esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Implementation Details
#### 3.2.2 Generation of the Inputs and Outputs Using the Sliding Window Technique  
This subsection describes the generation of the inputs and outputs. The daily open, high, and low prices, trading volume, and change were commonly used as input features in other studies. However, in the current study, we introduce a novel feature named medium, which is the average of high and low prices, to reduce the influence of the unusually extreme highest and lowest prices and to ensure generalizability.  
For each stock market index, the partial features of daily open, high, low, and medium prices, trading volume, and change (OHLMVC) were used as the input to train the model, and the daily close prices were used as the output to predict one-time-step and multi-time-step ahead.  
For the input and output generation, the normalized data were segmented using the sliding window technique, by which a fixed window size of time-series data was chosen as the input and a fixed number of the following observations was chosen as the output. This process was repeated for the entire dataset by sliding the window in intervals of one time step to obtain the next input and output. We trained the proposed models to look at \(m\) consecutive past data of features. The input at time \(t\) was denoted by  
\[\mathbf{X}_{t}=\left(\mathbf{x}_{t}^{O},\mathbf{x}_{t}^{H},\mathbf{x}_{t}^{L}, \mathbf{x}_{t}^{M},\mathbf{x}_{t}^{V},\mathbf{x}_{t}^{Ch}\right)\in\mathbb{R} ^{m\times 6}, \tag{12}\]  
where for each \(k\in\{O,H,L,M,V,Ch\}\),  
\[\mathbf{x}_{t}^{k}=\left(x_{t-m+1}^{k},\cdots,x_{t-1}^{k},x_{t}^{k}\right)^{T }\in\mathbb{R}^{m}, \tag{13}\]  
\(\mathbf{x}^{O},\mathbf{x}^{H},\mathbf{x}^{L},\mathbf{x}^{M},\mathbf{x}^{V}\), and \(\mathbf{x}^{Ch}\) are the daily open, high, low, and medium prices, trading volume, and change from time \(t-m+1\) to time \(t\), respectively.  
The input \(\mathbf{X}_{t}\) was fed sequentially into the proposed models to predict the following \(n\) daily close prices of stock market indices, with the output denoted by  
\[\mathbf{y}_{t+1}^{C}=\left(y_{t+1},y_{t+2},...y_{t+n}\right)^{T}\in\mathbb{R} ^{n}. \tag{14}\]  
The look-back periods of \(5,21\), and \(42\) days were considered as one week, one month, and two months, respectively; while the look-ahead periods of one and five days were considered to predict the future one-time-step or multi-time-step ahead. Figure 2 illustrates the sliding window technique.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods  
We describe the techniques and methods for secure and quality of service routing in MANETs in this section.
## 2 Materials and Methods
### Academic Performance  
The data were requested from the Agency for Quality Education (MINEDUC) through the official platform, following the agency's established protocol. The information, delivered within 30 business days, included school-level scores in mathematics and language for eighth-grade students, along with municipality and administrative dependency, without access to individual student data. The SIMCE test administration and analysis are regulated by Chilean Law No. 20,529, with a stratified sampling design based on region and school administrative type, excluding hard-to-reach areas [14].  
The SIMCE system is a standardized, nationwide learning assessment tool established in 1968. Its purpose is to improve the quality and equity of education by recording information on students' learning achievements, promoting transparency and a commitment to continuous learning [10; 15; 16]. According to national and international learning assessments from the period 2004-2018, Chile's SIMCE derives information from tests administered biennially at different levels, such as 4 th, 6 th, and 8 th grade and second year of high school. The subjects evaluated in SIMCE include language and communication, mathematics, natural sciences, and history, geography, and social sciences, depending on the students' grade level [10; 15; 16].  
In the language assessment for all levels, three skill axes are distinguished: locating, interpreting and relating, and reflecting. Similarly, the mathematics tests for all evaluated levels present three skill axes to be assessed: knowledge, application, and reasoning. In the 8 th-grade mathematics test, four thematic axes or content areas are identified: numbers, algebra and functions, geometry, and probability and statistics [16].
## 3 Seismic Shaking Table Tests
### Results of Shaking Table Tests  
#### 3.3.1 Strain Time History Seismic Responses  
From the measured strain data (\(\epsilon_{\text{a}}\), \(\epsilon_{\text{b}}\), and \(\epsilon_{\text{c}}\)) obtained from the triaxial 0\({}^{\circ}\)-45\({}^{\circ}\)-90\({}^{\circ}\) rosette strain gauges, as conceptually described in Figure 7, the maximum principal strain (\(\epsilon_{\text{max}}\)), the minimum principal strain (\(\epsilon_{\text{min}}\)), the maximum shear strain (\(\tau_{\text{max}}\)), and the directional angle of the principal strain (\(\theta\)) can be calculated using the equations, as follows [17]:  
\[\epsilon_{\text{max}}=\frac{1}{2}\left[\epsilon_{\text{a}}+\epsilon_{\text{c} }+\sqrt{2\big{\{}\big{(}\epsilon_{\text{a}}-\epsilon_{\text{b}}\big{)}^{2}+ \big{(}\epsilon_{\text{b}}-\epsilon_{\text{c}}\big{)}^{2}\big{\}}}\right] \tag{1}\]  
\[\epsilon_{\text{min}}=\frac{1}{2}\left[\epsilon_{\text{a}}+\epsilon_{\text{c} }-\sqrt{2\big{\{}\big{(}\epsilon_{\text{a}}-\epsilon_{\text{b}}\big{)}^{2}+ \big{(}\epsilon_{\text{b}}-\epsilon_{\text{c}}\big{)}^{2}\big{\}}}\right] \tag{2}\]  
\[\tau_{\text{max}}=\sqrt{2\big{\{}\big{(}\epsilon_{\text{a}}-\epsilon_{\text{b }}\big{)}^{2}+\big{(}\epsilon_{\text{b}}-\epsilon_{\text{c}}\big{)}^{2}\big{\}}} \tag{3}\]  
\[\theta=\frac{1}{2}\ \tan^{-1}\bigg{(}\frac{2\epsilon_{\text{b}}-\epsilon_{\text{a}}- \epsilon_{\text{c}}}{\epsilon_{\text{a}}-\epsilon_{\text{c}}}\bigg{)} \tag{4}\]  
In the above equation, the angle, \(\theta\), is the angle of the maximum principal strain to the \(\epsilon_{\text{a}}\) axis when \(\epsilon_{\text{a}}>\epsilon_{\text{c}}\) or the angle of the minimum principal strain to the \(\epsilon_{\text{a}}\) axis when \(\epsilon_{\text{a}}<\epsilon_{\text{c}}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specifications** \\ \hline Model Name & Type 8396A (KISTLER) \\ Sensing Type & MEMS Variable Capacitance, Silicon Sensing \\  & Element \\ Measuring Range (g) & 50 \\ Measuring Axis & Triaxial \\ Measuring Freq, Range (Hz) & 0.5–5000 \\ Operating Temperature (\({}^{\circ}\)C) & –55–125 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of the used accelerometers.  
Figure 7: Concept of principal strains from the triaxial 0\({}^{\circ}\)–45\({}^{\circ}\)–90\({}^{\circ}\) rosette gauge pattern.
# Two-Dimensional Constellation Shaping in Fiber-Optic Communications
## 2 Typical Constellation Shaping Schemes
### Geometric Shaping
When the amount of the constellation points is more than 64, GS may suffer more implementation penalties than PS.
# Special Issue on Light Beams in Liquid Crystals
## 1 Introduction
### Ring Pattern Formation  
[PERSON] et al. [10] investigate the amplified coupling of two coherent light beams in dye-doped liquid crystals. The authors experimentally study how light is able to induce ring patterns through the photo-isomerization of specific samples with photosensitive dopants. This process is modeled by a Swift-Hohenberg-type equation. An analysis of this model shows that the rings arise as a trans-critical bifurcation of the isotropic liquid phase. The article characterizes the bifurcation diagram of this complex opto-topological system.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 1 Introduction
It mainly filters various characteristics and information contained in security situation data by setting pre-rules. An information fusion method is adopted to synthesize information feedback values as situation evaluation results. The typical methods include clustering or D-S evidence theory. [PERSON] and [PERSON] [18] proposed a network security situation evaluation system based on a modified D-S evidence theory. Through the information fusion of internal factors and external threats, a multi-module evaluation system was constructed. [PERSON] [19] carried out grey clustering analysis on network attacks. Network attacks were divided into three levels of harm, including \"strong\", \"medium\", and \"weak\". On this basis, the AHP method was used to build a security situation assessment model. [PERSON] and [PERSON] [20] considered the information fusion of multi-information sources and multi-level heterogeneous systems. And a framework of comprehensive factors as weighted data for assessment for a network security situation was proposed. These methods can use data characteristics and information to analyze a network security state. The assessment process is objective. However, it cannot effectively analyze the hidden information in the data when faced with a large amount of high-dimensional network data. The third assessment method is to use traditional artificial intelligence models to evaluate the network security situation. The typical methods include the neural network model and the support vector machine (SVM) model, etc. These methods are widely used in the field of air traffic congestion research. [PERSON] [21] used artificial neural network modeling including an autoregressive property to predict the delays of a congested airport. [PERSON] [22] proposed a recurrent neural networks model to predict day-to-day delay status. [PERSON] [23] counted and predicted the air traffic flow based on support vector regression and long short-termmemory. [PERSON] [24] compared the performance of three classes of models: the Markov jump linear system (MJLS), classification and regression trees (CART), and the linear regression (LR) and artificial neural network (ANN) architectures in air traffic delay networks. However, delay and congestion are only typical manifestations when the air traffic situation is in a bad status. The status and change in air traffic operations should be accounted for in real time. Systematic and scientific assessment of the air traffic situation is the key method to solve these problems. In this paper, we turn the situation assessment of the air traffic system into the situation assessment of the air traffic network based on the complex network theory. And binary classification is used to quantitatively assess the situation of the air traffic network. In order to improve the classification accuracy, we use ensemble learning to build the assessment model.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 2 System Model  
### Spatial Network Model  
In this work, the downlink of an outdoor single-tier mmwave cellular network is considered. The BSs and UEs are located in \(\mathbb{R}^{2}\) according to Poisson point process (PPP) \(\phi_{B}\) and \(\phi_{u}\) of density \(\lambda\) and \(\lambda_{u}\) respectively. The network assumes universal frequency reuse with each BS having the same bandwidth that is allocated equally to all UEs. \(\lambda_{u}>>\lambda\), and all BS have a single active UE to serve in its coverage area. \({}^{\rm HM}\)The average cell radius (\(r_{c}\)) of the network defines the inter-site distance used in BS planning as well as characterizes the BS density in a network given by \(r_{c}=\sqrt{1/\pi\lambda}\). For tractability, all BSs as well as the UEs are assumed to transmit at fixed power. The UE for which performance analysis is being performed is termed as typical UE placed at the origin. \({}^{\rm HM}\)It is assumed that each user associates with the BS providing the highest SINR [41]. The probability distribution function (PDF) of distance is given by:  
\[f_{R}(r)=2\pi\lambda r\exp(-\pi\lambda r^{2}) \tag{1}\]
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 4 Inverse Problem
### Experimental Tests
#### 4.2.1 Damage Located at a Sensor Point: Case A  
The contour plots and damage estimates of the minimization procedure, Bartlett beamformer, and MVDR processor for the four damage scenarios D1.A-D4.A (damage below sensor #2) are presented in Figures 12-15. The actual values of the damage parameters are indicated by circles, while the estimates are indicated by crosses. The errors on the identified damage parameters are reported in Table 7, which shows that the damage location is identified exactly by all the estimators, apart from the objective function in the mildest case, D1.A. In all cases, the objective function and the Bartlett processor admit numerous local extrema and sidelobes, while MVDR eliminates the local maxima (or minima), provides much clearer estimates, and improves resolution both in location and intensity. This is especially true with regard to residual height: as evident from the pseudo-experimental analysis, the estimators have lower sensitivity to this parameter than to location. The reported contour plots make it obvious that the MVDR beamformer has the highest sensitivity with regard to \(h^{D}\). On comparing the errors in Table 7 with those obtained from other identification methods based on frequency variations [35], it can be concluded that, on measuring the curvature below the damage, comparable errors are obtained in the identification of the damage location and residual height.  
Figure 12: Contour plots of the estimators for damage scenario D1.A (**(a)** objective function, (**b**) Bartlett, (**c**) MVDR) (circle: correct damage parameters; cross: identified parameters).  
Figure 13: Contour plots of the estimators for damage scenario D2.A (**(a)** objective function, (**b**) Bartlett, (**c**) MVDR) (circle: correct damage parameters; cross: identified parameters).
## 4 Modeling and Optimization Concerns
### Verification by Employing a Complex Path  
To apply the developed model in real machining, a complex trajectory, illustrated in Figure 12a, was tested. The trajectory, including the G01, G02, and corner errors for the conjunction of each segment, is displayed in Figure 12b-e. The interpolation parameters listed in Table 5 were used in the experiments. Clearly, contour errors generally occurred in sharp corners. The contour errors for the smooth conjunctions between G01 and G02 and from G02 to G03 in the period K5-K7 were extremely small compared with those in the period K2-K4. As expected, the MCoE for Case 1 at the peak of the period K2-K4 was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**MCoE**} & \multicolumn{2}{c}{**MT**} & \multicolumn{2}{c}{**CVib**} \\  & & **(mm)** & **(s)** & **(mm/s\({}^{2}\))** \\ \hline
1 & Pred. & 15.9 & 24.012 & 10.4 \\  & Exp. & 17.1 & 24.085 & 14.1 \\  & Error * & \(-\)1.2 & \(-\)0.073 & \(-\)3.7 \\
2 & Pred. & 100.1 & 5.009 & 63.8 \\  & Exp. & 99.7 & 4.867 & 69.7 \\  & Error * & 0.4 & 0.142 & \(-\)5.9 \\
3 & Pred. & 24.5 & 24.029 & 5.0 \\  & Exp. & 25.0 & 24.075 & 4.6 \\  & Error * & \(-\)0.5 & \(-\)0.046 & 0.4 \\
4 & Pred. & 23.4 & 8.060 & 5.4 \\  & Exp. & 23.8 & 8.299 & 9.0 \\  & Error * & \(-\)0.4 & \(-\)0.239 & \(-\)3.6 \\ \hline \hline \end{tabular} Note*: The error was calculated between the predicted value of the experimental result.  
\end{table}
Table 6: Artificial neural network (ANN)-predicted and experimental results for MCoE, MT, and CVib.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Multi-Objective Function**} & \multicolumn{2}{c}{**Constraints**} \\  & & \(900\leq A_{max}\leq 2700\) \\ \(L=w_{1}\times f_{1}+w_{2}\times f_{2}+w_{3}\times f_{3}\) & \(35\leq T_{a}\leq 56\) \\  & & \(0\leq T2\leq 24\) \\ Minimum \(L\) & \(220\leq V_{c}\leq 580\) \\  & & \(300\leq V_{max}\leq 1500\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-objective function and constraints following the weighted sum approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **1** & **2** & **3** & **4** \\ \hline \((w_{1},w_{2},w_{3})\) & \((1,0,0)\) & \((0,1,0)\) & \((0,0,1)\) & \((0.5,0.2,0.5)\) \\ \hline \(A_{max}\) & 900 & 2079 & 1575 & 900 \\ (mm/s\({}^{2}\)) & 35 & 42 & 55 & 35 \\ \hline \(T_{a}\) & 23 & 0 & 6 & 23 \\ (ms) & 220 & 579 & 220 & 220 \\ \hline \(f_{max}\) & 300 & 1499 & 300 & 884 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for optimized interpolation parameters according to the optimized mode.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## Appendix C Proof of MRC  
Let the combiner be  
\[\hat{x}=\sum_{\ell=1}^{L}c_{\ell}^{H}y_{\ell}=\mathbf{c}^{H}\,\mathbf{y}, \tag{13}\]  
with weights \(\mathbf{c}\) from a received signal \(\mathbf{y}=\mathbf{h}\mathbf{x}+\iota\) where the \(j\)th entry of \(\mathbf{h}\) is \(h_{j}=\beta_{j}/r_{j}^{b}\) for distance \(r_{j}\), and the CF of the interference \(I_{j}=|I_{j}|^{2}\) is known \(\Psi_{\ell}(\omega)\). The MRC are designed to maximize the SIR Y, and thus the service probability \(P_{MRC}(\mathbf{c})\) where the instantaneous SIR is  
\[\text{Y}=\frac{\mathbf{c}^{H}\,\mathbf{h}\,\mathbf{h}^{H}\,\mathbf{c}}{ \mathbf{c}^{H}\,\mathbf{\bar{D}}_{\mathbf{I}}\,\mathbf{c}}, \tag{14}\]  
\(\mathbf{\bar{D}}_{\mathbf{I}}=E_{I}[\mathbf{D}_{\mathbf{I}}]\) for \(\mathbf{D}_{I}=diag(I_{1},I_{2},\cdots,I_{L})\), and \(I_{\ell}=E[I_{\ell}]\) that can be derived from CF, such as for \(R_{max}<\infty\). However, for skewed alpha-stable distributions for \(R_{max}\to\infty\) (UCA for \(h=0\), Section 3.2), the mean does not exist, and maximization for the choice \(I_{\ell}\) as median does not change the conclusions. The Rayleigh quotient (14) is known to be maximized for the choice  
\[\mathbf{c}_{opt}=\frac{\mathbf{\bar{D}}_{I}^{-1}\mathbf{h}}{\mathbf{h}^{H} \mathbf{\bar{D}}_{I}^{-1}\mathbf{h}} \tag{15}\]  
of the weights \(\mathbf{c}\). Finally, the service probability reduces to  
\[P_{MRC}=\mathbb{P}_{I}(\mathbf{c}_{opt}^{H}\mathbf{D}_{I}\mathbf{c}_{opt}< \mathbf{c}_{opt}^{H}\mathbf{h}\mathbf{h}^{H}\mathbf{c}_{opt}\frac{\sigma}{T}), \tag{16}\]  
and after some analytic it reduces to  
\[P_{MRC}=\mathbb{P}_{I}\biggl{(}I_{tot}<\frac{\beta}{T}\biggr{)}, \tag{17}\]where the aggregated weighted interference is \(l_{tot}=\sum_{\ell=1}^{L}l_{\ell}\alpha_{\ell}\) for \(\alpha_{\ell}=|\beta_{\ell}|^{2}/(l_{\ell}^{2}\tau_{\ell}^{2b})\) and \(B=\left(\sum_{\ell=1}^{L}|\beta_{\ell}|^{2}/\bar{l}_{\ell}\tau_{\ell}^{2b} \right)^{2}\). Thus, the analysis for the service (or complementary, for the outage) depends on the CDF of \(l_{tot}\) and in turn on the CF  
\[\Psi_{l_{tot}}(\omega)=\prod_{\ell=1}^{L}\Psi_{\ell}(a_{\ell}\omega), \tag{101}\]  
which evidences the multiple usage in the main text for service probability analysis. Note that, when noise is not negligible, given the noise power \(\sigma_{w}^{2}/N\) with \(N\) being the total number of antennas, one can repeat the same derivations by replacing \(\mathbf{D_{1}}=diag(l_{1}+\sigma_{w}^{2}/N,l_{2}+\sigma_{w}^{2}/N,\cdots,l_{L }+\sigma_{w}^{2}/N)\).
## 3 Results and Discussion
### Ablation Experiment Results and Analysis
Finally, an ablation study was conducted on the number of network stages used in the proposed method, with the results tested on the aforementioned eight sets of images, as shown in Figure 10. The number of network stages is a crucial hyperparameter in our method. As indicated in the graph, the homography estimation error significantly decreases \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Scale** & **Stage 1** & **Stage 2** & **Stage 3** \\ \hline The same 128 \(\times\) 128 & 4.57 & 3.44 & 2.12 \\ \(512\times 512\), \(256\times 256\), \(128\times 128\) & 3.91 & 2.85 & 1.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results for same-size image input versus multi-scale image input. Figure 9: Visualization results of same-size image input versus multi-scale image input. as the number of network stages increases from one to three, demonstrating that a multi-stage network structure can progressively refine the homography estimation task, reducing errors and improving accuracy. However, when the number of stages increases to four, the errors begin to rise, and at the fifth stage the errors significantly increase. This is attributed to the reduced image size for input when the network exceeds three stages, leading to unstable network training. Therefore, a three-stage network is optimal for this method.
Electronics_**2013**, 2, 387-399; doi:10.3390/electronics2040387  
_Article_  
**An Energy Conservative Wireless Sensor Networks Approach**  
**for Precision Agriculture**  
**[PERSON] and [PERSON] ***  
College of Information Science and Technology, Hainan 570228, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-898-6627-9057.  
_Received: 30 September 2013; in revised form: 27 November 2013 / Accepted: 2 December 2013 / Published: 11 December 2013_  
**Abstract:** Reducing energy consumption of sensor nodes to prolong the lifetime of finite-capacity batteries and how to enhance the fault-tolerant ability of networks are the major challenges in design of Wireless Sensor Networks (WSNs). In this paper, we present an energy-efficient system of WSNs for black pepper monitoring in tropical areas. At first, we optimized the base station antenna height in order to facilitate reliable communication, after which the Energy-efficient Sensor Protocol for Information via Negotiation (ESPIN) routing protocol was utilized to solve the energy saving challenge. We conducted radio propagation experiments in actual black pepper fields. The practical test results illustrate that the ESPIN protocol reduces redundant data transmission and whole energy consumption of network, and enhances the success rate of data transmission compared with traditional Sensor Protocol for Information via Negotiation (SPIN) protocol. To further optimize topology for improving the network lifetime, we designed a symmetrical double-chain (SDC) topology which is suitable to be deployed in farmland and compared the lifetime with traditional tree topology. Experiment results indicate SDC topology has a longer network lifetime than traditional tree topology. The system we designed will greatly help farmers to make more informed decisions on the efficient use of resources and hence improve black pepper productivity.  
**Keywords:** WSN; energy-efficient; fault-tolerant route; antenna height; precision agriculture
## 2 Preliminaries
### Secure Multi-Party Computation  
Secure Multi-party Computation (MPC) is a term for a broad range of cryptographic techniques and protocols, which can enable a set of parties \(P_{1},...,P_{n}\) to compute the function \(f\) with their private inputs \(x_{1},...,x_{n}\) without anything beyond the output \(f(x_{1},...,x_{n})\) of the computation. Importantly, an actively misbehaving participant should not be able to bias the outcome of the computation (except by choosing their input) or learn anything about the inputs of the honest parties (except for what is leaked by the output itself). With the progress of technology and network equipment, MPC has become a practical science from a purely theoretical research field. More and more companies and researchers focus on this topic.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 4 Simulation Results
### Scenario 3: Robustness Performance Test  
The robustness performance is considered in scenario 3. To this end, the parameter perturbation of the resistance \(R\) is introduced. It varies from 100 \(\Omega\) to 50 \(\Omega\) at 0.1 s. \(\gamma\) = 10,000, \(\lambda=1000,k_{p}=0.015,k_{i}=0.15,\mu=1\times 10^{-6}\). It is seen in Figure 7 that although the estimate \(i\) has the steady state error, the proposed controller can eliminate the influence of the variation in the resistance in the closed-loop system. Indeed, the output voltage stays around the desired value of 12 V.