esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Correlation Parameters for Interorganizational Workflow  
#### 3.1.1 Correlation Parameters  
Taking the detailed design phase of a prefabricated building project as an example, the correlation between the interorganizational workflows of TMOs can be expressed by a Binary Design Structure Matrix (BDSM), as shown in Figure 3. A BDSM qualitatively reflects the existence of correlation between interorganizational workflows. However, it does not reveal differences in workflow correlation or the  
Figure 2: Role-based interorganizational workflow model.  
strength of workflow correlation. In order to reveal the possibility and impact of correlations between interorganizational workflows, correlation can be described by a numerical Design Structure Matrix (NDSM). In this paper, the downstream sensitivity (\(s_{ij}\)) and probability of change (\(c_{ij}\)) are used to quantitatively express correlations between interorganizational workflows. The correlation \(C_{j-i}\) is illustrated in Figure 4.  
(1) Downstream sensitivity  
Downstream sensitivity (\(s_{ij}\)) refers to the degree of sensitivity (dependence) of the workflow of Organization \(i\) (downstream) to the processing workflow of Organization \(j\) (upstream). High sensitivity indicates that the processing workflow of Organization \(i\) is highly dependent on the workflow processing of Organization \(j\), and that adjustment to the workflow of Organization \(j\), therefore, leads to a high degree of adjustment to the workflow of Organization \(i\). The opposite is true for low sensitivity.  
(2) Probability of Change  
The probability of change (\(c_{ij}\)) refers to the change in the range of the iterative workflow processing of Organization \(i\) (downstream) that is caused by a change in the range of the iterative workflow processing of Organization \(j\) (upstream). High changeability means that a change in the iterative workflow processing of Organization \(j\) would lead to a large range of iterative workflow processing for Organization \(i\). The opposite is true for low changeability.  
The correlation for the interorganizational workflow is comprehensively reflected by the two aspects of downstream sensitivity and probability of change. On this basis, these two aspects are transformed into a single comprehensive indicator, \(r_{ij}\), to represent the correlation for the interorganizational workflow processing. In this paper, the product of downstream sensitivity and probability of change is used to represent \(r_{ij}\). For an n-dimensional NDSM, R, we have the following.  
\[R=r_{ij}=\left\{\begin{array}{ll}s_{ij}\cdot c_{ij},&\qquad(i\
eq j)\\ 0,&\qquad(i=j)\end{array}\right. \tag{1}\]
## 7 Risk Analysis
### Edge Gateway
#### 7.6.2 Denial of Service (DoS)  
1. **Component**  
_Security risk_: The risk of denial of service (DoS) of edge gateway poses a critical threat with potentially severe consequences. In this context, an attacker with access to the WiFi network and sufficient resources can orchestrate a DoS attack, effectively overwhelming the edge gateway. By exploiting vulnerabilities such as those leading to a SYN flood attack, the attacker can inundate the edge gateway with a barrage of malicious requests. This vulnerability could lead to a severe disruption of service, rendering the edge gateway incapable of processing legitimate emergency requests.  
_Security measures_: Employing DDoS mitigation solutions can help detect and mitigate such attacks in real time. These solutions analyze incoming traffic patterns and automatically filter out malicious traffic, ensuring that legitimate requests reach the edge gateway. For example, implementing traffic monitoring and anomaly detection systems to identify unusual spikes in traffic patterns that could be indicative of a DoS attack.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### U-Net Architecture
#### 3.2.2 U-Net Decoder with Transposed Convolutions and Skip Connections  
The decoder reflects the encoder's architecture but uses transposed convolutional layers for upsampling (Figure 5). In every decoder phase, features from the related encoder block are concatenated via skip connections, restoring fine-grained spatial details diminished during downsampling. Additional convolutional blocks enhance the upsampled feature maps, leading to better spatial reconstruction.  
The decoder uses transposed convolutions to upsample the feature maps. The formula for the transposed convolution is:  
\[F\_out\_prime=sigmoid(np.dot(W\_prime,\ X\_prime prime)+b\_prime) \tag{3}\]  
where \(F\_out\_prime\) is the output feature map after upsampling, \(W\_prime\) is the transposed convolution filter, \(X\_prime\) is the input feature map, \(b\_prime\) is the bias term, sigmoid represents the activation function (ReLU).  
Figure 4: Architecture of the U-Net Encoder model.
## 5 Experiments
### MOS Prediction  
Using the available objective measures and the MOS scores obtained in the subjective evaluation, we have built an estimator of the MOS based on linear regression. This predictor may then be applied to the thousands of synthetic voices from the voice bank in order to automatically select the ones with best MOS estimations to populate the voice catalog offered to the voice bank users.  
To be able to estimate the prediction ability of the linear regression a leave-one-out strategy was applied: 15 different linear regression polynomials were built using in each case data from 14 speakers to train and the remaining 1 to test. SIIB presents a special difficulty in being included in the regression, as it is not possible to obtain one SIIB value per sentence due to its minimum length requirement of 20 s to be reliably calculated. As commented in Section 4.2, several sentences were concatenated to be able to reliably calculate SIIB, and consequently there is no available value for each sentence. Taking this issue into account, and considering also that SIIB correlates negatively with the MOS obtained in the subjective evaluation and that three of the five voices selected according to this measure have less than 3 in MOS, we decided not to include SIIB in the regression.  
Each regression polynomial was evaluated over the 10 sentences from the speaker not included in the training set. The mean value of the predicted MOS together with the corresponding 95% confidence interval are shown in Figure 3.  
We can see that the prediction is accurate with no significant differences for 8 out of the 15 evaluated voices (voices SPK06, SPK12, SPK10, SPK02, SPK13, SPK09, SPK03, and SPK11). For voices SPK08, SPK05 and SPK07, the MOS was underestimated, while for SPK01 and SPK15, it was overestimated.  
The correlation between the predicted MOS and the actual MOS obtained in the evaluation is \(\rho=0.546\). Therefore, it is higher than the correlation obtained with all objective measures individually (see Table 4), except for ESTOI, which has a slightly higher correlation. This suggests that although using only ESTOI can produce a useful first selection, combining it with other objective measures can help in considering other high-quality voices that would otherwise be left out. Applying a suitable threshold over the predicted MOS (higher than 3.5 for instance) removes most of the poor quality voices and would populate the synthetic voice catalog mostly with voices of acceptable quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**STOI** & **ESTOI** & **NISQA** & **SIIB** \\ \hline
0.452 & 0.584 & 0.356 & \(-\)0.199 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlations between MOS and objective measures.
## 3 Test Results and Observations
### Accumulation of Carbon Powder and Ash Powder
#### 3.1.4 Analysis of Images of Substrate with Accumulated Ash Powder  
The resolution of each ash powder image was identified to be the same 1430 \(\times\) 1722 pixels as the carbon powder images. In addition, pixel values, shown in Figure 9 and Table 8, were obtained through histograms by setting the same ROI as in the analysis on the images of carbon powder accumulation. The gap between the pixel value of the substrate with the least accumulated ash powder of 12.5 g and that of the new substrate was 18.998, which is deemed valid considering the standard deviation. Therefore, it is confirmed that it is also possible to analyze the status of ash accumulation, as with carbon powder accumulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Carbon Powder** & **Mean Pixel Value** & **Standard Deviation** \\
**Accumulation (g)** & **Mean Pixel Value** & **Standard Deviation** \\ \hline
0 & 158.887 & 1.420 \\
12.5 & 167.390 & 3.273 \\
25 & 172.388 & 2.050 \\
62.5 & 178.212 & 1.781 \\
100 & 179.967 & 1.630 \\
125 & 183.624 & 1.623 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pixel values according to each accumulation condition (carbon powder).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
12.5 g & 25 g & 100 g & 125 g \\ \hline
**Outlet** & **Outlet** & **Outlet** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The substrate images of four different conditions (ash powder from 12.5 g to 125 g).
# A Study on Karst Cave Collapse Based on Improved Terzaghi Theory and Upper Limit Analysis
## 4 Discussion
### Parameter Analysis
#### 4.2.1 Effect of Karst Cave Development  
The diameter of the karst cave (\(d\)) and the overburden thickness (\(h\)) were adopted to represent the development of the karst cave. If the groundwater table is positioned below the karst cave, soil unit weight \(\gamma\) = 18.5 kN/m\({}^{3}\), \(c\) = 24 kPa, \(\varphi\) = 14\({}^{\circ}\), static earth pressure coefficient \(K_{0}\) = 0.58, the angle between perpendicular lines of the failure profile and surface \(\alpha\) = 0\({}^{\circ}\), and vacuum absorption erosion pressure is set as \(\sigma_{p}\) = 50 kPa [32]. The relationship between the critical surface load \(\sigma_{1}\) and the development of karst cave (\(d,h\)) is given in Figure 10.  
Figure 10: Relationship between the critical surface load \(\sigma_{1}\) and development of the karst cave (\(d,h\)).  
Figure 9: Relationship between load factor \(\sigma_{1}/c\) and depth ratio \(h/d\) of the karst cave.  
In Figure 10, the dashed line \"Limit Equilibrium\" denotes the critical surface load \(\sigma_{1}=0\) kPa. This implies that, without a surface load, karst cave is stable. The upper part of the dashed line indicates the stable state of the karst cave. The lower part of the dashed line represents the failure of the karst cave. Moreover, the critical surface load \(\sigma_{1}\) is positively correlated with overburden thickness \(h\) and negatively correlated with \(d\). This suggests that the larger \(h\), the smaller \(d\), and the more stable the karst cave. This result aligns with the relevant research [33; 34] and the actual situation.
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa
### Impdhi  
The Inosine-5\({}^{\prime}\)-Monophosphate Dehydrogenase genes, _IMPDH_, encode an enzyme that catalyzes the conversion of inosine 5\({}^{\prime}\)-phosphate (IMP) to xanthosine 5\({}^{\prime}\)-phosphate (XMP), a step in the synthesis of guanine nucleotide. _IMPDH_ is essential for cell growth, as inhibition of the enzyme leads to a stop in DNA synthesis [92]. There are two IMP dehydrogenases in humans encoded by the genes _IMPDH1_ and _IMPDH2_. While all cells express both isozymes, retinal cells express only IMPDH1. Furthermore, there are two retina-specific isoforms of _IMPDH1_, each produced by alternative splicing [93; 94].  
Mutations in _IMPDH1_ are found in 2.5%-3.5% of all adRP cases [55; 56; 95] and in some cases of autosomal dominant LCA type 11 [95]. In total, 24 mutations have been described, the vast majority of which are missense variants. The two most common _IMPDH1_ mutations are p.Asp226 Asn, comprising 2.2% of cases, and p.Asp311 Asn, 1.5% of cases [55]. All _IMPDH1_ mutations are found in the so-called CBS subdomain, which contains two cystathionine \(\beta\)-synthase repeats and shows the most structural differences between IMPDH1 and IMPDH2.  
While all _IMPDH1_ mutations are dominant, the mechanism of pathogenesis is unclear. There are different hypotheses on how these variants give rise RP, all of them related to decreased DNA binding. The first hypothesis is that IMPDH1 has a second role as a transcription repressor of histone genes and the E2F transcription factor by binding single-stranded DNA [96], and that adRP-causing mutations block this DNA binding [97]. The second is that IMPDH1 is associated with polyribosomes that translate rhodopsin, and that the causative mutations undermine this interaction [58]. The last hypothesis is that adRP mutations lead to structural changes in _IMPDH1_ that render it constantly active, and lead to an imbalance in purine nucleotides [98].  
As can be seen from above, _IMPDH1_ is a very interesting gene, with multiple functions. However, to date, the way that it gives rise to RP is not clear. In the absence of further evidence, it would be safer to treat adRP-causing _IMPDH1_ mutations either by correction of the mutant allele or by invalidation of both alleles and gene supplementation.
# Study on Filling Capacity of Optical Glass in a Novel Rapid Hot Embossing Process
## 4 Results and Discussion
### Effect of Embossing Temperature on the Filling Capacity  
It is known that the deformation of N-BK7 glass is not appreciable when the temperature is below its transition point (\(\sim\)557 \({}^{\circ}\)C), even under a considerably large embossing force. In fact, the difficulty of the deformation and flowing of glass is largely determined by its viscosity which is quite sensitive to the temperature. Therefore, it is not surprising that the embossing temperature shows the most significant influence on the filling capacity of glass. As shown in Figure 8a,b, the maximum height of microlens soars from 3.78 \(\upmu\)m to 48.99 \(\upmu\)m, as the embossing temperature increases from 700 \({}^{\circ}\)C to 740 \({}^{\circ}\)C. According to Figure 8a, the shape of the embossed microstructure gradually changes from trapezoidal platform to simple hemisphere, finally to cylindrical hemisphere with the inevitable corners caused by the adhesion and friction along the sidewall. Correspondingly, the curvature radius dramatically decreases as embossing temperature increases, as seen in Figure 8c.
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 3 Proposed Control Strategy
### Stability Analysis  
If \(x_{1}=i_{d}\), \(u=v_{cd}\), applying the delay method on Equation (13) gives Equation (21)  
\[F(t)=\dot{x}_{1}^{-}-\dot{\alpha}u^{-} \tag{21}\]  
With \(x_{1}^{-}=x(t-T_{e})\) and \(u^{-}=u(t-T_{e})\).  
The lag of the control law from Equation (14) is given by Equation (22)  
\[\alpha u^{-}=\dot{y}_{d}^{-}(t)-\ F(t)+k_{p}\varepsilon^{-}(t)+k_{i}\int \varepsilon^{-}(t) \tag{22}\]  
where \(\dot{y}_{d}^{-}(t)\) is the output delay and \(\dot{y}^{*}\) is the reference derivative.  
Subtracting the Equations (13) and (22), we find the Equation (23)  
\[\alpha\big{(}u-u^{-}\big{)}=\dot{y}_{d}(t)-\dot{y}_{d}^{-}(t)+k_{p}\varepsilon ^{-}(t)+k_{i}\int\varepsilon^{-}(t) \tag{23}\]  
With \(\dot{\varepsilon}=\dot{y}_{d}(t)-\dot{y}_{d}^{-}(t)\), Equation (23) becomes  
\[\alpha\big{(}u-u^{-}\big{)}=\dot{\varepsilon}(t)+k_{p}\varepsilon^{-}(t)+k_{i }\int\varepsilon^{-}(t) \tag{24}\]  
If the system in closed loop is stable, then in steady state \(u=u^{-}\), Equation (24) becomes:  
\[\dot{\varepsilon}(t)+k_{p}\varepsilon^{-}(t)+k_{i}\int\varepsilon^{-}(t)=0 \tag{25}\]  
\(u\) can be approximated by  
\[u=u^{-}+T_{e}\dot{u}^{-} \tag{26}\]  
Figure 7: Balancing technique.  
By injecting (26) into (24), we obtain  
\[\alpha T_{e}\dot{u}^{-}=\dot{\varepsilon}(t)+k_{p}\varepsilon^{-}(t)+k_{i}\int \varepsilon^{-}(t) \tag{27}\]  
By replacing it in Equation (7), the derivative of the control law can be established by Equation (28)  
\[\dot{u}=\frac{L_{eq}\ddot{x}_{1}+R_{eq}\dot{x}_{1}}{\alpha} \tag{28}\]  
Thus, the representation of the system given by Equation (28) can be expressed as follows:  
\[\alpha T_{e}\frac{L_{eq}\ddot{x}_{1}+R_{eq}\dot{x}_{1}}{\alpha}=\dot{x}_{1}^{* }-\dot{x}_{1}+k_{p}(x_{1}^{*}-x_{1})+k_{i}\int(x_{1}^{*}-x_{1}) \tag{29}\]  
So, the characteristic equation is  
\[T_{e}L_{eq}s^{3}+T_{e}R_{eq}s^{2}+k_{p}s+k_{i}=0 \tag{30}\]  
According to [PERSON]'s criterion: the first condition holds \(T_{e}L_{eq}\), \(T_{e}R_{eq}\), \(k_{p}\), \(k_{i}>0\), and the closed loop system is stable if \(R_{eq}k_{p}>L_{eq}k_{i}\).
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 2 Background
### Single-Agent RL Algorithms
#### 2.2.1 Q-Learning  
Q-learning [15] is a form of model-free, value-based reinforcement learning. It takes inspiration from dynamic programming and methods of temporal differences such as TD(0) [17]. The Q-learning agent faces the task of determining an optimal policy that maximizes the total discounted reward; this policy \(\pi^{*}\) is such that  
\[V^{*}(s)\equiv V^{\pi^{*}}(s)=\max_{a}\Biggl{\{}R_{s}(a)+\gamma\sum_{s^{\prime }}P_{ss^{\prime}}[a]V^{\pi^{*}}(s^{\prime})\Biggr{\}} \tag{9}\]  
Figure 3: The taxonomy of reinforcement learning algorithms.  
Figure 2: The structure of an actor–critic algorithm.  
where \(R_{s}(a)\) is the average reward received by an agent in the state \(s\) if it selects the action \(a\), and \(P_{ss^{\prime}}[a]\) is the transition probability from the state \(s\) to \(s^{\prime}\), given the action \(a\). The object is to estimate the state-action pair value function (Q-values); this is done by creating a matrix \(Q_{n}(s,a)\) of dimensions \(S\times A\) containing the estimates of all the value functions at the time-step \(n\). At each iteration \(n\) of the learning process, the Q-values are updated using the equation  
\[Q_{n}(s,a)=\begin{cases}(1-\alpha_{n})Q_{n-1}(s,a)+\alpha_{n}[r_{n}*\gamma V_{ n-1}(s_{n+1})]&\text{if $s=s_{n}$ and $a=a_{n}$}\\ Q_{n-a}(s,a)&\text{otherwise}\end{cases} \tag{10}\]  
where \(\alpha_{n}\) is the learning rate, \(r_{n}\) is the instant reward received, and  
\[V_{n-1}(s_{n+1})=\max_{a^{\prime}}Q_{n-1}(s_{n+1},a^{\prime})\]  
It was shown in [18] that, under the assumption of bounded rewards \(|r_{n}|\leq R\) and learning rates \(0\leq\alpha_{n}<1\) such that  
\[\sum_{i=1}^{\infty}\alpha_{n^{i}(s,a)}=\infty,\sum_{i=1}^{\infty}[\alpha_{n^{ i}(s,a)}]^{2}<\infty\ \forall s,a, \tag{11}\]  
the estimates \(Q_{n}(s,a)\) will converge to the optimal Q-value \(Q^{*}(s,a)\) with probability 1.
# Static Analysis of Composite Plates with Periodic Curvatures in Material Using Navier Method
## 3 Results and Discussion
### MATLAB Implementation  
The analytical solution was implemented in MATLAB using the Navier method, which is based on the Galerkin approach and combined with Gauss-Legendre quadrature for accurate evaluation of integrals. The algorithm starts by defining the geometry, material constants, and loading parameters. Periodic curvature functions and the associated material stiffness coefficients \(A_{\mathrm{ij}}\) (\(x_{1}\)) are computed symbolically, and their derivatives are evaluated within the integration domain. Harmonic terms (\(m\), \(n\)) are used to construct the load matrices \(\mathrm{P}_{\mathrm{mn}}\), while the transformation matrices are assembled through Gauss integration of the governing equations. The coefficients wmn are then solved, from which the deflection \(w\) (\(x_{1}\), \(x_{3}\)), bending moments \(M_{11}\), \(M_{33}\), and in-plane stresses \(\sigma_{11}\), \(\sigma_{33}\) are determined through structured loops.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Different Methods for Anomaly Score Calculation
#### 2.4.1 Cosine Similarity  
Cosine similarity [37] is a metric used to measure the degree of similarity between two vectors in a multi-dimensional space. Unlike direct comparisons of vector magnitudes, cosine similarity evaluates the angle between two vectors to determine their directional alignment. Cosine similarity has been widely used in various audio processing tasks. For instance, in speech recognition [42], it measures the similarity between different speech signals; in speaker identification [20], it compares the similarity of speaker-specific features. Furthermore, in the domain of anomalous sound detection, [PERSON] et al. [19] utilized cosine similarity to calculate anomaly scores. These applications collectively demonstrate the effectiveness and versatility of cosine similarity in speech and audio processing.  
In anomaly detection, let the feature vector of the test sample be \(z_{i}\), and the feature center of its corresponding normal class be \(\mu_{c}\), which represents the mean feature vector of the normal data for that class. The cosine similarity between \(z_{i}\) and \(\mu_{c}\) is computed as follows:  
\[\text{Cosine Similarity}=\cos(z_{i},\mu_{c})=\frac{z_{i}\cdot\mu_{c}}{\|z_{i} \|\|\mu_{c}\|} \tag{8}\]  
After calculating the similarity, the anomaly score \(A_{\text{cos}}\) is derived as follows:  
\[A_{\text{cos}}=-\ln(|\cos(z_{i},\mu_{c})|) \tag{9}\]  
\(A_{\text{cos}}\) is the anomaly score, with smaller scores indicating more typical data points and larger scores indicating higher likelihood of being an anomaly.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 4 MAC Protocol Leveraging Wake-up Receivers
### Analytical Study of Scalability
#### SNW-MAC:
Furthermore:  
\[\mathbb{E}\left[X|X\geq 1\right]=\sum_{k=1}^{\infty}k\Pr\left(X=k|X\geq 1 \right), \tag{21}\]  
and for \(k\geq 1\):  
\[\Pr\left(X=k|X\geq 1\right)\,=\frac{\Pr\left(X=k,X\geq 1\right)}{\Pr\left(X \geq 1\right)}=\frac{\Pr\left(X=k\right)}{p_{g}} \tag{22}\]  
leading to:  
\[\mathbb{E}\left[X|X\geq 1\right]=\frac{\mathbb{E}\left(X\right)}{p_{g}}=\frac{ \lambda T_{S}}{60p_{g}}. \tag{23}\]  
Therefore, the average number of packets received during a time slot is:  
\[\mathbb{E}\left[R^{\prime}\left(Y\right)\right]=\sum_{i=1}^{n}R^{\prime}\left( Y=i\right)\Pr\left(Y=i\right)\,=\mathbb{E}\left[X|X\geq 1\right]\Pr\left(Y=1 \right)=\frac{N\lambda T_{S}}{60}e^{-\left(N-1\right)\frac{\lambda T_{S}}{60 }}, \tag{24}\]  
and the average receiving rate \(\gamma_{PAM}\) in packets per minute is thus:  
\[\gamma_{PAM}=\frac{60\mathbb{E}\left[R^{\prime}\left(Y\right)\right]}{T_{S}}= N\lambda e^{-\left(N-1\right)\frac{\lambda T_{S}}{60}}. \tag{25}\]  
Figure 4 shows \(\gamma_{SNW-MAC}\) and \(\gamma_{PAM}\) for values of \(N\) ranging from 0-100 and values of \(\lambda\) ranging from values of 0-300 packets per minute. In real scenarios, the wake-up interval of pseudo-asynchronous protocols \(T_{S}\) is usually set to a much higher value than \(\tau_{R}\) to save energy. However, in order to compare SNW-MAC to the best case scenario of pseudo-asynchronous protocols regarding scalability, both \(\tau_{R}\) and \(T_{S}\) were set to 40 ms, leading to \(\Gamma=1500\) packets per minute for SNW-MAC. As we can see, \(\gamma_{SNW-MAC}\) increases until reaching \(\Gamma\). The sink then saturates, and  
the receiving packet rate stops increasing. On the other hand, \(\gamma_{PAM}\) first increases with \(\lambda\) and \(N\), but decreases after reaching a maximum because of collisions, limiting its scalability. Moreover, the maximum reached by \(\gamma_{PAM}\) is 674 packets per minutes, which is more than twice smaller than the maximum reached by \(\gamma_{SNW-MAC}\). These numerical results show the better scalability of SNW-MAC, even when compared to the best case scenario of pseudo-asynchronous MAC protocols. In the case where \(T_{S}\) is set to 250 ms and \(\tau_{R}\) to 40 ms, which are typical real scenario values, numerical results show that the highest packet rate achieved by pseudo-asynchronous MAC protocols is 300 packets per minute, which is 5-times lower than SNW-MAC.