esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Transducer Array Validation
### Results  
For the 0 m case, results are taken 20 cm away from the HPUT configuration (Figure 13). From comparing the HPUT configurations, there is a clear trend of an increase in achieved amplitude with an increase in number of HPUTs within the array. Over the length of the pipe, the displacement achieved at the monitored point reduces (Figures 14-16). The addition of water contributes to the reduction in amplitude due to the attenuation of the ultrasonics into the liquid medium. However, at 0 m, the non-marinized 4-HPUT configuration increases with water and can be due to generating high-pressure fields within the liquid.  
Figure 11: HPUT Collar CAD (**left**) and placed onto 6-inch pipe (**right**).  
Figure 12: Point data along the length of pipe using Polytec CLV-3D Laser Vibrometer.  
Figure 14: Performance of non-marinized configuration in air.  
Figure 13: Performance of minimized configuration in air.  
Figure 16: Performance of non-marinized configuration in water.  
Figure 15: Performance of marinized configuration in water.  
The results are not representative of whether there is a reduction in displacement as a result of the addition of the marinization material. This is due to having fixed monitored points that may not represent the maximum displacement achieved as there may be a shift in the distribution of standing waves across the structure due to the marinization. Overall, for both cases, there is a similar trend of a reduction in amplitude across the length of the structure.
# Performance Comparison of 500 MW Coal-Fired Thermal Power Plants with Final Feedwater Temperatures
## 3 Results and Discussion
### Gross Output, Net Output, and Net Efficiency of Power Plant
As the final feedwater temperature increased, the flow rate of HPT extraction steam increased, the flow rate of steam flowing inside the HPT after the extraction decreased and the flow rate of HPT exhaust steam decreased. The heat exchange rate of the reheater of the boiler decreased according to the decrease in the HPT exhaust steam flow rate. The feedwater flow rate was increased to keep the heat exchange rate of the boiler constant. According to the increased feedwater flow rate, the main steam flow rate supplied to the HPT increased, and consequently, the output of the HPT increased. When the final feedwater temperature and the net output increment of the power plant were proportional,  
Figure 14: Net efficiency increment versus final feedwater temperature. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Net Output** & \multicolumn{2}{c}{**Net Efficiency**} \\  & **Increment Ratio (kW/\({}^{\circ}\)C) 1** & **Increment Ratio (\%p/\({}^{\circ}\)C) 1** \\ \hline Case 1 & 63.05 & 0.00528 \\ Case 2 & 61.50 & 0.00515 \\ Case 3 & 63.99 & 0.00535 \\ Case 4 & 62.49 & 0.00523 \\ Case 5 & 64.06 & 0.00536 \\ Average & 63.02 & 0.00527 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Limited to the section in which the final feedwater temperature and the net output of the power plant were proportional.  
\end{table} 
Table 3: The average change rate of the net output increment and the net efficiency increment for different final feedwater temperatures. with an increase of 1 \({}^{\circ}\)C in the final feedwater temperature, the net output increment of the power plant increased by 63.02 kW on average in the five cases. This increased net output of the power plant was the added power output without CO\({}_{2}\) emissions due to the improvement in power plant efficiency without additional heat supplied to the boiler. The increased net output has the same effect as the increased gross output at the power transmission stage. Therefore, considering the CO\({}_{2}\) emission factor of a supercritical coal-fired power plant at the power transmission stage and Equation (13), if the final feedwater temperature was raised by 1 \({}^{\circ}\)C, CO\({}_{2}\) emissions were reduced by 60.52 kgCO\({}_{2}\)/h on average in the five cases. Considering the maximum value of the net output increment of the power plant according to the final feedwater temperature, the CO\({}_{2}\) emission reduction effect was 1132.44 kgCO\({}_{2}\)/h in case 1, 1104.30 kgCO\({}_{2}\)/h in case 2, 1176.61 kgCO\({}_{2}\)/h in case 3, 1149.05 kgCO\({}_{2}\)/h in case 4, and 1170.17 kgCO\({}_{2}\)/h in case 5.
## 2 Multi-Fidelity Monte Carlo Method
### Multi-Fidelity Monte Carlo Method  
#### 2.2.1 Control Variables in Multi-Fidelity Estimation  
The multi-fidelity method takes the random variable \(f^{(2)}(Z),\ldots,f^{(k)}(Z)\) generated by the low-fidelity models \(f^{(2)},\ldots,f^{(k)}\) as the control variable, to estimate the statistics of the random variable \(f^{(1)}(Z)\) of the high-fidelity model \(f^{(1)}\). Let the number of model calculations \(\mathbf{m}=[m_{1},\ldots,m_{k}]^{T}\in N^{k}\) be a vector with integer components \(0<m_{1}\leq\ldots\leq m_{k}\) and  
\[z_{1},\ldots,z_{m_{k}}\in D, \tag{4}\]  
so let \(m_{k}\) be independent and identically distributed realizations of the random variable \(Z\). For \(i=1,\ldots,k\), evaluate model \(f^{(i)}\) at the \(m_{i}\) realizations \(z_{1},\ldots,z_{m_{i}}\) of (4) to obtain  
\[f^{(i)}(z_{1}),\ldots,f^{(i)}(z_{m_{i}}),\]  
The component \(m_{i}\) of \(\mathbf{m}\) is the number of calculations of model \(f^{(i)}\) for \(i=1,\ldots,k\). Derive the Monte Carlo estimate \(\overline{y}_{m_{i}}^{(i)}\) as in (2) from the \(m_{i}\) model evaluations \(f^{(i)}(z_{1}),\ldots,f^{(i)}(z_{m_{i}})\) for \(i=1,\ldots,k\). Additionally, the Monte Carlo estimate \(\overline{y}_{m_{i-1}}^{(i)}\) computed from the \(m_{i-1}\) model evaluations \(f^{(i)}(z_{1}),\ldots,f^{(i)}(z_{m_{i-1}})\) for \(i=\ 2,\ldots,k\). The high-fidelity model expectation estimation is \(s=E[f^{(1)}(Z)]\). The MFMC estimate \(\hat{s}\) of \(s\) is then  
\[\hat{s}=\overline{y}_{m_{1}}^{(1)}+\sum_{i=2}^{k}\alpha_{i}(\overline{y}_{m_{ i}}^{(i)}-\overline{y}_{m_{i-1}}^{(i)}), \tag{5}\]  
where \(\alpha_{2},\ldots,\alpha_{k}\in\ R\) are control variable coefficients and \(\overline{y}_{m_{i}}^{(i)}\) is the Monte Carlo estimate using model \(i\) and the first \(m_{i}\) the number of calculations of model in the MFMC approach. The estimator (5) of MFMC is to correct the estimation of high-fidelity by using the sum of difference estimation of low-fidelity models. It can be seen from (5) that the estimation of MFMC method depends on number of model calculations \(\mathbf{m}\in N^{k}\) and coefficients \(\alpha_{2},\ldots,\alpha_{k}\in R\). In [8] (lemma3.1) show that the MFMC estimator is an unbiased estimator of \(s\).  
Consider the Monte Carlo estimates of \(\overline{y}_{m_{i}}^{(l)}\) and \(\overline{y}_{m_{j}}^{(l)}\) with \(1\leq i,j,l,t\leq k\), the variance \(Var[\hat{s}]\) of the MFMC estimator \(\hat{s}\) is:  
\[Var[\hat{s}]=\frac{\sigma_{1}^{2}}{m_{1}}+\sum_{i=2}^{k}\big{(}\frac{1}{m_{i-1 }}-\frac{1}{m_{i}}\big{)}(\alpha_{i}^{2}\sigma_{i}^{2}-2\alpha_{i}\rho_{1,i} \sigma_{1}\sigma_{i}), \tag{6}\]  
where \(\rho_{1,i}^{2}>0\) is the Pearson correlation coefficient of random variables \(f^{(1)}(Z)\) and \(f^{(i)}(Z)\) for \(i=2,\ldots,k\). The Pearson correlation coefficient is \(\rho_{ij}=\frac{Cov[f^{(i)}(Z),f^{(i)}(Z)]}{\sigma_{i}\sigma_{j}}\).
## 7 Applications in SDN-based VANETs
### Road Condition Warning  
Although sensors are utilized to gather the data related to the roads' circumstances, the OBUs evaluate the information received through the sensors and forward the investigation results to the SDN-based RSUs. The SDN-based RSUs forward the ready information to all the vehicles that are in the communication range of the RSUs. This program application shields vehicles from the crisis parts of mishaps that are being brought about by applying the crisis breakdowns of the vehicle. All the SDN-based VANET applications are listed in Figure 27.
## 2 Materials and Methods
### Multiuser MIMO-OFDM System
#### 2.1.2 Model for the Optimization Problem  
The pilot symbols of the \(i\)-th transmitter are:  
\[\widetilde{\Gamma}_{q_{x}}=\widetilde{X}_{q_{x}}h_{q_{x}}+\widetilde{W}_{q_{x }}, \tag{5}\]  
\[h_{q_{x}}=F_{q_{x}}l_{q_{x},\ q_{r}}, \tag{6}\]  
where \(F_{q_{x}}\) is the partial FFT matrix and \(l_{q_{x},\ q_{r}}\) is an impulse vector of the \(K\)-sparse channel.  
The partial FFT matrix can be represented as:  
\[\big{[}F_{q_{x}}\big{]}_{a,\ b}=\bigg{(}\frac{1}{\sqrt{N}}\bigg{)}e^{-2\pi k _{q_{x},a}\frac{b}{N}}, \tag{7}\]  
where \(a=1,\ 2,\ \ldots\ P;\ b=1,\ 2,\ \ldots\ L;\) and \(k_{q_{x},a}\in\Psi_{q_{x}}\).  
The mutual coherence for the sparse signal of the measurement matrix \(h_{q_{x}}\) is as follows:  
\[\mu\Big{\{}\hat{h}_{q_{x}}\Big{\}}=\operatorname*{argmin}_{h_{q_{x}}}\|h_{q_{ x}}\|. \tag{8}\]  
The optimization problem can be defined for CE by minimizing the mutual coherence.
## 3 Proposed System Model
### Equalizer and GPR Target
\right] \tag{28}\]  
For the three-way GPR target on the lower \(\Pi\Pi\), the matrix form \(\mathbf{G}_{l}\) is as follows:  
\[\mathbf{G}_{l}=\left[ \begin{array}{cccc}0&0&0\\ g_{0,-1}&1&g_{0,1}\\ 0&g_{1,0}&0\\ \end{array} \right] \tag{29}\]  
\[\mathbf{g}=\left[ \begin{array}{cccccc}0&0&0&g_{0,-1}&1&g_{0,1}&0&g_{1,0}&0 \\ \end{array} \right]^{T} \tag{30}\]  
The constraint can be expressed as follows:  
\[\mathbf{E}^{T}\mathbf{g}=\mathbf{c} \tag{31}\]  
where  
\[\mathbf{c}=\left[ \begin{array}{cccccc}0&0&0&1&0&0\\ \end{array} \right]^{T} \tag{32}\]  
and  
\[\mathbf{E}^{T}=\left[ \begin{array}{cccccc}1&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&1\\ \end{array} \right] \tag{33}\]Finally, for the four-way GPR target [20], the matrix form \(\mathbf{G}\) is as follows:  
\[\mathbf{G}=\left[ \begin{array}{ccc}0&g_{-1,0}&0\\ g_{0,-1}&1&g_{0,1}\\ 0&g_{1,0}&0\end{array} \right] \tag{34}\]  
\[\mathbf{g}=\left[ \begin{array}{cccccc}0&g_{-1,0}&0&g_{0,-1}&1&g_{0,1}&0&g_{1, 0}&0\end{array} \right]^{T} \tag{35}\]  
The constraint can be expressed as follows:  
\[\mathbf{E}^{T}\mathbf{g}=\mathbf{c} \tag{36}\]  
where  
\[\mathbf{c}=\left[ \begin{array}{cccccc}0&0&1&0&0\end{array} \right]^{T} \tag{37}\]  
and  
\[\mathbf{E}^{T}=\left[ \begin{array}{cccccc}1&0&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&1\end{array} \right] \tag{38}\]  
With these constraints, we can derive the following Lagrange function:  
\[J=\mathbf{f}^{T}\mathbf{R}\mathbf{f}-2\mathbf{f}^{T}\mathbf{Tg}+\mathbf{g}^{ T}\mathbf{Ag}-2\lambda^{T}\left(\mathbf{E}^{T}\mathbf{g}-\mathbf{c}\right) \tag{39}\]  
where \(\lambda\) is a vector containing the Lagrange multipliers. By setting the gradients of \(J\) with respect to \(\mathbf{f}\), \(\mathbf{g}\), and \(\lambda\) to zero vectors, we obtain the optimized target and equalizer coefficient vectors as follows:  
\[\lambda=\left(\mathbf{E}^{T}\left(\mathbf{A}-\mathbf{T}^{T}\mathbf{R}^{-1} \mathbf{T}\right)^{-1}\mathbf{E}\right)^{-1}\mathbf{c} \tag{40}\]  
\[\mathbf{g}=\left(\mathbf{A}-\mathbf{T}^{T}\mathbf{R}^{-1}\mathbf{T}\right)^{- 1}\mathbf{E}\lambda \tag{41}\]  
\[\mathbf{f}=\mathbf{R}^{-1}\mathbf{Tg} \tag{42}\]
# Current Context and Research Trends in Linear DC-DC Converters
## 8 Future Prospects for LDO Regulators in Power Supplies
### Limitations of SCALDO Technology  
Even though SCALDO technology has many salient advantages, there are a few areas with limitations; primarily minor effects due to the component constraints. The efficiency in SCALDO design is constrained by the ESR and the leakage current losses in the supercapacitors. As mentioned above, ESR values of supercapacitors are in the range of 0.1 m\(\Omega\) to 100 m\(\Omega\)[69] and the leakage current of supercapacitors is in the range of 5 to 50 \(\mu\)A [77]. Generally, both ESR and the leakage current of supercapacitors have very small values and the losses due to ESR and the leakage current can be negligible.  
The control circuitry power consumption is the other factor. In an integrated design, this can be minimised to a significantly small value with the right component selection.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 2 Background
### Software Solutions for Reliability Enhancement of NNs
#### 2.3.4 Median Filter  
Median filter is an HT developed to mitigate hardware faults on AL accelerators calculating the median operation to the outputs of convolutional and linear layers [13]. The typical hardware implementation of the median operation generally involves 2 main steps: sorting the array subject to median computation and selecting the second array element. However, sorting an array of length L (Sort-L) hinders the parallelization of the workload through CUDA-implemented functions. For this reason, as suggested by the authors of [13] taking inspiration from the work [60], a parallelizable, thus efficient, implementation of the median operation is implemented as the composition of L steps of Sort-2 algorithms. The median operation is applied to the input of the convolutional and linear layer feature maps (FMs). However, including the median operation requires the NN to be trained from scratch or fine-tuned so that the new layer can also process the gradients computed during the training process.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm
### Node
#### 5.3.3 Analysis of the Search Methods  
We now present a time analysis of the two search methods.  
**Optimistic Search method.** As it can be observed from Algorithm 8, this method is a one-hop task: the mobile node communicates with its neighbors, and they communicate back to it. If we assume that a one-hop point-to-point communication takes one unit of time, then the number of time units \(T_{opt}\) is proportional to the number of neighbors the in-use mobile node \(m\) has:  
\[T_{opt}=2|N_{m}|, \tag{3}\]  
where \(N_{m}\) is the set of the neighboring nodes of \(m\). Note that if \(m\) can broadcast the message to all its neighbors within one time unit (instead of sending one message at a time to each), then \(T_{opt}=|N_{m}|+1\). In any case, \(T_{opt}=O(|N_{m}|)\).  
**Allocation Search method.** As it can be observed from Algorithm 9, this method is a two-hop task: the in-use mobile node \(m\) communicates with its neighbors (\(N_{m}\)), and each of its neighbors \(i\) must communicate with its own neighbors (\(N_{i}\)) as well. Thus, the number of time units \(T_{al}\) is bounded by:  
\[T_{al}\leq 2|N_{m}|+2\sum_{i\in N_{m}}|N_{i}|. \tag{4}\]  
The equality occurs in the worst-case scenario where the nodes in \(N_{m}\) need to communicate with all of their neighboring nodes. Even if nodes can broadcast messages to their neighbors, it still follows that \(T_{al}=O(|N_{m}|+\sum_{i\in N_{m}}|N_{i}|)\).  
The above analysis suggests that the optimistic method is more efficient in terms of time. However, the allocation method seems to be more effective in the long run, as it ensures that the additional resources can be handled, taking into consideration what has already been \"reserved\" by the ongoing search mechanism (cf. Section 6.3.2).
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 3 Materials and Methods  
In order to simulate the wave propagation in a two-dimensional periodic geometry, this study was based on the Bloch theorem. The representation of a structure in a two-dimensional space can be achieved with the reproduction of a unit cell along the length of three independent mesh vectors that control periodicity, denoted by \(\alpha\)1, \(\alpha\)2, \(\alpha\)3. By utilizingfinite element discretization, the discretized equations of the motion for the displacements of the unit cell have the following form:  
\[M\ddot{q}+Kq=F \tag{1}\]  
Here, \(M\) and \(K\) denote the mass and stiffness matrices of the unit cell, \(q\) and \(\ddot{q}\) represent the displacement and the acceleration vector of nodes in the mesh, and \(F\) represents the forces applied to the nodes.  
Periodicity conditions are added in the system of Equation (1) and subsequent systems by considering the additional kinematic constraints that prescribe equal displacements between the opposite boundaries of the periodic unit cell. These constraints can be realized with the help of Lagrange multipliers or other tools available in commercial finite element packages under the description of'multipoint constraints'.  
Bloch analysis starts by assuming a harmonic excitation and subsequently solving for the amplitude of vibration q that, as a result, turns \(\dot{q}\) into \(-\omega^{2}q\); thus, Equation (1) takes the following form:  
\[\Big{(}-\omega^{2}M+K\Big{)}q=F \tag{2}\]  
Replacing the displacements for the unit cell \(n_{1}a_{1}+n_{2}a_{2}\) with \(q(n_{1},n_{2})\) the requirement is to find a solution that has the following property:  
\[q(n_{1},n_{2})=e^{\langle k,n_{1}a_{1}+n_{2}a_{2}\rangle}q(0,) \tag{3}\]  
where \(\langle k,n_{1}a_{1}+n_{2}a_{2}\rangle\) describes the general form of the transmission constant. Most of the published studies utilize Equation (1) for cases with eigenvalues. With \(\omega\), we represent the eigenvalue for the transmission frequency, and it is a function on \(M\), \(K\), and the wave vector \(k\). The given relations are valid for two-dimensional models and can be extended to three-dimensional ones in an obvious way.
## 6 Experimental Studies
### Encoder-Based Tracking
The average computation times for the proposed MPC scheme with encoder-based tracking follow from Figure 9. Since only the computation for the controller (1 ms) and the PowerCube I/O (14 ms), that is, the time to determine the motor currents and for measuring the angles and angular velocities by the encoders, are necessary, the overall computation time lies under the sampling interval of 20 ms assumed for the encoder-based tracking.
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
###### Abstract  
6D pose estimation of objects is essential for intelligent manufacturing. Current methods mainly place emphasis on the single object's pose estimation, which limit its use in real-world applications. In this paper, we propose a multi-instance framework of 6D pose estimation for textureless objects in an industrial environment. We use a two-stage pipeline for this purpose. In the detection stage, EfficientDet is used to detect target instances from the image. In the pose estimation stage, the cropped images are first interpolated into a fixed size, then fed into a pseudo-siamese graph matching network to calculate dense point correspondences. A modified circle loss is defined to measure the differences of positive and negative correspondences. Experiments on the antenna support demonstrate the effectiveness and advantages of our proposed method.  
6D pose estimation; metric learning; dense correspondences; antenna support +
Footnote †: journal: Applied
###### Abstract  
The use of organic components from plants as an alternative antimicrobial agent is becoming popular due to the development of drug-resistance in various pathogens. Essential oils from fresh (MF-1) and dried (MD-1) roots of _Salvadora persica_ L. were extracted and benzyl isothiocruate was determined as their chief constituent using GC-MS and GC-FID. The antibioticfilm and antimicrobial activities of MD-1 and MF-1 against _Streptococcus mutans_ a dental caries causing bacteria were determined using multiple assays. These activities were compared with chlorhexidine digluconate (CHX) and clove oil, well known antimicrobial agents for oral hygiene. Essential oils demonstrated IC\({}_{50}\) values (10-11 \(\mathrm{\SIUnitSymbolMicro mL}\)) comparable to that of CHX, showed a significant reduction (82 \(\pm\) 7-87 \(\pm\) 6%) of the biofilm formation at a very low concentration. These results were supported by RT-PCR studies showing change in the expression levels of _AtI_E, _gtf_/B, _ymc_A and _sod_A genes involved in autolysis, biofilm formation and oxidative stress, respectively. The results presented in this study show the robust bactericidal and antibioticfilm activity of MD-1 and MF-1 against _S. mutans_ which is comparable to Chlorhexidine digluconate. Our results suggest that these essential oils can be as effective as CHX and hence can serve as a good alternative antimicrobial agent for oral hygiene.  
Keywords:essential oils; _Salvadora persica_ L; antibiotic resistance; _S. mutans_; oral pathogens; Chlorhexidine
## 6 Functionalities and Results  
### Fleet Management System Functionalities  
The fleet manager allows for the efficient monitoring and management of both AGVs and AMRs. Among its main features is the protection of system integrity through an authentication system, in which each user is assigned a specific access level, thereby enabling  
Figure 17: The system architecture—data integration within the runtime.  
operators to perform actions with a greater or lesser impact on the system, according to their permissions.  
The adoption of static facility maps to reference the position of mobile robots in fleet management systems is not ideal. Considering that a factory environment can undergo occasional and temporary changes, the use of static maps would result in outdated layouts. Furthermore, it would hinder the system's adoption in other environments where updated maps are not available. An additional challenge includes managing the identifiers that define AGV routes. These can occasionally be repositioned, either due to route redefinition or simply the replacement of damaged identifiers.  
Taking this into account, an important feature is the visualization of predefined routes for both types of robots, as shown in Figure 18. In the case of AGVs, it is possible to edit the position of the tags directly in the fleet manager, providing greater flexibility in managing the trajectories. Regarding the AMRs, they use the SLAM-generated map complemented by a topological map with lines that define the zones through which they will navigate.  
The fleet manager also allows for the real-time consultation (as Figure 19 demonstrates) of various data related to AGVs and AMRs, including the name, IP address, battery level, current route, and operational status. The current position of each mobile robot is also available for viewing. In AMRs, the X, and Y coordinates and orientation angle are displayed, while in AGVs, it is possible to view the tag on which it is located, and from there, consult the X and Y position of the respective tag.  
To facilitate the supervision of the mobile robots' status, the fleet manager offers a visual indication through a color synoptic, as illustrated in Figure 20. This system allows operators to quickly and intuitively monitor the navigation status of each robot at a glance.  
Figure 18: Routes page.  
Based on the information regarding the robot states (presented in Figure 18), the following are real photographic records that illustrate the robot's different operational states. Each state is identified by a distinct color, making it easier to visually recognize during the operation, as shown in Figures 21-23.  
Figure 19: A map with all robots imported into the system.  
Figure 20: The color code corresponding to the navigation status of the robots: (**a**) green—the AGV is functioning correctly; (**b**) yellow—the AGV is stopped due to an obstacle; (**c**) red—the AGV is stopped due to a failure or malfunction; and (**d**) blue—the AGV is charging its batteries.  
Figure 21: Obstacle detection, robot’s icon changes to yellow.  
Figure 22: Emergency state, robot’s icon changes to red.