esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 AE Clustering in Time as a Seismic Precursor or an Aftershock  
Since the cumulative probabilities \(C^{\pm}\)(\(\tau\)) are represented in a time domain ranging from 1 to 14-16 weeks, shorter values of \(\tau\) are further investigated.  
In particular, time coupling of regional seismicity to AE activity from Chapel XVII and the Asinelli Tower is analyzed in terms of \(D_{t}\) for \(\tau\) ranging from 3 to 24 h after an earthquake occurrence, and from 10 min to 24 h before an earthquake occurrence.  
The varying local slope \(D_{t}\) of Log \(C^{-}\) vs. Log \(\tau\) reveals that the AE activity following seismic events is not equally probable over the time (see Figure 10a,b). For short time delays, \(\tau\) = 3-10 min, high values of the time fractal dimension, \(D_{t}\) = 1.38 and 1.24, indicate tight coupling of the AE activity to the earthquakes. In other words, AE bursts following an earthquake are more likely to occur within \(\tau\) = 3-10 min after the seismic event. Then, such short time delays suggest a triggering action exerted by nearby earthquakes on damage processes of Chapel XVII and the Asinelli Tower. Contrarily, for \(\tau\) ranging from 1.0 \(\times\) 10\({}^{3}\) to 1.440 \(\times\) 10\({}^{3}\) min, the obtained low values of \(D_{t}\), 0.48 and 0.45, suggest that the effects of nearby earthquakes on the structural damage evolution disappear after 24 h.  
In regards to the AE events preceding earthquakes, _i.e._, considering AE as \"seismic precursors\", the linear bi-logarithmic plots of \(C^{+}\) (\(D_{t}\) = 1.06 and 0.94) shown in Figure 11 describe a uniform probability density of finding AE events prior to an earthquake for a wide range of time intervals, up to \(\tau\) = 1.440\(\times\)10\({}^{3}\) min. In other words, we observe a constant AE activity in the 24 h preceding an earthquake. The absence of accelerating precursive activity in the presented case studies confirms the need for further investigation, possibly with the aid of electromagnetic seismic precursors.  
Figure 9: Modified correlation integrals \(C^{\pm}\)(\(\tau\)), both considering AEs as earthquake “precursors” (+) and as “damage aftershocks” (–), plotted as functions of the time separation \(\tau\) for different values of the spatial range \(r\) for: (**a**) Chapel XVII and area around the city of Varallo; (**b**) Asinelli Tower and area around the city of Bologna.  
Figure 11: _Cont._  
Figure 10: Analysis of time-clustering features of AE events considered as “seismic aftershocks” by the time correlation dimension \(D_{t}\), as a local slope of Log \(C\)_– vs._ Log \(\tau\). The time range for \(\tau\) is 3 min to 24 h. The selected seismic events are all those considered during the defined monitoring periods. (**a**) Chapel XVII (Varallo); (**b**) Asinelli Tower (Bologna).
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
###### Abstract  
This paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading scenarios while the number of sensors is kept smaller than the number of load cases. Building on prior work in which machine learning models predicted loads using only sensor readings without information on load location, this study continues that approach. It demonstrates that prediction models perform better when sensor networks are optimized. The novelty lies in a newly formulated objective function, allowing for optimization of sensor number, positions, and orientations across multiple load cases and measurement types. The goal is to minimize the differences between maximal responses of the structure and detected responses by the sensors (for all possible load cases). The method is validated on a numerical model of a composite structure with 1-3 sensors under seven different load cases. Load predictions from sensors in optimized locations are compared to predictions from measurements of randomly positioned sensors. Statistical comparison proved that the developed methods and algorithms allow us to significantly reduce the prediction errors.  
optimization; sensor placement; structural health monitoring; finite element method; genetic algorithm; artificial neural network; operational load monitoring Article
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 3 Hybrid State Estimation  
Suppose a target can have \(n_{\text{m}}\) modes or dynamic models. Let \(m_{k}\) be the mode during the time interval \((t_{k-1},t_{k}]\). The dynamic and measurement models for a hybrid system are given by [8; 20; 25]  
\[\mathbf{x}_{k}=\mathbf{f}_{k-1}(\mathbf{x}_{k-1},m_{k})+\mathbf{w}_{k-1}(m_{k }), \tag{20}\]  
\[\mathbf{z}_{k}=\mathbf{h}_{k}(\mathbf{x}_{k},m_{k})+\mathbf{n}_{k}(m_{k}). \tag{21}\]  
The time variation of the modal state is described by a homogeneous Markov chain with known \(n_{\text{m}}\times n_{\text{m}}\) TPM \(\mathbf{\Pi}\)[8; 20; 25]:  
\[\pi_{ij}:=P(m_{k}=j|m_{k-1}=i),\ \ i,j=1,2,...,n_{\text{m}}, \tag{22}\]  
where \(\pi_{ij}\) is the (\(i\),\(j\))th matrix element of \(\mathbf{\Pi}\). The transition probabilities satisfy  
\[\pi_{ij}>0\text{ and }\sum_{j=1}^{n_{\text{m}}}\pi_{ij}=1,\text{ for }\ \ i,j=1,2,...,n_{\text{m}}. \tag{23}\]  
Let \(\{\mu_{i}\}\) denote the initial model probabilities defined by \(\mu_{i}:=P(m_{1}=i),\ \ i=1,2,...,n_{\text{m}}\) and satisfying  
\[\mu_{i}>0,\ \ \text{for}\ i=1,2,...,n_{\text{m}}\text{ and }\sum_{i=1}^{n_{\text{m}}}\mu_{i}=1. \tag{24}\]  
The state of the hybrid system at time \(t_{k}\) is the augmented state \(\mathbf{y}_{k}=\left[\mathbf{x}_{k}^{\prime}\ \ \ m_{k}\right]^{\prime}\). For the formal recursive equations, interested readers may refer to Section 1.4 of [20]. Detailed steps of the hybrid state estimation algorithm using the IMM-CKF algorithm are described in Section 5.
## 3 Results and Discussion
### Findings from the Experimental Results  
After comparing and analyzing the experimental results of vibration transmissibility and frequency curves of PE25, PE30, and XPE cushioning materials at different static loads and temperatures, the following observations were obtained.  
The results showed an influence of temperature on the performance of all tested materials. At all static loads, as the temperature decreased from 40 \({}^{\circ}\)C to \(-20\)\({}^{\circ}\)C, the frequency of maximum vibration amplification, or resonant frequency, increased. These results also showed a stronger influence of temperature on the resonant frequency of XPE and PE30 compared to PE25. For instance, when a static stress of 3.488 kPa was exerted on XPE or PE30, the resonant frequency at \(-20\)\({}^{\circ}\)C was about two times as much as that at 40 \({}^{\circ}\)C. However, for the same static stress level, the difference in resonant frequency of PE25 at \(-20\)\({}^{\circ}\)C and 40 \({}^{\circ}\)C was only about 27%.  
When the frequency was over 70 Hz, the frequency range reached the attenuation zone of PE25. This point was approximately 138 Hz for PE30 and approximately 222 Hz for XPE.  
Generally, XPE has the lowest, second-lowest, and third-lowest vibration transmissibility values when considering all materials, temperatures, and static loads. The resonant frequencies were 48.8345 Hz (+20 \({}^{\circ}\)C, 6.976 kPa), 50.6682 Hz (+40 \({}^{\circ}\)C, 4.651 kPa), and 62.6298 Hz (+40 \({}^{\circ}\)C, 3.488 kPa); while their vibration transmissibilities were 4.9942, 4.1833, and 3.7688.  
The lowest peak vibration transmissibility value was 7.7215 at \(-20\)\({}^{\circ}\)C (PE30, 6.976 kPa). At 0 \({}^{\circ}\)C, it was 6.2941 (XPE, 6.976 kPa). The lowest vibration transmissibility values for the other two temperatures were 4.9942 and 3.7688.  
Vibration transmissibility is influenced by the static load of the cushioning system. Similarly, regarding the effect of temperature, the results also showed a stronger influence of static load on the resonant frequency of XPE and PE30 compared to PE25. For example, the average increase of resonance frequencies when the static load was decreased from 6.976 kPa to 3.488 kPa was 59% and 46% for XPE and PE 30, respectively, but only 19% for PE25.  
For the XPE material at loads of 3.488 kPa and 4.651 kPa and at 40 \({}^{\circ}\)C, a second peak frequency was observed around 100 Hz. This phenomenon was attributed to the softer XPE material at 40 \({}^{\circ}\)C and its shorter compression distance, which resulted in a higher amplitude in the cross section of the specimen.
## 2 Methodology  
### Governing Equations  
The air and water phases are treated as incompressible viscous fluid in the present numerical model, which are solved using URANS equations:  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}= -\frac{1}{\rho}\frac{\partial p_{\_}{rgb}}{\partial x_{i}}+v\frac{\partial^{2 }u_{i}}{\partial x_{j}^{2}}-\frac{\partial\overline{u_{\_}{i}^{\prime}u_{\_}{ j}^{\prime}}}{\partial x_{j}} \tag{1}\]  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{2}\]  
where \(u_{\_}{i}\) are the components of the mean fluid velocity, \(p_{\_}{rgb}}\) is the pressure in excess of the static pressure, \(\rho\) is the fluid density (air or water), and \(v\) is the kinematic viscosity. \(\overline{u_{\_}{i}^{\prime}u_{\_}{j}^{\prime}}\) denotes the Reynolds stress component expressed by the Boussinesq q approximation.  
\[\overline{u_{\_}{i}^{\prime}u_{\_}{j}^{\prime}}=v_{\_}{i}\bigg{(}\frac{ \partial u_{\_}{i}}{\partial x_{\_}{j}}+\frac{\partial u_{\_}{j}}{\partial x_ {\_}{i}}\bigg{)}-\frac{2}{3}k\delta_{\_}{ij} \tag{3}\]  
Here, \(k\) is turbulent kinetic energy, \(v_{\_}{t}\) is the turbulent kinematic viscosity, and \(\delta_{\_}{ij}\) is the Kronecker delta.  
The \(k-\omega\)_SST_ model proposed by [PERSON] [16] has a good performance for wave elevation predictions during the wave breaking according to [PERSON] et al. [17]. The application of the \(k-\omega\)_SST_ model to numerically simulate breaking wave past a vertical cylinder can also be found in [PERSON] et al. [14,15]. Therefore, the \(k-\omega\)_SST_ turbulence model is employed in the present simulations. The transport equations for turbulent kinetic energy \(k\) and specific turbulent dissipation rate \(\omega\) are given by  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho u_{\_}k)}{\partial x_ {\_}{i}}=\frac{\partial}{\partial x_{\_}{i}}\bigg{[}(\mu+\sigma_{\_}k\mu_{ \_}t)\frac{\partial k}{\partial x_{\  
where \(y\) is the distance from the center of the first layer mesh to the wall. The dynamic turbulent viscosity \(\mu_{t}\) is defined by  
\[\mu_{t}=\frac{\rho a_{1}k}{\max(a_{1}\omega,SF_{2})} \tag{8}\]  
The invariant measure of the strain rate \(S=\sqrt{2S_{ij}S_{ij}}\). Here, \(S_{ij}=\frac{1}{2}\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\bigg{)}\). The second blending function \(F_{2}\) is expressed as  
\[F_{2}=\tanh\left[\left[\max\left(\frac{2\sqrt{k}}{\beta^{*}\omega y},\frac{500v }{y^{2}\omega}\right)\right]^{2}\right] \tag{9}\]  
The production term \(P_{k}=\min(G,10\beta^{*}\rho k\omega)\), where \(G\) is given by  
\[G=\mu_{t}\frac{\partial u_{i}}{\partial x_{j}}\bigg{(}\frac{\partial u_{i}}{ \partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\bigg{)} \tag{10}\]  
The constants of \(\alpha_{k}\), \(\alpha_{\omega}\), \(\alpha\) and \(\beta\) are blended using equation  
\[\phi=F_{1}\phi_{1}+(1-F_{1})\phi_{2} \tag{11}\]  
The constants of this model are \(\beta^{*}=0.09\), \(\alpha_{1}=5/9\), \(\beta_{1}=0.075\), \(\sigma_{k1}=0.85\), \(\sigma_{\omega 1}=0.5\), \(\alpha_{2}=0.44\), \(\beta_{2}=0.0828\), \(\sigma_{k2}=1\), \(\sigma_{\omega 2}=0.856\).
## 3 Proposed Design
In the scenario shown in Figure 1, the case is depicted where node 2 generates high priority packet during the transmission of fragments by node 1. Node 2 begins listening to the channel; when it detects the channel idle for the duration \(T_{CH}\), it begins sending its packet. The FROG-MAC is a pseudo-preemptive protocol because although it reduces the waiting time for high priority data significantly as compared to the traditional preemptive protocols, this data still has to wait for the transmission of short fragment which is already in transmission. Once the high priority transmission gets completed, the low priority data continues its transmission. The future components of FROG-MAC would include acknowledgement and buffer mechanisms. The buffer would help to store the previously received low priority fragments, so their retransmission could be avoided. Figure 1 illustrated the case where node 2 had high priority data. In case if node 2 had low priority data, it would not transmit because during the handshaking, it would already know that the medium would be occupied due to the data transmission by node 1. When the node 1 finished its transmission and receives acknowledgement (also received by node 2), both nodes would run back-off process in their attempt to win the channel. Similarly, in case both the nodes have high priority data at same instant, random value of back-off would decide the winning node. It is to be noted that the major focus of FROG-MAC is to reduce the channel access delay for the high priority traffic in the time-critical applications of WSN. The delay is expected to reduce by the proposed fragmentation scheme. However, the drawbacks of FROG-MAC include high energy cost and the single hop topology. The impact of these limitations would be minimized by authorizing the high priority traffic to immediately access the channel. Hence, the scope of FROG-MAC might be limited to the application scenarios where energy is not a constraint and reducing delay is a crucial requirement.
## 2 Materials and Methods
### Statistical Analysis  
The following three models were constructed in order to analyze the tree growth results: model SB, for spring burned plots; model AB, for autumn-burned plots; and model NB, for non-burned plots. For each treatment, a linear mixed model (Equation (1)) was used to describe short-term impact of PB on tree growth. BAI was selected as the target variable. A collinearity analysis was performed; tree height (H), which had a strong collinearity with DBH, was excluded. Likewise, nested effect between random variables were considered. According to the collinearity and significance, the best fit model considered the following potential predictors (fixed factors) (Table 4): tree height (H), percentage of live crown height (Hc), diameter at breast height (DBH), maximum scorch height (SMx), time during which the temperature in the cambium area was higher than 60 \({}^{\circ}\)C (t60) and time during which the temperature in the bark surface was higher than 300 \({}^{\circ}\)C (t300). Possible differences in composition and characteristics of stand (S), variability between plots (P) and total amount of litterfall collected per plot one year after PB (L) were resolved by adding a random effect.  
\[ln\left(BAI_{k(jy)}+1\right)=\beta_{0}+\beta_{1}\cdot Hc+\beta_{2}\cdot DBH+ \beta_{3}\cdot SMx+\beta_{4}\cdot t60+\beta_{5}\cdot t300+\alpha_{0k(j)}+L \alpha_{k(jy)}+\varepsilon_{k(jy)} \tag{1}\]  
where \(\beta_{0}\) is the overall intercept; \(\beta_{i}\) (\(i=1,\,\ldots,\,5\)) are the parameters adjusting the fixed effects; \(k\) is the study site index; \(k(j)\) is the tree index nested in the study site; \(y\) is the month of measurement index; \(\gamma_{0k(j)}\), \(\alpha_{0k(i)}\) represents the random effects associated with study plots and stand, respectively; \(L\alpha_{k(jy)}\) is the interaction between stand and litterfall; and \(\varepsilon_{k(jy)}\) is the error term. No pattern was observed in the residuals of any models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline – & **Both Stands** & **Mixed Stand** & **Pure Stand** \\ \hline Series intercorrelation & 0.439 & 0.542 & 0.431 \\ Average mean sensitivity & 0.337 & 0.374 & 0.308 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main dendrochronology statistics.  
All statistical tests were performed using R software (v. 3.0.1, R Foundation for Statistical Computing), specifically with the _nlme_ and _lme4_ packages for linear mixed-effects modelling. A significance level of 95% was established for detecting differences between treatments.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 5: Active Learning  
Systems used in production inevitably produce false alarms or encounter novel drift patterns. Incorporating human (engineer) feedback can dynamically reduce false positives and enhance coverage of new anomalies [22].  
1. Human Feedback: If a flagged sample is deemed normal by experts, it is re-labeled as a \"false alarm,\" and the threshold prior \(p(\tau)\) can be adjusted accordingly. On the other hand, if an anomaly is confirmed, it is added to a \"labeled anomaly\" set for retraining.
2. Retraining with Semi-Supervision: \[\theta\leftarrow\theta-\eta\
abla_{\theta}\Big{[}\mathcal{L}_{recon}+\lambda \mathcal{L}_{contrast}+\beta\mathcal{R}_{anomaly}\Big{]}.\] (11) Here, \(\mathcal{R}_{anomaly}\) is an additional penalty term used to reinforce the model's discrimination of newly confirmed drift samples.  
An updated model is shown by \(\theta^{**}\) and a refined threshold is shown by \(\tau_{t}^{\star}\). Over time, the model adapts to evolving operating conditions, reducing false positives and the number of missed detections.
## 4 Case Study
### Selection of Replacement Plan
#### 4.3.1 Construction of Comparison Matrix  
Under the suggestions of the expert group, the priority of each index, criterion, and subject of AHP hierarchical model was distinguished through the 1-9 evaluation scale method, and the comparison matrices are constructed as follows:  
\[X=\left(\begin{array}{rrrrrr}1&1&2\\ 1&1&2\\ 1/2&1/2&1\end{array}\right),Y_{1}=\left(\begin{array}{rrrrrr}1&6&6&4&4&3\\ 1/6&1&1&1/5&1/5&1/3\\ 1/6&1&1&1/5&1/5&1/3\\ 1/4&5&5&1&1&1/4\\ 1/4&5&5&1&1&1/4\\ 1/3&3&3&4&4&1\end{array}\right),Y_{2}=\left(\begin{array}{rrrrrr}1&1/3&2\\ 3&1&4\\ 1/2&1/4&1\end{array}\right),Y_{3}=\left(\begin{array}{rrrrrr}1&1/2&1\\ 2&1&2\\ 1&1/2&1\end{array}\right)\]  
Figure 8: Operation interface of BECIA software.  
In accordance with Equation (6), the eigenvectors and maximum eigenvalues of comparison matrices are obtained:  
\[\begin{array}{ll}w_{X}=\left(0.40,0.40,0.20\right)^{T}&\lambda_{\max X}=3.15\\ w_{Y_{1}}=\left(0.42,0.04,0.04,0.13,0.13,0.24\right)^{T}&\lambda_{\max Y_{1}}=6. 66\\ w_{Y_{2}}=\left(0.24,0.63,0.14\right)^{T}&\lambda_{\max Y_{2}}=3.06\\ w_{Y_{3}}=\left(0.25,0.50,0.25\right)^{T}&\lambda_{\max Y_{3}}=3.00\\ \end{array}\]  
Next comes the result of the consistency test:  
\[\begin{array}{ll}CI_{X}=0R&I_{X}=0.58&CR_{X}=0<0.10\\ CI_{Y_{1}}=0.13&RI_{Y_{1}}=1.24&CR_{Y_{1}}=0.11>0.10\\ CI_{Y_{2}}=0.03&RI_{Y_{2}}=0.58&CR_{Y_{2}}=0.05<0.10\\ CI_{Y_{3}}^{2}=0&RI_{Y_{3}}^{2}=0.58&CR_{Y_{3}}^{2}=0<0.10\\ \end{array}\]  
Unfortunately, as a high-order matrix, \(Y_{1}\) proves incapable of meeting the requirements of consistency test, which indicates that the acquired hierarchical weight \(w_{Y_{1}}\) is not acceptable and modification should be performed via the hybrid chaotic whale optimization algorithm.
## 3 Results  
The results provide precise answers to the six research questions, highlighting how the integration of disruptive technologies enhances surveillance and protection in urban environments through physical infrastructure and communication networks, which are essential for smart city service management. RQ1--What Are the Primary Deep Learning Models That Have Been Applied to Detect Violence and Crime in Smart Cities? Several studies have proposed models for IoT security, intrusion detection, and urban surveillance, facilitating the identification of threats such as violent incidents, cyberattacks, and unauthorized access. The results summarized in Table 2 highlight the key characteristics of each proposal, emphasizing the neural networks trained to manage violence and crime in smart city environments. Most studies also provide details on the datasets used to train and test these models. The detection of cyberattacks and threats in IoT environments has led to the development of diverse deep learning models designed to identify anomalous patterns and sophisticated attacks. Among these, Convolutional Neural Networks (CNNs) have been widely employed due to their capability to extract spatial features efficiently and classify network traffic anomalies. This trend is evident in S1, S5, S7, S10, S19, S20, S24, S26, S28, and S42 where CNN-based models are used for intrusion detection, DDoS attack mitigation, and cybercrime analysis in smart cities and IoT-based infrastructures. In these studies,  
Figure 4: Number of studies published by country. CNNs have been deployed both individually and in combination with other architectures, improving the accuracy and generalization of cybersecurity systems.
## 3 Materials and Fabrication Techniques
### Fabrication Techniques  
#### 3.3.1 Spin-Coating  
Spin-coating is by far the most frequently utilized fabrication technique. Two main different approaches may be distinguished [87], namely a _top-down_ or _subtractive_ approach where PEDOT:PSS is first deposited on the substrate and subsequently patterned, and a _bottom-up_ or _additive_ approach in which PEDOT:PSS is spin-coated on a pre-patterned layout (Figure 9). Prior to spin-coating, additives are often added to the PEDOT:PSS suspension to increase the final film electrical conductivity (such as ethylene glycol or dimethyl sulfoxide) or to improve film formation and adhesion (such as dodecylbenzenesulfonic acid (DBSA), (3-glycidyloxypropyl) trimethoxysilane (GOPS) or alkylbenzenesulfonic acid (ABSA)), as discussed in the previous sections. In most cases the channel final thickness is approximately 100 nm but values larger than 400 nm were also reported [88]. After spin-coating, devices are thermally annealed (T \(\geq\) 125 \({}^{\circ}\)C) for several tens of minutes to promote solvent evaporation; annealing may be performed either in air or  
Figure 9: Schematic of subtractive (**a**) and additive (**b**) photolithographic patterning approaches for organic films. The asterisks indicate the steps where the organic film would be damaged during conventional photolithography. The chemical structure of parylene-C is shown on the bottom. Reprinted from [87]. Copyright 2005, with permission from Elsevier. in inert atmosphere. Two-step annealing processes, with increasing temperatures, are also reported. In a few cases, after thermal annealing, devices were soaked in deionized water to remove loosely bound polymer chains or excess additives. The most important characteristics of spin-coating fabrication processes are summarized in Table 1.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Results
#### 4.4.5 Comparative Analysis 5  
The propagation model (TK) being constant, routing protocols are compared in this case. The simulation is run for varying simulation times for realistic scenario with 71 vehicles communicating for BSM transmission. The comparative analysis graph depicted in Figure 9 shows that OLSR outperforms AODV and DSDV in terms of throughput, end-to-end delay, packet delivery, average routing goodput, MAC/PHY overhead despite an increase in simulation time.  
Figure 8: Performance Analysis of Routing Protocols varying Simulation Time. (**a**) Simulation Time vs. End-to-End Delay; (**b**) Simulation Time vs. Packet Delivery Ratio; (**c**) Simulation Time vs. Throughput; (**d**) Simulation Time vs. AverageRoutingGoodput; (**e**) Simulation Time vs. MAC/PHY Overhead.