esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 5 Experimental Evaluation and Discussion
### Evaluation Criteria  
Receiver operating characteristic (ROC) curves and the area under ROC curve (AUC) are used as performance measurement methods. The ROC curve is a graphical plot that illustrates classification characteristics of a classifier as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR) for all possible values of the discrimination threshold. This means any concrete threshold cannot be chosen as the ROC curve captures all possible choices.  
The AUC performance measure is simply the area under a ROC curve. It summarizes classifier performance as one number inside the [0, 1] interval, where 1 is perfect classification and 0.5 is equivalent to the performance of random guessing. Another interpretation of AUC is that it is equal to the probability that a classifier would rank a randomly chosen positive sample higher than a randomly chosen negative sample. For convenience, AUC scores are often written as percentages.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### SLR Validation
#### 3.3.3 QZSS  
The SLR residuals of QZSS satellites are shown in Figure 11 with respect to the beta angle. The mean bias and STD values of the SLR residuals are summarized in Table 11. SLR residuals with absolute values larger than 1 m are regarded as outliers and are excluded from the statistics.  
For J01, JAX showed the smallest STD values of 12.4 and 4.6 cm during the ON and YS periods, respectively. Again, the orbit accuracy degraded significantly once the attitude mode switched to ON mode. With the SRP model designed for the ON period (applied since DOY198 (July), 2018), the J01 orbit of COD significantly improved by 49% in terms of STD values [36]. WUM also established an a priori SRP model for J01 POD to enhance the ECOM model, and an additional three parameters were used to compensate for the remaining modeling deficiencies during ON periods [37]. As a result, small STD values of 18.2 and 7.1 cm were achieved in the ON and YS modes, respectively. As for GFZ, the SLR residuals of J01 showed clear half-year periodical variations, and the absolute values increased as the absolute values of the beta angle increased. Further, GFZ showed the most outliers exceeding an absolute value of 1 m during YS periods, which may have been caused by insufficient SRP modeling.  
For J02 and J03, ESM showed the smallest STD values of 6-7 cm, while WUM exhibited large STD values of 16-19 cm and large mean biases of 22-33 cm. As for GFZ, the STD values improved by about  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **COD** & **ESM** & **GFZ** & **JAX** & **TUM** & **WUM** \\ \hline J01(ON) & \(\begin{matrix}16.1\pm 27.3\end{matrix}\)1 [FOOTNOTE:1]Footnote 1: Statistics before DOY198 2018. 2 Statistics after DOY198 2018. 3 Statistics before DOY330 2018. 4 Statistics after DOY330 2018. 5 Statistics after DOY330 2018. 5 Statistics before DOY281 2018. 6 Statistics after DOY281 2018. & 6 Statistics after DOY281 2018.  
65% beginning DOY330 2018. Considering that improvements of 45% and 19% were also observed for J01 during ON and YS periods, respectively, a refined SRP model (instead of ECOM) was assumed to be used for the QZSS IGSO POD in GFZ solution beginning DOY330 2018. TUM also improved SRP modelling for QZSS by using a priori box-wing models beginning DOY281 (July) 2018, and the STD values were reduced by 50% and 83% for J02 and J03, respectively. For the GEO satellite J07, GFZ and WUM showed large STD values of 16.9 and 19.0 cm.
## 3 Methods
### Machine Learning Models
#### 3.3.1 Regression Model Used to Predict the Quantitative Parameters  
A Multiple Linear Regression(MLR) model was used to predict quantitative parameters. An MLR model predicts the linear relationship between a dependent variable and other variables. A multiple linear regression model with \(p\) predictor variables \(x1\), \(x2\),..., \(xp\) and response \(Y\), can be formulated as  
\[Y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+...\ \beta_{p}x_{p}+\epsilon, \tag{5}\]  
where \(\beta_{0},\beta_{1},...,\beta_{p}\) are known as model coefficients or parameters and \(\epsilon\) is a noise term which is a random error. Training data can be used to estimate \(\hat{\beta_{0}},\hat{\beta_{1}},...,\hat{\beta_{p}}\), and the coefficients being known, predictions can be made using the following equation:  
\[\hat{y}=\hat{\beta_{0}}+\hat{\beta_{1}}x_{1}+\hat{\beta_{2}}x_{2}+...+\hat{ \beta_{p}}x_{p}, \tag{6}\]  
where \(\hat{y}\) represents a prediction of \(Y\) on the basis of \(X=x\). In the previous equation, the hat\({}^{*}\) symbol refers to the estimated coefficients or predicted response. Values of \(\beta\) must be estimated. The least square approach is used in this model to minimise the following equation:  
\[RSS=\sum_{i=1}^{n}(Y_{i}-\hat{y}_{i})^{2}=\sum_{i=1}^{n}(Y_{i}-\hat{\beta_{0}}- \hat{\beta_{1}}x_{i1}-\hat{\beta_{2}}x_{i2}-...-\hat{\beta_{p}}x_{ip})^{2}. \tag{7}\]  
The following metrics have been selected from Table 2 to evaluate regression model:
# Investigation of Quadrate Fresnel Zone Plates Fabricated by Femtosecond Laser Direct Writing
## 4 Conclusions  
In this paper, the structure of circular FZP was optimized, and the diffraction performances of quadrate FZPs were studied. Theoretically, the relationship between half side length \(L_{n}\) of S-FZP and radius of circular FZP \(r_{n}\) is determined by \(L_{n}=r_{n}\), and the focal length formula of S-FZP is derived as \(f_{\text{Q-FZP}}=l_{1}^{2}/\lambda\).  
The similarities and differences of square, orthogonal, linear, and circular FZPs were studied through simulations and experiments. The quadrate FZPs can achieve linear and cross focus. The influence of zone number on diffraction patterns was also studied. As the number of zones increased, the focal depth and focal point became smaller, and the focal point's peak reached a larger value. By shifting the phase of quadrate FZPs, the double-cross lines focusing could be realized. Square Fresnel zone plate patterns were obtained by adjusting phase shift and transmittance parameters. Compared with circular FZPs, the quadrate FZPs have more vital stability. They are more suitable for rectangular pixel arrays with beam shaping, optical vortex, array generator applications, and other fields. By adjusting the phase shift parameter in different directions, the rectangular PB-FZPs were fabricated. The simulated results were consistent with the experimental results in diffraction performance.  
Writing-original draft preparation, X.S.; writing-review and editing, F.Z.; investigation, F.Z. and L.D.; methodology, X.S. and L.D.; data curation, J.-a.D.; validation, X.S., F.Z. and L.D.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Nos. 51875584, 51875585, and 51935013).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Head Election Strategy
#### 4.2.1 Adaptive Node Degree  
In order to improve the utilization of network bandwidth resources, the number of neighbor nodes around the CH is controlled. The adaptive node degree [12] is used in this paper to avoid network congestion. The degree is positively correlated to the probability of a node being selected as the CH. In the network, each node obtains the node degree of itself by exchanging HELLO messages. The node degree of node \(i\) is defined as  
\[d_{i}=\sum_{j=0}^{N}d_{j}^{i}, \tag{9}\]  
\[d_{j}^{i}=\begin{cases}0&d_{ij}\geq r\\ 1&0<d_{ij}<r,\end{cases} \tag{10}\]  
where \(d_{ij}\) is the distance between the node \(i\) and the node \(j\), and \(r\) is the node communication radius. The adaptive node degree difference is defined as  
\[\varepsilon_{i}=|d_{i}-\frac{N}{n}|, \tag{11}\]  
where \(n\) is the optimal number of clusters determined by (8). After normalization, the adaptive node degree of node \(i\) is  
\[D_{i}=e^{-\varepsilon_{i}}. \tag{12}\]
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DEM & Digital Elevation Model \\ DOP & Dilution Of Precision \\ DTE & Direct-to-Earth \\ ELFO & Elliptic Lunar Frozen Orbits \\ EKF & Extended Kalman Filter \\ ENU & East-North-Up \\ GNC & Guidance Navigation Control \\ GNSS & Global Navigation Satellite System \\ IMU & Inertial Measurement Unit \\ INS & Inertial Navigation System \\ LNS & Lunar Navigation Service \\ LLO & Low Lunar Orbit \\ LME2000 & Lunar Mean Equator at J2000 \\ LPO & Lagrange Point Orbits \\ LoS & Line of Sight \\ ODTS & Orbit Determination and Time Synchronisation \\ PID & Proportional Integral Derivative \\ RF & Radio Frequency \\ RWFM & Random Walk Frequency Modulation \\ RMSE & Root Mean Squared Error \\ SISE & Signal In Space Error \\ SHE & Spherical Harmonic Expansion \\ SRP & Solar Radiation Pressure \\ WFM & White Frequency Modulation \\ \end{tabular}
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 1 Introduction
### Requirements  
For the purpose of design and implement the MViDO system, it would be necessary to find a specific solution to meet certain requirements. One of the requirements for the development of that system was that its implementation was done on a computer that was small enough to be accommodated onboard the AUV-embedded low-cost hardware. Such a requirement implied a more limited processing feature, i.e., a high performance (low computationally demanding) system. By commitment to the control system of the AUV, the NViDO system should not exceed 0.011 s to process each frame received from the camera. Another requirement was that the system be based only on a single camera so that the AUV dynamics were the least changed possible, i.e., it was not intended to alter the MARES body, so any extra equipment (sensor) to be installed in the vehicle would necessarily have to be incorporated into the housing available for that purpose. On the other hand,the space on board the vehicle is limited and the energy available as well. The housing available to add a sensor only supports a camera and a light source. Furthermore, using more than one camera would create more drag and consume more energy. These were the starting points for the development of the MViDO system.  
Components Specifications  
The developed system is integrated as an independent module in the MARES AUV system. All the developed algorithms, presented here, were implemented on a Raspberry Pi 2 model B, a computer that is small enough to be accommodated onboard the MARES AUV. The single camera used for the vision system was a Bowtech underwater camera with a Sony 1/3\({}^{\prime\prime}\) EX-View HAD CCD sensor. The camera and lens specifications are those shown in Tables 1 and 2.
## 3 The Investigated Rotor System and Its Modeling
### The Equations of Motion of the Model System  
Lateral vibration of the rotor is governed by a set of differential equations of the first and second orders.  
\[m\ddot{y}+(b_{p}+b_{M})\dot{y}-b_{M}\dot{y}_{D}+ky-ky_{D}+\dot{\vartheta}b_{M}z- \dot{\vartheta}b_{M}z_{D}=me_{T}\dot{\vartheta}^{2}\cos(\vartheta+\psi)+me_{T} \ddot{\vartheta}\text{sin}(\vartheta+\psi), \tag{9}\]  
\[m\ddot{z}+(b_{p}+b_{M})\dot{z}-b_{M}\dot{z}_{D}+kz-kz_{D}-\dot{\vartheta}b_{M}y +\dot{\vartheta}b_{M}y_{D}=me_{T}\dot{\vartheta}^{2}\sin(\vartheta+\psi)-me_{ T}\ddot{\vartheta}\text{cos}(\vartheta+\psi)-mg, \tag{10}\]  
\[-b_{M}\dot{y}+b_{M}\dot{y}_{D}-ky+(k+2k_{D})y_{D}-\dot{\vartheta}b_{M}z+\dot{ \vartheta}b_{M}z_{D}=2E_{mry}, \tag{11}\]  
\[-b_{M}\dot{z}+b_{M}\dot{z}_{D}-kz+(k+2k_{D})z_{D}+\dot{\vartheta}b_{M}y-\dot{ \vartheta}b_{M}y_{D}=2E_{mrz}. \tag{12}\]  
where; \(m\) is the disc mass, \(b_{p}\) is the coefficient of the disc external damping, \(b_{M}\) is the coefficient of the shaft material damping, \(k\) is the shaft stiffness, \(k_{D}\) is the stiffness of the damper cage spring, \(e_{T}\) is eccentricity of the disc center of gravity, \(g\) is the gravity acceleration, \(y\), \(z\) are the y and z displacements of the disc center, \(y_{D}\), \(z_{D}\) are displacements of the shaft journal center, \(\vartheta\) is the angle of the rotor rotation, \(\psi\) is the phase shift of the excitation effect caused by the disc unbalance, and (.), (.) denote the first and second derivatives with respect to time, respectively.  
The governing equations were derived by means of utilization of the first and second impulse theorems. In detail, the procedure is described in [37; 40].  
The implicit Adams-Moulton method was applied in the simulations to solve the governing equations.
## 3 Results and Discussion  
In order to validate our theoretical estimations, for the computation of the interference values and the interference limited communication range, we implemented extensive Monte-Carlo simulations [34] using the network model presented above. The parameters for our simulation are chosen to reflect the description of our system model (Figure 1) for a general-purpose wireless biomedical network used as the medium to transfer different bio-signals. In this scenario we may have many biomedical sensors, control/sink nodes and gateways each one operating in proximity of each other forming different wireless body/personal area networks.
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Do You Eu Raw Salit/n**} & \multicolumn{3}{c}{**Is Less Salt Good for Managing Blood Pressure?, n (\%)**} & \multicolumn{3}{c}{**Are You Considering Reducing Raw Salt Use?, n (\%)**} \\ \hline  & & **No at Risk** & **Yes** & **\(p\)-Value** & **Not at All** & **A Little** & **A Lot** & \(p\)**-Value** & **Not at All** & **A Little** & **A Lot** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Sex} & Total & 307 & 271 (78.5) & & 54 (17.6) & 124 (40.4) & 129 (42.0) & & 16 (5.2) & 182 (99.3) & 109 (35.5) & \multirow{3}{*}{0.001} \\  & Female & 154 & 123 (09.9) & 0.56 & 26 (16.9) & 66 (42.9) & 62 (40.3) & 0.68 & 8 (5.2) & 101 (65.6) & 45 (29.2) & 0.05 \\  & Male & 153 & 118 (77.7) & & 28 (18.3) & 58 (57.9) & 67 (34.8) & 8 (5.2) & 81 (52.9) & 64 (41.8) & \\ \hline \multirow{3}{*}{Age in years} & 30–40 & 46 & 35 (76.1) & 0.81 & 5 (10.9) & 16 (34.8) & 25 (54.3) & 0.09 & 1 (2.2) & 28 (60.9) & 17 (37) & 0.58 \\  & 40–59 & 160 & 129 (80.6) & & 31 (19.4) & 62 (38.8) & 67 (41.9) & & 8 (5) & 96 (60) & 56 (35) & \\  & \(\geq\)60 years & 101 & 77 (76.2) & & 18 (17.8) & 46 (45.5) & 37 (36.6) & & 7 (6.9) & 58 (57.4) & 36 (35.6) & \\ \hline \multirow{3}{*}{Level of Education} & No & \multirow{3}{*}{99} & 85 (88.9) & 0.002 & 29 (29.3) & 49 (49.5) & 21 (21.2) & -0.001 & 6 (6.1) & 69 (69.7) & 24 (24.2) & -0.001 \\  & education & & & & & & & & & & \\  & Primary to & & & & & & & & & \\  & high school & 148 & 117 (79.1) & & 25 (16.9) & 60 (40.5) & 63 (42.6) & & 8 (5.4) & 91 (61.5) & 49 (33.1) & \\  & SSC or & & & & & & & & & & \\  & above & & & & & & & & & & \\ \hline \multirow{3}{*}{SES *} & Poor & 92 & 74 (80.4) & 0.65 & 24 (28.1) & 39 (42.4) & 29 (31.5) & 0.003 & 3 (3.3) & 64 (69.6) & 25 (27.2) & 0.22 \\  & Middle & 214 & 166 (77.6) & & 30 (14) & 85 (39.7) & 99 (46.3) & & 13 (6.1) & 117 (54.7) & 84 (93.3) & \\ \cline{1-1}  & \multirow{3}{*}{Occupation} & Farmer & 66 & 54 (81.8) & 0.24 & 22 (33.3) & 22 (33.3) & 0.001 & 3 (4.5) & 42 (63.6) & 21 (31.8) & 0.03 \\ \cline{1-1}  & Houseville & 146 & 116 (79.5) & & 25 (17.1) & 61 (41.8) & 6 (40.11) & & 8 (5.5) & 94 (64.4) & 40 (40.1) & \\ \cline{1-1}  & Employment & 53 & 39 (72.6) & & 5 (9.4) & 23 (43.4) & 25 (47.2) & & 1 (1.9) & 26 (49.1) & 2 (69.1) & \\ \cline{1-1}  & Business & 24 & 19 (79.2) & & 1 (4.2) & 11 (45.8) & 12 (50.0) & & 2 (8.3) & 9 (57.5) & 13 (54.2) & \\ \hline \multirow{3}{*}{D Diabetes status *} & No diabetes & 217 & 170 (78.3) & 0.48 & 39 (18) & 95 (43.8) & 83 (38.2) & 0.02 & 10 (4.6) & 131 (60.4) & 76 (35) & 0.62 \\ \cline{1-1}  & Diabetes & 41 & 30 (73.2) & & 3 (7.3) & 12 (29.3) & 26 (63.4) & & 2 (4.9) & 21 (51.2) & 18 (43.9) & \\ \hline \hline \end{tabular}  
* \({}^{*}\) Indicates some missing cases for example, information for one participant’ SIS-vas missing  
\end{table} 
Table 2: Raw salt consumption habits, and knowledge and attitudes towards raw salt consumption and their associated factors.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 1 Introduction
At the same time, the forecast date for hitting '1 Trillion' sensors worldwide has already been brought forward from 2030 to 2025, adding further strength to these predictions [18]. In this regard, there are a multitude of sensor technologies currently available on the market for environmental monitoring in buildings with varying levels of complexities and functionalities, such as temperature, carbon dioxide, humidity, occupancy, light, and airflow sensors. To complement these sensors, a plethora of communication and networking technologies, such as Ethernet, Power Line Carrier, Zigbee, Bluetooth, Wifi, EnOcean, BACnet, Modbus, 6 LoWPAN, Z-Wave and LoRaWAN are also commercially available today. These communication technologies are essential for the reliable and secure communication and storage of high volumes of data acquired from sensors, and are expected to play an even more significant role in the integration of smart buildings and grids of the future [19]. In the following sections, we present a detailed review of various sensors and environmental monitoring technologies commercially available and widely used today for building applications, and in doing so, we aim to answer the following key questions:  
1. What is the current state-of-the art of commercially available sensors and monitoring devices in terms of operation principles, accuracy, measurement range, response times, portability, power consumption, and cost? (Section 2)
2. What is the current state-of-the-art of commercially available wired and wireless communication technologies in terms of operation principles, connectivity and scalability, measurement range, data rates, power consumption and market adoption? (Section 3)
3. What are the key characteristics, limitations and potential of various battery and energy harvesting technologies for sensing and monitoring applications in buildings? (Section 4)
4. What are the key aspects regarding the application, challenges and future perspectives of sensing and monitoring technologies in buildings? (Section 5)
## 4 Numerical Method and Verification
### Grid Independence  
The impeller and guide vane body are modeled and meshed in TurboGrid (Figure 9). The conduit and its extension are modeled in UG, and ICEM CFD is used for structural meshing (Figure 10).  
The dimensionless distance between the first node and the wall is indicated by introducing the y\({}^{+}\) value. The flow field in the pump unit is studied in detail. Especially in order to effectively capture all kinds of vortices, the calculation domain should meet the conditions of \(30<y^{+}<500\)[21]. The grid y\({}^{+}\) values of each overcurrent component of the vertical axial flow pump unit are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fluid-Passing** & **Elbow Inlet** & **Impeller** & **Guide Vane** & **Elbow** & **Outlet** \\
**Component** & **Conduit** & **Impeller** & **Body** & **Elbow** & **Conduit** \\ \hline y\({}^{+}\) & 236.210 & 99.345 & 62.497 & 203.546 & 237.312 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid y+ values for each fluid-passing component.  
Figure 10: Flow conduit grid sections.  
Figure 9: Grids of the impeller and guide vane.  
The grid quality was greater than 0.4. According to the analysis of the number independence of the grid cells, when the number of grid cells was about 5.4 million, the absolute error of the efficiency change of the pump device was less than 0.2%, and the efficiency tended to be stable. The number of grid nodes in the whole computational fluid domain was 5,672,753, and the number of grid cells was 5,470,378. The number independence of the grid cells is shown in Figure 11. Based on the Reynolds time-averaged Navier-Stokes equation [22; 23; 24], the RNG \(k\)-\(\varepsilon\) turbulence model was used to solve the flow field [25; 26], the inlet boundary condition was the total pressure, the outlet boundary condition was the mass flow, the wall condition was nonslip, the dynamic and static interfaces were between the elbow inlet passage and impeller, the impeller and guide vane body were treated by the mixed-plane method, the guide vane body and siphon outlet passage were treated by no interface, the calculation convergence residual was set to \(10^{-5}\) and the monitoring points were set to monitor the change of the pump head.
## 5 Networking Parameter Adjustment Method for Optimized Systematic Performance  
The underwater environment and monitoring mission will vary as time changes. UWSNs need to have adaptive ability of optimization and adjustment with the variation of the environment and mission. In this section, a Networking Parameters Adjustment Method (NPAM) for optimized systematic performance of UWSNs is presented.  
There are three categories of parameters influencing an UWSN's performance: constraint parameters, device performance parameters and networking parameters. Table 2 presents the categories of various performance items and parameters. In practice, it is necessary to achieve an optimal combination of these parameters so that the network can achieve the best effect for different missions or in different environments, with minimal cost.  
When the underwater environment changes or the current monitoring mission varies, the network needs to be adjusted optimally. In order to ensure optimization and adjustment are done reliably and  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Systematic Performance** & **Constraint Parameters** & **Device Performance Parameters** & **Networking Parameters** \\ \hline  & \(\Theta=\alpha\cdot\frac{n_{b}\cdot\pi\cdot d_{b}^{3}}{c_{b}}[\cdot 1(1- 1h_{b})^{\alpha}]+\beta\cdot n_{u}\cdot d_{u}^{3}\cdot h_{u}\) & \\ \hline Coverage & \(\alpha\), \(\beta\), \(\gamma\) & \(h_{b}\), \(n_{u}\), \(d_{u}\), \(h_{u}\) & \(d_{b}\), \(n_{b}\), \(c_{b}\) \\ \hline  & \(\Phi=\alpha\cdot k_{b}\cdot[l\cdot\omega\cdot(1-\eta)\cdot b_{b}+(1-\omega) \cdot b_{b}^{\prime}]+\beta\cdot l\cdot v_{u}\cdot n_{u}\cdot b_{u}\) & \\ \hline Connectivity & \(\alpha\), \(\omega\), \(\beta\) & \(v_{u}\), \(n_{s}\), \(b_{s}\), \(b_{y}\), \(b_{y}\), \(b_{u}\), \(v_{u}\), \(n_{u}\) & \(k_{b}\) \\ \hline  & \(\overline{\psi}=\alpha\cdot(\frac{\
abla(\frac{c_{b}}{b_{b}})+\beta\cdot \frac{c_{u}}{p_{u}})}\) & \\ \hline Durability & \(\alpha\), \(\beta\), \(m\) & \(c_{u}\), \(c_{b}\), \(p_{u}\) & \(p_{b}\) \\ \hline  & \(\Omega=\frac{\alpha\cdot k_{b}\cdot b_{b}}{\frac{L}{V}\cdot(1+\frac{s\cdot l}{ \varphi})}\) & \\ \hline Rapid-reactivity & \(\alpha\), \(s\), \(l\) & \(b_{b}\) & \(r_{b}\), \(k_{b}\), \(c_{b}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter categories in the Systematic Performance Measurement Model.  
Figure 5: Map relationship between performances and parameters.  
in a timely way, there is a need to have a control center in the network. It is reasonable that the master node be regarded as the control center, so that all data should be collected at the master node, and then be processed. It dominates the network to be adaptive for optimal adjustment. The process of network optimization is shown in Figure 6.