esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Ricardo Wave Engine Modelling
### GT1548 Turbocharger Matching to Engine
#### 4.2.3 Turbocharger Speeds for Full Load Modelling  
To identify the correct turbocharger speeds to be used for each full load engine speed, a speed sweep on the turbocharger shaft speed was conducted from 4000 rpm to 150,000 rpm in intervals of 4000 rpm. While this speed sweep analysis excessively uses low speeds that would likely not be used for full loading, the data would be used for part load analysis to identify the correct speeds. The reason for this is, during a DoE optimum search procedure run in WAVE, it was identified that the throttle position had minimal impact on the flow rate. The results of the sweep to identify the full load turbocharger shaft speed are shown in Figures 11 and 12.  
Figure 11: Variation of engine torque during compressor speed sweep at different engine speeds.  
Figure 10: Scaled turbine map.  
The turbocharger speeds were found by interpolating the results for the torque and power that matched the provided data, and then, the average speed from both interpolations was used to identify the correct turbocharger speed for each engine speed. The choice of linear interpolation was used although the profiles of the sweeps are slightly curved. This can be observed in the figure depicting the comparison of power wherein a linear interpolation was used, since it can be reasonably assumed that, between relatively small changes in turbocharger speed (4000 rpm), the response is linear. This assumption is confirmed from Figures 11 and 12. For the lower engine speeds (1000 and 2000 rpm), it was noted that the shaft speed would not increase enough to provide the conditions to match the provided data. However, this is to be expected for a few reasons. Firstly, the scaling point of the map (5000 rpm), while chosen to ensure the design point operating conditions were matched to the provided data, may not have been ideal for the lower speeds, resulting in the map performing well at high engine speeds only. The second reason is that the provided data is likely the engine data with the stock turbocharger mounted; therefore, it is not easy to match all operating points of the turbocharger, as the GT1548 is designed to provide more power than the stock turbocharger. Another reason is due to the choice of assuming constant turbine efficiency for the map scaling process. This means the turbine is in theory producing less power as discussed in the previous section, thus resulting in the compressor not receiving enough power to reach higher shaft speeds.
## 3 Lillgrund Offshore Wind Farm  
The total rated power of the Lillgrund wind farm is 110.4 MW which consists of 48 Siemens SWT-2.9-93 wind turbines with a rated power of 2.3 MW each. The rotor diameter of the Siemens SWT-2.9-93 wind turbine is 92.6 m and the hub height is at 65 m. The wind farm layout is shown in Figure 4. The convention used herein to label each individual wind turbine is to assign them a letter and number, the letter represents the column in which the wind turbine is located ranging from A-H and the number represents the appointed row ranging from 1-8. A distinct feature of the Lillgrund wind farm is the very tight configuration of the wind turbines, with a separation distance of 6.6 rotor radii (\(R\)), for the columns and 8.6\(R\) for the rows. The reason behind this unusually compact inter-row spacing according to [42] is that the layout was initially developed for a smaller wind turbine model. This model was however unavailable when the construction permit for the wind farm was finally given. As a result, the developers decided to use a larger wind turbine while keeping the original layout. Another noteworthy feature of this layout is the gap in the middle of the farm where wind turbines D5 and E5 should have been placed. This gap increases the separation distance locally and will influence the total wake recovery and power production of the wind turbines downstream of the gap.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Domain**} & \multicolumn{3}{c}{**Inner Equidistant Region**} & \multicolumn{1}{c}{**Total Number of Cells**} \\ \hline \(\mathbf{Lx}\) & \(\mathbf{Ly}\) & \(\mathbf{Lz}\) & \(\mathbf{lx}\) & \(\mathbf{ly}\) & \(\mathbf{lz}\) & **Cell Size** & \\ \hline
86\(R\) & 86\(R\) & 11\(R\) & 54\(R\) & 60\(R\) & 5\(R\) & \(R/6\) & \(3.4\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Domain set-up.  
Figure 3: Top view of WindSim generated mesh. (Grid resolution at ground level, \(R\) is the wind turbine radius).  
The turbulence intensity per direction used in this study was estimated by data extracted from a met mast, located to the south-west as seen in Figure 4, during approximately a two-year period prior to the erection of the wind turbines (1 st of September 2013 to the 28 th of February 2016) [43]. The inflow directions on which this study will focus are shown in Table 3 along with the respective turbulence intensities at a 65 m height. Specifically, for the wind flow direction of 120\({}^{\circ}\), data are used from rows 3 and 5 at a wind velocity of 9 m s\({}^{-1}\). Similarly, for the 222
# Image-Based Feature Representation for Insider Threat Classification
## 3 Proposed Method
### Feature Vector Construction  
In the first step of our proposed approach, user access log pre-processing and feature extraction is performed. In this process, the log files that contain the raw resource access information of employees in the form of rows and columns are extracted to create a feature vector with useful and meaningful features. We used the popular CMU CERT Insider Threat dataset V4.2 [11] for simulation. The dataset comprises of various log files with information regarding the user's logon/logoff details (logon.csv), details of the user browsing history (http.csv), file access patterns (files.csv), external device usage within the organization (device.csv), and email communications sent/received by the user (email.csv). The feature vector is constructed using a frequency-based approach as in Reference [12; 13; 14; 15; 18]. Figure 2 depicts how the normalized feature vector is created from log files.  
The logon.csv is used to extract the information about computer usage, such as the session duration, the number of sessions within and outside of office hours, etc. This information provides insights to the computer access patterns of the user. The file.csv gives details about the files copied/read/written by the user on each day. This information is used to analyze the file access patterns of the user. Device.csv log file contains the external device access details of the user on each day. It indicates a user's access to any device within and outside of office hours. Usage of devices outside of office hours may be considered a suspicious activity.  
The email.csv file contains information about emails sent/received by the employee within and outside of office hours. It also indicates if the recipient/sender was an external/internal entity. The http.csv provides information about the user's browsing history. The log files described above are used to construct feature vectors. Each feature vector is comprised of 18 features, as shown in Table 1.  
In this paper, we use the features used in the paper [18]. The CMU CERT dataset provides the ground truth to label the users as malicious or non-malicious. The ground truth contains the details of user ID, the malicious events that have occurred, and the date/time of the event. There is a list of malicious users with their user IDs and the period in which their activity is marked as suspicious. Separate files are provided for each malicious user with the details of the activity that marked the user as a malicious insider, the timestamp of the event, and the log file.
## 3 Method
### Spherical Harmonics  
Spherical harmonics are the basis functions for irreducible representations of a group of rotations \(\mathsf{SO}(3)\) in \(\mathbb{R}^{3}\). These orthonormal functions form a basis of the Hilbert space of square-integrable function \(L^{2}_{\mathbb{R}}\big{(}S^{2}\big{)}\).  
Let us denote \(S^{2}\) a unit sphere. Any square-integrable function \(f:S^{2}\to\mathbb{R}\) can be represented as a sum [33] as follows:  
\[f(\theta,\varphi)=\sum\limits_{l=0}^{\infty}\sum\limits_{m=-l}^{l}f_{lm}Y_{lm} (\theta,\varphi)\]  
where spherical harmonics coefficients (or Fourier-Legendre's coefficients) are  
\[f_{lm}=\int\limits_{0}^{2\pi}\left(\int\limits_{0}^{\pi}\sin\theta\;f(\theta, \varphi)Y_{lm}(\theta,\varphi)\;d\theta\;\right)d\varphi \tag{3}\]  
and spherical harmonics \(Y_{lm}\) are defined by  
\[Y_{lm}(\theta,\varphi)=\begin{cases}(-  
Functions \(f\) and \(g\) could be expanded on a unit sphere \(S^{2}\) as  
\[f(\theta,\varphi)=\sum_{l=0}^{\infty}\sum_{m=-l}^{l}f_{lm}Y_{lm}(\theta,\varphi), \quad g(\theta,\varphi)=\sum_{l=0}^{\infty}\sum_{m=-l}^{l}g_{lm}Y_{lm}(\theta,\varphi) \tag{4}\]  
where  
\[\begin{array}{l}f_{lm}=\int_{0}^{2\pi}d\varphi\int_{0}^{\pi}d\theta\;\text{ sin}\theta\;g(\theta,\varphi)Y_{lm}(\theta,\varphi),\\ g_{lm}=\int_{0}^{2\pi}d\varphi\int_{0}^{\pi}d\theta\;\text{sin}\theta\;g( \theta,\varphi)Y_{lm}(\theta,\varphi).\end{array}\]  
If we only consider a specific degree \(l\) in Formula (4), we will obtain a partial reconstruction of the functions \(f\) and \(g\) for this degree. This reconstruction will use \(2l+1\) coefficients \(g_{lm}\) and \(f_{lm}\), \(m=-l\), \(\ldots\), \(l\) for the whole expansion.  
Expansions \(f\) and \(g\) are given by arrays of their spherical harmonic coefficients, \(\{f_{lm}\}\) and \(\{g_{lm}\}\). Let us denote the partial expansion of the degree \(L\) of the function \(f\) as \(f_{L}\) and \(g_{L}\) for \(g\) as follows:  
\[f_{L}(\theta,\varphi)=\sum_{l=0}^{L}\sum_{m=-l}^{l}f_{lm}Y_{lm}(\theta,\varphi ),\qquad g_{L}(\theta,\varphi)=\sum_{l=0}^{L}\sum_{m=-l}^{l}g_{lm}Y_{lm}( \theta,\varphi). \tag{5}\]  
Function \(f_{L}\) is defined by coefficients \(\{f_{lm}\}\) with \(l\leq L\). The norm of deviation of functions \(f_{L}\) and \(g_{L}\) could be defined as a cosine similarity of arrays \(\{f_{lm}\}\) and \(\{g_{lm}\}\) for \(l\leq L\).  
As we postulated, function \(g\) is a result of rotation \(R\) of function \(f\), and the results of the partial reconstruction will have the same relationship--they are the rotated versions of each other. For every degree \(l\) (as well as for any combinations of such degrees), the partial reconstructions of \(f\) and \(g\) in \(\mathbb{R}^{3}\) could be mapped by the same rotations \(R\).
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 2 Factors Impacting OPGW Lifespan
### Intrinsic Influences on OPGW Lifespan  
The lifespan of a fiber optic cable is largely dependent on the integrity of its core material, the fiber itself. Micro-cracks represent a critical factor influencing the overall lifespan of these fibers. These tiny fissures, stemming from manufacturing imperfections or extended exposure to environmental elements over prolonged use, possess the potential to gradually expand. As these fissures grow, they compromise the mechanical robustness of the optical fiber, eventually leading to its structural failure. Furthermore, sustained stress due to various adverse conditions also contributes to the gradual deterioration of optical fiber cable lifespan.
# Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device +
## 3 Results
### Optical Reconstruction Results  
Figure 10 shows the results of an optical experiment with a single color. Each method in Figure 10a-f corresponds to methods (a)-(f) in Table 2. Figure 10d has a higher contrast than Figure 10a. Compared to Figure 10a, applying the multi-random phase or pixel separation methods reduces speckle noise and improves the image quality in each reconstructed image. Subjectively, there is no significant difference in the quality of the reconstructed image using the multi-random phase method and the pixel separation method.  
\begin{table}
\begin{tabular}{c c l c} \hline \hline  & & **Methods** & **MSE** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} **Comparison** \\ **Method** \\ \end{tabular} } & (a) & Pixel pitch expansion & 9648.8 \\  & (b) & Pixel pitch expansion + Multi-random phase & 4509.9 \\  & (c) & Pixel pitch expansion + Pixel separation & 4316.4 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} } & (d) & Time-division reconstruction + Uneven color correction & 9640.3 \\  & (e) & Time-division reconstruction + Uneven color correction + Multi-random phase & 4107.0 \\ \cline{1-1}  & (f) & Time-division reconstruction + Uneven color correction + Pixel separation & 4085.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image quality evaluation of simulation results.  
Figure 9: Reconstruction images by simulation: the methods used in (**a–f**) correspond to Table 2.  
In Figure 10d, obtained using the proposed time-division method by averaging six reconstructed images, the projection time per one reconstructed image was about 5.3 ms. Thus, the projection time per average reconstruction was 31.8 ms in Figure 10e,f as six images were projected in each division block. The image quality of the reconstructed image can be improved by increasing the number of projections for each divided block. However, due to the upper limit used in the frame rate of DMD, it is necessary to determine the number of projections for each divided block so that flicker does not occur in the reconstructed image.
# An Improved Skewness Balancing Filtering Algorithm Based on Thin Plate Spline Interpolation
## 2 Materials and Methods  
The proposed filtering algorithm based on progressive TPS interpolation is explained in the following subsections. Section 2.1 introduces the principle of the traditional skewness balancing algorithm, while the main problem of this traditional unsupervised method is illustrated by Figure 1 in Section 2.2. To resolve this problem, Section 2.3 presents the principle of the proposed method and explains the detailed implementation procedure, including point cloud denoising, selecting ground seeds, TPS interpolation, and filtering judgment.
## 5 Evaluation Measures
### Miscellaneous
#### 5.4.6 Situational Awareness  
Situational awareness is mentioned in some studies, mainly with regard to AI-supported decision-making in dynamic, collaborative environments [66]. The concept refers to the understanding of elements in the environment, including their meaning and the ability to predict their future status [144]. However, intelligent systems could threaten situational awareness, as automation may lead users to check out and become over trusting [66]. To evaluate the role of explanations within such a context, [PERSON] et al. [66] and [PERSON] et al. [64], for instance, conducted experiments where participants worked with an AI system to solve collaborative tasks. In their experimental design, one group used a system with explanations, while the control group used it without explanations. In both groups, situational awareness was measured. In [PERSON] et al.'s [66] study, situational awareness was assessed by measuring how accurately participants could predict the behavior of their co-players in a game. Similarly, [PERSON] et al. [64] used the Situation Awareness Global Assessment Technique (SAGAT) [144], where participants were prompted at random points during a task with a series of fact-based questions to assess their knowledge of the current situation.
## 3 Results and Discussion
### Regional Comparison of the Energy Saving Achieved by Using Adaptive Setpoint Temperatures

Table 5: Energy saving achieved in the analyzed Greek cities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Energy Consumption Saving (kWh/Year)**} \\ \cline{2-4}
**City** & \multicolumn{1}{c}{**Cooling**} & **Heating** & **Total** \\ \cline{2-4}
**Aoosta** & **204.24** & **1084.31** & **1288.56** \\
**Bari** & **700.56** & **509.13** & **1209.69** \\
**Bologna** & **672.49** & **625.28** & **1297.77** \\
**Cagliari** & **848.81** & **350.98** & **1199.79** \\
**Campobasso** & **451.21** & **732.20** & **1183.41** \\
**Catanzaro** & **797.75** & **397.85** & **1195.59** \\
**Florence** & **636.49** & **519.75** & **1156.24** \\
**Genoa** & **767.03** & **445.21** & **1212.24** \\
**LAquila** & **776.30** & **540.22** & **1316.51** \\
**Milan** & **595.17** & **707.58** & **1302.75** \\
**Naples** & **724.73** & **426.26** & **1150.99** \\
**Palermo** & **980.20** & **223.24** & **1203.44** \\
**Perugia** & **666.04** & **578.03** & **1244.08** \\
**Potenza** & **353.65** & **940.69** & **1294.35** \\
**Rome** & **756.70** & **446.12** & **1202.81** \\
**Trento** & **617.32** & **720.22** & **1337.54** \\
**Trieste** & **670.65** & **560.01** & **1230.66** \\
**Turin** & **532.89** & **797.58** & **1330.47** \\
**Venice** & **623.05** & **634.71** & **1257.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Energy saving achieved in the analyzed Italian cities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Energy Consumption Saving (kWh/Year)**} \\ \cline{2-4}
**City** & \multicolumn{1}{c}{**Cooling**} & **Heating** & **Total** \\ \cline{2-4}
**Aveiro** & **345.90** & **483.52** & **829.42** \\
**Beja** & **708.61** & **466.41** & **1175.01** \\
**Braga** & **256.75** & **579.06** & **835.81** \\
**Braganca** & **442.92** & **775.50** & **1218.43** \\
**Castele Branco** & **621.81** & **578.06** & **1199.87** \\
**Coimbra** & **433.53** & **506.42** & **939.95** \\
**Evora** & **678.97** & **503.43** & **1182.41** \\
**Faro** & **892.65** & **241.81** & **1134.46** \\
**Guarda** & **126.63** & **1123.06** & **1249.69** \\
**Leiria** & **413.69** & **429.77** & **843.46** \\
**Lisboa** & **781.05** & **306.81** & **1087.86** \\
**Portalegre** & **310.35** & **493.64** & **803.99** \\
**Porto** & **640.37** & **624.21** & **1264.57** \\
**Santarem** & **634.48** & **363.84** & **998.32** \\
**Setubal** & **737.15** & **324.72** & **1061.87** \\
**Viana Castelo** & **353.28** & **463.47** & **816.75** \\
**Vila Real** & **415.88** & **701.77** & **1117.65** \\
**Viseu** & **328.34** & **767.78** & **1096.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Energy saving achieved in the analyzed Portuguese cities.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 4 Experimental Design and Result Analysis
Each obstacle scenario (initial, mid-borehole, terminal, double obstacles, lateral obstacle) was repeated five times to reduce randomness and improve result reliability. The evaluation criteria included: (1) convergence speed (number of steps to reach stable average reward); (2) trajectory smoothness (quantified by dogleg severity, with lower values indicating better smoothness); (3) obstacle avoidance success rate (percentage of trajectories that avoid all obstacles without collision); and (4) average reward value (comprehensive metric reflecting target accuracy, safety, and efficiency). The hyperparameter values listed in Table 2 were determined based on a combination of prior literature benchmarks in deep reinforcement learning (e.g., SAC implementations in control tasks), experimental tuning, and practical constraints of drilling optimization. The learning rate, discount factor, and entropy coefficient follow standard ranges recommended by prior studies [15; 27]. Parameters such as batch size, buffer size, and update frequency were fine-tuned through repeated pretraining trials to balance training stability and convergence speed. The hidden layer size [28] is a commonly used configuration that offers sufficient expressive power without excessive computation. These settings were found to provide robust performance across multiple experimental scenarios. The experimental process steps are as follows:  
Step 1: Generating 3D geological models with obstacles using WGAN;  
Step 2: Constructing the reinforcement learning environment by defining state space, action space, and multi-objective reward functions;  
Step 3: Training the SAC algorithm integrated with the self-attention mechanism;  
Step 4: Testing the trained model in typical obstacle scenarios (initial, mid-borehole, terminal, double obstacles, lateral obstacles);  
Step 5: Evaluating performance via metrics such as convergence speed, trajectory smoothness, and obstacle avoidance success rate. In actual drilling engineering, the downhole environment is often complex and changeable, often accompanied by the existence of obstacles such as faults, abandoned wellbores, and high-pressure abnormal zones. If the wellbore path crosses or approaches these areas, it may not only cause engineering accidents such as lost circulation and well collapse, but also greatly increase drilling costs and safety risks. Therefore, the design of the obstacle avoidance trajectory is of great significance for ensuring drilling safety and improving wellbore quality. Obstacle avoidance design not only requires avoiding risk areas, but also takes into account the constructability and target orientation of the trajectory. It is one of the core problems of modern intelligent wellbore trajectory planning to ensure a smooth trajectory and accurate target entry while meeting the safe space distance. A detailed pseudocode of the proposed SAC + Attention-based trajectory optimization method is provided in Appendix A to support reproducibility.
## 3 Results and Discussion
### Numerical Tests on Linear Time-Invariant Individual System Models
#### 3.3.1 Mathematical Models of Each Machine in the Chosen Plant System
\begin{bmatrix}T_{CPfo}\\ T_{CPo}\end{bmatrix} \) from the above equations become  
\[\textit{HWT}:\ \dot{x}_{5}=-\Bigg{(}\frac{c_{w}\dot{m}_{CP}+\eta_{CPf}\dot{m}_{ CPf}c_{CPf}}{M_{CP}c_{w}}\Bigg{)}x_{5}+\frac{\dot{m}_{CP}}{M_{CP}}y_{5}+ \frac{\eta_{CPf}\dot{m}_{CPf}c_{CPf}}{M_{CP}c_{w}}u_{5}.\]  
\[\textit{HP}:\ y_{51}=\eta_{CPf}x_{5}+\Big{(}1-\eta_{CPf}\Big{)}u_{5}.\]  
\[\textit{HWT output pipe}:\ y_{52}=(1-\eta_{PCP})x_{5}+\eta_{PCP}u_{1}.\]  
Solution of the Whole Process Equations  
Another condition is generally hypothesized as the no-delay condition; pumps' response time is small compared with controlled parameters variation, and the work to operate them is neglected. With the hypothesis above, we conclude that the flow rate does not vary within each circuit, so we have \(\dot{m}_{HPWT}=\dot{m}_{WTB}\) and \(\dot{m}_{RPWTi}=\dot{m}_{WTRPo}\) leading to  
\[\dot{m}_{HPWT}=\dot{m}_{WTB}=\dot{m}_{B}=\dot{m}_{HP}=\dot{m}_{H},\]  
\[\dot{m}_{RPWTi}=\dot{m}_{WTRPo}=\dot{m}_{RPC}=\dot{m}_{W},\]  
\[\dot{m}_{RPE}=\dot{m}_{RPC}=\dot{m}_{CP}=\dot{m}_{C}.\]Moreover, the presence of Algebraic Equations leads to the following system of DAEs:  
\[ \begin{cases}\dot{x}=Ax+Bv+Pu\\ y=Cx+Du+Ev\,,\\ v=My\end{cases} \]  
where:  
\[x= \begin{pmatrix}x_{1}^{(0)}\\ T_{HP}\\ T_{WT}\\ x_{4}^{(0)}\\ T_{CP}\end{pmatrix} u= \begin{pmatrix}T_{E}\\ \dot{m}_{CH_{4}}\\ T_{HPfi}\\ \dot{L}\\ T_{CPfi}\end{pmatrix} ,\]  
\[v= \begin{pmatrix}v_{1}\\ v_{2}\\ v_{31}\\ v_{32}\\ v_{41}\\ v_{42}\\ v_{5}\end{pmatrix} = \begin{pmatrix}T_{Bi}\\ T_{HPi}\\ T_{HPWi}\\ T_{RPVi}\\ T_{RPCi}\\ T_{CPi}\end{pmatrix} y= \begin{pmatrix}y_{1}\\ y_{21}\\ y_{22}\\ y_{31}\\ y_{32}\\ y_{41}\\ y_{42}\\ y_{51}\\ y_{52}\end{pmatrix} = \begin{pmatrix}T_{Bo}\\ T_{HPfo}\\ T_{HPfo}\\ T_{HPPo}\\ T_{WTBPo}\\ T_{RPCo}\\ T_{RPPo}\\ T_{CPPo}\end{pmatrix} .\]  
With the constant external input \(u\), we have the following analytical solution  
\[\begin{cases}x=e^{Kt}(x_{0}+K^{-1}P_{1}u)-K^{-1}P_{1}u\\ y=(I-EM)^{-1}(Cx+Du)\end{pmatrix},\]  
with:  
\[ \begin{array}{l}K=A+BM(I-EM)^{-1}C\\ P_{1}=P+BM(I-EM)^{-1}D\end{array} \]  
\(K\) contains two null rows and columns as two subsystems do not contain ODEs:  
\begin{tabular}{c c c c c c} \hline  & B1 & B2 & B3 & B4 & B5 & \\  & 0 & 0 & 0 & 0 & B1 \\ \cline{2-6}  & 0 & \(-\left(\frac{c_{w}\dot{m}_{H}+\gamma_{HP  
In this case, the explicit form of the analytical solution is  
\[x=\left\{ \begin{array}{c}T_{HP}=6.03-23.31\;e^{-0.00628\;t}+6.032\;e^{-0.0637\;t} \\ T_{WT}=77.81-27.64\;e^{-0.00628\;t}-10.17\;e^{-0.0637\;t}\\ T_{CP}=19.19+30.81\;e^{-0.005\;t}\end{array} \right..\]