set structure overtime. A reduction in material strength could result from this pore formation. Additionally, as mentioned by a previous study [33], voids within the sealer or unset sealer may be the reason that hand files can navigate through the sealer to gain patency. Nevertheless, further studies are needed to confirm and expand upon the effects of these solutions on the microstructure of AH Plus Bioceramic sealer. The lack of differences between the various solutions tested in this study highlights the importance of other factors, such as the chemical composition of the sealer and its bond strength to dentin. The higher solubility and weak bond strength to dentin presented by AH Plus Bioceramic [25,43] may facilitate its removal, thereby aiding in the re-establishment of patency. Additionally, NSER was performed four weeks after canal filling, which may have been insufficient for the formation of a mineralized layer between the root canal dentin and the sealer. It is also important to note that this study only assessed straight, single-root canals. Therefore, future studies should focus on investigating the re-establishment of apical patency in teeth obturated with different CSSs in more challenging canal anatomies.
## 2 Related Work
### LoS blockage Probability
Recently, the authors in [21] proposed a detailed and versatile UAV LoS blockage probability that accounts for most critical parameters including different UAV and BS heights, different building height distribution, and various widths of streets and building blocks. The standard city deployment is however limited to the regular one and captured by the Manhattan Poisson line process (MPLP). Owing to the model complexity, closed-form expressions have been provided for specific building height distributions only. The authors demonstrated that the LoS blockage probability is highly sensitive to the type of deployment, the distribution of building heights, and the flight altitude of the UAV. Also, according to the authors, the existing standardized models developed by 3 GPP and ITU-R provides an overly optimistic approximation of the UAV LoS blockage probability.
###### Abstract  
Noninvasive monitoring of blood flow in the carotid artery is important for evaluating not only cerebrovascular but also cardiovascular diseases. In this paper, a wireless neckband ultrasound Doppler system, in which two 2.5-MHz ultrasonic sensors are utilized for acquiring Doppler signals from both carotid arteries, is presented for continuously evaluating blood flow dynamics. In the developed wireless neckband Doppler system, the acquired Doppler signals are quantized by 14-bit analog-to-digital-converters running at 40 MHz, and pre-processing operations (i.e., demodulation and clutter filtering) are performed in an embedded field programmable gate array chip. Then, these data are transferred to an external smartphone (i.e., Galaxy S7, Samsung Electronics Co., Suwon, Korea) via Bluetooth 2.0. Post-processing (i.e., Fourier transform and image processing) is performed using an embedded application processor in the smartphone. The developed carotid neckband Doppler system was evaluated with phantom and in vivo studies. In a phantom study, the neckband Doppler system showed comparable results with a commercial ultrasound machine in terms of peak systolic velocity and resistive index, i.e., \(131.49\pm 3.97\) and \(0.75\pm 0.02\) vs. \(131.89\pm 2.06\) and \(0.74\pm 0.02\), respectively. In addition, in the in vivo study, the neckband Doppler system successfully demonstrated its capability to continuously evaluate hemodynamics in both common carotid arteries. These results indicate that the developed wireless neckband Doppler system can be used for continuous monitoring of blood flow dynamics in the common carotid arteries in point-of-care settings.  
caroid ultrasonography; neckband; wearable ultrasound; point-of-care 17 April 2019; Accepted: 24 May 2019; Published: 29 May 2019
## 2 Materials and Methods
### Drying Process Optimization Test
#### 2.3.3 Experimental Design, Statistical Analysis and Optimization  
The RSM was used to estimate the effects of the process variables on DR, DQ, MSE and \(\Delta\)E* during the drying of walnuts. HA temperature (\(x_{1}\)), MC of the transition point (\(x_{2}\)), microwave power (\(x_{3}\)) and MV thermostatic temperature (\(x_{4}\)) were selected as independent variables by means of literature survey and preliminary experiments. A central composite rotatable design (CCRD) was arranged in the experimental scheme; coded values corresponding to the natural values of each variable and CCRD are shown in Table 4. The dataset was fitted with the following second-order polynomial model:  
Figure 7: Diagram of the HA-MVD system.  
\[Y_{k}=\beta_{k0}+\sum_{i=1}^{4}\beta_{ki}x_{i}+\sum_{i=1}^{4}\beta_{kii}x_{i}^{2}+ \sum_{i=1}^{3}\sum_{j=i+1}^{4}\beta_{kij}x_{i}x_{j},(k=1,\ 2,\ 3,\ 4) \tag{6}\]  
where \(\beta_{k0}\), \(\beta_{ki}\), \(\beta_{kii}\) and \(\beta_{kij}\) denote constant regression coefficients, and \(x\) represents the coded independent variable. Multiple regression analysis was employed to assess the mathematical model for each response variable. The significance of terms within the model for each response was determined using analysis of variance (ANOVA), with significance gauged by the F-statistic within a 95% confidence interval. The model adequacies were checked by R\({}^{2}\), adjusted-R\({}^{2}\), predicted-R\({}^{2}\) and prediction error sum of squares (PRESS). After model fitting, residual analyses were conducted to validate assumptions used in ANOVA. The smaller MSE is the main goal of this study. In addition, minimum DQ, \(\Delta\)E\({}^{\star}\) and maximum DR should also be guaranteed. Therefore, the desirability function method was performed, and the importance value 5 was specified for the MSE response and the same importance value 3 were appointed to other responses. Note that the Design Expert 10 was used to fit response surfaces and optimize the drying process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Run \# & \(x_{1}\) (\({}^{\circ}\)C) & \(x_{2}\) (\%) & \(x_{3}\) (W) & \(x_{4}\) (\({}^{\circ}\)C) & **DR (\%/min)** & **DQ (kW-h/kg)** & **MSE (\%)** & \(\Delta\)E\({}^{\star}\) \\ \hline
1 & 55 (\(-\)1) & 21 (\(-\)1) & 550 (\(-\)1) & 45 (\(-\)1) & 0.141 & 5.31 & 2.30 & 8.46 \\
2 & 65 (1)
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 6 Regulation of Signaling Pathways by RAC1B
### Wnt/\(\beta\)-catenin  
RAC1B overexpression stimulates TCF-mediated gene transcription, whereas depletion of RAC1B results in decreased expression of the Wnt target gene _CCND1_. RAC1B was capable of functionally interacting with DVL3 but not \(\beta\)-catenin to mediate synergistic induction of Wnt target genes. In agreement, DVL3 but not \(\beta\)-catenin caused increased activation of RAC1B [20]. Follow-up studies have shown that RAC1B resides at the promoters of Wnt target genes, c-Myc and _CCND1_, in HCT116 cells with aberrant Wnt pathway [64]. In HEK293T cells with intact Wnt signaling, RAC1B is tied to these same gene promoters independent of WNT3A stimulation and recruits DVL3 and \(\beta\)-catenin in the absence of WNT3A stimulation, suggesting a novel transcriptional coactivator role of RAC1B in \(\beta\)-catenin/TCF-mediated transcription [64]. By enhancing DVL3-mediated Wnt pathway signaling and induction of Wnt target genes involved in decreasing adhesion of CRC cells, RAC1B overexpression may facilitate tumor progression [64]. RAC1B activation of the Wnt pathway requires the NLS located in the polybasic region (see Section 4.4). Earlier data also suggested that nuclear Wnt signaling negatively regulates RAC1B protein stability (see Section 4.3).
# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 3 Robust Grey Relational Analysis-Based Accuracy Evaluation Method
### Bias Tolerance
After adding the perturbation tolerance index, the original grey correlation analysis method evaluation formula becomes as follows:  
\[ \begin{split}\gamma(\mathbf{X},\mathbf{Y})&=\frac{1}{n}\sum \limits_{i=1}^{n}\frac{\underset{i}{\sum}\ \underset{k}{\min}\ \underset{k}{\min}\ \left|x_{i}(k)-y_{i}(k)\right|+\tau\ \underset{i}{\max}\ \underset{k}{\max}\ \left|x_{i}(k)-y_{i}(k)\right|}{\left|\delta_{i} \right|+\tau\ \underset{i}{\max}\ \underset{k}{\max}\ \left|x_{i}(k)-y_{i}(k)\right|}\\ s.t.&\delta_{i}=\begin{cases}x_{i}(k)-y_{i}(k)&if\ \ |x_{i}(k)-y_{i}(k)|\geq r_{i}\\ f(z_{i},r_{i})[x_{i}(k)-y_{i}(k)]&if\ \ |x_{i}(k)-y_{i}(k)|<r_{i}\end{cases}\end{split}  \tag{13}\]  
where \(f(z_{i},r_{i})\) is a weight function that is satisfied as follows:  
\[\left\{ \begin{array}{l}\underset{z_{i}\to 0}{\lim}f(z_{i},r_{i})=0\\ \underset{z_{i}\to r_{i}}{\lim}f(z_{i},r_{i})=1\\ z_{i}=|x_{i}-y_{i}|\end{array} \right. \tag{14}\]  
In this paper, the Sigmoid function is used as the basis, and Equation (14) is the boundary condition for function fitting to achieve a small slope and a smooth transformation near the boundary of the error tolerance band, which is used to construct an S-shaped distribution of the weights, so that the outputs are restricted to a specific range, which is conducive to the stability and explanatory nature of the model's weight update. Using the experimental time series data as the reference sequence b, the disturbance tolerance dimensionless parameter of deviation acceptability p satisfies Equation (15):  
\[\text{r}=\text{p}\times\text{b} \tag{15}\]  
The grey correlation formula, after adding the fitted weight function, is shown in Equation  
Figure 7a and Figure 5a, it is found that the use of this paper's method increases the grey correlation coefficient of each data point within a certain deviation range of the reference sequence. This strengthens the weight coefficient of the distance when calculating the grey correlation coefficient, which is obtained by calculating the TIC inequality coefficient \(TIC=0.01032\), which proves that the fitting accuracy of the sequence is higher; in the case using both the through-valve interpolation method and bias acceptability, the overall grey correlation degree of the sequence reaches 68.439%, and the accuracy reaches 87.602%. Compared with the traditional grey correlation analysis method, the method suggested in this paper is more in line with the TIC discrimination conclusion.
Sensors_**2013**, _13_, 3951-3974; doi:10.3390/s130303951  
_Seriesors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 1**  
1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan; E-Mails: [EMAIL_ADDRESS] (Y.-T.C.); [EMAIL_ADDRESS] (J.-S.P.); [EMAIL_ADDRESS] (B.-Y.L.)
2 Chung-Shan Institute of Science and Technology, Zuoying 90008, Taiwan; E-Mail: [EMAIL_ADDRESS]
3 School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide 5068, Australia; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 30 January 2013; in revised form: 13 March 2013 / Accepted: 14 March 2013 / Published: 20 March 2013_  
**Abstract:** Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.  
keywords:transmission optimization; minimum node degree; energy-efficient; full reachability networks +
Footnote †: journal: Journal of the Royal Society Interface
## 4 Results and Discussion
### RQ9 and RQ10: What Is the Research Focus and Limitations According to the PS?
#### 4.9.1 CNN or CNN Combined with LSTM or GRU
In [90], the authors propose a novel approach for the detection of malicious HTTP requests in the particular case where the datasets available for the target system are limited in size and of low degree of diversity to build performant DL models. The approach consists of three phases. (i) In model initialization, a large public dataset is used to build a DL model that detects malicious HTTP requests. They used word2 vec for the vectorization of HTTP payloads and TextCNN for their classification. (ii) In data augmentation, noise is added to the original samples of the targeted system-based dataset while keeping the keywords unchanged. This way, the samples get diversified but keep their semantic meaning. They used TF-IDF to define keywords. (iii) Third, in Transfer Semi-Supervised Learning, they froze the first n layers of the TextCNN model built at the first phase, and they trained the rest on the original and generated labeled and unlabeled targeted system-based datasets. This way, the obtained model has the knowledge learned by the initial model and adapted to the new target system without the risk of over-fitting due to the small size of the dataset. The results of the conducted experiments show that the proposed approach achieved better performance in comparison with other baseline models and methods such as Bi-LSTM, SVM, AE, character-level embedding, and N-gram. However, as future work, they plan to (i) include more complex web attacks, (ii) handle encrypted malicious HTTP requests, (iii) consider non-textual elements of the HTTP web requests, and (iv) build anomaly-based detection models.
## 3 Design of the DAB Converter Prototype
### Transformer Selection  
In order to achieve bidirectional power transfer, the DC bus voltage and the battery voltage are used as design criteria. Formula (1) is used to obtain the turns ratio.  
\[n=\frac{N_{P}}{N_{S}}=\frac{V_{DChus}}{V_{bat}}=8 \tag{1}\]  
The transformer part number PC95 PQ50/50Z-12, produced by TDK (Tokyo, Japan), is used as the high-frequency transformer of the proposed DAB prototype. The core is MnZn PC 95 ferrite core, and the coil former is PQ 50/50. According to the specifications provided by TDK, the effective cross-sectional area of the core is \(A_{e}\) = 3.28 cm\({}^{2}\). By using the temperature characteristics curve of the transformer, it can be known that the saturation magnetic flux density of the iron core is about 410 mT at the temperature of 100 \({}^{\circ}\)C. To avoid the saturation of the transformer core, the maximum magnetic flux density \(B_{max}\) is designed to be 0.31 times the saturation value, that is, 127 mT to ensure the normal operation of the transformer [14; 15]. Since the transformer voltage is the square wave, the form factor \(K_{f}\) is 4. Finally, Formulas (2) and (3) are used to obtain the number of turns required for the high-voltage side winding, \(N_{P}\) is 24 turns, and for the low-voltage side winding, \(N_{S}\) is 3 turns.  
\[N_{p}=\frac{V_{DChus}\times 10^{4}}{B_{max}\times K_{f}\times A_{e}\times f_{ sw}}\cong 24\text{ turns} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Primary voltage, \(V_{Dchus}\) & 400 V \\ Secondary voltage, \(V_{bat}\) & 50 V \\ Maximum output power, \(P_{o,max}\) & 1 kW \\ Maximum power, \(P_{Max}\) & 4.5 kW \\ Switching frequency, \(f_{sw}\) & 100 kHz \\ Maximum phase-shift, \(D_{max}\) & 0.2 \\ Voltage ripple, \(\Delta V_{Dchus}\), \(\Delta V_{bat}\) & \textless{}5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of the DAB converter prototype.