esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 4 State Feedback Controller for Nonlinear Magnetic Levitation System  
This section presents main results--controllers designed for Magnetic levitation using methods described in Section 3, and corresponding nonlinear ML responses. Several different discrete-time robust pole-placement controllers are designed in Section 4.1. In Section 4.2, nonlinear continuous time controllers are designed for different choice of closed-loop poles. Finally, the comparison of best controllers from both approaches is presented in Section 4.3 together with simulation experiments to show the limits of the designed controllers.  
Discrete-Time Robust Pole-Placement State Feedback Controller Design Based on LMI Solution for Magnetic Levitation  
In this section we further develop our recent results on discrete-time robust pole-placement controller design using LMI approach, in this case applied to working points for different ball mass. We evaluate the design parameters of the proposed approach and compare the results for several variants of robust controller.  
A linearized ML plant model (6) is evaluated in the determined working points--for different ball masses:  
\[A_{c}=\begin{bmatrix}0&1&0\\ 1684.7&0&a_{2 ci}\\ 0&0&-0.2888\end{bmatrix},B_{c}=\begin{bmatrix}0\\ 0\\ 1270.6\end{bmatrix}. \tag{30}\]  
Element \(a_{2 ci}\) varies with the mass of the ball. The following 3 working points, denoted as WP1, WP2, WP3, and parameters of the corresponding continuous-time linearized models are in the Table 2.  
The continuous linearized model (30) was discretized for all three considered working points with sampling period \(T_{s}=0.001\) s. The resulting discretized polytopic model is described by (7), where matrices \(A(\xi),B(\xi)\) have the following form  
\[A(\xi)=\begin{bmatrix}1.0008&0.001&0\\ a_{21 di}&1.0008&a_{23 di}\\ 0&0&0.7492\end{bmatrix},B(\xi)=\begin{bmatrix}0\\ b_{2 di}\\ 1.1036\end{bmatrix}. \tag{31}\]  
Elements \(a_{21 di},a_{23 di},b_{2 di}\) vary with the mass of the ball. In the next Subsection, the discrete-time polytopic model (7), (8) with vertices corresponding to working points WP1, WP2, WP3 in Table 3, is considered for robust pole-placement controller.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 7 Principal Component Analysis  
Principal component analysis (PCA) is a data manipulation or reduction technique that reduces the dimensions of a data set while minimizing the amount of information lost, as presented in Figure 7. To do this, the initial variables are transformed into a new set of variables called principal components. PCA also works around the fact that the principal components are ordered. As a result, the beginning components keep most of the information [43].  
The principle of simplicity is aesthetically desirable, but it can also have advantages in data analysis. Focusing on critical parameters, such as those provided by PCA, within almost any data analysis technique will improve its efficiency and the quality of the result. Including parameters that do not contribute to the model's behavior makes the computation less efficient, and other parameters contribute noise to the problem.
## 3 Results  
### Diel and Seasonal Variations of Mercury Species in Surface Water  
The water column was oxic during both seasons, with the lowest oxygen levels (O\({}_{2}\) = 76 \(\pm\) 23%\({}_{\text{saturation}}\)) and redox potential values [i.e., oxydo-reduction potential (ORP) = \(-\)8 to -72 mV] found during the dry season compared to the wet one (O\({}_{2}\) = 93 \(\pm\) 17%\({}_{\text{sat}}\)), where redox (i.e., ORP = 192 to -115 mV) was only negative during the night, resulting from algal respiration and sulfate reduction (H\({}_{2}\)S = 51 \(\pm\) 42 ug L\({}^{-1}\), Figure 2a,b).  
Dissolved gaseous mercury (DGM = 11.2 \(\pm\) 9.9 pg L\({}^{-1}\)) in surface water (SW) was mainly in the form of Hg(0) [19] and was overall low and never exceeded 1% of THgUNF for both seasons (Figure 2a), similar to previous studies in Lake Uru Uru [19] and Lake Titicaca [24] but in the lowest range of values reported for temperate freshwater lakes [36; 37]. Higher DGM levels were found during the dry season (21.0 \(\pm\) 7.5 pg L\({}^{-1}\)), which were around four times higher than during the wet one (5.6 \(\pm\) 5.5 pg L\({}^{-1}\)).  
Average unfiltered (UNF) concentrations of THg (3.3 \(\pm\) 1.0 ng L\({}^{-1}\)) and MMHg (0.6 \(\pm\) 0.2 ng L\({}^{-1}\)) were 3 to 10 times higher than those measured in Lake Titicaca [2; 24] reflecting the local mining contamination and high MMHg production in this ecosystem [18; 19]. Both the average filtered (F) and unfiltered (UNF) THg and MMHg levels in surface water (Figure 2c,d) were similar between the two seasons. In contrast, filtered alkali and alkaline-earth metal concentrations (e.g., Li, Na, K, Mg, Ca, and Sr) and DOC increased by two to three times during the dry season due to the evaporation and concentration of ions in water (Figure 2b and Figure S1).  
The diel variability of F and UNF MMHg concentrations overwhelmed the seasonal one, with the highest values measured during the night-time when oxygen levels dropped (Figure 2a), resulting from the aquatic ecotopes' respiration. MMHg/THg percentages were high in surface waters for both seasons. However, they presented larger diel variations, with lower average values during the dry season (i.e., 35 \(\pm\) 20% and 26 \(\pm\) 7% for F and UNF, respectively) compared to the wet one (i.e., 42 \(\pm\) 4% and 35 \(\pm\) 6%, Figure 2e).
## 2 Simulation Model Establishment
### Mathematical Model of SICL
The change in the axial component of the magnetic field results in the current in the armature segment, thus the rate of change of the axial component of the magnetic field is calculated as:  
\[\frac{\mathrm{d}H_{z}}{\mathrm{d}t}=\frac{\partial H_{z}}{\partial I_{di}} \cdot\frac{\mathrm{d}I_{di}}{\mathrm{d}t}+\frac{\partial H_{z}}{\partial z} \cdot\frac{\mathrm{d}z}{\mathrm{d}t} \tag{9}\]  
Therefore, the armature winding current can be represented as:  
\[I_{pj}=\frac{\mu}{Z}\int\left(\frac{\partial H_{z}}{\partial I_{di}}\cdot \frac{\mathrm{d}I_{di}}{\mathrm{d}t}+\frac{\partial H_{z}}{\partial z}\cdot \frac{\mathrm{d}z}{\mathrm{d}t}\right)\mathrm{d}A \tag{10}\]  
From Equation (10), it can be seen that the armature segment current consists of two parts: one is caused by the change in drive coil current, defined as induced current; and the other is caused by the movement of the armature segment, defined as motion-induced current. The current in the armature segment is the sum of induced current and motion-induced current. During the motion of the armature, the magnetic field changes unevenly along the axial direction, leading to inconsistent current distribution among the armature segments. At the same moment, different current densities with opposite directions may exist on the cross-section of the armature. This results in significant force differences among the armature segments, adding complexity to the analysis of the armature's motion mechanism and patterns.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 6 Experimental Results and Discussion
### System Throughput Improvement
The subcarrier utilization of different OFDM can be written as follows:  
1. The subcarrier utilization of the CP-OFDM and ZP-OFDM schemes can be written as: \[\xi_{1}=\frac{N_{Data}}{N_{Data+N_{Plots}}}\times\frac{N}{N+M}\] (46)
2. The subcarrier utilization of the TDS-OFDM can be written as: \[\xi_{2}=\frac{N}{N+M}\] (47) where \(N_{Data}\) and \(N_{Plots}\) represent the number of OFDM subcarriers used for data transmission and the number of the OFDM subcarrier used for pilot signal transmission. The system throughput of the ZP-OFDM-NOMA and CP-OFDM-NOMA can be written as: \[\gamma_{1}=\xi_{1}R\] (48) and the system throughput of the TDS-OFDM-NOMA can be written as: \[\gamma_{2}=\xi_{2}R\] (49)  
Figure 7 shows the behavior of the total system throughput of the near and far user versus the SNDR in the case of using the proposed TDS-OFDM-NOMA scheme and conventional CP-OFDM-NOMA scheme with different pilots' length. As known, as the pilot's length increases, the total system throughput decreases. Thanks to the TDS-OFDM pilot's freedom, the TDS-OFDM-NOMA provides the highest system throughput for the near and far users. At a SNDR of 20 dB, the near user of the TDS-OFDM-NOMA scheme  
achieved 2.33 bits per second per hertz (\(\text{pbs}/\text{Hz}\)), while the conventional CP-OFDM-NOMA achieves 2.26, 2.18, 2.04, and 1.75 \(\text{pbs}/\text{Hz}\) for the pilot's length of \(N/32,\ N/16,\ N/8\), and \(N/4\), respectively. In the case of far user, at a SNDR of 20 dB, the TDS-OFDM-NOMA scheme achieved 2.14 \(\text{pbs}/\text{Hz}\), and the convention CP-OFDM-NOMA achieved 2.07, 2, 1.87, and 1.6 \(\text{pbs}/\text{Hz}\) for a pilot's length equal to \(N/32,\ N/16,\ N/8\), and \(N/4\), respectively.
## 5 Sustainable Development within Solar-Driven ORC System  
Sustainable development is the progress that sustains the necessaries of the current without conceding the forthcoming demands. Since ORC is being operated through solar energy, it significantly reduces the production of greenhouse gas emissions if fossil fuels are used instead, and provides an alternative way to produce electricity which can be beneficial for especially developing countries or remote locations lacking electricity. In the event that a solar-driven ORC system is utilised as an alternative of petroleum-based plants for electricity purposes, the saved petroleum \(P_{s}\Big{(}\frac{L}{year}\Big{)}\) and the reduced CO\({}_{2}\) emissions, \(R_{CO2}\Big{(}\frac{tounce}{year}\Big{)}\) can be estimated by the following equations [45; 46]:  
\[P_{s}=t_{year}*a_{1}(W_{N}+Q_{out}) \tag{28}\]  
\[R_{CO2}=\frac{t_{year}*a_{2}(W_{N}+Q_{out})}{1000} \tag{29}\]  
where \(t_{year}\) is the annual sunlight hours of the city; for this analysis, the city of Ottawa's average annual sunlight hours, 2084 h, is considered; \(a_{1}\) and \(a_{2}\) are the quantity of petroleum consumed to produce 1 kW of electricity and the quantity of CO\({}_{2}\) emissions if 1 kW electric energy is generated by a petroleum-based plant, respectively. For this analysis, these amounts are taken as 0.266 L/kWh and 0.894 kg/kWh, respectively [45; 46].  
Figure 25a,b depict a comparison of predicted savings in petroleum and reduction in CO\({}_{2}\) emissions for different mixtures. Two cases were considered for each fluid; one with normal sunlight hours and the other with a storage tank that would double the normal hours. It can be clearly observed from these figures that the mixtures R245 fa/R152a (0.9/0.1) followed by the R245/propane have the most savings in petroleum as well as the highest reduction in CO\({}_{2}\) emissions. These values significantly increase if a storage tank is added to the system.  
Sustainability Index is also evaluated for these mixtures using Equation (20). Figure 26 illustrates a comparison of the Sustainability Index for the different mixtures assessed. It can be seen that R245 fa/Propane (0.4/0.6) has the highest sustainability index followed by butane/propane (0.8/0.2) and R245 fa/R152a (0.9/0.1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**NPV**} & \multirow{2}{*}{**IRR**} & \multirow{2}{*}{**BCR**} & \multicolumn{2}{c}{**Selling Price of**} \\  & & & & **Payback** & **Electricity** & **Electricity** \\  & & & & **Period (years)** & **(CAD/kWh)** & **Electricity** \\  & & & & & **(Annual Benefit)** & **(CAD/kWh)** & **(EAC)** \\ \hline
**1** & CAD 0.00 & 6.00\% & 1.00 & 20 & 0.19 & 0.15 \\
**2** & \(-\) CAD 6485.50 & 3.86\% & 0.87 & 30 & 0.19 & 0.18 \\
**3** & CAD 6485.50 & 8.93\% & 1.18 & 14 & 0.19 & 0.12 \\
**4** & CAD 8390.
## 4 Simulation Results  
The performance of the proposed F-SBL algorithm was compared with conventional techniques, LS [13], OMP [5], SBL, and TMSBL [7] for a UWA OFDM system with the parameters in Table 1. For the F-SBL algorithm, optimal weights (\(w_{1}^{*}=0.4\) and \(w_{2}^{*}=0.6\)) were employed. In order to investigate the effectiveness of the proposed LR in (21), TMSBL adopted with the proposed LR (F-TMSBL) was also considered. The optimal weights for F-TMSBL were numerically found to be \(w_{1}^{*}=0.1\) and \(w_{2}^{*}=2.2\). The maximum number of iterations \(N_{iter}\) was set to 5 for the F-SBL and F-TMSBL algorithms. For other considered methods, \(N_{iter}\) was set to 10, large enough to ensure the best possible performance. The channel having random delay, as described in the Section 3.3, also assessed the performance under diverse underwater conditions.  
Figure 4 shows the MSE performance for the time-invariant underwater channel. We can see that the MSE of all considered SBL-based methods decreased for the increasing SNR, while that of OMP exhibited an error floor. The proposed F-SBL outperformed the conventional SBL and performed very close to that of TMSBL. The proposed F-TMSBL had the best MSE performance. TMSBL [7] used multiple OFDM symbols to exploit temporal correlation of the UWA channel, and, subsequently, had a better estimation performance than the original SBL method. It can be confirmed by the superior performance of F-SBL and F-TMSBL algorithms that the proposed LR enhanced the estimation performance.  
Figure 4: MSE performance versus SNR.  
Figure 3: The convergence of the proposed method compared with the conventional SBL CE for SNR = 10 dB.  
Figure 5 showed the BLER performance for the 16 QAM modulation for the time-varying UWA channel. Note that, to induce time-varying characteristics, the Doppler rate of each path was set to \(\alpha_{l}=v_{p}/c\), \(c=1500\) m/s, where \(v_{p}\) was the relative speed between the transmitter and receiver and had a uniform distribution with a standard velocity deviation of 0.1 m/s. We can observe that the proposed algorithms outperformed the other considered methods. In particular, F-SBL and F-TMSBL algorithms exhibited gains of 0.5 and 0.15 dB at BLER = 0.01 over the original SBL and TMSBL, respectively. Note that this performance gain was obtained even with the number of iterations half of that of other methods. Therefore, the performance analysis confirmed that the proposed method achieved an accurate channel estimation performance with much fewer iterations, which greatly reduced the computational delay and power consumption of the receiver.
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Noise Reduction Filter  
Robustness and accuracy of MMG are affected by the presence of noise in the signal. Therefore, noise reduction filters (NRF) should be incorporated into MMG technique to address robustness and accuracy [101].  
MMG term \(\rho_{g}^{a}\) is employed in power system protection of MT-HVdc system to identify the arrival time of wave head of traveling wave generated at sensing terminal of converter station. MMG is basically an advancement in MG. It employs a series of scalable flat SEs with inverter origins \(g^{+}\) and \(g^{-}\) for each level of decomposition a as shown in following equations:  
\[p_{g^{+}}^{a}=\Big{(}\rho^{a-1}\oplus g^{+}\Big{)}(x)-\Big{(}\rho^{a-1}\odot g ^{+}\Big{)}(x) \tag{94}\]  
\[p_{g^{-}}^{a}=\Big{(}\rho^{a-1}\odot g^{-}\Big{)}(x)-\Big{(}\rho^{a-1}\oplus g ^{-}\Big{)}(x) \tag{95}\]  
\[\rho_{g}^{a}=\ p_{g^{+}}^{a}+\ p_{g^{-}}^{a} \tag{96}\]  
The SEs \(g^{+}\) and \(g^{-}\) are defined as:  
\[g^{+}=[g_{1},g_{2},\ \dots,\ g_{l-1},\ g_{l}] \tag{97}\]  
\[g^{-}=[g_{l},g_{l-1},\ \dots,\ g_{2},\ g_{1}] \tag{98}\]  
where \(g_{l}\) is the origin of the SE with length \(l=\ 2^{l-a}l_{g}\). \(l_{g}\) is the length of SE of first decomposition level. When \(a=1,\ \rho^{o}=f\).  
Noise reduction filters (NRFs) are Morphological Median Filter [94; 95; 96], Multi resolution Morphological Opening--Closing Filter [96; 102], Opening--Closing/Closing--Opening [103] and Opening--Closing maximal/Closing--Opening minimal [103]. In NRFs, signals are countered between upper and lower edge and average between them is evaluated. Flat line SE is employed to maintain the feature of original signal. Moreover, length of SE is calculated for each level of NRF as given by:  
\[l=\ \lambda^{1-b}l_{1} \tag{99}\]  
where \(\lambda\) is the dilating factor, b is the level filter and \(l_{1}\) is the length of g at level 1. For \(b=1,\ g^{o}\) is the SE with length \(l_{1}\) and \(f^{1}\) is the initial signal. In the level 1, the initial signal is filtered by an NRF with SE length \(l_{1}\). In the next level, \(b\) is updated with \(b=b+1\). Filtered signal is employed as an input of NRF. SE length used for filtering is updated by Equation (99). This process is repeated with the intention of requirement of level of filtration.
## 1 Introduction
On the other hand, it is desirable to have an efficient and effective tool for determining the modal characteristics of thick composite cantilever plates for solving engineering problems such as resonance mitigation, material constants identification, and damage detection. For instance, the natural frequencies of composite cantilever plates can be used to characterize the material properties of the plates. In particular, the identification of the current material constants for a plate can help assess the material degradation/structural health of the plate. As mentioned above, many methods have been proposed to identify the material constants of composite plates from natural frequencies. In general, most of the existing material constant identification methods have attempted to determine all the material constants at the same time. However, when the number of the material constants increases, many of the existing methods may face one or both of the two difficulties, i.e., it is difficult to make the solution converge and computationally expensive. For practical applications, many methods may not be efficient enough to be used in material degradation assessment. Therefore, regarding practical applications, it is still desirable to search for more efficient and effective methods for material degradation assessment. Hence, the incorporation of the proposed SFSDT-based Ritz method and a useful procedure for material degradation assessment of composite cantilever plates using natural frequencies is the aim of this paper. In this paper, a new SFSDT-based Ritz method is presented to study the free vibration of composite cantilever plates with different length-to-thickness ratios. Appropriate characteristic functions are proposed to approximate the deflection components which lead to the total deflection of the plate. The theoretical and experimental natural frequencies obtained using different methods are used to verify the accuracy of the proposed method. The effects of plate thickness and fiber angles on the natural frequencies and mode shapes of composite cantilever plates are studied by means of several numerical examples. A novel procedure with the consideration of the information obtained from the sensitivity analysis of natural frequency is presented for material degradation assessment. The proposed procedure is able to produce a good estimation of any material constant in a few iterations in the identification process. The material degradation assessment of composite cantilever plates is performed to show one of the applications of the proposed method.
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 11 Models and Scenario
### Familiarity-Based Definitions  
#### 11.4.1 Exposure Intensity  
When \(q_{i}\) or \(r_{i}\) is transmitted, a set of features, \(Z\) (Equation (23)), is created and used to compute exposure intensity, \(E_{i}\), as shown in Equation (29), where \(E_{i}\rightarrow[0,1]\). An alert description, \(\kappa_{E_{i}}\), associated with the value of \(E_{i}\). The description of each feature is available in the table of notations. The sender's current message's arrival time, \(t_{i}\), the sender's previous message's arrival time, \(t_{i-1}\), the sender's first message's arrival time, \(t_{0}\), the sender's last message's arrival time, \(t_{n}\), and the recipient's dispatched message's time, \(t_{d_{i}}\), are required to define the features in Equations (24)-(28).  
\[Z=\{\zeta_{pt},\zeta_{qq},\zeta_{qr},\zeta_{tt},\zeta_{to}\} \tag{23}\]  
\[\zeta_{qq}\ ||\ \zeta_{rr}=t_{i}-t_{i-1} \tag{24}\]  
\[\zeta_{qr}=t_{i}-t_{d_{i}} \tag{25}\]  
\[\zeta_{tt}=\begin{cases}0,\ \texttt{if}\ i=q0\\ t_{i}-t_{0},\ \texttt{if}\ \texttt{otherwise}\end{cases} \tag{26}\]  
\[\zeta_{to}=\begin{cases}t_{n}-t_{i},\ \texttt{if}\ i=0\\ 0,\ \texttt{if}\ \texttt{otherwise}\end{cases} \tag{27}\]  
\[\zeta_{pt}=\begin{cases}+1,\ \texttt{if}\ \zeta_{qq}<\zeta_{qq}^{T},\\ +1,\ \texttt{if}\ \zeta_{rr}<\zeta_{rr}^{T},\\ +1,\ \texttt{if}\ \zeta_{qr}<\zeta_{qr}^{T},\\ +1,\ \texttt{if}\ \zeta_{qq}<\zeta_{qr},\\ +1,\ \texttt{if}\ \zeta_{rr}<\zeta_{qr},\\ +1,\ \texttt{if}\ \zeta_{to}<\zeta_{to}^{T},\\ 0,\ \texttt{if}\ \texttt{otherwise}\end{cases} \tag{28}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Address Type** & **Access Type** & **Address Size** & **Function/Query** &
\begin{tabular}{c} **Function** \\ **Code (Hex)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Coil} & \multirow{3}{*}{Write/Read} & \multirow{3}{*}{1 bit} & Read Coil & 0x01 \\ \cline{3-4}  & & & Write Multiple Coils & 0x0F \\ \cline{3-4}  & & & Write Single Coil & 0x05 \\ \hline \multirow{3}{*}{Holding Register} & \multirow{3}{*}{Write/Read} & \multirow{3}{*}{2 bytes} & Read Holding Register & 0x03 \\ \cline{3-4}  & & & Write Multiple Registers & 0x10 \\ \cline{3-4}  & & & Write Single Register & 0x06 \\ \hline Discrete Input & Read & 1 bit & Read Discrete Input & 0x02 \\ \hline Input Register & Read & 2 bytes & Read Input Register & 0x04 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Selected modbus function codes.  
Figure 7: Modbus TCP/IP frame.  
\[E_{i}=\begin{cases}1,\text{ if }\theta=1\\ 0,\text{ if }\zeta_{pt}>\zeta_{pt}^{T},\kappa_{E_{i}}=1\\ \frac{Z_{g}\cdot Z_{i}}{\|Z_{g}\|\|Z_{i}\|},\kappa_{E_{i}}=0\text{ if }E_{i}>E_{i}^{T}\\ \frac{Z_{g}\cdot Z_{i}}{\|Z_{g}\|\|Z_{i}\|},\kappa_{E_{i}}=3\text{ if }E_{i}<E_{i}^{T} \end{cases} \tag{29}\]