esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Electrochemistry of ZnFe2O4  
The anode materials for LIBs are conventionally divided into three groups, depending on their reaction mechanism [78; 79; 80; 81]:  
1. intercalation/de-intercalation (such as carbon-based materials, TiO2, Li4 Ti5O12, etc.);
2. alloying/de-alloying (such as Si, Zn, Ge, Sn, Al, Bi, Sb, etc.);
3. conversion (such as transition metal oxides, sulphides, phosphides, and nitrides).  
The materials based on the conversion reaction mechanism are particularly intriguing for their high theoretical capacity and improved discharge potential as compared to graphite and for the ability to avoid lithium dendrites' formation under fast discharge rates.  
ZnFe2O4, unlike other oxides, has a lithium insertion mechanism that involves both conversion and alloying reactions. After the conversion reaction of ZFO with lithium ions and the formation of metallic Zn, Fe, and Li2O, the resulting Zn can further react with lithium to form a LixZn alloy, thus contributing additional capacity.  
The Li storage mechanism of ZFO is proposed as follows: during discharge  
\[\mathrm{ZnFe_{2}O_{4}+0.5 Li^{+}+0.5e^{-}\to Li_{0.5}ZnFe_{2}O_{4}} \tag{1}\]  
\[\mathrm{Li_{0.5}ZnFe_{2}O_{4}+1.5 Li^{+}+1.5e^{-}\to Li_{2}ZnFe_{2}O_{4}} \tag{2}\]  
\[\mathrm{Li_{2}ZnFe_{2}O_{4}+6 Li^{+}+6e^{-}\to 4 Li_{2}O+2 Fe+Zn} \tag{3}\]  
\[\mathrm{Zn+Li^{+}+e^{-}\to LiZn(alloy)} \tag{4}\]  
and in the following recharge process, the ferrite is not recovered and the reaction proceeds as follows:  
\[\mathrm{LiZn(alloy)\to Li^{+}+Zn+e^{-}} \tag{5}\]  
\[\mathrm{Li_{2}O+Fe\to 6 Li^{+}+Fe_{2}O_{3}+6e^{-}} \tag{6}\]  
\[\mathrm{Li_{2}O+Zn\to ZnO+2 Li^{+}+2e^{-}} \tag{7}\]  
Based on the aforementioned mechanisms, the discharge in the first cycle consists of the destruction of the spinel crystal structure, which yields a mixture of Li2O and metallic phases. The overall capacity obtained in the first discharge is provided by nine Li\({}^{+}\) ions' insertion into the ZFO molecule based on reactions (1)-(4). The formation of Li2O is partially irreversible and contributes to the irreversible capacity loss in the first cycle, together with the formation of a SEI layer. In the first recharge (5)-(7), only the simple oxides Fe2O3 and ZnO can be recovered. In the following cycles, the electrochemically active Li-Zn alloys conveys reversible electrochemical reactivity towards Li.  
Unfortunately, as is clear from the reported mechanism, ZFO experiences first-cycle irreversibility and fast decay in capacity with cycling, mainly resulting from poor electrical conductivity and large volumetric changes associated with the conversion reaction.  
However, thanks to the downsizing of the particles, the addition of proper carbon sources, and to peculiar morphologies, the long-term cycling and the capacity values of ZnFe2O4 are, nowadays, very appealing.  
Some of the most encouraging electrochemical results obtained in these last years on the ZFO active material are reported in Table 1. The ink composition, carbon type, and the electrolyte mixture are reported together with the discharge capacity values and the long-cycling performances.
## 1 Introduction
The main objective of this work is to differentiate the AE signals obtained from the tank under normal and crack conditions: A binary classification problem. Quantitative assessment of signals has been carried out in many fields, such as speech recognition, natural language processing, and fault diagnosis [15; 16; 17; 18; 19; 20; 21]. We apply this concept to identify the structural integrity of a spherical tank. The current study has two main contributions: (1) Conversion of the AE signals into lower-dimensional feature space using time-domain statistical features, and (2) classification of nonlinear instances in the feature space by using a radial basis function (RBF) kernel-based support vector machine. AE signals are collected from the storage tank under normal conditions (no crack) and under cracked conditions. Additionally, statistical features were extracted from the AE signals to quantify the signals from the two states and then input them to the support-vector-machine, which classifies the statistical features extracted from the AE signals. The results show that the proposed crack detection algorithm was able to correctly classify instances obtained from the spherical storage tank having crack. The rest of this paper is comprised of the following sections. Section 2 presents the proposed spherical tank crack detection methodology. Section 3 describes the experimental setup and the data obtained. Section 4 provides the results of the current work. Section 5 gives this study's conclusions.
## 3 Results
### Nitrogen Fixation  
Total N\({}_{2}\) fixation varied between species and amongst treatments. N\({}_{2}\) was significantly lower in cba than in cst; overall for the two species, N\({}_{2}\) fixation efficiency was greater in the high P treatment than in the low P treatment (Figure 2a). The amount of N\({}_{2}\) fixed biologically was significantly lower in plants supplied with NH\({}_{4}\)NO\({}_{3}\), as indicated by the decline in %Ndfa in plants in the +N+R treatment compared with plants grown in the inoculated treatment (\(-\)N+R) (Figure 2a). With the exception of the \(-\)N\(-\)R treatment, for which N concentration was significantly low (1.07 \(\pm\) 0.06; \(p\) = 0.038), there were no differences for this variable in the +N\(-\)R (1.91 \(\pm\) 0.23; \(p\) < 0.05), \(-\)N+R (2.08 \(\pm\) 0.09; \(p\) < 0.043) and +N+R (2.46 \(\pm\) 0.11; \(p\) < 0.021) treatments. N\({}_{2}\) fixation efficiency was greater in cst than in cba. Plants solely reliant on N\({}_{2}\) fixation were more efficient at fixing N at the two levels of P according to the amounts of N fixed per nodule (Figure 2b).  
Figure 2: (**a**) Percentage N derived from the atmosphere (%Ndfa) and (**b**) N\({}_{2}\) fixation efficiency of 22-week-old _Cytisus halansae_ (cba) and _Cytisus striatus_ (cst) seedlings, grown in sand culture treated with \(-\)N\(-\)R, +N\(-\)R, +N\(-\)R and +N+R, under two levels of phosphate (Low and High). Values are means (\(n\) = 10, except for \(-\)N\(-\)R where \(n\) = 6) \(\pm\) standard deviation. Different letters indicate significant differences among treatments (* \(p\)\(\leq\) 0.05).
# A Real Case of Implementation of the Future 5G City
## 2 5G Network Architecture  
One of the key drivers of 5G systems is the need to support a wide variety of vertical markets that originate extremely different use cases and, consequently, extremely different requirements. Such a wide range of requirements cannot be met through a common network setting, and deploying individual mobile network solutions for each use case is unfeasible. Hence, there is a general consensus in considering network slicing as a key element of 5G networks. With network slicing it is possible to compose and manage dedicated end-to-end logical networks with specific functions, without losing the economies of scale of a common physical infrastructure [20]. This is the approach considered in the 5G experimental test-bed, described here.  
The network is logically divided in three layers as represented in Figure 2: (i) physical infrastructure, (ii) network function, and (iii) service layers. The physical infrastructure layer is in charge of the control and the management of the physical resources (i.e., radio access networks (RANs), computing, storage, CN), while the second layer configures the network functions (both in the user and control plane) customized for each slice. The third layer translates the services in the slice's configurations in terms of quality of service (QoS) requirements, service level agreements (SLAs), and needed functionality. The three layers are managed by an orchestrator that maps the resources available at different layers to the slices.  
In particular, two different network models will be implemented:  
* the _\"open access_\" model proposed and deployed by Open Fiber in the city of Prato, where the operator that deploys the network is only an infrastructure provider (InP) that offers network  
Figure 1: Project timeline.  
slices to virtual operators, enterprises, and public administrations with the goal of providing services to the end user;
* the _\"vertically integrated operator\"_ model proposed and deployed by Wind-Tre in the city of L'Aquila, where the InP is also the operator that provides services to the end users (no third parties are involved). This means that different network slices are owned and managed by the InP itself.
## 3 Results  
### Characterization of the Membranes  
The measured properties of the MicroPES 2F membrane used as base membrane for the coatings are shown in Table 2, together with the properties of the commercial hydrophobic membranes commonly used in membrane distillation.  
SEM images reveal a difference in the surface porosity on both sides of the PES-membrane (Table 3). While the pore size is larger on the surface side 2, the pore density and porosity are the highest on surface side 1. The cross-section shows an hourglass-shaped pore structure [46]. The densest zone of the membrane is located in the region of 10 to 50 \(\upmu\)m distance from surface 1. All coatings are applied on the surface 1 because this side has the lowest pore size, which is preferred to increase the wetting resistance.  
Figure 6: Schematic of the membrane distillation setup.  
The SEM images in Table 4 show a difference in membrane structure for the stretched PE membrane, which is more porous compared to the PVDF membrane. Both membranes have a symmetric cross-section. The PVDF membrane has a more open surface structure compared to the PES-membrane, while it has lower bulk porosity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Membrane** & \(\Theta\) (\({}^{\circ}\)) & \(d_{max}\) (\(\mu\)m) & \(d_{av}\) (\(\mu\)m) & \(\Delta\) (\(\mu\)m) & \(E\) (\%) & **LEP (bar)** \\ \hline PES (Membrane, MicroPES) & 26 \(\pm\) 4 & 0.61 \(\pm\) 0.04 & 0.56 \(\pm\) 0.03 & 115 \(\pm\) 1 & 75.2 \(\pm\) 0.3 & 0 \\ PE (Lydall, Solupor) & 120 \(\pm\) 1 & 0.32 \(\pm\) 0.02 & 0.43 \(\pm\) 0.02 & 99 \(\pm\) 1 & 75.6 \(\pm\) 0.6 & 3.9 \(\pm\) 0.1 \\ PVDF (Millipore, GVHP) & 120 \(\pm\) 3 & 0.44 \(\pm\) 0.01 & 0.60 \(\pm\) 0.01 & 119 \(\pm\) 1 & 65.7 \(\pm\) 0.9 & 2.3 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the hydrophilic support membrane (polyethersulfone (PES)) and the membranes commonly used for membrane distillation (polyethylene (PE) and polyvinylidene fluoride (PVDF)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SEM** & **Surface 1** & **Surface 2** & **Cross-Section** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Properties \\ \end{tabular} } & \(\varepsilon_{s}\) = 22 \(\pm\) 4\% & \(\varepsilon_{s}\) = 8.8 \(\pm\) 0.5\% & \\  & \(d_{min,s}\) = 0.03 \(\pm\) 0.1 \(\mu\)m & \(d_{min,s}\) = 0.24 \(\pm\) 0.1 \(\mu\)m & \\  & \(d_{avg}\) = 0.47 \(\pm\) 0.2 \(\mu\)m & \(d_{avg}\) = 0.91 \(\pm\) 0.7 \(\mu\)m & \\  & \(d_{max,s}\) = 1.90 \(\pm\) 0.5 \(\mu\)m & \(d_{max,s}\) = 2.2 \(\pm\) 0.3 \(\mu\)m & \\  & Pore density = 4.1 \(\pm\) 0.7 & Pore density = 1.2 \(\pm\) 0.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface and cross-section images of the commercial polyethersulfone (PES) membrane and properties obtained using image analysis.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Tensile Test
#### 3.1.4 Strength Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & \(\eta\)**/\%** & \(F_{\rm{yc}}\)**/**kN** & \(F_{\rm{uc}}\)**/**kN** & \(f_{\rm{yc}}\)**/MPa** & \(f_{\rm{uc}}\)**/MPa** & **Failure Mode** \\ \hline T-0-1 & – & 26.38 & 39.10 & 329.75 & 488.75 & – \\ \hline T-0-2 & – & 25.80 & 37.88 & 322.50 & 473.50 & – \\ \hline T-0-3 & – & 26.00 & 36.60 & 325.00 & 457.50 & – \\ \hline T-60-1 & 4.10 & 22.28 & 32.36 & 290.41 & 421.79 & A \\ T-60-2 & 3.12 & 22.82 & 32.94 & 294.45 & 425.03 & A \\ T-60-3 & 4.46 & 21.08 & 31.34 & 275.81 & 410.05 & B \\ T-120-1 & 8.39 & 20.52 & 29.18 & 279.98 & 398.14 & A \\ T-120-2 & 8.30 & 20.14 & 29.34 & 274.54 & 399.95 & B \\ T-120-3 & 8.67 & 19.38 & 28.66 & 265.26 & 392.28 & C \\ T-180-1 & 11.52 & 18.16 & 27.54 & 256.57 & 389.09 & B \\ T-180-2 & 11.37 & 18.43 & 27.52 & 259.94 & 388.15 & C \\ T-180-3 & 10.93 & 18.52 & 28.04 & 259.89 & 393.49 & D \\ \hline \hline \end{tabular} Notes: \(\eta\) is the corrosion rate; \(f_{\rm{yc}}\) is the yield strength; \(f_{\rm{uc}}\) is the ultimate strength; A denotes flat fracture; B denotes oblique fracture; C denotes step fracture; D denotes multiple fracture.  
\end{table} 
Table 4: The strength of tensile test. Figure 9: Strength normalization value—(**a**)/\(f_{\rm{yc}}\)/\(f_{\rm{y}}\)—corrosion rate, (**b**)/\(f_{\rm{uc}}\)/\(f_{\rm{u}}\)—corrosion rate.
## 5 Empirical Model
### The Statistical Reliability of the Results
First for soils with a \(PI\) below 20%, and second for soils with \(PI\) above 20%. The last part of the empirical model should describe test conditions, such as effective stress. The power function of effective stress with reference to atmospheric pressure was proposed based on [45]. Finally, based on articles [44; 45; 46; 47; 48] and acquired knowledge, the main core of the empirical model was created:  
\[D=\left[\text{a}\left(\frac{G}{G_{MAX}}\right)^{2}-b\left(\frac{G}{G_{MAX}} \right)+c\right]+dI_{p}+\left(\frac{p^{\prime}}{P_{a}}\right)^{\prime} \tag{5}\]  
where: \(a\), \(b\), \(c\), \(d\), \(e\), and \(f\)--constants, different for both analyzed soil groups, and \(Pa\)--atmospheric pressure equal to 100 kPa. Next, the authors employed the software _STATISTCIA 13_ with the nonlinear regression module. Thanks to least squares method and the Gauss-Newton estimation, two groups of constants were found: for low and medium cohesive soils \(PI<20\%\): a = 14.8, b = 34.3, c = 26, d = -0.31, e = 1.36, and f = -0.32; and for very cohesive soil \(PI>20\%\): a = 6.32, b = 20.36, c = 14.43, d = 0.062, e = 0.75, and f = -1.49. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Low and Medium Cohesive Soil \(PI<20\%\)**} & \multicolumn{4}{c}{**Very Cohesive Soil \(PI>20\%\)**} \\ \hline  & \(PI\) & \(p^{\prime}\) & \(e\) & \(\gamma\) & \(G/G_{MAX}\) & \(D\) & \(PI\) & \(p^{\prime}\) & \(e\) & \(\gamma\) & \(G/G_{MAX}\) & \(D\) \\  & (\%) & (\(\mathrm{kPa}\)) & (\(\cdot\)) & (\%) & (\(\cdot\)) & (\%) & (\(\%\)) & (\(\mathrm{kPa}\)) & (\(\cdot\)) & (\(\%\)) & (\(\cdot\)) & (\(\%\)) & (\(\%\)) \\ \(PI\) (\%) & 1.00 & 0.06 & 0.65 & 0.00 & 0.07 & \(-0.44\) & 1.00 & \(-0.13\) & 0.58 & 0.07 & \(-0.16\) & 0.27 \\ \(p^{\prime}\) (\(\mathrm{kPa}\)) & 0.06 & 1.00 & \(-0.27\) & \(-0.20\) & 0.20 & \(-0.28\) & \(-0.13\) & 1.00 & \(-0.02\) & \(-0.22\) & 0.26 & \(-0.38\) \\ \(e\) (\(\cdot\)) & 0.65 & \(-0.27\) & 1.00 & \(-0.04\) & 0.04 & \(-0.28\) & 0.58 & \(-0.02\) & 1.00 & \(-0.07\) & 0.13 & \(-0.07\) \\ \(\gamma\) (\%) & 0.00 & \(-0.20\) & \(-0.04\) & 1.00 & \(-0.88\) & 0.85 & 0.07 & \(-0.22\) & \(-0.07\) & 1.00 & \(-0.83\) & 0.88 \\ \(G/G_{MAX}\) (\(\cdot\)) & 0.07 & 0.20 & 0.04 & \(-0.88\) & 1.00 & \(-0.88\) & \(-0.16\) & 0.26 & 0.13 & \(-0.83\) & 1.00 & \(-0.93\) \\ \(D\) (\%) & \(-0.44\) & \(-0.28\) & \(-0.28\) & 0.85 & \(-0.88\) & 1.00 & 0.27 & \(-0.38\) & \(-0.07\) & 0.88 & \(-0.93\) & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation matrix for low, medium, and very cohesive soils. The proposed equation allows for the calculation of the damping ratio in every range of shear strain. To obtain a minimal damping ratio in the elastic shear strain zone, one should insert 1 in place of \(G/G_{MAX}\). For the damping ratio in elastoplastic strain zone, \(G/G_{MAX}\) from 0.98 to 0.8, and for damping ratio in plastic strain range, \(G/G_{MAX}\) below 0.8. Thus, the lower the normalized shear modulus value, the greater the shear strain.
## 2 Experiments
### Scanning Laser Vibrometer Measurements  
The structural response is measured using a scanning laser vibrometer situated in an adjacent room and protected by a glass window (see Figure 5). Scanning laser vibrometry enables non-intrusive measurements (no mass added to the surface) and a large number of measurement points. This large amount of experimental data proves to be useful for the validation of the SEA model--the responses of which are averaged in space. However, measurements can only be performed on the external surfaces that are visible from the adjacent room through the protective window. Two configurations of the structure were set up.  
In the vertical configuration, shown in Figure 3, the response of the external envelope was measured, while the response of the bottom was measured in the horizontal configuration, shown in Figure 5. Preliminary tests have shown that the presence of the test structure, regardless of its orientation, in the middle of the chamber, has a negligible effect on the acoustic sound field. Furthermore, with scanning laser vibrometry, the measurements at different points are not synchronous but sequential. The Power Spectral Density of each measurement point is computed; however, the cross power spectral densities cannot be computed.
## 3 \(l\)-\(k\)-Nearest-Neighbors Query Processing
### Overall Process  
The basic concept of our _l- k_NN query processing is to divide the search space into several track-sectors whose sides are all larger than \(l\). We then select one Q-node in each of the other track-sectors. Therefore, the Q-nodes are at least distance \(l\) from each other because a track-sector with a _Q_-node is surrounded by margin tracks and sectors. For example, \(TS_{2,1}\) is surrounded by two margin tracks \(T_{1}\) and \(T_{3}\), and two margin sectors \(S_{2}\) and \(S_{8}\), as shown in Figure 2. The overall processing of a _l- k_NN query consists of four phases:  
* Routing phase: A query message is routed from the source node to the H-node. GPSR [19] is adopted as the routing algorithm. In this phase, the sensor network information, such as the number of nodes, is collected without the assistance of any infrastructure.
* _l- k_NN boundary estimation phase: The H-node estimates a searching boundary. The _l-k_NN boundary is dynamically estimated according to the distance constraint \(l\) and the number of neighbors \(k\).
* Query dissemination phase: The H-node disseminates the query message to other sensor nodes within the _l- k_NN boundary. In order to guarantee the \(l\) distance, the query message is propagated to the query tracks and query sectors.
* Aggregation phase: The H-node aggregates the query results. The aggregated query results are transmitted back to the query source by the GPSR.  
Figure 2: Basic concept for _l-k_NN query processing.  
The routing and aggregation phases are very similar to existing \(k\)NN query processing techniques. Compared with \(k\)NN query processing, our proposed \(l\)- \(k\)NN query processing is mainly different in the \(l\)- \(k\)NN boundary estimation and query dissemination phases. We explain the details of these phases in Sections 3.2 and 3.3.
## 3 Fast Channel Information Prediction Based Random Forest
### CC Allocation Algorithm for Energy-Efficient Optimization
The proposed CC allocation (DDJBNBC) algorithm that combines the iterative pruning method and the algorithm to avoid frequent handover of narrowband users. The specific process of the DDJBNBC algorithm is shown in Algorithm 4. The flow chart of Algorithm 4 (DDJBNBC) is shown in Figure 2.  
```
1 Input : the random set \(\mathbb{C}_{k}\)\(\forall k\); user number \(k\); maximum slot \(T_{slot}\) ; EE\({}_{th}\);
2 Output : CC allocation scheme \(a\);
3 for \(t=1\) to \(T_{slot}\) do
4 compute narrowband user's CC set \(\left\{m(k)_{pre}\,\forall k\in K_{1}\right\}\);
5 for \(k=1\) to \(K\) do
6 while\(\left|\mathbb{C}_{k}\right|>C_{k}\) do
7 obtain \(n\) /, \(k\) / by Equation (26);
8 update \(C_{k}\), namely \(\mathbb{C}_{k}=C_{k}-n\) /;
9 end while
10 for \(k\in K_{1}\) do
11 if\(EE_{k}^{t}\leq EE_{th}\) then
12\(m(k)_{new}\gets m(k)_{update}\);
13 else\(m(k)_{new}\gets m(k)_{pre}\)
14 end if
15 end for
16 end for
17\(a=\left\{\mathbb{C}_{1}\,,\,\mathbb{C}_{2}\,,\,\ldots,\mathbb{C}_{k}\right\}\) end for;
18 end for
```  
**Algorithm 4**DDJBnBC Algorithm  
Figure 2: The flow chart of Algorithm 4 (DDJBnBC). On the basis of energy-efficient optimized RB and power allocation algorithm ERPA and CC allocation algorithm DDJBNBC, the energy efficiency joint resource optimization allocation (PEJA) algorithm based on channel information prediction is proposed. The PEJA algorithm uses the online predicted channel information and iterates the ERPA algorithm and the DDJBNBC algorithm. The detailed PEJA algorithm steps are shown in Algorithm 5. Algorithm 5 PEJA is the optimal energy-efficient resources allocation algorithm, which includes the iteration of two sub algorithms, they are Algorithm 3 EERPA and Algorithm 4 DDJBNBC separately. Algorithm 3 EERPA is the RB and power allocation sub algorithm and Algorithm 4 DDJBNBC is the CC allocation sub algorithm. Figure 3 shows the flow chart of PEJA algorithm 5. Figure 3: The flow chart of PEJA Algorithm 5.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 4 Online SOC Estimation by LSTM  
Real-time SOC estimates were based on a real-time estimate of the SOC of the lithium battery pack by combining the 6 voltage data with the lowest error rate results in Section 5.  
Figure 4: Structure of LSTM.  
Figure 5 presents the experimental configuration of the real-time estimation of the lithium battery pack. Real-time SOC estimation of the lithium battery was performed in a state wherein all four cells are fully charged, that is, at 100% SOC. An eight-channel relay module was added to the battery controller to achieve sequential SOC estimation of the four cells. Currently, studies on the real-time estimation of SOC are scarce. Therefore, the real-time SOC estimation method, which has been researched and tested in this paper, is considered as a new direction of SOC estimation, and the research and experiments were conducted.  
The experiment process was as follows. The four cells were completely charged at a constant voltage of 4.2 V and constant current of 1300 mA using an electronic load. Subsequently, the voltage and temperature variable data were sent to the PC through the battery controller. The SOC of the lithium battery was estimated in real time using the LSTM method utilizing only 6 voltage as the input value and the temperature variable data. The real-time estimation is as shown in Figure 6.  
The real-time SOC estimation result of Cell 1 to Cell 4 is shown in Figure 6. The experimental result confirmed that the trend of SOC estimation of the battery pack was similar to that of the SOC estimation simulation experiment.  
Figure 5: Experimental environment for real-time SOC estimation.  
Figure 6: Real-time SOC estimation.