esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Discussion  
In the collision avoidance techniques for UAVs, the collision avoidance algorithm is the key function module. This paper presents a CA algorithm for multiple cooperative fixed-wing UAVs, which provides an aggressive maneuver to avoid collision based on maneuver coordination and planned trajectory prediction. This algorithm consists of five modules: generating available maneuvers, predicting the corresponding planned trajectories, managing threats, evaluating combination of maneuvers and activating the maneuvers.  
The main advantages of the proposed algorithm are as follows: first, the algorithm presented in this paper eliminates the uncertainty of the traditional CA. This algorithm depends on the prediction of the planned trajectory instead of original trajectories. The former is determined and predictable, while the latter is unstable because the flying condition is easily broken, especially in high-dynamic environments, such as formation flying and clusters of aircraft. Second, the algorithm reduces the interference to the original trajectories to the greatest extent. The collision avoidance maneuver is activated at the last moment, with no more than 4 s before the collision. Third, the algorithm shows the greatest advantage in continuous conflicts in multiple, high-dynamic, and high-density environments and formation flying for rapid response, real-time, accuracy, low intervention and generality.  
Recommendations for future work are as follows: (1) further improve the stability and accuracy of aircraft controls, such as acceleration, angle acceleration and attitude angle, (2) further improve performances by solving continuous conflicts, especially in clusters of UAVs, and (3) further improve the operational efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **Number** & \
\begin{tabular}{c} **Control** \\ **Parameter** \\ \end{tabular} & **Maneuver** & **Time** & **Number** &
\begin{tabular}{c} **Control** \\ **Parameter** \\ \end{tabular} & **Maneuver** \\ \hline
9.8 & A & \(\begin{array}{c}(-8,-12,-8,\\ -6,0,-8)\end{array}\) & Roll to left & 9.8 & E & \(\begin{array}{c}(-8,-12,-8,\\ -6,0,-8)\end{array}\)  
**Author Contributions:** Conceptualization, Y.W. and J.T.; methodology, Y.W.; software, Y.W.; validation, Y.W.; formal analysis, Y.W.; investigation, Y.W.; resources, Y.W., J.T., S.L.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., J.T.; visualization, Y.W.; supervision, J.T., S.L.; project administration, J.T., S.L.; funding acquisition, J.T., S.L.  
**Funding:** This research received no external funding.  
**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# FEM-Based Compression Fracture Risk Assessment in Osteoporotic Lumbar Vertebra L1
## 2 Methods and Materials
### Risk Evaluation  
The following three random variables were used to evaluate risk: external load \(P\), the cortical shell thickness of lumbar verteb \(\Delta\), and bone volume to total volume ratio \(\beta_{BVTV}\) which was obtained from TBS interdependence. In contrast, TBS was obtained from the 2-D vertebra CT scan. These variables were used for solving the FEM for the proposed mechanical model strength of the lumbar vertebra. Finally, the obtained set of points values for different combinations of independent variables were used for least square fitting by cubic polynomial as follows  
\[\sigma_{s}(\beta,\delta,p)=\sum_{i,j,k=0}^{i+j+k\leq 3}a_{ijk}\beta^{i}\delta^{j}p ^{k} \tag{5}\]  
where \(p\) is the external load, \(\delta\) is the cortical shell thickness, and \(\beta\) is the BV/TV ratio.  
Furthermore, the approximation was obtained by using logarithmic values of FEM stresses \(\log(\sigma_{vontMises})\) by searching minima of the following expression:  
\[\min|\sum_{i,j,k}\log(\sigma_{vontMises}(i,j,k))-\sigma_{s}(\beta_{i},\delta_ {j},p_{k})| \tag{6}\]  
Figure 4: Time curve of dynamic load.  
Figure 3: Load conditions of the compression test.  
where indexes \(i,j,k\) denote values of discrete independent variables used in FEM computations.  
Fracture risk \(F_{R}\) of lumbar vertebra can be expressed in term of reliability:  
\[R=P(Z\leq 0)=1-F_{R}(Z\leq 0), \tag{7}\]  
where \(Z=X_{\sigma}-Y_{\sigma_{Y}}\), \(X_{\sigma}\) is max stress, and \(Y_{\sigma_{Y}}\) is yield strength. The general expression for the fracture risk of a stress-strength system has the following cumulative density function (CDF) [29]:  
\[F_{R}=1-P(Z\leq 0)=1-\int_{0}^{\infty}\int_{0}^{\infty}f_{X_{\sigma}}(\sigma+z)f _{Y_{\sigma_{Y}}}(\sigma)d\sigma dz \tag{8}\]  
where \(f_{X_{\sigma}}\) and \(f_{Y_{\sigma_{Y}}}\) are probability density functions (PDFs) of the random values \(X_{\sigma}\) and \(Y_{\sigma_{Y}}\) of the maximum stress caused by the external load \(p\) and the strength, respectively.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 5 Assets in HE
### Student and Employee Personal Identifiable Information (PII)  
[PERSON] writes that Universities _\"store thousands of pieces of information from each student, teacher and staff member. Bank accounts, addresses, school transcripts and other valuable data\"_[42]. Additionally, PII processed in HE can include information about applicants, students, employees, guests, alumni and participants in research projects [6]. Several of the information assets are reoccurring in the review, such as PII, research data, and student records. [PERSON] and [PERSON] [50] propose the following assets as critical for cybersecurity: Students' PII such as email id, contact number and financial information, and records. Admission, examination, and administration details, together with employee PII. Lastly, the authors describe financial data as a critical asset. FireEye [5] lists PII and distinguishes health and medical information from PII as they are handled at student health centers. FireEye also adds law enforcement data as a part of the assets that must be protected, as _\"many schools have their own forces that keep records on students who run into trouble on campus\"_. NCSC [10] writes that bulk PII (Personal Identifiable Information) on staff and students are typical targets of a cyberattack. [PERSON] [53] conducted an in-depth analysis of the type of data records lost in her breach investigation. The results illustrate the diversity of the University PII portfolio Figure 1. PII about both students and employees are processed for multiple purposes at the University, and Table 4 shows that PII is mentioned in most of the reviewed sources.  
\begin{table}
\begin{tabular}{l l l} \hline
**KPI** & **Critical Information Assets** & **Reference** \\ \hline Enrollment \& Graduation & Student PII and records & [5,6,9,10,40–42,50,51,53,54,67] \\  & Learning and teaching information & [40,41,50,54] \\  & Financial management information & [5,6,9,40–42,50,53,54,67] \\ \hline Stakeholder satisfaction & Sensitive Research information/data and IP & [5,9,10,40–42,50,51,54,67] \\  & Government and Third-party data & [5,67] \\ \hline Employee \& HR & Employee \& Student PII & [5,9,10,40–42,50,51,54,67] \\  & Administration details & [40,41,50,51,54,67] \\  & User and administrator accounts & [6,8,9,45,53,58,65,66] \\ \hline IT Supporting services & Bandwidth and Internet Connection & [6,9,10,51] \\  & Computing power and resources & [6,9,10,52] \\  & Communication systems and data & [6,10,52,53] \\ \hline \end{tabular}
\end{table}
Table 4: Proposition of the most valuable information assets based on KPI. References for each asset is listed in the right column.  
Figure 1: Overview of breached data record types (\(y\)-axis) and percentages (\(x\)-axis) from Mello [53], approximate \(n=1150\).
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 4 Experiments and Results
### Evaluation Metrics  
* SLA violation  
In our experiment, the QoS is defined via SLA violation, as the ratio of unallocated resources demanded by applications and the total requested resources. The unallocated resource can be calculated as the difference between the requested resource (\(R^{*}_{p_{i}p_{i}}\)) of all VMs and the actual allocated resources (\(U^{*}_{pmi}\)) of all PMs (1). Here * can be represented as CPU, RAM and Bandwidth, respectively and each parameter of this metrics considers the whole life time on each VM.  
\[\begin{split} SLA=\ \frac{\sum_{j=1}^{N}\int_{t}^{T}\left(U^{*}_{pmj}(t)- \sum_{i=1}^{M}R^{*}_{p_{i}p_{i}}\right)dt}{\sum_{j=1}^{N}\int_{t}^{T}U^{*}_{pmj }(t)dt}\end{split} \tag{11}\]  
where \(M\) is the number of VMs in \(PM(j)\), \(N\) is the number of PMs.  
* Energy consumption  
Our method will take CPU utilization in priority when considering energy-efficiency, compared to the RAM and Bandwidth. Research on [15] shows that an idle host consumes approximately 70% power of a fully utilized one and the energy costs of each server can be described on both of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Number of PMs & & 50 \\ Number of VMs & & 200 \\ CPU core performance of PMs & MIPS & \{2000, 2500, 3000, 4000\} \\ Memory of each PM & G & \{1, 2, 4, 8] \\ Bandwidth of each PM & Mbps & \{500, 800, 1000\} \\ CPU core performance of VMs & MIPS & \{1000, 1500, 2000, 3000\} \\ Memory of each VM & G & \{0.5, 1, 2, 4] \\ Bandwidth of each VM & Mbps & \{100, 200, 400\} \\ initial threshold of \(T_{l}\) & \(<t_{c},\ t_{r},t_{b}>\) & \(<0.3,0.2,0.2>\) \\ initial threshold of \(T_{u}\) & \(<T_{c},\ T_{r},T_{b}>\) & \(<0.7,0.8,0.8>\) \\ initial threshold of \(T_{SLA}\) & & 4\% \\ initial threshold of \(T_{m}\) & & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment configuration table.  
power cost and the CPU cost, which can be calculated within a linear relationship. Based on that, we defined our power consumption metrics as follows (2), where \(P_{max}\) is set to 250 W and c represents CPU utilization.  
\[P(c)=0.7\cdot P_{max}+0.3\cdot P_{max}\cdot c=P_{max}\cdot(0.7+0.3\cdot c) \tag{12}\]  
Furthermore, according to each different workload, the changing utilization of CPU can be represented as a function of time: \(c(t)\), Therefore, \(E\) in (3) is used to define the total energy consumption by a host.  
\[E=\int\limits_{t}^{T}P(c(t))dt \tag{13}\]
## 4 Discussion
### Odor Thresholds  
First of all, it must be noted that the odor threshold values compiled from the literature (Table 6) are from different studies, which employed different methods and present variations of many folds. Furthermore, they were determined at room temperature using water as a matrix. In the case of mate tea _chimarnao_-type infusions, prepared with warm/hot water, these values change greatly once the vapor pressure of a substance increases exponentially with the temperature [11]. Therefore, caution is necessary when considering these data.  
In between the values at the extremes of Table 6, a difference of 3.2 million fold can be observed. This indicates that the perception of the different volatiles is greatly driven by these values. Some of them possess such low odor thresholds that their occurrence above the threshold and consequent odor contribution already become likely upon their detection by a gas chromatograph, which is frequently less sensitive than the human olfactory system [11]. Examples of these compounds with extremely low values are: damascenone <(Z)-\(\beta\)->, damascone <(E)-\(\beta\)->, ionone <(E)-\(\beta\)->, 2-decenal <(E)->, octanal, ionene, <\(\alpha\)->, hexanal, decanal <n>, ionone <(E)-\(\alpha\)->, 2-octenal <(E)->, cyclocitral <\(\beta\)->, and linalool. On the other hand, compounds at the bottom of the table are more unlikely to impart their specific individual notes, e.g., camphene, methyl hexadecanoate, menthol, and menthene.
## 4 GPS L1/L5 Receiver: L5 Pattern Assumption
### Received Signal Power Levels  
Figures 19-22 show the four highest received power levels of the GPS L5Q signals at the receiver position as a function of the altitude, during the full considered trajectory, when assuming a 0 dBi receiver antenna gain, for the three assumed L5 antenna patterns. As we can see from these figures, the highest and the second-highest received signal powers have similar plots for the three assumed L5 patterns, while the third- and fourth-highest received signal powers plots are more different. From the four figures, we can notice that assuming L5 pattern 1 would result in higher received signal powers, always at the end of the MTO, and also in general for the fourth-highest received power. Again, the most important plot is the fourth-highest received power, in order to have at least four signals, and therefore to be able to compute the navigation solution. From Figure 22 we can see that when assuming L5 pattern 1 the signal power level is never smaller than \(-169\) dBm; when assuming L5 pattern 2 it goes below \(-169\) dBm at a few points at the very end of the trajectory; and when assuming L5 pattern 3 the received signal power level is below \(-169\) dBm for a considerable amount of time after 310,000 km altitude. Moreover, most of the time, the highest fourth signal power has higher values in the case of L5 pattern 1, while it has lower values in case of L5 pattern 3 for most of the time. In the next session we will investigate the signals' availability, in order see the impact of the three assumed L5 patterns.  
Figure 19: Highest received power level of GPS L5 signals, during the considered MTO, for a 0 dBi receiver gain pattern.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Portion**} & \multicolumn{4}{c}{**L1 Antenna Pattern 3D**} & \multicolumn{4}{c}{**L1 Antenna Pattern 2D**} \\ \cline{2-9}  & **1** & **2** & **3** & **All MTO** & **1** & **2** & **3** & **All MTO** \\ \hline Position & **Mean** & 2.93 & 16.57 & 88.45 & 36.12 & 3.16 & 22.18 & 89.86 & 45.07 \\ error & **Std** & 2.51 & 11.26 & 60.04 & 35.09 & 2.86 & 15.70 & 59.82 & 44.31 \\ (m) & **Max** & 30.42 & 72.42 & 232.03 & 232.03 & 25.33 & 98.61 & 302.79 & 302.79 \\ \hline Velocity & **Mean** & 0.008 & 0.017 & 0.097 & 0.027 & 0.006 & 0.015 & 0.108 & 0.033 \\ error & **Std** & 0.005 & 0.007 & 0.030 & 0.025 & 0.004 & 0.007 & 0.035 & 0.029 \\ (m/s) & **Max** & 0.021 & 0.041 & 0.205 & 0.205 & 0.018 & 0.039 & 0.208 & 0.208 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of the range-based orbital filter estimation error.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 4 Result and Analysis
### Application
In the case of software encryption, the data processing and transmission times can be reduced by 77.21%, 72.26%, 49.11%, and 14.45%, respectively, whereas in the case of hardware encryption, the data processing and transmission times can be reduced by 69.68%, 64.28%, 48.84%, and 21.87%, respectively.
## 1 Introduction
Finally, its performance has been evaluated with 10 Gb/s NRZ traffic transmitted through non-dispersion compensated 25 km Standard Single Mode Fiber (SSMF), achieving error free operation with a 1.43 dB negative power penalty, validating its potential to perform successfully in realistic burst-mode traffic conditions where both propagation losses and dispersion accumulation vary on a per packet level.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Ablation Study
#### 4.5.4 Effect of ADIIoU  
To tackle the challenges arising from object scale variations in RSI, the auxiliary dynamic IoU (ADIoU) loss function is proposed as the localization loss in our model. By incorporating ADIoU, the mAP further improves from 96.79% to 97.12% on RSOD and from 96.01% to 96.56% on NWPU VHR-10. These results confirm that ADIIoU effectively enhances the model's accuracy by dynamically adjusting the impact of scale and position during localization. To better showcase the interpretability of ADIIoU, we perform a qualitative comparison on the RSOD dataset, as illustrated in Figure 9. This includes visualizing the detection outcomes of ProSAF-Net both with and without ADIIoU, in addition to those from the baseline model. Additionally, to highlight the optimization effect of ADIIoU on object scale variations, all selected images contain objects with varying scales. As observed in Figure 10, compared to the third-row images, the fourth-row images demonstrate overall improved detection performance after adopting ADIIoU as the localization loss. Moreover, the results exhibit a noticeable enhancement in contrast to the baseline, providing additional evidence of ADIIo's effectiveness.  
Figure 10: Qualitative comparison of detection results: (**a**) original image, (**b**) baseline model detection, (**c**) ProSAF-Net detection without ADIIoU, and (**d**) ProSAF-Net detection with ADIIoU.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam  
The Kirchhoff-Love hypothesis [27] was used as the basic assumption to derive the relationship between the strain and displacement of the laminated T-beam. The lateral centroid position of the beam was also identified using the stress-strain relationship. Subsequently, the effective stiffness of the 1D T-beam was deduced using the relationship between the resultant moment and stress. Furthermore, the stress on each layer of the flange was calculated.  
The laminated T-beam was assumed to comprise two laminates of the flange and web. The stress and moment of the beam were calculated according to the laminated beam theory. The simply supported beam was assumed to consist of two types of beams: narrow-flange and wide-flange beams. Owing to the non-symmetrical structure of the T-beam geometry, the centroid position of the structure varied with the sequence and shape of the laminates. Once the centroid position was derived using the beam theory, the effective stiffness of the beam could be calculated by the sum of the moments of the web and flange. Subsequently, the deflection and stress of the symmetric and non-symmetric laminated T-beams were deduced.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 7 Performance Evaluation
A long traveling path of a mobile agent is likely to induce a high package loss rate. Figure 10. The traveling path length of the mobile agent. Figure 9. Total energy consumption of the network. The input of PSO needs to use the output of ACO. Specifically, when we construct the particles \(P\) which represent anchor nodes, we need to use the sequence the ACO created to order the CHs. We also compare it with the anchor nodes ordered in a random way. The simulation result is shown as Figure 12. We can clearly see that the schema without ACO performs worse than the schema using ACO. The upper limit of the schema which doesn't adopts ACO is close to twice that of the schema which adopts ACO. Even if the sets of anchor nodes in the two schemas are the same, when the mobile agent travels them in a different order, the result could be very different. That is also the reason why we need to combine the ACO with PSO rather than only using PSO algorithm.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Finite-Element Model of the Laminated Composite Beam
#### 2.1.3 Variational Formulation
Similarly, using Equations (2)-(9), the first variation of the mechanical and electrical potential energy for a piezoelectric element can be written as:\[\delta U_{m}^{e}=\int\limits_{0}^{L_{x}}\int\limits_{A_{b}}\Bigl{[}( \delta\varepsilon_{x})\sigma_{x}^{(b)}+(\delta\gamma_{xz})\tau_{xz}^{(b)}\Bigr{]} Addx+\sum\limits_{i=1}^{2}\int\limits_{0}^{L_{x}}\int\limits_{A_{p_{i}}}\Bigl{[}( \delta\varepsilon_{x})\sigma_{x}^{(p_{i})}+(\delta\gamma_{xz})\tau_{xz}^{(p_{i })}\Bigr{]}dAdx\] \[=\int\limits_{0}^{L_{x}}[\delta\varepsilon_{x}^{0}A_{11}\varepsilon _{x}^{0}+\delta\varepsilon_{x}^{0}B_{11}\kappa_{x}+\delta\kappa_{x}B_{11} \varepsilon_{x}^{0}+\delta\kappa_{x}D_{11}\kappa_{x}+\delta\gamma_{xz}^{0}A_{5 5}\gamma_{xz}^{0}]dx\] \[-\int\limits_{0}^{L_{x}}[\delta\varepsilon_{x}^{0}A_{31}^{(p)}+ \delta\kappa_{x}B_{31}^{(p)}\varepsilon_{x}^{0}]\Bigr{]}E_{z}dx= \tag{17}\] \[=\int\limits_{0}^{L_{x}}(\delta\overline{\varepsilon})^{T} \mathcal{D}\overline{\varepsilon}dx-\int\limits_{0}^{L_{x}}(\delta\overline{ \varepsilon})^{T}\mathcal{E}^{(p)}Edx\] \[\delta U_{E}^{e}=\int\limits_{0}^{L_{e}}\int\limits_{0}^{b}\int \limits_{0}^{-\frac{h_{p}}{2}-h_{p_{1}}}\Bigl{(}\delta E_{2}^{(1)}\Bigr{)}D_{ z}^{(p_{1})}dzdydx+\int\limits_{0}^{L_{e}}\int\limits_{0}^{b}\int\limits_{\frac{h_{p}}{2} +h_{p_{2}}}^{\frac{h_{p}}{2}+h_{p_{2}}}\Bigl{(}\delta E_{2}^{(2)}\Bigr{)}D_{z} ^{(p_{2})}dzdydx\] \[=\sum\limits_{i=1}^{2}\delta E_{2}^{(i)}\int\limits_{0}^{L_{e}} \Bigl{[}A_{31}^{(p_{i})}\varepsilon_{x}^{0}+B_{31}^{(p_{i})}\kappa_{x}+A_{33} ^{(p_{i})}E_{z}^{(i)}\Bigr{]}dx\] (18) \[=\int\limits_{0}^{L_{e}}\left\{ \begin{array}{c}\delta E_{z}^{(1 )}\\ \delta E_{z}^{(2)}\end{array} \right\}^{T}\!\!\left[ \begin{array}{cc}A_{31}^{(p _{1})}&B_{31}^{(p_{1})}&0\\ A_{31}^{(p_{2})}&B_{31}^{(p_{2})}&0\end{array} \right]\left\{ \begin{array}{c} \varepsilon_{x}^{0}\\ \kappa_{x}\\ \gamma_{xz}^{0}\end{array} \right\}dx+\int\limits_{0}^{L_{e}}\left\{ \begin{array} []{c}\delta E_{z}^{(1)}\\ \delta E_{z}^{(2)}\end{array} \right\}^{T}\!\!\left[ \begin{array}{cc}A_{33}^{( p_{1})}&0\\ 0&A_{33}^{(p_{2})}\end{array} \right]\left\{ \begin{array}{c}E_{z}^{(2)}\\ E_{z}^{(2)}\end{array} \right\}dx\] \[=\int\limits_{0}^{L_{e}}\delta\mathcal{E}^{T}\mathcal{E}^{(p)}{}^ {T}\overline{\varepsilon}dx+\int\limits_{0}^{L_{e}}\delta\mathcal{E}^{T} \mathcal{G}^{(p)}Edx\]  
In the above equations, the cross-sectional coefficients of the composite are given by:  
\[A_{11}=A_{11}^{(b)}+\sum\limits_{i=1}^{2}A_{11}^{(p_{i})},\ B_{11}=B_{11}^{(b )}+\sum\limits_{i=1}^{2}B_{11}^{(p_{i})},\ D_{11}=D_{11}^{(b)}+\sum\limits_{i=1 }^{2}D_{11}^{(p_{i})},\ A_{55}=A_{55}^{(b)} \tag{19}\]  
with  
\[\Bigl{[}A_{11}^{(a)}\ B_{11}^{(a)}\ D_{11}^{(a)}\Bigr{]}=b\int\limits_{z}Q_{1 1}^{(a)}\Bigl{[}1\,z\,z^{2}\Bigr{]}dz,\ a=b,p_{1},p_{2}\ A_{55}=k_{sc}b\int \limits_{z}Q_{55}dz \tag{20}\]  
\[\Bigl{[}A_{31}^{(p_{i})}\quad B_{31}^{(p_{i})}\Bigr{]}=b\int\limits_{z}\widehat {\varepsilon}_{31}^{(p_{i})}\bigl{[}1\quad z\bigr{]}dzdy,\ i=1,2 \tag{21}\]
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## 3 Simulation of MED-TVC Desalination Plant
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Actual Design** & **Simulation** & **\%Error** \\ \hline Distillate (ton/h) & 3768 & 3771.753 & 0.10\% \\ \hline Brine (ton/h) & 9442.708 & 9428.249 & 0.15\% \\ \hline Performance ratio (PR) & 14.62 & 13.86 \({}^{*}\) & 5.2\% \\ \hline \hline \end{tabular} \({}^{*}\)The performance ratio (PR) is calculated using the simulation results as in Equation (6).  
\[\text{PR}=\frac{\dot{m}_{\text{Distillate}}}{\left(\frac{\dot{m}_{\text{motive  steam}}\left(\frac{\dot{m}_{\text{motive steam}}-\dot{l}_{\text{cooling  water}}}{2326\text{ kJ}}\right)}\right)}=\frac{3771.753}{\left(\frac{238.050 \left(2797.16-138.63\right)}{2326\text{ kJ}}\right)}=13.86 \tag{6}\]  
\end{table} 
Table 5: Comparison between the actual design of the exiting MED plant and the simulation results.