esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 13 Conclusions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Network Performance** \\ \hline Transit delay & The time it takes an application to travel from the ingress (entry) point to a network’s egress (exit) point is referred to as network latency. Delay can cause severe QoS problems with applications such as video conferencing and fax delivery, which time out and terminate because of an unreasonable delay. Network propagation delay can cause ingress queuing delays for traffic entering a network node, traffic conflict at each network node, and egress queuing delays for traffic leaving a network node. At each network hop, data is distributed over the physical network medium. \\ \hline Jitter & The difference in delay reported by different packets in the same traffic flow is known as jitter, as is high-frequency delay variance. The most crucial problem for QoS is jitter caused by variations in queue wait times for consecutive packets in a flow. Jitter is not tolerated by some forms of traffic, especially real-time traffic such as video conferencing. Jitter is present in all transportation networks. Jitter thresholds have little effect on service quality if they are below the specified tolerance level. \\ \hline Resilience & Quality of service (QoS) is a critical consideration in the architecture of IP-based multimedia and multiservice networks. Network resilience refers to a network’s ability to survive network assaults and poor results. If the network becomes more vulnerable to attacks, such as sniffing, spoofing, and malicious operations, data confidentiality will be compromised, and data loss will occur. Every well-designed recovery plan must account for the multiple reliability needs of individual traffic flows to prevent unnecessary bandwidth consumption for standby links and decide which flows to defend against network failures as well as the degree to which they must be defended. \\ \hline Loss & If a network node gets overburdened, it may lose packets and fail. TCP (Transmission Control Protocol) is a networking protocol that defends against packet loss by retransmitting packets lost by the network. As network congestion increases, more packets are lost, resulting in increased TCP transmission. Since most B.W. is used to retransmit lost packets, network capacity can deteriorate if congestion persists. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
**Author Contributions:** [PERSON]--main author of this review article who search and analyse the research idea; [PERSON]--the supervisor who moderated & approved the review as well as provide the funding for the project. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by Geran Putra Berimpak Universiti Putra Malaysia, Vote Number 9659400. Our sincere thanks to Geran Putra Berimpak Universiti Putra Malaysia for their support. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Cell Assembly and Electrochemical Testing  
CNTs and sublimed sulfur were mixed in an 8:2 mass ratio and ground together, then reacted at 155 \({}^{\circ}\)C for 12 h under an Ar atmosphere in a tube furnace to obtain the CNTs/S composite. A cathode slurry was prepared by ball-milling S/CNTs, Super P, and LA133 in a 7:2:1 mass ratio, with water and ethanol as solvents. The resulting slurry was coated onto an aluminum foil current collector using a coating machine, with the coating thickness adjusted to 10 \(\upmu\)m and an active material loading of 1 mg cm\({}^{-2}\). After drying overnight at 60 \({}^{\circ}\)C, the CNTs/S electrodes were cut into 12 mm diameter disks for use.  
Using CNTs/S as the cathode, MoS\({}_{2}\)-NG/PP, MoS\({}_{2}\)-G/PP, and MoS\({}_{2}\)/PP as separators, lithium foil as the anode, and a 1 M LiTFSI solution dissolved in a 1:1 volume ratio of DOL/DME with 2 wt% LiNO\({}_{3}\) as the electrolyte, CR2032-type coin cells were assembled in an argon-filled glovebox and tested for electrochemical performance. Constant current charge/discharge tests and rate performance tests were conducted using a LAND CT2001A battery tester (Landt Instruments, Vestal, NY, USA) with a voltage window of 1.7 to 2.8 V. The current density and specific capacity were calculated based on the sulfur content in the cathode (1 C = 1675 mAh g\({}^{-1}\)). CV and electrochemical impedance spectroscopy (EIS) were performed using an Autolab electrochemical workstation (Metrohm, Herisau, Switzerland). The CV potential window was set to 1.7 to 2.8 V, with a scan rate of 0.1 to 0.5 mV s\({}^{-1}\), and the EIS frequency range was 0.01 to 100 kHz, with a perturbation voltage of 5 mV.
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 4 LSTM for Load Estimation and PPO for Task Offloading Strategy
### PPO for Task Offloading Strategy  
#### 4.2.1 PPO Principles  
The PPO algorithm is a DRL approach, which is primarily used to solve problems in continuous action spaces. The core principle of PPO involves transforming on-policy methods based on a policy gradient into off-policy methods and improving the utilization efficiency of interaction data through importance sampling [29]. Importance sampling corrects the value function of samples generated by the current policy, optimizing the policy parameters. We use the PPO-Clip method.  
PPO-Clip restricts the difference between the new and old policies using a clipping function to control the magnitude of policy updates. The clipping function computes the ratio of the new and old policies on each action sample and limits it within a predetermined range, ensuring that policy updates do not result in excessive changes, to improve the stability and performance of the algorithm.  
Based on the MDP \(\{s_{t},a_{t},r_{t},s_{t+1}\}\), and where \(\pi_{\phi}(s_{t},a_{t})\) is the agent's policy at time step \(t\), the comparison of the new policy and old policy can be calculated as  
\[c_{t}(\phi)=\frac{\pi_{\phi}(s_{t},a_{t})}{\pi_{\phi^{\prime}}(s_{t},a_{t})}, \tag{23}\]  
where \(\phi\) is the parameter of the new policy, and \(\phi^{\prime}\) is the parameter of the old policy.  
PPO uses a value learning approach, where an advantage function \(\hat{A}_{t}\) is constructed to measure the payoff of the current policy, and it is calculated as follows:  
\[\hat{A}_{t}=-V(s_{t})+r_{t}+\lambda r_{t+1}+...+\lambda^{T-t+1}r_{T-1}+\lambda ^{T-t}V(s_{T}), \tag{24}\]  
where \(\lambda\) is the discount factor, which is used to weigh the importance of real-time rewards against past rewards. Therefore, \(\epsilon\) is denoted as the clip factors, and the loss function can be constructed as  
\[L^{CLIP}(\phi)=\hat{\mathbb{E}}_{t}[\min(c_{t}(\phi)\hat{A}_{t},clip(c_{t}( \phi),1-\epsilon,1+\epsilon)\hat{A}_{t})]. \tag{25}\]  
Figure 2 shows the learning process of the agent for the task offloading policy based on the PPO-Clip method as well as the deployment of this policy. It also provides a detailed and clear depiction of the algorithmic workflow of the PPO-Clip method for task offloading. In this process, vehicle users transmit their data to the cloud computing center via the RSU. At the cloud computing center, the agent is trained using the PPO algorithm to generate the appropriate offloading strategy. Once training is completed, the cloud computing center sends the resulting offloading policy back to the vehicle, where the policy is executed.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### Environment Configuration
#### 3.4.4 Reward  
In order for an agent to perform well in learning, appropriate rewards must be given. Algorithm 2 shows the details of the reward determination procedure.  
```
1:remained_traffic\({}_{t}\)= CalRemTraff (M\({}_{r}\))
2:MoveUAV (action)
3:remained_traffic\({}_{t+1}\)= CalRemTraff (M\({}_{r}\))
4:reward = remained_traffic\({}_{t}\) - remained_traffic\({}_{t+1}\)
5:if reward < 0 then
6:reward = \(-\)0.5
7:elseif reward == 0 then
8:if action =='staying' then
9:if A UAV consumes all of its network capability then
10:reward = 0.5
11:else
12:reward = \(-\)2
13:endif
14:else
15:reward = 0.5
16:endif
17:else
18:reward = 2
19:endif
```  
**Algorithm 2** Algorithm for determining the reward value  
Figure 4: An example of providing network services to the ground.  
In the algorithm, a function, CalRemTraff, calculates the remained traffic in the traffic map, M\({}_{r}\). The other function, MoveUAV, makes a UAV move according to the input action, which forwards the step of episode in a learning process. As shown in Figure 4, in the assumed scenario, each UAV provides a network service for traffic on the ground around one's location. The most ideal result is that UAVs are well placed to minimize the sum of the required traffic remaining in the traffic map. Therefore, it is reasonable to receive a reward if the remaining traffic decreases as the UAV moves, and line 18 in Algorithm 2 indicates this situation. Conversely, if the remaining traffic increases due to the movement of the UAV, the penalty, the negative reward, should be given, and lines 5 and 6 are relevant to these operations.  
In addition to the above situations when the remaining traffic increases or not, there is also a situation in which there is no change in the remaining traffic, and we divide this situation into two cases. The first case is when the UAV stays in the position without moving, and we again divide this case into two sub-cases. The first sub-case is when a UAV has already consumed all of its network service capability. In this sub-case, it is a reasonable to continue to provide services with maintaining its position because it makes the most of the UAV's capability, so the UAV receives a reward as indicated by lines 9 to 10 in Algorithm 2. The second sub-case is a situation in which a UAV does not consume all of its capability, which means that it can provide network services more. In this sub-case, staying in the position can be abandoning the opportunity for movement to explore a better position, so the UAV receives a negative reward. Lines 11 and 12 are relevant to these operations. The second case is when a UAV moves, but the remaining traffic does not change. In this case, although the remaining traffic is not decreased, the environment gives a positive reward to the UAV to encourage the UAV to move for finding a better location. Lines 14 and 15 in Algorithm 2 are relevant to these operations.
## 2 Single-Degree-of-Freedom Control Design
\begin{matrix}s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}&-\left(2\zeta \omega_{n}s+\omega_{n}^{2}\right)\\ -g\left(2\zeta_{f}\omega_{n}s+\omega_{n}^{2}\right)&s^{2}+2\zeta_{f}\omega_{n }s+\omega_{n}^{2}\end{matrix} \right]\left\{ \begin{matrix}X(s)\\ U(s)\end{matrix} \right\}=\left[ \begin{matrix}\omega_{n}^{2}\\ 0\end{matrix} \right]\overline{D}(s) \tag{7}\]  
where \(\overline{D}(s)=\mathcal{L}\left\{\overline{d}(t)\right\}\). Then, the determinant of the matrix becomes:  
\[det=s^{4}+2(\zeta+\zeta_{f})\omega_{n}s^{3}+2\left[1+2(1-g)\zeta\zeta_{f} \right]\omega_{n}^{2}s^{2}+2(1-g)(\zeta+\zeta_{f})\omega_{n}^{3}s+(1-g)\omega_ {n}^{4} \tag{8}\]  
Applying the Routh-Hurwitz stability criteria [27] to Equation (8), the following stability condition can be obtained:  
\[\text{Stable if }0<g<1 \tag{9}\]  
Such as the PPF controller, the stability condition is static, which means that the stability condition does not depend on frequency, which is a very desirable stability condition from the point of view of controller design. The closed-loop system, Equation (6), can be drawn by the block diagram shown in Figure 3.  
Figure 4 shows the Bode diagram of the closed-loop transfer function for various gains, where \(\zeta_{f}=0.3\) was employed. It can be seen from Figure 4 that \(g=0.1\) is good enough, and a large gain rather degrades the performance of the closed loop system. Figure 5 shows the uncontrolled and controlled impulse responses when \(\zeta=0.01\), \(\zeta_{f}=0.3\), \(g=0.1\), \(\omega_{n}=20.1\) rad/s. Using logarithmic decrement, the damping factor of the controlled system was found to be 0.107. Figure 6 shows the displacement response the controller is turned on at 4 s. As shown in Figures 5 and 6, it can be seen that the PIPO controller proposed in this study can reduce the response very effectively. Figure 2: Bode diagram for the proposed compensator. (**a**) Magnitude; (**b**) Phase. Figure 4: The Bode diagram of the closed-loop system. (**a**) Magnitude; (**b**) Phase. Figure 5: Free vibration simulation. Figure 3: Block diagram of the closed-loop system.
## 5 Simulation Experimental Results and Discussions
### Range and Variance Analyses
As illustrated in the following, it is calculated as the ratio of the mean square between levels to the mean square within levels:  
\[\text{F}_{fac}=\frac{MS_{fac}}{MS_{e}}=\frac{\frac{SS_{fac}}{\frac{4f_{fac}}{ \text{SS}_{e}}}}{\frac{SS_{e}}{df_{e}}} \tag{14}\]  
where df\({}_{fac}=l-1\) represents the degree of freedom between levels, d\({}_{e}=t-l\) denotes the degree of freedom within levels, \(SS_{fac}=\sum\limits_{m=1}^{l}t_{m}(\overline{y}_{m}-\overline{y})^{2}\) denotes the sum of squares of deviations between levels, and \(SS_{e}=\sum\limits_{m=1}^{l}\sum\limits_{g=1}^{I_{m}}\left(y_{mg}-\overline{y} _{m}\right)^{2}\) signifies the sum of squares of deviations within levels. Based on the obtained F\({}_{fac}\) values, significance level analysis should be performed via the accompanying probability \(p\)-value from the F-distribution table. The case of \(p<0.01\) indicates that the factor possesses a very significant effect on the indicator, whereas the case of \(0.01<p<0.05\) reveals a substantial effect; and finally, the case of \(p>0.05\) indicates that the factor has no significant effect on the indicator [52]. Figure 9 demonstrates the trend graph of the orthogonal experiments, and Table 11 lists the range and variance analysis results of the orthogonal experiments. From the \(R\) and \(SS_{T}\) values in Figure 9 and Table 11, it can be seen that for the hexahedral mesh, the impact weights of the factors on the compliance, maximum stress, weight reduction ratio, and computation time follow the following order: C \(>\) E \(>\) D, C \(>\) D \(>\) E, E \(>\) C \(>\) D, and C \(>\) E \(>\) D, respectively. In addition, for the optimal solution for each indicator, lower values for compliance, maximum stress, and computation time indicate better optimization results. Therefore, the optimal factor level for each indicator is the one with the minimum \(k_{\text{m}}\) value. The optimal solutions for compliance, maximum stress, the weight reduction ratio, and computation time also follow the following order: C1E1D2, C2D2E3, E2C3D2, and C3E1D2, respectively. For the tetrahedral mesh, the impact weights of the factors on the compliance, maximum stress, weight reduction ratio, and computation time follow the following order:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & & \multicolumn{4}{c}{**Hexahedral Mesh**} & \multicolumn{4}{c}{**Tetrahedral Mesh**} \\ \cline{3-10}  & & & **C** & **D** & **E** & **C** & **D** & **E** \\ \hline \multirow{6}{*}{ \begin{tabular}{c} Compliance \\ (m) \(\times\) 10\({}^{4}\) \\ \end{tabular}  } & \(k_{\text{m}}\) & \(k_{1}\) & 6.943 & 6.990 & 6.960 & 6.873 & 6.877 & 6.877 \\  & \(k_{2}\) & 6.987 & 6.973 & 6.997 & 6.877 & 6.873 & 6. C = D > E, C > D > E, C > D > E, and C > E > D, respectively. The corresponding optimal solutions are C1D2E1(C1D2E2), C3D2E3, C1D2E1, and C3E3D3.
## 4 Fabrication and Measurement Challenges  
Although the proposed design had a suitable configuration acting as a LWA with superior characteristics at Sub-THz frequencies, its fabrication might have some level of complexity. For example, there would be slight discrepancies between the simulated and measured results due to the unavoidable air gap between the ground plane and the dielectric layer in the fabrication. However, to address this issue, it is recommended to consider a Teflon-like low-permittivity insulating layer such as a 50 \(\upmu\)m thick fluorinated ethylene propylene (FEP) copolymer film at the beginning of the design [52; 53]. It should be noted that this layer should not be considered too thick in order to achieve a wide single-mode bandwidth. With thermal bonding at 300 \({}^{\circ}\)C, the dielectric is attached on the metal without any adhesive. Moreover, achieving low reflection levels at such high frequencies is relatively challenging, as even some minor deviation in the feature sizes or dielectric constant can potentially impact the antenna performance. As a result, a high-precision fabrication method such as micro-dispensed silver paste should be selected [54] for the metal loading in the proposed antenna. Furthermore, measuring the antenna radiation patterns over the full 180-degree range may also be challenging because of technical limitations. Indeed, at angles near to the backfire, many sources of diffraction associated with antenna-feeding effects (waveguide flange, plastic screw, etc.) or effects related to the absorbing materials can modify the radiation pattern at such high frequencies. Additionally, due to the lack of connectors at sub-THz frequencies, most test equipment is waveguide-based, making the transitions essential for the proposed design. In this work, since the microstrip line is used for feeding, a simple inline transition between this and a rectangular waveguide may also be required for the measurement. The transitions to rectangular waveguide were not modeled in the simulation.
## 2 The Standard PTP
### PTP Operation
#### 2.1.2 Timing-Exchange Mechanism  
The second step of the clock synchronization in the PTP system is a timing exchange between the MC and the slave ports using event and general messages. In the standard PTP, the timing-exchange mechanism is called a delay request-response mechanism. This mechanism identifies the path of the time information exchange by using Sync, Follow_Up, Delay_Req and Delay-Resp messages to measure and calculate the clock drifting between the master and slave ports, as shown in Figure 3.  
The synchronization mechanism begins by transmitting a Sync message from the MC toward the SC; consequently, this message will be time-stamped at the instant of the message departure and arrival at the MC physical layer (t\({}_{1}\)) and the SC physical layer (t\({}_{2}\)), respectively. After that, the MC transmits a Follow_Up message towards SC that contains the time-stamp value of the Sync message (t\({}_{1}\)). Therefore, the SC responds with a Delay_Req message; thus, this message will be time-stamped at the instant of the message departure and arrival at the SC physical layer (t\({}_{3}\)) and the MC physical layer (t\({}_{4}\)), respectively. Finally, the MC forwards a Delay_Resp message towards the SC that contains the time-stamp information of the Delay_Req. Hence, the SC becomes aware of all the required time stamps (t\({}_{1}\), t\({}_{2}\), t\({}_{3}\), t\({}_{4}\)) that are used to correct its local clock.  
Figure 2: A sample PTP network.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 5 Discussion
The developed approach works without any user intervention for parameter selection compared to segmentation-based techniques.The proposed approach can be extended for the development of mobile or web apps, and growers can obtain benefits for accurate and swift crop health monitoring. It is an automatic, straightforward, cost-effective, efficient, scalable, and reproducible approach. It may be extended for leaf disease diagnosis of other crops such as sugar beet, cotton, tomato, and apple.
# Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches
## 3 Results
Sustainability is an aspect that has been investigated by several companies in recent years [201], which shows that concerns regarding the environment have been growing. Hence, it has become increasingly important to include this variable as a criterion in business decision-making. [PERSON] et al. (2018) [150] created a design in a study based on OEE and its relationship with sustainability in Industry 4.0. Meanwhile, other authors incorporated the concept of sustainability in OEE, e.g., [PERSON] et al. (2015) [12] developed the overall environmental equipment effectiveness to identify and measure losses due to sustainability. Likewise, [PERSON] et al. (2018) [191] designed the Sustainable Overall throughput effectiveness indicator to measure the operating performance and factory level sustainability. The OEE has been adapted for the transport sector. To the best of our knowledge, it first occurred in the mining industry [16] and was used to identify possible losses in the availability, performance and quality of equipment such as shovels and trucks. In recent years, the efficiency framework in the port terminal [18] that considers manageable and unmanageable variables has been studied to create indicators based on OEE. Additionally, the OEE has been adapted to road transportation [17] based on distance, load capacity, route time, stops and services. Furthermore, it has been used to evaluate the effectiveness of urban freight transportation [11] as well as optimize availability, performance and quality metrics. Accordingly, some authors have established interesting frameworks that can be developed in future studies. Some of them proposed future studies based on the frameworks that they have developed thus far, whereas others developed innovations in new areas. [PERSON] et al. (2019) [193] used a new OEE formula to identify and implement process improvements. [PERSON] et al. (2018) [190] extended the proposed methodology, including the analysis of material losses based on the finished product. [PERSON] et al. (2018) [150] proposed re-performing experiments for long time periods and as a case study in the manufacturing industry. [PERSON] and [PERSON], (2019) [76] applied the same value stream mapping technique that is used to identify improvements in other factories. By contrast, other authors proposed different areas in which OEE is applicable. In the study by [PERSON] et al. (2018) [17] where OEE was adapted to transport management, they assume that the same methodology is applicable to the service sector and other logistics processes, such as goods reception or performing selection in a warehouse. [PERSON] et al. (2018) [137] and [PERSON] and [PERSON] (2018) [59] reported that their studies can be replicated in the service sector as well as in other industries, such as pharmaceutical, electrical/electronic, textile and transportation (rail and air travel).