esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Analysis of Two-Port Interconnections
### Sum of Integer and Fractional-Order Integrator Responses  
Practical circuitry implementing the fractional-order integrator and integer-order integrator derived from the general concept in Figure 1 is shown in Figure 3. Its transfer function is given by:  
\[K_{\_}{\_}{\_}{f\_}{(s)}\ =\ \frac{A_{\_}{1}}{\tau_{\_}{1}s}+\frac{A_{\_}{2}}{ \tau_{\_}{2}s^{\alpha}} \tag{10}\]  
Figure 2: Practical implementation of the Constant Phase Element (CPE) solution from resistor–capacitor circuit (RC) segments: (**a**) circuit topology; (**b**) magnitude of impedance; (**c**) phase of impedance.  
Its representation in complex form is quite extensive:  
\[K_{\_}{I\_}{I}(\omega)\ =\ \frac{1}{\tau_{1}\tau_{2}\omega^{1+\alpha}}\Big{(}A_{ \_}2\tau_{1}\omega\sin\!\left((1+\alpha)\frac{\pi}{2}\right)+\hat{J}\!\left[A_{ \_}2\tau_{1}\omega\cos\!\left((1+\alpha)\frac{\pi}{2}\right)-A_{\_}1\tau_{2} \omega^{\alpha}\right]\Big{)} \tag{11}\]  
Magnitude and phase part can be expressed as, respectively:  
\[\left|K_{\_}{I\_}{I}(\omega)\right|\ =\ \frac{1}{\tau_{1}\tau_{2}\omega^{1+ \alpha}}\sqrt{\left[A_{\_}2\tau_{1}\omega\sin\!\left((1+\alpha)\frac{\pi}{2} \right)\right]^{2}+\left[A_{\_}2\tau_{1}\omega\cos\!\left((1+\alpha)\frac{\pi} {2}\right)-A_{\_}1\tau_{2}\omega^{\alpha}\right]^{2}} \tag{12}\] \[\varphi_{\_}{I\_}{I\_}{I}(\omega)\ =\ \tan^{-1}\!\left[\frac{A_{\_}2\tau_{1} \omega\cos\!\left((1+\alpha)\frac{\pi}{2}\right)-A_{\_}1\tau_{2}\omega^{\alpha }}{A_{\_}2\tau_{1}\omega\sin\!\left((1+\alpha)\frac{\pi}{2}\right)}\right] \tag{13}\]  
The Mathcad analysis of ideal transfer function (10) is shown in Figure 4 as three dimensional (3D) plots. Variation of order \(\alpha\) between 0 and 1 indicates a clear point of break where the slope divides into two parts (integer-order low-frequency zone and fractional-order high-frequency zone), and the movement of position of this intentional zero from high frequencies to low frequencies, as well as increasing the character of the corner phase shift at the end of the operational band (from \(-90^{\circ}\) up to \(0^{\circ}\)).  
Figure 4: The three dimensional (3D) plot of studied ideal transfer response (10) in dependence on the value of the \(\alpha\) variable: (**a**) magnitude vs. order vs. frequency dependence, (**b**) phase vs. order vs. frequency dependence.  
Figure 3: Practical solution of circuitry providing summing responses of the fractional- and integer-order integrators.
## 3 Mathematical Model
### Equations for the Flow Field  
The continuity equation:  
\[\frac{\partial}{\partial t}+\
abla\cdot\left(\rho U\right)=0 \tag{2}\]  
where \(\rho\) is the air density, and \(U\) is the air velocity.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Units** & **Value** \\ \hline Particle model & & \multicolumn{1}{c}{Rosin–Rammler} \\ Total flow rate & \(q_{m}\) (kg\(\cdot\)s\({}^{-1}\)) & 0.5 \\ Gas density & \(\rho_{g}\) (kg\(\cdot\)m\({}^{-3}\)) & 1.225 \\ Particle density & \(\rho_{p}\) (kg\(\cdot\)m\({}^{-3}\)) & Depending on the particle type \\ Distribution density & \(\rho_{d}\) (kg\(\cdot\)m\({}^{-2}\)) & 0.15 \\ Particle mean diameter & \(d_{m}\) (\(\mu\)m) & 100 \\ Spread parameter & n & 5.95 \\ Normal restitution coefficient & \(e_{max}\) & 0.95 \\ Tangential restitution coefficient & \(e_{tang}\) & 0.85 \\ Near-wall treatment & & Scalable wall function \\ TKE Prandtl number & & 1 \\ TDR Prandtl number & & 1.3 \\ Energy Prandtl number & & 0.85 \\ Wall Prandtl number & & 0.85 \\ Coefficient & \(C_{min}\) & 0.89 \\ Coefficient & C\({}_{1x}\) & 1.46 \\ Coefficient & C\({}_{2x}\) & 1.89 \\ Coefficient & \(\delta_{e}\) & 1.4 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3: Mesh model of the regenerative air sweeper pickup head.  
The mass conservation equation:  
\[\frac{\partial u_{i}}{\partial\chi_{i}}=0 \tag{3}\]  
The momentum conservation equation [4; 8]:  
\[\frac{\partial}{\partial\chi_{i}}\big{(}\rho u_{i}u_{j}\big{)}=-\frac{\partial p }{\partial\chi_{i}}+\frac{\partial}{\partial\chi_{j}}\left(u\frac{\partial u_ {i}}{\partial\chi_{j}}+\tau_{ij}\right)+\rho g_{i} \tag{4}\]  
where \(u_{i}\) is the time-averaged air velocity, \(\chi_{i}\) is the Cartesian coordinate component, \(\rho\) represents the density, \(\tau_{ij}=-\rho u_{i}^{\prime}u_{j}^{\prime}\) is the Reynolds stress, \(p\) is the air pressure, and \(g\) represents the acceleration of gravity.  
The turbulence energy transfer equation is given by [9]  
\[\rho\frac{dk}{dt}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{ \varepsilon_{m}}{\sigma_{k}}\bigg{)}\frac{\partial k}{\partial xj}\bigg{]}+G_ {k}-\rho\varepsilon \tag{5}\]  
The turbulent flow energy dissipation rate transmission equation is [9; 19]  
\[\rho\frac{d\varepsilon}{dt}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(} \mu+\frac{\varepsilon_{m}}{\sigma_{k}}\bigg{)}\frac{\partial k}{\partial xj} \bigg{]}+C_{1\varepsilon}\frac{\varepsilon}{K}G_{k}-C_{2\varepsilon}\ \rho\frac{ \varepsilon^{2}}{k} \tag{6}\]  
where \(G_{k}=-\rho u_{i}^{\prime}u_{j}^{\prime}\frac{\partial u_{j}}{\partial xj}\) is the turbulent kinetic energy production, \(k\) is the turbulent kinetic energy dissipation, \(\mu\) is the viscosity coefficient of laminar flow, \(p_{0}\) represents the static pressure, \(\sigma_{k}=1\) and \(\sigma_{\varepsilon}=1.3\) represent the turbulent Prandtl numbers of \(k\) and \(\varepsilon\), respectively, and \(C_{1\varepsilon}=1.44\) and \(C_{2\varepsilon}=1.92\) are constants.
# A Highly Interactive Honeypot-Based Approach to Network Threat Management
## 3 Central Node Model in High-Interactivity Honeypot System
### Information Capture Module  
As one of the most core components of the honeypot system, the performance of the information capture module is a key indicator to determine the effectiveness of the whole honeypot system.  
In our system, we use high-interactive honeypots for information capture. High-interactive honeypots provide a realistic operating system environment and can provide more attack information than low-interactive honeypots, and are less likely to be detected by attackers. After being subjected to a network attack by an adversary, a high-interaction honeypot acquires and records the attack data, compiles it into a package, and submits it to the central node for analysis. We use the form of loadable kernel modules (LKMs) to run internally in the system to avoid being easily discovered by the attacker and causing exposure to the honeypot system. If the honeypot is compromised by the attacker, the user can also disconnect the interaction between the central node and the honeypot to ensure the overall security of the system.  
As shown in Figure 2, the high-interactivity honeypot generates an attack environment for simulated attacks. After the attacker launches an attack on the attack environment, the central node in the honeypot generates and packages the attack information. Once the packaging is complete, the specific attack information is transmitted to the central node of the entire system for further information processing.  
It is noteworthy that to maintain a high level of interactivity within the honeypot system, a realistic operating system environment is provided to the honeypot kernel. Additionally, the honeypot kernel furnishes a dependable operating system for the simulated attack environment generated by the honeypot. In this way, the attacker is not easily aware that they are attacking a honeypot when carrying out an attack. Although this poses a certain challenge to the security of the entire system, it also protects the confidentiality of the entire system and the authenticity of the captured attack information.  
Figure 1: High-interaction honeypot system framework.
## 2 Results
_GPI_ (glucose-phosphate isomerase, Solyc04g076090), _PMI_ (phosphommannose isomerase, Solyc02g086090), _PMM_ (phosphomman0 utase, Solyc05g048760), _GMP_ (GDP-Man pyrophospholylase, Solyc03g096730), _GME1_ (GDP-Man-3\({}^{\prime}\),5\({}^{\prime}\)-epimerase 1, Solyc01g097340), _GME2_ (GDP-Man-3\({}^{\prime}\),5\({}^{\prime}\)-epimerase 2, Solyc04g077020), _GGP1_ (GDP-L-Gal phosphorphylase/L-Gal  
guanylyltransferase 1, Soly06g073320), _GGP2_ (GDP-L-Gal phosphorylase/L-Gal guanylyltransferase 2, Soly02g091510), _GPP1_ (L-Gal 1-phosphate phosphatase 1, Solycdq014800), _GPP2_ (L-Gal 1-phosphate phosphatase 2, Solycd1 lg012410), _GalDH_ (L-Gal dehydrogenase, Solycd1 lg016450), _GLDH_ (L-GalL dehydrogenase, Solycd1 lg0079470), _MIX_ (_myo_-inositol oxygenase, Solycd1 lg008650), _MDHAR1_ (monodehydroascorbate reductase, Solycd09g009390), _DHAR1_ (dehydroascorbate reductase, Solycd05g054760). Three replicate experiments were performed. Error bars represent standard error, means \(\pm\) SE. FW, fresh weight. The asterisks represented significant differences from wild type (AC), as indicated by the \(t\)-test (*_p_ < 0.05; **_p_ < 0.01). Different Biosynthesis Pathways Contributed to the Total AsA Content in Leaves and Fruit of Pyramiding Lines  
According to the above-mentioned results, we assumed that increase in the AsA content after pyramiding is connected with the contribution of different AsA biosynthesis pathways in leaves and fruits. Thus, three substances representing the three biosynthesis pathways, inositol (MI) corresponding to inositol pathway, glucose (GLc) corresponding to d-Man/L-Gal pathway and galacturonic acid (GLA) corresponding to the galacturonic acid pathway, were selected for feeding experiments. AsA and H\({}_{2}\)O were utilized as a positive and negative controls, respectively. In leaves, feeding with MI and GLA significantly decreased AsA content in both pyramiding lines and AC. On the contrary, the AsA content in wild-type and pyramiding lines were significantly increased by feeding with GLc which is the primary product of photosynthesis in leaves and a precursor of d-Man/L-Gal pathway (Table 1). Glucose plays a decisive role in AsA biosynthesis suggesting that d-Man/L-Gal pathway was the main contributor of AsA accumulation in tomato leaves. Feeding with both MI and GLc can increase the content of AsA in fruits of pyramiding lines. However, the AsA content in the fruit of AC and the _GGP_\(\times\)_GPP_ pyramiding line showed no change after feeding with GLA (Table 1). This result suggested that d-Man/L-Gal pathway, the d-galacturonate pathway, and the myo-inositol pathway all contributed to AsA biosynthesis in tomato red fruit in pyramiding lines. Taken together, gene pyramiding enhanced the AsA biosynthesis pathway in which the d-Man/L-Gal pathway plays a role in leaves and the three pathways all function in fruits, and subsequently affect AsA content.
# A Micro-Coordinate Measurement Machine (CMM) for Large-Scale Dimensional Measurement of Micro-Slits
## 3 Experimental Result and Discussion
### Evaluation of Alignment Errors of the Measurement System
\[L_{y\_Abbe} =\left(a_{1}\tan\frac{\theta_{y\_X-PZT}}{2}+a_{2}\right)\tan\, \theta_{y\_X-PZT}+\left(a_{1}\tan\frac{\theta_{y\_X-Servo}}{2}+a_{2}\right) \tan\,\theta_{y\_X-Servo} \tag{12}\] \[L_{z\_Abbe} =\left(a_{1}\tan\frac{\theta_{z\_X-PZT}}{2}+a_{2}\right)\tan\, \theta_{z\_X-PZT}+\left(a_{1}\tan\frac{\theta_{z\_X-Servo}}{2}+a_{2}\right) \tan\,\theta_{z\_X-Servo} \tag{13}\]  
Figure 11: Geometrical model of the alignment error of the probing points during the gap width measurement. Figure 12: Schematic of Abbe error caused by the tilting motion of \(X\)-directional moving stage. Consequently, the combined Abbe error can be expressed in the following equation. \[L_{Abbe\_error}=\sqrt{L_{y\_Abbe}^{2}+L_{z\_Abbe}^{2}} \tag{14}\]  
The measurement error for the gap width measurement caused by the tilt errors of the positioning stages is then evaluated. When the probing points on the slot die surface are changed by moving the slot die with the \(Y\)-linear slide, errors will be introduced in the measurement result of the gap width by the tilt error about the \(Y\)-axis (Rolling) \(\theta_{slide\_rolling}\) and the tilt error about the \(X\)-axis (Yawing) \(\theta_{slide\_wwing}\). The gap width measurement error \(\Delta w_{y\_slide}\) caused by the tilt errors of the \(Y\)-linear slide can be indicated as follows. \[\Delta w_{y\_slide}=\frac{w_{s}}{\cos\left(\theta_{slide\_rolling}\right)\cos \left(\theta_{slide\_wwing}\right)}-w_{s} \tag{15}\]  
When the probing points are changed by moving the slot die with the \(Z\)-directional servo motor stage, errors will be introduced to the measurement result of the gap width by the tilt error \(\theta_{z\_serve\_rolling}\) about the \(Y\)-axis (Rolling) and the tilt error \(\theta_{z\_serve\_pitching}\) about the \(X\)-axis (Yawing). The gap width measurement error \(\Delta w_{z\_serve\_zero}\) caused by the tilt errors of the \(Y\)-linear slide can be indicated as follows. \[\Delta w_{z\_serve}=\frac{w_{s}}{\cos\left(\theta_{z\_serve\_rolling}\right) \cos\left(\theta_{z\_serve\_pitching}\right)}-w_{s} \tag{16}\]  
Table 2 shows a summary of the tilt errors of the positioning stages. The tilt errors are measured by a laser autocollimator with a resolution of 1.21 \(\upmu\)rad. The measurement uncertainties for on-line qualification and gap width measurement can then be evaluated based on Equations (4)-(15) as well as Tables 1 and 2.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
###### Abstract  
In this paper, a new Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar (SSS-RSSAR) is proposed to generate a high-resolution image of a Region of Interest (ROI) tilted with respect to the satellite track. Comparing to the traditional Spaceborne Sliding Spotlight Synthetic Aperture Radar (SSS-SAR), the SSS-RSSAR is superior in contributing to less data amount, lighter computational load and hence higher observation efficiency. Unlike the Spaceborne Stripmap Range Sweep Synthetic Aperture Radar (SSS-RSSAR) proposed in a previous paper, the SSS-RSSAR not only continuously sweeps the beam in range for the ROI tracking, but also in azimuth to enlarge the synthetic aperture for an improved azimuth resolution. Two aspects of the SSS-RSSAR are focused: system and imaging. For the system part, a Continuous Varying Pulse Interval (CVPI) technique is proposed to avoid the transmission blockage problem by non-uniformly adjusting the pulse intervals based on the geometry. For the imaging part, a Modified Polar Format Algorithm (MPFA) is proposed to accommodate the original polar format algorithm to the echo received with the CVPI technique. Moreover, an integrate system parameter design flow for the SSS-RSSAR is also suggested. The presented approach is evaluated by exploiting the point target simulations.  
SSS-RSSAR; CVPI;MPFA; system parameter design Article
## 2 Numerical Models
### Comparison of the Numerical and Analytical Model for the Circular Tunnel Case Study
#### 2.3.2 Effect of the Soil Young's Modulus E  
The soil Young's modulus is assumed to fall in a range from 10 to 350 MPa. The other parameters presented in Table 1, based on the reference case study, were used as the input data. The numerical results obtained by using the FLAC\({}^{\text{3D}}\) comparison with [PERSON]'s analytical method for the full-slip and no-slip conditions are presented in Figure 6. The observations discussed below were made.  
Figure 6 shows good agreement of the incremental bending moments and normal forces induced in the tunnel lining under seismic loading, obtained by the numerical model and the analytical solution for both no-slip and full-slip conditions when considering the \(E\) variation. The maximum difference is smaller than 2%.  
The extreme incremental bending moments are strongly dependent on the \(E\) value as seen in Figure 6a. The absolute values of the extreme incremental bending moments are reached for \(E\) values close to 50 MPa. There is a rapid decrease in the absolute extreme incremental bending moments when the \(E\) value reduces from 25 to 10 MPa. When the \(E\) values are larger than 50 MPa, an increase in \(E\) induces a decrease in the absolute extreme incremental bending moments. This correlation of the extreme incremental bending moments is observed in both full-slip and no-slip conditions. It should be noted that for the same \(E\) value, the absolute extreme incremental bending moments induced in the tunnel lining for the no-slip condition are always 10 to 15% smaller than the full-slip ones.  
While the extreme incremental normal forces in the full-slip conditions are insignificantly dependent on the \(E\) value (Figure 6b), an increase in \(E\) can cause a rapid increase in the maximum and minimum incremental normal forces in the tunnel lining for the no-slip condition. As predicted, incremental normal forces for the no-slip condition are always larger than the full-slip ones.
## 3 Results and Discussion
### Question 8 Distribution of Respondents  
Question 8 may speak directly to the concerns of those who do not feel that they have the experience (or perhaps realize the complexity and time requirements of software evaluation). It asks respondents to identify whether review by a panel of trusted experts would increase their trust in computer data analysis. Agreement (between agree-no preference and strongly agree) was indicated by 76% of respondents, 12% indicated no preference and 12% indicated some form of disagreement (between disagree-no preference and strongly disagree). This data is presented in Table 7 and Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Strongly** & \multirow{2}{*}{**Agree**} & \multicolumn{3}{c}{**No**} & \multirow{2}{*}{**Disagree**} & \multicolumn{2}{c}{**Strongly**} & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Invalid**} \\
**Agree** & & & **Preference** & & & **Disagree** & **Response** & **Response** \\ \hline
36 & 19 & 87 & 29 & 41 & 4 & 12 & 1 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Responses to the statement “I would trust computer analysis of data if I had access to or could evaluate the source code”.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Strongly** & \multirow{2}{*}{**Agree**} & \multicolumn{3}{c}{**No**} & \multirow{2}{*}{**Disagree**} & \multicolumn{2}{c}{**Strongly**} & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Invalid**} \\
**Agree** & & & & **Preference** & & & **Disagree** & **Response** & **Response** \\ \hline
30 & 23 & 72 & 49 & 27 & 10 & 14 & 2 & 1 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Responses to the statement “I would trust a computer analysis of data if the software had been reviewed by a panel of trusted experts”.  
Figure 7: Graph of responses to the statement “I would trust computer analysis of data if I had access to or could evaluate the source code”.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Sliding Time Window Size Experiment  
The size of the sliding time window is a variable parameter and has a significant impact on the predictive performance of the model. Therefore, in order to obtain a better prediction effect, we set up the sliding time window size experiment. The size of the time window was increased successively according to the values of 6, 9, 12, 18, 24, 30, and 42. Single-step prediction was conducted on the three datasets, and the changing trend of the results was observed to find the optimal sliding window size.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Learning rate & 0.001 \\ \hline Batch size & 128 \\ \hline Optimizer & Adam [44] \\ \hline Epochs & 200 \\ \hline Dropout & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameter settings.  
Figure 4 shows the influence of the sliding time window size on the model's prediction performance based on different datasets. For the EITH1 dataset (Figure 4a), RMSE and MAE reach their lowest values when the window size is 12, while R\({}^{2}\) reaches its highest value, indicating that 12 is the best prediction window. For the ETTm1 dataset (Figure 4b), RMSE and MAE are the lowest when the window size is 18, and R\({}^{2}\) is the highest. It is determined that 18 is the optimal window. For the Australian power load dataset (Figure 4c), RMSE and MAE are the lowest when the window size is 24, while R\({}^{2}\) is the highest. Therefore, 24 is selected as the best prediction window size. Overall, the optimal prediction window sizes corresponding to each dataset are 12, 18, and 24, respectively.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### Route Request  
When a node, \(i\), receives an RREQ from another node, which is here referred to as the \"requesting node\", in order to be sure that the same packet has not been previously received, node \(i\) checks the tag on the packet. Node \(i\) drops the packet if has been received previously. Otherwise, it proceeds to implement the eligibility determination algorithm. If its eligibility (\(El\)) determination operation yields a \"1\", node \(i\), which now becomes a requesting node, will re-broadcast the RREQ packet. If, on the other hand, (\(El\)) returned a zero, the packet is dropped. (\(El\)) is fully a binary operation, which is determined as below:  
\[El=\left\{\begin{aligned} & 1,\begin{cases}d_{sk}<d_{sk}^{req}\\ E_{th}\geq E_{th}^{min}\\ & clc=clc_{req}\\ & R(\varpi)\geq R_{\varpi_{t}}\\ &\text{else}=0\end{cases}\right.\end{aligned} \tag{26}\]  
Node \(i\), has to meet four conditions before its \(El\) operation returns a one. These conditions include the following. Its POS has to be smaller in value than that of the requesting node, _i.e._, \(d_{sk}\) has to be smaller than \(d_{sk}^{req}\). This condition ensures the packet keeps a forward flow to the sink. The condition \(E_{th}\geq E_{th}^{\min}\) is used to check if the energy of node \(i\), \(E_{th}\), is above the minimum value, \(E_{th}^{\min}\). Node \(i\) uses the third condition, \(clc=clc_{req}\), to check if the offered data channel in the \(clc_{req}\) field is available. The last condition, \(R(\varpi)\geq R_{\varpi_{t}}\), helps node \(i\) to confirm if the spatio-temporal characteristic of the chosen channel in \(clc_{req}\) supports the needed traffic. At this point, if the RREQ channels passes node \(i\)'s local rate test, it is retained in \(clc_{req}\) as its chosen data channel. Thus, the value for the channel switch count will not be altered. On the other hand, if node \(i\) has another channel that exhibit better performance, it is replaced, and the channel switch count is incremented by \"1\". This thus means that different channels will be used by node \(i\) for its upstream and downstream communication. Node \(i\) will then cache the channel(s) and position to the sink (POS) of the requesting node. If the path was finally selected as the best path by the sink, this requesting node will be known as node \(i\)'s upstream LMR node. Before the node \(i\) re-broadcasts the RREQ packet, it performs some bookkeeping tasks as follows: it replaces the requesting node's POS with its POS and increments the hop count. If, on the other hand, none or any of the conditions were met by node \(i\), it immediately drops the RREQ packet. This adopted strategy helps keep the RREQ packet short, as against the method used in [9], wherein the size of RREQ packet grows together with the hop count. This route request is continued until all possible disjoint routes are discovered.