esa-sceva/satcom-chunk-collection huggingface dataset:
# PAM-\(N\)--Fundamental Limits in Chromatic Dispersive-Uncompensated Channels
## 1 Introduction  
The dramatic growth in the capacity requirement for optical communication systems has increased the motivation to encode digital information in multilevel optical signals [1, 2]. \(N\)-level pulse amplitude modulation (PAM-\(N\)) introduces a lower symbol rate and narrower spectra, which relax the bandwidth requirements of optoelectronic components. In addition, PAM-\(N\) is a cost-effective solution compared to coherent detection, which is not an affordable option in many cases. Consequently, the prevalence of this technology has recently increased (see, for example, [3, 4, 5, 6, 7]). On the other hand, multilevel signaling reduces the distance between the levels, resulting in a lower immunity to electrical noise, optical noise, non-linear distortion, and other impairments. In recent years, one of the leading multilevel modulation techniques for 100 Gbit/s (and beyond) and both intra- and inter-data center optical links has been 4-level pulse amplitude modulation (PAM-4) [2, 8, 9, 10] mainly because of its higher signal-to-noise ratio (SNR). Consequently, multilevel modulation is the obvious solution for increasing the data rate without raising the cost of the detectors, and the use of PAM-\(N\) modulation is very advantageous. In this modulation format, instead of transmitting only two intensity levels (i.e., zero and one), \(N\) different intensity levels are transmitted. This clearly increases the channel capacity by a factor of log2\(N\)[11]. Because a data center has a relatively short network, the effects of optical noise and nonlinear effects can be reduced substantially and can even be negated. Moreover, in current data centers where the networks operate in the \(\circ\)-band, dispersion can be negated (it should be noted that 850 nm networks, which are based on multimode optical fibers, can work only at very short distances). In high-rate next-generation C-band networks, the chromatic dispersion effects cannot be negated, and in fact, can determine the maximum distance that the network can operate with low-complexity dispersion mitigation elements and without the loss of data.  
The main object of this paper is to quantify the fundamental upper limits that dispersion imposes on the PAM-\(N\) optical channel without any mean, neither passive nor active, dispersion compensation or optical amplification.  
When these networks limits (such as maximum distance, maximum capacity, and power penalty) are quantified analytically, they can be used by the network designer to evaluate the maximum network performances without using laborious simulations and expensive experiments.
## 4 Material and Methods
### Chemotaxis Activity of VOCs to the Meliodogyne hapla J2s  
The chemotaxis activity test was carried out in square Petri dishes (width = 10 cm) containing 2% water agar (WA), as described previously [42], with some modifications. Firstly, filter paper discs (5 mm) soaked with volatile compound solutions of different concentrations were placed on the test area (area A) of square Petri dishes, and filter paper discs soaked with sterile distilled water containing dimethyl sulfoxide were placed on the control area (area B) of square Petri dishes as a control (Figure 8). Subsequently, approximately 150 _M. hapla_ J2s in 10 \(\upmu\)L of sterilized water were inoculated at the center (area C) of the Petri dishes and cultured in the dark at 25 \({}^{\circ}\)C for 12 h. The chemotaxis index (C.I.) was calculated by recording the number of J2s in areas A and B under the anatomical microscope [43]. The experiments were performed three times, and every treatment was replicated three times.  
\[\text{C.I.}=\text{(the number of nematodes in the test area $-$ the number of nematodes in the control area)}/\text{(the total number of nematodes)} \tag{3}\]  
If 0 \(<\) C.I. \(<\) 1, the _M. hapla_ J2s were attracted by the tested sample; if \(-\)1 \(<\) C.I. \(<\) 0, the tested sample repelled _M. hapla_ J2s; and if C.I. = 0, the sample had no effect on the _M. hapla_ J2s.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Experimental Comparison and Analyses
#### 4.3.4 Comparison with the State-of-the-Arts
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **mAP** \\ \hline RRPN [26] & 88.52 & 71.20 & 31.66 & 59.30 & 51.85 & 56.19 & 57.25 & 90.81 & 72.84 & 67.38 & 56.69 & 52.84 & 53.08 & 51.94 & 53.58 & 61.01 \\ R2 CNN [27] & 80.94 & 65.75 & 35.34 & 67.44 & 59.92 & 50.91 & 55.81 & 90.67 & 69.52 & 72.39 & 55.06 & 52.23 & 55.14 & 53.35 & 48.22 & 60.67 \\ ICN [16] & 81.36 & 74.30 & 47.70 & 70.32 & 64.89 & 67.82 & 69.98 & 90.76 & 79.66 & 78.20 & 53.64 & 62.90 & 67.02 & 64.17 & 50.23 & 68.16 \\ RoI-Trans [28] & 88.64 & 78.52 & 43.44 & 75.92 & 68.81 & 73.68 & 83.59 & 90.74 & 77.27 & 81.46 & 58.39 & 53.54 & 62.83 & 58.93 & 47.67 & 69.56 \\ CADNet [17] & 87.80 & 82.40 & 49.40 & 73.50 & 71.10 & 63.05 & 76.70 & 90.90 & 79.20 & 73.30 & 48.40 & 69.09 & 62.00 & 67.00 & 62.20 & 69.90 \\ SCRDet [18] & 89.66 & 81.22 & 45.50 & 75.10 & 68.27 & 60.17 & 68.93 & 90.90 & 86.96 & 86.15 & 64.05 & 63.48 & 68.01 & 62.05 & 71.16 \\ BBAV [19] & 88.35 & 79.96 & 50.69 & 62.18 & 78.43 & 78.98 & 87.94 & 90.85 & 83.58 & 84.35 & 54.13 & 60.24 & 65.22 & 64.28 & 55.70 & 72.32 \\ DRN [31] & 89.71 & 82.34 & 47.22 & 64.10 & 76.22 & 74.43 & 85.84 & 90.57 & 86.18 & 84.89 & 57.65 & 61.93 & 69.30 & 69.63 & 58.48 & 73.23 \\ FEHA-OD & 89.62 & 82.72 & 51.02 & 67.92 & 77.23 & 81.12 & 87.09 & 90.87 & 87.32 & 86.92 & 59.62 & 67.68 & 65.89 & 67.34 & 61.87 & 75.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results comparing different methods.
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Comparison with Observer-Based Methods  
Recent advancements in observer-based methods for nonlinear systems have shown promising results in handling uncertainties. Notably, the works presented in references [48; 49] utilize intermediate observers to estimate unknown states and disturbances. However, prescribed-time control offers several advantages over these methods:  
Time specification: Unlike observer-based methods which typically provide asymptotic convergence, prescribed-time control guarantees convergence within a user-defined time interval, regardless of initial conditions.  
Robustness to initial conditions: Observer-based methods often require accurate initial state estimates, whereas prescribed-time control maintains consistent performance across varying initial conditions.  
Computational efficiency: The proposed approach avoids the need for separate observer design and implementation, potentially reducing computational overhead.  
Adaptability: Our method combines adaptive techniques with prescribed-time control, allowing simultaneous parameter estimation and robust control within the specified timeframe.  
Deterministic performance: While observer-based methods typically provide statistical guarantees of performance, prescribed-time control offers deterministic convergence guarantees.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 5 Analysis of Selected QoS Metrics
### Network Lifetime Modeling  
Since the implanted sensor is difficult to replace or recharge, it is crucial to maximize the implanted sensors' lifetime. Two performance metrics--stability period and network lifetime--are chosen for further analysis. The stability period and total network lifetime represent the lifecycle of the wireless biomedical implant systems until the first implanted sensor is energy depleted and all implant sensors' power is consumed, respectively. The aim of the proposed protocol is to maximize the network lifetime \(T\), which is the summation of all rounds:  
\[\max_{k,\mathcal{R}_{SD},d_{SR_{i}},d_{R_{j}D}}\sum_{r}t_{r} \tag{15}\]where \(r\) and \(t_{r}\) describe the current round and the summation of all rounds, respectively.  
This is subject to  
\[t_{r}\leq\frac{NE_{0}}{\sum_{i}k\Big{(}E_{sen}+E_{pro}+E_{trans}+nE_{amp}d_{SR_{i}} \Big{)}},\ i=1,2,3\ldots,\ N, \tag{16}\]  
\[0\gets E_{0}<E_{Initial},\ i=1,2,3\ldots,N, \tag{17}\]  
\[\mathcal{R}_{SD}=\min\Bigl{\{}\mathcal{R}_{SR_{i}},\mathcal{R}_{R_{j}D} \Bigr{\}},\ j=1,2, \tag{18}\]  
\[\underset{C(i)}{\text{min}}d_{S_{i}R_{j}},\ i=1,2,3\ldots,N,\ j=1,2, \tag{19}\]  
\[\underset{c(i)}{\text{min}}d_{R_{j}D},\ j=1,2. \tag{20}\]  
As shown in Equation (15), the primary objective target is to maximize the network lifetime \(T\). The \(E_{sen}\) and \(E_{pro}\) represent the energy consumption of sensing per bit and data processing per bit, respectively. The network lifetime is the summation of rounds until all implanted sensors' energy \(NE_{0}\) is depleted. Equation (16) gives the minimal energy consumption for one round. By employing an energy-efficient communication protocol, the energy consumption regarding \(E_{trans}\) and \(nE_{amp}d_{SR_{i}}\) can be significantly reduced. The function (17) points out that all implanted sensors are provided with the same initial power \(E_{Initial}\). Once the network starts to work, the residual energy status of \(E_{0}\) steps down and finally reaches zero. Equation (18) illustrates that due to the nature of the communication channel, numerous data packets will be dropped. Equations (19) and (20) demonstrate that the cooperative routing protocol should ensure the transmission distances \(d_{SR_{i}}\) and \(d_{R_{i}D}\) are kept to the minimal possible values, where path selection schemes \(C(i)\) and \(d(i)\) are mentioned in Section 4.2.
# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 5 Conclusions  
In this study, we propose an intelligent scheme based on fuzzy control, RFID, image processing, fuzzy color classification, and mean shift tracking to control omnidirectional WMR for indoor patrol. In image processing, we integrate the webcam and vision builder to process images and navigate the robot to the destination. A normal image is in RGB color space; however, the composition of three color components and the produced colors are not so intuitive and are susceptible to light interference. So we use HSL color space to solve the problem of light interference. We apply fuzzy color histogram classification to recognize all colors from the pattern image and define the background color. Mean shift tracking can find the highest color density of sample points from the fuzzy color space of the target objects and the objects can be tracked continuously. In the control scheme we used a human-machine interface by LabVIEW 2014 (National Instruments Corporation, Austin, TX, USA) and integrated the MATLAB 2015b (The MathWorks, Inc., Natick, MA, USA) codes and image information by vision builder to control the robot to track the target and reach the destination. RFID reader and tag's UID can verify the destination to avoid recognition error from image processing. A fuzzy controller can drive the WMR to avoid obstacles easily. This study develops a dual-robot patrol system. The second robot acts as a backup to the first robot while on patrol. Intruder information can be sent to a remote site via the Internet by both robots. The experimental results show that the proposed control scheme can make the dual-robot system perform indoor security service. The proposed system can therefore free up human resources. Further improvements of this study will be to have the robots extinguish unnecessary sidewalk lights and lock unlocked office doors. Although the goal of this study has been achieved, the disadvantage of the proposed system is that the robots can only be applied to indoor service. In the future, GPS will be installed on the robots so that they can also perform outdoor patrols.  
Hardware and software integration was performed by the first author. The corresponding author provided theory analysis and paper writing.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### SC Hydration of the Dorsal Skin of Nude Mice  
The SC is the outermost layer of the skin and functions as a barrier to prevent moisture loss. It is composed primarily of lipids and keratinocytes and contains ceramides, cholesterol, and free fatty acids [32]. In particular, ceramides maintain SC hydration, which indicates the moisture content of the skin and also protects the skin from external damage and pathogens [32]. When the skin barrier is compromised, an increase in TEWL is accompanied by a decrease in the SC hydration level [33]. At week 13 of UVB irradiation, hydration in the UVB group was significantly (\(p\) < 0.05) lower than that in the NC group by 47.37% (Figure 2B). At week 13, compared with the UVB group, the mice administered low, medium, and high doses of LPTWK10 exhibited significant increases of approximately 63.32, 90.31, and 112.24% (\(p\) < 0.05), respectively, in the dorsal skin hydration level, whereas those administered low, medium, and high doses of the spray-dried LPTWK10 supernatant exhibited significant increases of approximately 47.45, 79.89, and 76.97% (\(p\) < 0.05), respectively. These results indicate that low, medium, and high doses of LPTWK10 and the  
Figure 2: Effects of the administration of LPTWK10 or LPTWK10 spray-dried supernatant powders on (**A**) dorsal TEWL and (**B**) dorsal hydration of the stratum corneum in UVB-induced nude mice. Data are presented as mean \(\pm\) SD (\(n\) = 7 or 8). Values with different uppercase letters were significantly different by [PERSON]’s multiple range test (\(p\) < 0.05). TEWL: transepidermal water loss; UVB: ultraviolet B; NC: normal control; PC: positive control; LPLD, LPLD and LPHID: administration of LPTWK10 viable cells at 2 \(\times\) 10\({}^{8}\) CFU/kg bw/day, 2 \(\times\) 10\({}^{9}\) CFU/kg bw/day and 2 \(\times\) 10\({}^{10}\) CFU/kg bw/day, respectively; SPLD, SPMD and SPHD: administration of TWK10 spray-dried supernatant powders at 0.30 g/kg bw/day, 0.60 g/kg bw/day, and 1.20 g/kg bw/day, respectively.  
spray-dried LPTWK10 supernatant increase skin moisture content, and LPTWK10 exhibits dose-dependent effects.
## 3 Application Cases
### Multidimensional Scaling and Cluster Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Class 1**} \\ \hline \multicolumn{5}{c}{**Dimension**} \\ \cline{2-5} \multicolumn{1}{c}{**Sample**} & **1** & **2** & **3** & **Distance** \\ \hline \(S_{3}\) & \(\mathbf{-0.4119}\) & \(\mathbf{0.7416}\) & \(\mathbf{-0.7197}\) & \(\mathbf{0.7629}\) \\ \(S_{16}\) & \(1.4572\) & \(1.6278\) & \(0.1601\) & \(1.5307\) \\ \(S_{9}\) & \(0.7154\) & \(1.0202\) & \(-0.3183\) & \(1.5151\) \\ \(S_{4}\) & \(-0.8741\) & \(0.4618\) & \(-0.3864\) & \(1.3643\) \\ \(S_{25}\) & \(-0.1068\) & \(0.8086\) & \(0.0288\) & \(1.4865\) \\ Center & \(0.15596\) & \(0.932\) & \(-0.2471\) & \\ \hline \multicolumn{5}{c}{**Class 2**} \\ \hline \multicolumn{5}{c}{**Dimension**} \\ \cline{2-5} \multicolumn{1}{c}{**Sample**} & **1** & **2** & **3** & **Distance** \\ \hline \(S_{10}\) & \(2.362\) & \(0.7122\) & \(0.0004\) & \(1.5824\) \\ \(S_{22}\) & \(3.1748\) & \(1.2389\) & \(0.1626\) & \(2.5164\) \\ \(S_{14}\) & \(-0.2611\) & \(-0.1965\) & \(-0.5328\) & \(1.3575\) \\ \(S_{21}\) & \(0.5911\) & \(0.1578\) & \(-1.1127\) & \(0.6130\) \\ \(S_{5}\) & \(\mathbf{0.6673}\) & \(\mathbf{0.0392}\) & \(\mathbf{-0.9629}\) & \(\mathbf{0.5176}\) \\ \(S_{20}\) & \(-0.2508\) & \(-0.2347\) & \(-0.5213\) & \(1.3647\) \\ \(S_{11}\) & \(0.5901\) & \(0.5983\) & \(-1.746\) & \(1.1730\) \\ Center & \(0.9819\) & \(0.3307\) & \(-0.6732\) & \\ \hline \multicolumn{5}{c}{**Class 3**} \\ \hline \multicolumn{5}{c}{**Dimension**} \\ \cline{2-5} \multicolumn{1}{c}{**Sample**} & **1** & **2** & **3** & **Distance** \\ \hline \(S_{13}\) & \(0.9113\) & \(1.4356\) & \(1.0489\) & \(2.4478\) \\ \(S_{35}\) & \(-2.2680\) & \(-0.0564\) & \(-0.1352\) & \(1.3029\) \\ \(S_{18}\) & \(-1.4942\) & \(0.1296\) & \(-0.0106\) & \(0.5052\) \\ \(S_{19}\) & \(-1.0845\) & \(-0.3144\) & \(-0.2335\) & \(0.6908\) \\ \(S_{26}\) & \(\mathbf{-1.4371}\) & \(\mathbf{0.1768}\) & \(\mathbf{0.1862}\) & \(\mathbf{0.4040}\) \\ \(S_{30}\) & \(-0.2032\) & \(0.2148\) & \(0.2165\) & \(0.8362\) \\ \(S_{32}\) & \(-1.6912\) & \(0.0893\) & \(0.1662\) & \(0.6702\) \\ Center & \(-1.0381\) & \(0.2393\) & \(0.1769\) & \\ \hline \multicolumn{5}{c}{**Class 4**} \\ \hline \multicolumn{5}{c}{**Dimension**} \\ \cline{2-5} \multicolumn{1}{c}{**Sample**} & **1** & **2** & **3** & **Distance** \\ \hline \(S_{15}\) & \(\mathbf{-0.8167}\) & \(\mathbf{0.1166}\) & \(\mathbf{0.3782}\) & \(\mathbf{0.3471}\) \\ \(S_{24}\) & \(-1.4354\) & \(0.0874\) & \(0.4419\) & \(0.3554\) \\ \(S_{17}\) & \(-1.4476\) & \(0.0971\) & \(0.6543\) & \(0.3783\) \\ \(S_{27}\) & \(-1.1690\) & \(0.0907\) & \(0.6821\) & \(0.1883\) \\ \(S_{12}\) & \(-1.4459\) & \(-0.2839\) & \(0.6865\) & \(0.4687\) \\ \(S_{6}\) & \(-0.3079\) & \(-0.1257\) & \(0.3506\) & \(0.8255\) \\ Center & \(-1.1038\) & \(-0.0030\) & \(0.5323\) & \\ \hline \end{tabular}
\end{table} 
Table 5: Clustering division and selection of representative samples.
# Detecting Anatomical Leg Length Discrepancy Using the Plug-in-Gait Model
## 1 Introduction  
Leg length discrepancy (LLD) is a significant factor influencing several pathological and physiological conditions such as foot pathologies [1; 2], low back pain [3; 4], functional scoliosis in children [5], osteoarthritis of the hip and knee [6], impaired functional outcomes and patient satisfaction after total hip replacement [7; 8]. Anatomic LLD is defined as the structural difference between the lengths of the two limbs measured from the femoral head to the distal tibia [9; 10; 11].  
Various imaging techniques have been used to measure anatomic leg length [6; 12]. Radiography is considered the gold standard for measurement, with accepted methods such as full limb radiographs, scanograms, computerized tomography and computerized digital radiographs. These methods are highly reliable and valid but are also expensive, not feasible for everyone and expose the subject to radiation, which limits their use in routine clinical settings.  
Three-dimensional motion analysis is extensively used today thus, improving a comprehensive understanding of gait in musculoskeletal disorders. It also assists in detecting gait deviations and impairments underlying reduced function, clinical decision making, quantifying rehabilitationeffectiveness and treatment intervention [13; 14]. Gait analysis is based on biomechanical gait models calculating joint kinematics [15; 16]. No previous study has validated the ability of a gait model to detect anatomical LLD. If a LLD measurement is found valid, three-dimensional motion analysis can assist in detecting anatomic LLD.  
The Plug-in-Gait model (PGM) (Vicon(r), Oxford, UK) is one of the widely used biomechanical gait models, implementing a well-established predictive model [15; 17]. The lower-body PGM divides the body into seven segments linked by joints with three degrees of freedom. The position of each joint is defined within its proximal segment and used to define its distal segment. The PGM extracts the experimental marker trajectories and generates virtual marker trajectories including joint centers. Assumed rigid body segments include the pelvis, femurs, tibias and feet. Femur and tibia segment length are calculated from joint center to joint center; the femur segment length is measured from the hip joint center (HJC) to the knee joint center (KJC); the tibia segment length is measured from the knee joint center (KJC) to the ankle joint center (AJC) [18].  
The main aim of the present study was to evaluate the capability of the PGM in detecting anatomical LLD. Specifically, to test the correlation between the femur and tibia segmental length discrepancy as measured by the PGM comparing to gold standard imaging such as standing anteroposterior (AP) radiography and computed tomography (CT) supine scanogram, as well as the correlation between segmental lengths measured by both methods.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Results and Analysis
#### 5.2.3 Experiments in the Different Slice Request Arrival Rates Scenario  
We further validated the performance of our proposed algorithm by experimenting with different slice arrival rates. There are 100 substrate nodes in the substrate network and the slice nodes are connected by a probability of 0.5 in this scenario. Figures 6 and 7 show the results of the slice acceptance ratio and slice provisioning revenue performance with mean slice arrival rates of 0.02, 0.04, 0.06, 0.08, and 0.1.  
As can be seen in Figure 6, RT-CSP+ algorithm always has the highest slice acceptance ratio when slices arrive at different rates. For example, when the slice request arrival rate is 0.06, the slice acceptance ratio of RT-CSP+ is 85.82%, which is 14.43%, 17.59%, 22.65%, and 56.15% higher than those of RT-CSP, VNE-DCC, NRM-VNE, and CC, respectively. This is because RT-CSP+ can comprehensively optimize node provisioning using the resource and topology attributes and the _minMaxBWUtilHops_  
Figure 6: Slice acceptance ratio in the different arrival rates scenario.  
Figure 7: (**a**) Long-term average slice provisioning revenue in the different arrival rates scenario; and (**b**) slice provisioning revenue-to-cost ratio in the different arrival rates scenario.  
strategy increases the probability of successfully provisioning slice links. In addition, slice acceptance ratio of all algorithms decreases as the slice arrival rate increases. The reason is that the larger the slice arrival rate, the more slices enter the slice provisioning system per unit time. Due to the limited physical resources, the probability of slice provisioning failure increases when more slices compete for limited physical resources, resulting in low slice acceptance ratio.  
Figure 7a shows that the RT-CSP+ and RT-CSP algorithm always have better long-term average slice provisioning revenue with different slice arrival rates. This is because RT-CSP+ and RT-CSP can reasonably evaluate nodes in the node provisioning stage, resulting in more slices to be received. For each algorithm, the reason the long-term average revenue grows as the arrival rate increases is that more slice requests arrive per time unit under higher arrival rate scenario. Thus, more revenue can be obtained per time unit. Figure 7b presents that the average slice provisioning revenue-to-cost ratio during the steady stage is relatively stable with different slice arrival rates because the arrival and departure of the slices can reach a relatively balanced state. Our algorithms still have higher revenue-to-cost ratio.
## 2 The Traversal Time and Hop Count Analysis (THCA) Algorithm  
In TTHCA, a source node firstly measures the RTT of the AODV route discovery packets, which is the time between sending the RREQ packet and receiving the RREP packet. Each intermediate node measures the processing time of the RREQ and RREP packets (\(\Delta T_{i}\)) and this is added to the \(\Delta T_{TOT}\) parameter in the RREP packet. Hence, once a RREP packet is received by the source node:  
\[\Delta T_{TOT}=\sum_{l=1}^{HC}\Delta T_{i} \tag{1}\]  
and the PTT is calculated from:  
\[PTT=\frac{RTT\ -\ \Delta T_{TOT}}{2} \tag{2}\]  
A wormhole is then suspected if:  
\[\frac{PTT}{HC}>\frac{R}{S} \tag{3}\]  
where \(R\) and \(S\) are respectively the maximum radio range per node and the propagation speed (_i.e._, \(3\times 10^{8}\) m/s).  
When a wormhole is suspected, all intermediate nodes on the route are added to a _graylist_[12] which is broadcasted throughout the MANET together with a new RREQ. All graylist nodes are then omitted during the next route discovery procedure resulting in a new unique route. Graylist broadcasting is repeated until a healthy route is found.