esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Investigation and Results
### Secure Running Safety Comparison  
The load acting on the structure during the installation of the side-swing doors of the refrigerated truck container is significant. A side-swing door was installed on the left side of the CBF structure for the ease of transporting ice bags out of the refrigerated container. The installation of the side-swing door leads to an imbalance in the total weight of the truck. Once the modified truck structure was complete, it was weighed to check the weight  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Type**} & **AB’s Thickness** & **Section Sizes** & **Total Def.** & **Stress** & **Strain** & \multirow{2}{*}{**FOS**} \\ \cline{3-4} \cline{6-9}  & & & **inch (mm)** & **inch (mm)** & **(mm)** & **(MPa)** & **(mm/mm)** & \\ \hline
01 & IB & ST & 1/16 & 3 \(\times\) 1/16 & 0.41 & 85 & 4.59 \(\times\) 10\({}^{-4}\) & 2.95 \\
02 & IB + SD & ST & 1/16 & 3 \(\times\) 1/16 & 0.49 & 92 & 5.01 \(\times\) 10\({}^{-4}\) & 2.71 \\
03 & IB + SD & CL & 1/16 & 2/4\(\times\) 1/4 & 3.16 & 363 & 2.02 \(\times\) 10\({}^{-3}\) & 0.69 \\
04 & IB + SD & CL & 1/8 & 2/4\(\times\) 1/4 & 3.10 & 197 & 1.05 \(\times\) 10\({}^{-3}\) & 1.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing simulation results of reducing the size of the box beam.  
Figure 14: The position of maximum stress and strain.  
distribution on each side of the wheels. The weight indicator in the rack version of the Dini Argeo, complete with a connector for linking the platforms, is shown in Figure 15. It was found that the weight on each side of the wheels was not balanced, with the weight falling on the left wheel being greater than the right. The details of the topic, Securing Basic Performance and Safety Requirements, will be shown last. Factors affecting the left side structure of the vehicle were the position of the fuel tank combined with the weight of the side-swing doors, and the structure that supports the door force causing the center of mass of the structure to be skewed to the left and located at the rear of the vehicle. This feature was not beneficial for the stability and safety of the vehicle while driving. Therefore, it was necessary to analyze the center of mass of the structure and adjust the appropriate installation position of the side-swing doors.  
The maximum allowable load was reduced when the mass of the finished vehicle (C.W. + A = T) increased, while keeping the G.V.W. fixed. Table 5 shows the chassis and cab curb mass of the base vehicle (C.W.) and the gross vehicle mass of the finished vehicle (G.V.W.) for the models GUN122R-BTFXYT3 and -BTFLXT3.
## 4 The Cloud-Enabled Emergency Navigation System  
Figure 1 shows the architecture of the cloud-enabled system. The system is composed of four layers. The cloud infrastructure layer is a hardware layer that provides the platform for the deployment of the system. The intelligence layer comprises a data interpretation module and a navigation module. The data interpretation module aims to provide better QoI for decision making, while the navigation module is used to guide evacuees to exits in a cooperative manner. The system interface layer is responsible for information exchanges from diverse gateways, such as the Universal Mobile Telecommunications System (UMTS) or Internet. Civilians with smart phones form the user layer to collect sensory data and convey it to the cloud. An AHCPN-based energy management module is installed among the handsets to search routes to realise energy-sensitive communication between the cloud and handheld devices.  
Figure 1: The architecture of the cloud-enabled system.  
The main objectives of the system are to provide appropriate routes for evacuees in a real-time manner and to balance the energy dissipation of portable devices. When an evacuation process starts, hazard information will be gathered by individuals and then uploaded to the cloud. On the cloud side, the CPN algorithm with a time metric will be performed to search paths with the shortest \"time\" to exits. Furthermore, a smart group motion will be generated by the SPN algorithm, which employs attractive forces and repulsive forces to maintain adjacent evacuees as a loose group. The suggested directions will be brought back by ACKs and visualised on the screens of portable devices. The simplified workflow of the system is shown in Pseudocode 2, and a list of symbols used is summarised in Table 2. In the following subsections, details of the coordinated emergency navigation algorithm in the navigation module and the power-aware protocol in the energy management module are introduced.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 5 Our Presented HM-ACOPSO Algorithm
### Sub-Domain Division  
We utilize a virtual grid to divide the sensor field into subdomains, and each subdomain represents a cluster. In order to simplify the intra-cluster communication, cluster members only communicate with their corresponding cluster head directly. In order to guarantee that any two nodes in the same subdomain can communicate with each other, the length of each subdomain should satisfy the following formula:  
\[w=\frac{\sqrt{2}}{2}R \tag{4}\]  
where \(R\) is the communication range of ordinary sensors. Each subdomain elects a cluster head in accordance with the weight of each node, and the weight can be calculated using Formulas (5)-(7):  
Figure 4: The workflow of PSO.  
\[W_{i}=\frac{E_{residual}}{D_{total}} \tag{5}\]  
\[D_{total}=\sum_{i\in A}dis(i,j)^{2} \tag{6}\]  
\[dis(i,j)=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{7}\]  
where \(E_{residual}\) is the residual energy of node \(i\) and \(A\) is the set of neighbors of node \(i\).  
During the initial phase of election, nodes close to the subdomain center take the lead in broadcasting a competition package which contains the node ID and weight. Neighboring nodes compare their own weights with the receiving package. If the neighbor nodes own the bigger weight, they will broadcast their own competition package. Finally, nodes with the biggest weight will be chosen as CH. The process of clustering needs to create many control messages which increase the extra energy consumption. Therefore, the network conducts the clustering procedure after several rounds. Figure 5 describes the result of the clustering.
## 3 Optimal Feature Set Acquisition
### Recursive Feature Elimination Method  
Recursive Feature Elimination (RFE) is a feature selection technique utilized to select the optimal subset from a given feature set. Its core concept involves progressively eliminating the least important features, repeatedly training the model, and assessing feature importance until the required number of features or performance indicators are met. The algorithm operates in a cyclical process encompassing three steps: (1) training the model based on the dataset and obtaining the corresponding weight of each feature; (2) calculating the evaluation scores of all features using predetermined evaluation indices; and (3) eliminating the feature with the lowest evaluation score in the current dataset. This looping process continues until only the final variable remains in the feature set. Ultimately, a list sorted by feature importance is generated. The RFE method can filter out the feature subset that significantly influences the target task and remove irrelevant features.  
Figure 1: Random forest structure.
# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
## 2 Materials and Methods
### Mathematical Model of Multi-GNSS Kinematic RT-PPP
Hence, the reformulated RT-PPP model can be written as follows:  
\[P_{3}^{G}=\rho_{r}^{G}+\widetilde{cdt_{r,G}}-cdt^{G,corr}+T_{r}^{G}+ \varepsilon_{p,3}^{G} \tag{7}\]  
\[\Phi_{3}^{G}=\rho_{r}^{G}+\widetilde{cdt_{r,G}}+T_{r}^{G}+\overline{\lambda N_ {3}^{G}}+\varepsilon_{\Phi,3}^{G} \tag{8}\]  
\[P_{3}^{I}=\rho_{r}^{I}+\widetilde{cdt_{r,G}}-cdt^{I,corr}+T_{r}^{I}+ISB^{I}+ \varepsilon_{p,3}^{I} \tag{9}\]\[\Phi_{3}^{J}=\rho_{r}^{J}+\widetilde{cdt_{r,G}}+T_{r}^{J}+ISB^{J}+\overline{ \lambda N_{3}^{J}}+\epsilon_{\Phi,3}^{J} \tag{10}\]  
where \(\widetilde{dt_{r,G}}\) refers to the GPS receiver clock error plus the receiver DCB (i.e., \(dt_{r,G}+b_{r,G,3}\)); \(dt^{G,corr}\) and \(dt^{I,corr}\) represent the corrected satellite clock parameter; and \(\overline{\lambda N_{3}^{G}}\) and \(\overline{\lambda N_{3}^{J}}\) are the ambiguity terms for GPS and other satellite navigation systems, respectively, taking the following formula:  
\[\Phi_{i}^{I}=\rho_{r}^{I}+cdt_{r,J}-cdt^{J}-\frac{f_{i}^{2}}{f_{0}^{2}}I_{i}^{I} +T_{r}^{I}+\lambda_{i}N_{i}+\varepsilon_{\Phi,i}^{I} \tag{2}\]0  
\[\Phi_{i}^{I}=\rho_{r}^{I}+cdt_{r,J}-cdt^{J}-\frac{f_{i}^{2}}{f_{0}^{2}}I_{i}^{I} +T_{r}^{I}+\lambda_{i}N_{i}+\varepsilon_{\Phi,i}^{I} \tag{2}\]1  
The tropospheric delay factor is divided into two parts: hydrostatic delay and wet delay. The hydrostatic tropospheric component is determined using an empirical tropospheric model, but the wet tropospheric component is estimated as an unknown parameter; thus, the vector of the estimated variables (\(\overrightarrow{X}\)) can be expressed as follows:  
\[\Phi_{i}^{I}=\rho_{r}^{I}+cdt_{r,J}-cdt^{J}-\frac{f_{i}^{2}}{f_{0}^{2}}I_{i}^{I} +T_{r}^{I}+\lambda_{i}N_{i}+\varepsilon_{\Phi,i}^{I} \tag{2}\]2  
where \(\Delta x,\ \Delta y,\) and \(\Delta z\) indicate the receiver's coordinate corrections, and \(T_{w}\) denotes the wet tropospheric delay parameter.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 4 System Model
### Communication Model  
Next, the communication model for wireless access in space edge computing will be introduced. For terrestrial terminals, it sends all tasks to the satellite for processing. If the satellite resources are sufficient, the mission is performed on the satellite; otherwise, the satellite will further offload the mission to the terrestrial cloud computing platform.  
Therefore, the communication links include ground terminals transmitting tasks to satellites, satellites offloading tasks to terrestrial cloud computing platforms, terrestrial cloud computing platforms transmitting results to satellites and satellites reporting results to terrestrial terminals. According to the research in [27], the transmission time consumption from the cloud computing platform to the satellite and the satellite to the terminal is neglected in this work, due to the face that the size of the computation outcome data is much smaller than that of the computation input data. Only the communication links from terminal to satellite and satellite to ground stations will be analyzed.  
Considering the mutual interference between the terminals and background noise, the uplink data rate for terminal \(t\) can be calculated as follows [30]:  
\[R_{T2S}=W_{T2S}\log_{2}\left(1+\frac{p_{t}g_{t,s}}{n_{0}+\sum\limits_{j\in N,j \
eq i}p_{j}g_{j,s}}\right) \tag{4}\]  
where \(W_{T2S}\) is the channel bandwidth, and \(p_{t}\) is terminal \(t\)'s transmission power, which is determined by the satellite. Besides, \(g_{t,s}\) is the channel gain between the terminal \(t\) and the satellite \(s\), and \(n_{0}\) denotes the background noise power. According to Equation (4), the transmission time of task \(i\) generated by terminal \(t\) to the satellite can be calculated as follows:  
\[T_{i,upload}=\frac{d_{i}}{R_{T2S}}+\frac{D_{T2S}}{c} \tag{5}\]  
where \(D_{T2S}\) is the distance between the terminal that generates the task \(i\) and satellite in communication, and \(c\) is the speed of light.  
Then, the energy consumed by the satellite receiving data is described as follow:  
\[E_{i,reactive}=p_{sr}\frac{d_{i}}{R_{T2S}} \tag{6}\]  
where \(p_{sr}\) is the satellite receiving power. Similarly, the transmission rate between satellite \(s\) and ground station can be described as follow:  
\[R_{S2C}=W_{S2C}\log_{2}\!\left(1+\frac{p_{s}g_{s,c}}{n_{0}+\sum\limits_{j\in SA _{i},j\
eq s}p_{j}g_{j,c}}\right) \tag{7}\]  
where \(SA\) is a set of satellites that can communicate with the ground station. Other parameters are like Equation (3). The transmission time of satellite \(s\) for offloading the task is described as follows:  
\[T_{ioffload}=\frac{d_{i}}{R_{S2C}}+\frac{D_{S2C}}{c} \tag{8}\]  
The energy consumed by the satellite transmitting data is described as follow:  
\[E_{i,transunit}=p_{s}\frac{d_{i}}{R_{S2C}} \tag{9}\]
## 4 Simulation Results
### Applications of KC200 GT PV Module
#### 4.2.3 Case 3: The Effect of Varying the Weather on SD and DD Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline Applied technique & GO & EVO & HTS & FPA & MGA \\ \hline \(I_{S2}\) (A) & \(1.00\times 10^{-6}\) & \(4.26\times 10^{-7}\) & \(0.000001\) & \(1.00\times 10^{-6}\) & \(7.18\times 10^{-8}\) \\ \hline \(H2\) & 1.266877 & 1.210971 & 1.266909 & 1.268029 & 1.281869 \\ \hline \(R_{S}\) (\(\Omega\)) & 0.005411 & 0.005719 & 0.005527 & 0.005411 & 0.004455 \\ \hline RMSE & 0.029191 & 0.055535 & 0.037941 & 0.029195 & 0.010833 \\ \hline \multicolumn{5}{c}{(e) Irradiance of 1000 W/m\({}^{2}\) at temperature of 50 \({}^{\circ}\)C} \\ \hline Applied technique & GO & EVO & HTS & FPA & MGA \\ \hline \(I_{Ph}\) (A) & 8.300973 & 8.273769 & 8.285531 & 8.298369 & 8.301133 \\ \hline \(R_{sh}\) (\(\Omega\)) & 5.643489 & 20.07308 & 10.45826 & 6.19124 & 5.624185 \\ \hline \(I_{S1}\) (A) & \(5.03\times 10^{-9}\) & \(9.90\times 10^{-7}\) & \(7.66\times 10^{-7}\) & \(6.72\times 10^{-7}\) & \(6.15\times 10^{-7}\) \\ \hline \(\eta 1\) & 1.700812 & 1.754878 & 1.757351 & 1.317407 & 1.208789 \\ \hline \(I_{S2}\) (A) & \(6.14\times 10^{-7}\) & \(6.76\times 10^{-7}\) & \(7.61\times 10^{-7}\) & \(2.27\times 10^{-7}\) & \(1.25\times 10^{-15}\) \\ \hline \(H2\) & 1.310004 & 1.319326 & 1.328574 & 1.822645 & 1.000148 \\ \hline \(R_{S}\) (\(\Omega\)) & 0.004841 & 0.004803 & 0.004766 & 0.0048 & 0.00484 \\ \hline RMSE & 0.001564 & 0.009872 & 0.00667 & 0.002403 & 0.001562 \\ \hline \multicolumn{5}{c}{(f) Irradiance of 1000 W/m\({}^{2}\) at temperature of 75 \({}^{\circ}\)C} \\ \hline Applied technique & GO & EVO & HTS & FPA & MGA \\ \hline \(I_{Ph}\) (A) & 8.423041 & 8.311589 & 8.358966 & 8.423242 & 8.424172 \\ \hline \(R_{sh}\) (\(\Omega\)) & 1.993537 & 7.206907 & 4.84717 & 1.992924 & 1.970199 \\ \hline \(I_{S1}\) (A) & \(1.00\times 10^{-6}\) & \(6.71\times 10^{-7}\) & 0.000001 & 0.000001 & 0.000001 \\ \hline \(\eta 1\) & 1.266804 & 1.223431 & 1.265694 & 1.265644 & 1.257094 \\ \hline \(I_{S2}\) (A) & \(1.00\times 10^{-6}\) & \(4.26\times 10^{-7}\) & 0.000001 & \(1.00\times 10^{-6}\) & \(9.93\times 10^{-7}\) \\ \hline \(H2\) & 1.266877 & 1.210971 & 1.266909 & 1.268029 & 1.277537 \\ \hline \(R_{S}\) (\(\Omega\)) & 0.005411 & 0.005719 & 0.005527 & 0.005411 & 0.005414 \\ \hline RMSE & 0.029191 & 0.055535 & 0.037941 & 0.029195 & 0.029331 \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._Figure 14: Acquired I-V characteristics for DD model of KC200 GT PV module under various weather scenarios using MGA. Figure 15: Acquired P–V characteristics for DD model of KC200 GT PV module under various weather scenarios using MGA.
## 3 Experiments and Results
### Experiments Analysis
Although MSACN pays more attention to the boundaries and details of cloud layers, its focus on details results in poor performance of the Recall indicator. Figure 8 shows an image of thick clouds. The underlying surface of the entire image includes a body of water, large areas of land, and hillsides. These make the cloud detection task difficult. As exhibited in Figure 8, the process of extracting cloud details by MSACN is more effective than the other two methods. The results of MSACN are better than U-Net, but the thin cloud segments of those neighborhood pixels are very blurry. There are two reasons for this. First, cloud images are of high resolution and contain a variety of objects. Second, cloud segmentation is different from natural image segmentation. The task is a pixel-to-pixel binary classification problem, which focuses on retaining edge details. However, the network we proposed obtains inter-class distance information by combining the advantages of multi-head soft attention modules and CNN, and strengthens the control of important features, thereby making the model have better spatial adaptability. This integrated method provides an effective solution for the fine division of cloud layer boundaries and brings significant improvements and enhancements to cloud detection tasks. In the area chosen by the red rectangle in Figure 8, the advantages of MSACN can be clearly seen. As shown in the indicators in Table 3, MSACN's accuracy is 2.75% higher than SwinUNet and 1.93% higher than U-Net; MSACN's IoU is 4.74% higher than Transformer-based SwinUNet and 1.19% higher than CNN-based DeeplabV3+. Figure 8: Experimental outputs of Cloud Extraction Results using Different models in Image2 (local enlarged in the red rectangle). (**a**) Raw image; (**b**) TransUNet; (**c**) SwinUNet; (**d**) U-Net; (**e**) DeeplabV3+; (**f**) MSACN-small; (**g**) MSACN; (**h**) Ground Truth. Figure 9: Experimental outputs of Cloud Extraction Results using Different models in Image3 (local enlarged in the red rectangle). (**a**) Raw image; (**b**) TransUNet; (**c**) SwinUNet; (**d**) U-Net; (**e**) DeeplabV3+; (**f**) MSACN-small; (**g**) MSACN; (**h**) Ground Truth. In Figure 9, there is haze and clouds of varying thickness covering half of the image. In contrast, U-Net and DeeplabV3+ perform poorly in distinguishing thin clouds and cloud-free areas. We use the image3 part in Figure 9 to evaluate the performance of MSACN when facing various boundary thin and thick clouds. As shown in Figure 9e, it can be seen that DeeplabV3+ cannot process detailed information, resulting in difficulties in distinguishing fine clouds. According to the indicators of image3 in Table 3, MSACN's accuracy is 3.58% higher than that based on Transformer and 2.43% higher than DeeplabV3+; MSACN's F1 is 5.63% higher than SwinUNet and 3.01% higher than U-Net. Compared results show that MSACN can obtain excellent results regardless of complex backgrounds or unevenly thick clouds. The results of various indicators evident the feasibility and effectiveness of MSACN architecture.
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 3 System Model
### Radio Model  
In WBAN, the radio frequency model for packet transmission plays a pivotal role and it needs to be intelligently selected when considering the design requirements. TAEO is based on the first order electromagnetic transmission scheme [21]. Equations (1)-(3) estimate the power consumed by a sensor during transmission (transmit or receive).  
\[P_{Tx}\left(N,l\right)=\ P_{Circuit-Tx}(N)+\ P_{Tx-Amp}(N,l)\]  
\[P_{Tx}\left(N,l\right)=\ P_{Circuit-Tx}.N.l^{2} \tag{1}\]  
\[P_{Rx}\left(N\right)=\ P_{Circuit-Rx}(N)\]  
\[P_{Rx}\left(N\right)=\ P_{Circuit-Rx}.N \tag{2}\]  
where, \(l\) is the distance between transmitter and receiver sensor. \(l^{2}\) indicates the power that was consumed by the transmission channel and \(P_{Tx}\) is the energy utilized to send a packet from transmitter to receiver node. It is sum of the power that is consumed by the internal circuit \(P_{Circuit-Tx}\) and signal amplifier \(P_{Tx-Amp}\).  
\(P_{Rx}\) shows the power consumption of the sensor on the receiving side, and it depends on the energy that is utilized by internal circuitry only i.e., \(P_{Circuit-Rx}\), as no signal amplification is required.  
In WBAN, the human body also adds some attenuation in the signal. Therefore, Equation (1) includes coefficient \(c\) depicting the path loss:  
\[P_{Tx}\left(N,l\right)=\ P_{Circuit-Tx}*N+\ P_{Tx-Amp}.N.c.l^{2} \tag{3}\]  
The equations that are derived above also highlights the importance of hardware selection in a routing protocol. In TAEO, the Nordic nRF 2401A sensor device is opted due to its low power consumption, which has been further improved by effectively controlling the transmission power.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
### Simulation
#### 2.1.4 Turbine Model  
The turbine is the same as the pump (pump-turbine reversible machine); therefore, the turbine pipe diameter (\(D_{HT}\)) and length (\(L_{HT}\)) are the same as those defined for the pump (\(D_{HT}=D_{\text{{P}}}\); \(L_{HT}=L_{\text{{P}}}\)).  
For each time step, the control strategy determines the hydro turbine output power \(P_{HT}(t)\) (W), which is limited to the maximum value \(P_{HT\_max}(t)\).  
\[P_{HT}(t)=\min\,(P_{HT}(t),\ P_{HT\_max}(t)). \tag{20}\]  
The maximum hydro turbine output is limited by its nominal rated power \(P_{HT\_N}\) (W) and nominal flow rate \(Q_{HT\_N}\) (m\({}^{3}\)/s). Further, at the end of the time step, the lower reservoir cannot exceed its maximum volume \(V_{LR\_max}\) (m\({}^{3}\)), and the upper reservoir volume cannot be lower than its minimum value \(V_{UR\_min}\) (m\({}^{3}\)).  
\[P_{HT\_max}(t)=\min\,\bigg{(}P_{HT\_N},\ \min\,\bigg{(}Q_{HT\_N},\frac{V_{LR\_ max}-V_{LR}(t)}{\Delta t},\frac{V_{UR}(t)-V_{UR\_min}}{ \Delta t}\bigg{)}\rho gH_{HT}(t)\eta_{HT}(t)\bigg{)}. \tag{21}\]  
The turbine flow rate (obtained from the upper reservoir and stored in the lower reservoir) depends on the turbine efficiency and turbine head. The turbine model [40] calculates the flow rate of the turbine \(Q_{HT}(t)\) (m\({}^{3}\)/s) as a function of the input power, which cannot be higher than the nominal flow \(Q_{HT\_N}\) (m\({}^{3}\)/s). At the end of the time step, the lower reservoir cannot exceed \(V_{LR\_max}\) and the upper reservoir volume cannot be lower than \(V_{UR\_min}\).  
\[Q_{HT}(t)=\min\,\bigg{(}\frac{P_{HT}(t)}{\rho gH_{HT}(t)\eta_{HT}(t)},\ Q_{HT\_N}, \frac{V_{LR\_max}-V_{LR}(t)}{\Delta t},\frac{V_{UR}(t)-V_{UR\_min}}{\Delta t} \bigg{)}, \tag{22}\]  
where \(\eta_{HT}(t)\) represents turbine efficiency (which includes the multiplier efficiency and the generator efficiency) and is a function of the flow, which can be found in the turbine technical manual. Further, \(H_{HT}(t)\) (m) represents the turbine head, which is calculated as the static head in turbine mode \(H_{TS}(t)\) minus the head loss in the turbine mode \(H_{HTl}(t)\).  
\[H_{HT}(t)=H_{TS}(t)-H_{HTl}(t) \tag{23}\]  
A pump-turbine reversible machine was considered, in which the static head was defined in the same manner as that for the pump (Equation (10)). The loss in the turbine mode \(H_{HTl}(t)\) is calculated using the same equations as those for the loss in the pump mode (Equations (13)-(19)) using the turbine flow rate (\(Q_{HT}(t)\)).