esa-sceva/satcom-chunk-collection huggingface dataset:
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 4 Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
### Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
#### 4.2.2 Dynamic Convergence Factor  
To balance the global and local search capabilities of MSSA, a new dynamic convergence factor is introduced during the leader's position update. This dynamic convergence factor draws inspiration from PSO and introduces an inertia weight strategy. It also employs a sine function to control the variation of the inertia weight. This function allows the salp swarm to explore the optimal solution at a relatively high speed at the beginning, and then gradually stabilizes the local search with a smoother rate of change towards the end, facilitating the discovery of the best solution. The Formula (17) is modified as follows  
\[c_{1}^{\prime}=\sin(\pi+\pi*(\frac{t}{2T}))+\frac{w_{\text{max}}+w_{\text{min} }}{2} \tag{17}\]  
When the inertia weight varies between 0.4 and 0.9, the algorithm exhibits relatively good optimization performance. As the iterations progress, the inertia weight is nonlinearly decreased from 0.9 to 0.4, achieving dynamic weight changes. This allows for a better balance between global exploration and local exploitation in the algorithm and, to some extent, increases the probability of escaping local optimal regions [32]. Therefore, in this paper, the value of \(w_{\text{max}}\) is set to 0.9, and \(w_{\text{min}}\) is set to 0.4.  
The updated equation for the leader position is shown in Formula (18).  
\[X_{d}^{1}=\left\{\begin{array}{ll}F_{j}+c_{1}^{\prime}((ub_{j}-lb_{j})c_{2}+ lb_{j})&\quad c_{3}\geq 0.8\\ F_{j}-c_{1}^{\prime}((ub_{j}-lb_{j})c_{2}+lb_{j})&\quad c_{3}<0.8\end{array}\right. \tag{18}\]  
where \(c_{1}^{\prime}\) represents the new dynamic convergence factor, with the meanings of the other parameters remaining the same as in Formula (13).
## 2 Materials and Methods
### Analysis of Phenolic Powder and Encapsulated Phenolic Powder
#### 2.6.1 Total Phenolic Content  
The total phenolic content (TPC) of the uncoated phenolic extract was estimated adopting the Folin-Ciocalteu method as described by [PERSON] et al. [28] with a simple adjustment. For the coated phenolic powder, approximately 120 mg of encapsulated phenolic powder was precisely weighed and dissolved in 1 mL of an ethanol:acetic acid:water (50:8:42) mixture [29]. The absorbance at 765 nm was measured using a UV-visible spectrophotometer (UV-3100 PC, VWR, Radnor, PA, USA) and the total phenolic content of the extracts (TPC) and microcapsules (TPCC) was calculated using gallic acid calibration curves and expressed as gallic acid equivalents (mg GAE/g dry weight (DW)).
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 3 Results and Discussion
### Sensitivity Analysis  
#### 3.2.1 Model's Behaviour under Changing Service Values  
Ratios between suitability calculated applying the supply-based approach and the demand-based approach are reported in Figure 6 for the different scenarios under investigation. Results show that the values are lower than 1. Moreover, the graphs show few relevant differences between the results in different landscape classes, while there are some evident differences in ratios between the different scenarios.  
In the analysed cases, demand-based suitability is higher than supply-based suitability, meaning that the area has an excellent capacity to fulfil human needs even in landscapes with a relatively low quantity of service supplied. Demand-based suitability is generally higher and flatter than supply-based suitability. A reason for that is that some services typically satisfy all the potential demand in the area, despite varying the supplied quantity.  
Figure 6: Average ratios between suitability calculated with supply-based scaling and demand-based scaling for each scenario and each landscape class (descriptions of landscape codes are reported in Table 1, scenario codes in Table 2). (**A**) economic perspective, (**B**) infrastructure perspective.  
For example, soil fertility varies across the area, generating variability in supply-based suitability. However, the soil fertility is generally rated as \"very good\" across the whole area, flattening the differences between the different landscapes. This consideration is also valid for other services, such as water provision for drinking purposes. This evidence is also supported by the fact that in those landscape classes where the supply-demand ratio shows high values, suitability is also high. The contrary happens in the areas with low liveability values, where the differences between supply-based and demand-based indicators are more evident. For example, in COMM, a scenario mainly benefiting from services produced and delivered in urban areas, the supply-based suitability of urbanized landscapes is high, and the supply-based and demand-based values of the index are more similar. In the scenarios INF and HOUS, landscapes NHI, NMI, OMI, and OHI show the highest ratios, meaning that in these cases, supply-based and demand-based suitability are pretty similar. So, landscape type is relatively suitable to the purpose identified because it offers a high density of maximized services.
## 2 System Model and Problem Formulation
### Communication Model  
In our work, we assume that the communication links between a vehicle and a UAV (V2U) are LoS. Due to that, the size of the processing result is in general much smaller than the size of the task, we ignore the transmission time of the downlink channel [24; 25].  
According to the Euclidean formula, the distance between vehicle \(n\) and UAV \(u\) at time slot \(t\) is given by  
\[L_{n,u}(t)=\sqrt{(X_{u}(t)-X_{n}(t))^{2}+(Y_{u}(t)-Y_{n}(t))^{2}+Z_{u}(t)^{2}}. \tag{6}\]  
We assume that the wireless channel from ground vehicles to the UAV is dominated by the LoS transmission link. It is noted that real-world scenarios, particularly in IoV, often utilize 3 GPP Urban Macro (UMa) [41; 42] or Urban Micro (UMi) [43] environments. In this case, the channel remains unchanged within each fading block and is subject to distance-dependent power attenuation. Therefore, at the \(t\)-th time slot, the channel power gain between the \(n\)-th vehicle and the \(u\)-th UAV can be formulated as  
\[h_{n,u}(t)=\eta_{0}L_{n,u}^{-\zeta}(t)=\frac{\eta_{0}}{(\|\mathbf{l_{u}}(t)- \mathbf{l_{n}}(t)\|)^{\zeta}}, \tag{7}\]where \(\eta_{0}\) is the channel power gain at a reference distance \(L_{0}=1m\), and \(\zeta\geq 2\) is the path-loss exponent [34].  
The data transmission rate for offloading the task from vehicle \(n\) to UAV \(u\) can be calculated as:  
\[R_{n,u}(t)=B_{n,u}\log_{2}\biggl{(}1+\frac{P_{n}G_{0}h_{n,u}(t)}{\sigma^{2}} \biggr{)}, \tag{8}\]  
where \(B_{n,u}\) is the spectrum bandwidth of the vehicle-to-UAV (V2U) communication channel, \(P_{n}\) is the transmission power of vehicle \(n\), \(G_{0}\approx 2.2846\)[44], and \(\sigma^{2}\) is the power of the white Gaussian noise. In this formula, we assume that all vehicles are connected to UAV \(u\) using Orthogonal Frequency-Division Multiple Access (OFDMA), which is allocated by the spectrum resource. Thus, different V2U links do not interfere with each other, and the coverage areas of the UAVs do not overlap, eliminating interference between UAVs [45]. Based on (8), the transmission delay from vehicle \(n\) to UAV \(u\) is given by:  
\[T_{n,u}^{\mathrm{comm}}(t)=\frac{s_{n}^{d}(t)}{R_{n,u}(t)}. \tag{9}\]  
If a vehicle requests service from a UAV, the condition that must be satisfied is that the UAV is within the vehicle's coverage range. This spatial condition is illustrated in Figure 2, which shows the geometric relationship between the UAV and the vehicle. Whether the vehicle is within the UAV's coverage range is represented by a binary 0-1 indicator \(I\), given by  
\[I_{n,u}(t)=\left\{\begin{array}{ll}1,&ifR_{n,u}(t)\geq R_{th}\\ 0,&else.\end{array}\right. \tag{10}\]  
where \(R_{th}\) represents the threshold of the communication rate.
## 3 Dynamic Object Identification and Tracking
### Object Tracking
The tracker parameters \(d_{th}\), \(max\_skip\), and \(max\_trace\) refer to the distance threshold for associating detections, the maximum number of frames a track can be skipped before being removed, and the maximum length of the object's trace, respectively. The notation \((pos,vel)\) indicates the current position and velocity of a tracked object, which are visualized and published as \((ID,pos,vel)\). Additionally, the MOTA metrics are computed to evaluate tracking performance. ```
1:Initialize tracker with \((d_{th},max\_skip,max\_trace)\)
2:Set visualization window \((w,h)\leftarrow(960,1000)\)
3:Initialize ROS node, publishers, and subscribers
4:while ROS running do
5: Receive message \(msg\) from Segment_centers
6:BirdEyeViewTracking(\(msg\))
7:endwhile
8:procedureBirdEyeViewTracking(\(msg\))
9:\(C\leftarrow[]\), \(S\leftarrow[]\)\(\triangleright\) C: centers, \(S\): states
10:for each object in \(msg\)do
11:if valid position \((x,y,z)\)then
12: Map \((x,z)\) to image coordinates, append to C
13: Append \((x,y,z,t)\) to \(S\)
14:endif
15:endfor
16: Update tracker with \(C\), \(S\)
17: Generate visualization frame
18:for each tracked object in tracker do
19: Plot \((pos,vel)\) on frame
20: Publish \((ID,pos,vel)\)
21:endfor
22: Display frame
23: Compute and print MOTA
24:if 'q' key pressed then
25: Exit visualization
26:endif
27:endprocedure
```  
**Algorithm 2** Bird's-eye-view object tracking
# Investigating the Adsorption Behavior of Zwitterionic Surfactants on Middle Bakken Minerals
## 2 Materials and Methods
### Adsorbent
#### 2.2.3 Evaluation of Critical Micelle Concentration (CMC)  
The CMC of zwitterionic surfactants was determined using the interfacial tension technique [36; 37; 38; 39]. To measure the IFT of CG3, ME1, and CD2 solutions at varying concentrations, a pendant drop tensiometer from Core lab, Midland, TX, USA was employed. The instrument used in this study to measure interfacial tension (IFT) falls under the category of static methods for determining interfacial tension. The pendant drop technique is particularly suitable for measuring liquid-liquid interfacial tension and involves analyzing the shape of a droplet suspended in an immiscible fluid. In this study, the droplet's geometry was captured using a high-resolution camera, and the interfacial tension was calculated using DROPimage Advanced software v2.x, which applies the Young-Laplace equation.  
Figure 1: Adsorbent preparation process from large core slab to powder.  
To conduct the measurements, the experimental setup included an IFT cell, accumulators for loading immiscible fluids, and a precision pump for controlled droplet formation as shown in Figure 2. The aqueous and hydrocarbon phases were injected into the chamber, and an oil droplet was carefully formed at the tip of a 1.05 mm needle. The software analyzed the droplet shape in real-time to provide accurate IFT values.  
A graph was generated by plotting surfactant concentration against IFT values, identifying the CMC value at the point where the descending line intersects with the horizontal line, indicating an inflection point.
## 4 Calibration of Electrical Inertia Simulation Accuracy
### Calibration of Standard Inertia
Observing the two deceleration processes, it can be found that the speed decline curve of the system during the deceleration process is smooth, and there is no obvious fluctuation in the \(T_{d1}\) and \(T_{d2}\) of the torque curve section. The deceleration of the system is 0.24, 0.23, and 0.23 rad/s\({}^{2}\), respectively, when analyzing the deceleration of the system at the intervals of 1, 2, and 3. Therefore, the influence of system resistance moment on the inertia calibration of the test-bed in a high speed range can be neglected. The standard inertia of 750 kg\(\cdot\)m\({}^{2}\) can be calibrated in the same way at the transmission ratio of 1:2.36, shown in Figure 17. The equivalent inertia is 4177.2 kg\(\cdot\)m\({}^{2}\). Under the condition of average torque \(T_{t1}=4220.38\) Nm, the average acceleration of the system is 0.94 rad/s\({}^{2}\), while under the condition of \(T_{t2}=4919.48\) Nm, the average acceleration of the system is 1.11 rad/s\({}^{2}\). Therefore, the inertia of the system is 4157.79 kg\(\cdot\)m\({}^{2}\) and the measurement deviation is 0.46%. Figure 16: Calibration test results under equivalent inertia of 2227.84 kg\(\cdot\)m\({}^{2}\). Figure 17: Calibration test results under equivalent inertia of 4177.2 kg\(\cdot\)m\({}^{2}\). The standard inertia of 250 kg\(\cdot\)m\({}^{2}\) can be calibrated at the transmission ratio of 1:6.48, shown in \[T_{t1}-T_{d1}=J_{s}\alpha_{1} \tag{45}\]0 The equivalent inertia is 10,497.6 kg\(\cdot\)m\({}^{2}\). Under the condition of average torque \(T_{t1}=7206.53\) Nm, the average acceleration of the system is 0.67 rad/s\({}^{2}\), while under the condition of \(T_{t2}=4919.48\) Nm, the average acceleration of the system is 0.73 rad/s\({}^{2}\). The inertia of the system is 10,522.34 kg\(\cdot\)m\({}^{2}\) and the measurement error is 0.23%. The variation of standard inertia deviation with the increase of equivalent inertia is shown in \[T_{t1}-T_{d1}=J_{s}\alpha_{1} \tag{45}\]1 It is evident that the mechanical inertia under large inertia is less than 0.5%, except in the case of small inertia, where the inertia calibration deviation is too large due to fluctuation of the sensor signal.
## 4 Simulation Results
### Physical Simulation Results
#### 4.2.3 Triangle Formation in a Single Water Scene  
The start point and final point in the single scene were set as \((x,y)=(-4\) m, \(-3.1\) m) and \((x,y)=(6.5\) m, \(7.3\) m), respectively. The formation shape was a triangle, as shown in Figure 19b. The initial position of the three spherical robots were \(position_{1}=(-4.5\) m, \(-4.5\) m), \(position_{2}=(-7.5\) m, \(-5.5\) m), and \(position_{3}=(-5.3\) m, \(-7\) m). The real formation tracking result is depicted in Figure 20. It can be seen that the initial shape composed of three robots was different than the desired formation shape, and each robot was far away from the desired trajectory. Under the adjustment of the controller, the three robots gradually converged to the corresponding reference trajectory while maintaining a basic triangular formation. The trajectory tracking error and formation error are shown in Figure 21. Most of the tracking errors of each of the robots converged to 0. The convergence error of Robot 2 in the x-direction did not exceed 0.1 m. The formation errors of \(y\) and \(\phi\) converged to zero, and the formation error of \(x\) did not exceed 0.2 m. Table 5 lists the root mean square errors of the trajectory tracking and formation constraints. The maximum formation error between the robots was 0.1532 m, which is 0.05 times the size of the formation shape. The control inputs of the three spherical robots are shown in Figure 22. The input values of each of the robots followed the constraints all of the time during the formation process.  
Figure 21: State errors during the formation process in a single water scene. (**a**) Tracking error. (**b**) Formation error.  
Figure 20: Real formation tracking results in a single water scene.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Experimental Environment and Parameter Settings  
Table 1 presents the specific structural parameters of the model. All models were trained and tested multiple times until the statistical metrics stabilized. All experiments in this paper were conducted on a GeForce RTX 3080 using PyTorch-v1.13.0, with Python v3.8 as the programming language. Adam was used as the optimizer for model training. Given the effectiveness of dynamically adjusting the learning rate, we adopted the Poly learning rate strategy, where the learning rate for each epoch was \(\text{lr}\times(1-\frac{\text{epoch}}{\text{max}\_\text{epoch}})^{power}\). The batch size was set to 64, the maximum number of iterations (max_epoch) was set to 100, the initial learning rate (lr) was set to 0.001, and the power was set to 0.9.
## 3 Results
### Hyperparameter Tuning Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **CV Strategy** & **Best Loss (F1)** & **Best Hyperparameters** \\ \hline \multirow{4}{*}{ANN} & \multirow{2}{*}{\(10\times\) CV} & \multirow{2}{*}{\(-0.8069\)} & S: ‘adam’, HLS: (50,50), AT: ‘relu’, LR: 0.021, A: 0.0068, MI: 300, \\  & & BS: auto, BI: 0.9408, B2: 0.9254, E: 0.0002, T: 0.0172, ES: ‘False’ \\ \cline{2-3}  & \multirow{2}{*}{\(5\times 2\) CV} & \multirow{2}{*}{\(-0.7921\)} & S: ‘adam’, HLS: (10,), AT: ‘tanh’, A: 0.079, LR: 0.101, MI: 300, BS: 256, \\  & & B1: 0.8716, B2: 0.9221, E: 0.0002, T: 0.0028, ES: ‘False’ \\ \hline \multirow{4}{*}{RF} & \(10\times\) CV & \(-0.8038\) & N: 75, MD: 9, MSS: 7, MSL: 5, MF: 1, OOB: 1, MS: 0.7529 \\  & & \(5\times 2\) CV & \(-0.7912\) & N: 436, MD: 16, MSS: 9, MSL: 1, MF: ‘log2’, OOB: ‘False’, MS: 0.5361 \\ \hline \multirow{4}{*}{LR} & \(10\times\) CV & \(-0.7228\) & S: ‘saga’, P: ‘elasticnet’, C: 2.6549, LR: 0.7288, MI: 250 \\  & & \(5\times 2\) CV & \(-0.7210\) & S: ‘lbfgs’, P: ‘l2’, C: 0.0218, MI: 300 \\ \hline \multirow{4}{*}{SVM} & \(10\times\) CV & \(-0.8096\) & K: ‘rbf’, C: 0.3528, G: 0.3727, T: 0.1306, MI: 900 \\  & & \(5\times 2\) CV & \(-0.7960\) & K: ‘rbf’, C: 52.9159, G: 0.5289, T: 0.0083, MI: 500 \\ \hline \multirow{4}{*}{LGBM} & \multirow{2}{*}{\(10\times\) CV} & \multirow{2}{*}{\(-0.8002\)} & N: 289, MD: 7, LR: 0.1859, SS: 0.5553, CT: 0.5743, MDL: 11, MSG: 0.0026, \\  & & & FF: 0.9745, RA: 0.0003, RL: 2.4946, V: \(-1\) \\ \cline{2-3}  & \multirow{2}{*}{\(5\times 2\) CV} & \multirow{2}{*}{\(-0.7786\)} & N: 245, MD: 8, LR: 0.0642, SS: 0.9525, CT: 0.7017, MDL: 10, \\  & & MSG: \(4.53\times 10^{-05}\), FF: 0.8595, RA: 0.0224, RL: 1.4106, V: \(-1\) \\ \hline \multirow{4}{*}{CB} & \multirow{2}{*}{\(10\times\) CV} & \multirow{2}{*}{\(-0.8119\)} & I: 66, D: 6, LR: 0.1162, SS: 0.8651, RSM: 0.8029, MDL: 42, L2R: 2.0748, \\  & & BT: 0.1544, RS: 0.8062, GP: ‘SymmetricTree’, LEI: 2, EM: ‘Logloss’ \\ \cline{2-3}  & \multirow{2}{*}{\(5\times 2\) CV} & \multirow{2}{*}{\(-0.7909\)} & I: 50, D: 10, LR: 0.0194, SS: 0.5672, RSM: 0.7175, MDL: 54, L2R: 2.3315, \\  & & BT: 0.5752, RS: 0.714, GP: ‘SymmetricTree’, LEI: 3, EM: ‘Logloss’ \\ \hline \multirow{4}{*}{XGB} & \multirow{2}{*}{\(10\times\) CV} & \multirow{2}{*}{\(-0.8073\)} & N: 383, MD: 7, LR: 0.2573, SS: 0.7641, CT: 0.7576, CL: 0.9269, G: 0.0020, \\  & & MCW: 1, MDS: 7.9246 RA: 0.1115, RL: 0.5026 \\ \cline{1-1} \cline{2-3}  & \multirow{2}{*}{\(5\times 2\) CV} & \multirow{2}{*}{\(-0.7844\)} & N: 75, MD: 7, LR: 0.1032, SS: 0.8089, CT: 0.9174, CL: 0.9684, G: 0.056, \\  & & MCW: 1, MDS: 6.3958 RA: 0.3124, RL: 1.5803 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of final hyperparameter configurations for ML models tuned with 10-fold and \(5\times 2\) cross-validation procedures. Figure 2: Analysis of computational overhead in hyperparameter tuning times for \(10\times\) CV and \(5\times 2\) CV methods across ML models.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Data Sources  
The lack of labeled data and missing categories under actual conditions were inconvenient to quantify, so it was difficult to control variables in experiments. Therefore, this study artificially generated corresponding simulation datasets based on actual data to simulate different application conditions.  
The original data in the experiment were the civil aviation trajectory data recorded by the ADS-B (Automatic Dependent Surveillance-Broadcast) system [21], which was downloaded from the ([[https://flightadsb.variflight.com](https://flightadsb.variflight.com)]([https://flightadsb.variflight.com](https://flightadsb.variflight.com)), accessed on 1 March 2021) website, including flight number, time, latitude and longitude, altitude, speed, heading angle, and other information. To reduce the variables in the experiment, this study only retained the two most basic spatiotemporal features of time and location in the data and normalized them. After preprocessing, such as filtering, interpolation, filtering, and smoothing, taking the channel as the category label, the trajectories on the 12 complex routes were taken to create a simulated trajectory dataset. The original trajectory latitude and longitude images are shown in Figure 7(a), and the normalized trajectory images are shown in Figure 7(b).  
Based on the above datasets, different application conditions were simulated by controlling the number of samples and categories of the labeled datasets.
## 2 Unit Cell Design Architecture  
The design view of proposed planar-square, left-handed, metamaterial SRR unit cell is shown in Figure 1a. The desired goal of the unit cell is to attain resonance within the frequency band 3-10 GHz for microwave imaging. The unit cell is comprised of four square copper strip ring patches with a thickness of 0.035 mm. The length and width of each square are 7 \(\times\) 7 mm\({}^{2}\), 5 \(\times\) 5 mm\({}^{2}\), 3 \(\times\) 3 mm\({}^{2}\), 1 \(\times\) 1 mm\({}^{2}\), respectively. The width of first three metal stripe squares is 0.5 mm and the width of the inner stripe is 0.25 mm. The gap (g) between each stripe is 0.5 mm and the split width of the first three (g\({}_{1}\), g\({}_{2}\)) is 0.25 mm, whereas the inner one (g\({}_{3}\)) is 0.125 mm. The design parameters of the proposed unit cell are demonstrated in Table 1. A magnetic field is induced in the SRR which is responsible for creating negative permeability. The split gaps in each ring induce a series capacitance which control the resonant characteristics. The unit cell structure is printed on FR4 substrate, which has a dielectric constant of 4.6 with a 0.002 loss tangent. The modification of the SRR structure decreases the series capacitance and increases the coupling between the inner and outer rings. The introduction of the inner rectangle and the optimization of the gap between the stripes insist that the unit cell obtains the resonant UWB band.
## 2 Methodology  
Figure 1 shows the overall experimental and modeling workflow. The dashed boxes in Figure 1 indicate the potential applications of this battery state prediction model, including redundant backup and fault detection support for battery parameters.  
1. Battery Experimental Data Collection: time-series data of essential parameters, including voltage and current, were collected from practical battery operations.
2. Parameter Identification: this process determines critical parameters in the equivalent circuit, such as internal resistance and time constants of RC networks.
3. Equivalent Circuit Model Construction: a second-order RC equivalent circuit model was established in the Simulink environment.
4. Battery Data Simulation: the constructed ECM was employed to simulate charge-discharge cycles, generating extensive training data.
5. Model Training: the simulated data were used to train a bidirectional LSTM network integrated with multi-head attention mechanism.
6. Battery State Prediction: the trained model is applied to predict battery voltage and SOC.  
In this study, an NCM Li-ion battery was chosen as the experimental object. NCM Li-ion batteries are widely used in electric vehicles, consumer electronics, grid energy storage, and portable energy storage systems due to their high energy density and long cycle life. The parameters of the NCM battery are presented in Table 1. The tested parameters used are the basic parameters of the ECM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Value** \\ \hline Nominal capacity & 32 Ah \\ Nominal voltage & 3.7 V \\ Charge cutoff voltage & 4.2 V \\ Discharge cutoff voltage & 3.4 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: The lithium battery parameters.  
Figure 1: Flowchart of the workflow for this study, including battery ECM establishment and state prediction model development.