esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Frequencies of a Tethered Satellite System
## 4 Natural Frequencies of a Tensioned Beam Model  
It is valuable to investigate the natural frequencies of the flexible tether when the satellite system moves in a circular orbit. Figure 12 shows a tensioned beam model for the tethered satellite system. The tether is subjected to tension due to the gravitational gradients and centrifugal force difference between the two satellites. By applying [PERSON]'s second law to an infinitesimal element \(\mathrm{d}x\), as shown in Figure 12a, the tensile stress \(\sigma\) as a function of \(x\) can be represented by  
\[\sigma(x)=-\frac{GM\rho}{x+r_{1}}-\frac{1}{2}\rho\omega_{c}^{2}(x+r_{1})^{2}+C, \tag{42}\]  
where \(\omega_{c}\) is the angular velocity of the tethered satellite system in a circular orbit around Earth, and \(C\) is an integration constant. To determine \(\omega_{c}\) and \(C\), the application of [PERSON]'s second law to each satellite \(m_{1}\) and \(m_{2}\) yields the following two boundary conditions:  
\[\sigma(0)=\frac{GMm_{1}}{Ar_{1}^{2}}-\frac{m_{1}r_{1}\omega_{c}^{2}}{A}, \tag{43}\]  
\[\sigma(L)=-\frac{GMm_{2}}{A(r_{1}+L)^{2}}+\frac{m_{2}(r_{1}+L)\omega_{c}^{2}}{ A}. \tag{44}\]  
By applying Equations (43) and (44) to Equation (42), the unknowns \(\omega_{c}\) and \(C\) can be determined as  
\[\omega_{c}=\sqrt{\frac{GM\left[m_{1}/r_{1}^{2}+m_{2}/(r_{1}+L)^{2}+\rho AL/r_ {1}(r_{1}+L)\right]}{\left[m_{1}r_{1}+m_{2}(r_{1}+L)+\rho AL/(r_{1}+L/2) \right]}}, \tag{45}\]  
\[C=C_{n}/C_{d}, \tag{46}\]  
where  
\[ \begin{array}{ll}C_{n}&=2 GMm_{1}m_{2}L\left[r_{1}^{2}+r_{1}(r_{1}+L)+(r_{1}+ L)^{2}\right]\\ &+GM\rho A\left\{m_{1}(r_{1}+L)\left[2r_{1}^{3}+(r_{1}+L)^{3}\right]+m_{2}r_{1} \left[r_{1}^{3}+2(r_{1}+L)^{3}\right]\right\}\\ &+GM\rho^{2}A^{2}r_{1}(r_{1}+L)\left[(r_{1}+L)^{3}-r_{1}^{3}\right],\end{array}  \tag{47}\]  
Figure 12: Tethered satellite system in a circular orbit around Earth: (**a**) schematic diagram for calculating tensile stress and (**b**) transverse and longitudinal deformations of the tether. \[C_{d}=2 Ar_{1}^{2}(r_{1}+L)^{2}[m_{1}r_{1}+m_{2}(r_{1}+L)+\rho AL(r_{1}+L/2)]. \tag{48}\]  
Note that Equation (45) reduces to Equation (6), which represents the angular velocity of the dumbbell model, when the mass of the tether is neglected (i.e., when \(\rho=0\)). It is necessary to determine the average value of the tensile stress \(\sigma\), which is a function of position \(x\) as indicated in Equation (42). To derive the natural frequencies for both transverse and longitudinal vibrations of the tether, it must be modeled as a linearized tensioned beam. This linearized model necessitates the use of the average stress over the entire length of the tether, rather than the position-dependent stress.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 3 Direct Tensile Test of HMAC
### Test Matrix  
As shown in Figure 3, fatigue tests were performed by Material Test System (MTS) with a strain-controlled load on asphalt concrete prisms and were conducted in the laboratory.  
Specimens must be a suitable shape and size for strain control and to ensure the reliability of test results. Several 300 mm \(\times\) 300 mm \(\times\) 50 mm plate specimens were firstly prepared and then they were cut into 250 mm \(\times\) 50 mm \(\times\) 50 mm prism samples in accordance with test procedures of direct tension testing in the literature [17].  
As shown in Figure 4, both ends of the specimen were bonded with a steel plate, and a spherical hinge was set at the end of the active loading position, which ensured the central tension on the tip of the specimen. The strain-controlled cyclic loads were applied on a steel plate, and the spherical hinge could be automatically set back when tension was applied to the specimen. In order to avoid uncontrolled tension, the load was set on the middle of the prism.  
Figure 3: Material Test System (MTS).  
Figure 2: Gradation of the aggregates used in this study.
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 3 Proposed Approach
### SVM Kernel Selection  
In this sub-section, we explain our method for SVM kernel selection. The most commonly applied kernels are the linear kernel, polynomial kernel, radial basis function (RBF) kernel, sigmoid kernel, and intersection kernel. In [16; 17], a typical procedure was suggested by [PERSON] and [PERSON]. The procedure consists of trying a linear kernel first and then checking if the performance is better when using a non-linear kernel (e.g., a polynomial kernel or RBF). In [14], an approach based on the RBF kernel showed better accuracy and convergence time compared to the polynomial kernel. Consequently, we have chosen to first try the linear kernel and then the RBF kernel.  
In [18], a grid search for the support vector machine was carried out, and the accuracy was considered to evaluate the performance of different models. In fact, for the RBF kernel, besides the hyper-parameter \(\gamma\), the selection of another parameter, the penalty coefficient \(C\), is also important, as SVM adjusts the trade-off between the margin and fitting error by modifying the parameter \(C\). Thus, the model needs to be tuned by applying several tests in order to avoid overfitting or underfitting as much as possible. A search of combinations for the values of the \(C\) and \(\gamma\) parameters was performed. Indeed, in this study, the RBF kernel, where \(\gamma=\frac{1}{\text{sample}\text{ nbr}}\), outperforms in SVM training process tests; the penalty coefficient parameter \(C\) was set to 0.1. This combination showed an accuracy of 0.9905 in the tests.
## 4 Spatial Pattern Formation and Correction of System Aberrations
The respective results are summarized in Table 1. Whilst aberration correction boosted the overall performance of the blue beam path over the whole field of view, for the yellow beam path, an optimization in just one major axis can be observed at a slight loss of resolution in the other major axis. Here, foci size at the edge of the FOV increases by an average of 14% compared to the center. From these measurements, we can estimate that the maximum power density of a single focus will be in the order of 6 Wmm\({}^{-2}\) for the blue and 11 Wmm\({}^{-2}\) for the yellow beam path, roughly 3 orders of magnitude more than usual power densities for optogenetics experiments. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **488 nm** & **589 nm** \\ \hline \multirow{2}{*}{Before optimization} & x & \(1.7\pm 0.4\) & \(1.2\pm 0.1\) \\ \cline{2-4}  & y & \(1.6\pm 0.3\) & \(1.2\pm 0.2\) \\ \hline \multirow{2}{*}{After optimization} & x & \(1.3\pm 0.2\) & \(1.3\pm 0.2\) \\ \cline{2-4}  & y & \(1.3\pm 0.1\) & \(1.01\pm 0.08\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Beam waist diameters before and after aberration correction, normalized to the diffraction limit according to Equation (5). Figure 3: Single focus features over the field of view. Top row: 488 nm beam path \(D_{4\
u}\) focus diameters along the major axes after aberration correction.
Furthermore, with the continuous proliferation of network devices and users [19], the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation [20]. The dynamic traffic grooming (DTG) algorithm, based on elastic optical networks, proves to be an effective solution to address the conflict between user needs and network limitations [21; 22]. Elastic services, characterized by diverse time slot requirements and dynamic arrivals or departures, result in numerous resource fragments. The DTG algorithm is well-suited to resolving this challenge. As for the traffic grooming method of satellite optical networks, the researchers solve the problem of network resources and a high degree of resource fragmentation by describing resource fragmentation and designing a two-step traffic grooming method [23]. However, the above algorithm needs to pay attention to the needs of services for slot allocation. At the same time, it is essential to note that the DTG algorithm is rarely used in satellites, so designing it based on the relevant characteristics of the EOSNs is necessary. In this paper, leveraging the SD-EOSN architecture applied to EOSNs, a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) is proposed. This algorithm takes advantage of the time elasticity characteristics of ARservices and consists of three main components: establishing an elastic optical satellite network virtualization model based on an enhanced twin-layer (EOSNV-ETL), designing an enhanced twin-layer elastic optical satellite network virtualization algorithm based on path evaluation (EOSNV-ETL-PE), and proposing a dynamic traffic grooming based on link fragment assessment (DTG-LFA).
## 2 Second configuration: The Improved ADRC (IADRC)
### Results of the Decentralized ADRC Control Scheme
The nominal model is represented by the  
Figure 11: The output response of (42) using IADRC, (**a**) output curve \(y_{1}\), (**b**) output curve \(y_{2}\), (**c**) control signals \(u_{1}\) and \(u_{2}\), and (**d**) estimated generalized disturbances \(\hat{\xi}_{1,3}\) and \(\hat{\xi}_{2,3}\). following mathematical model with the functions \(f_{1}\) and \(f_{2}\) without the external disturbances \(w_{1}\) and \(w_{2}\), as shown below:  
\[\left\{ \begin{array}{c}\dot{\xi}_{1,1}=\xi_{1,2}\\ \dot{\xi}_{1,2}=\xi_{1,1}+\xi_{2,1}+\eta+b_{1,1}u_{1}+b_{1,2}u_{2}\\ y_{1}=\xi_{1,1}\\ \dot{\xi}_{2,1}=\xi_{2,2}\\ \dot{\xi}_{2,2}=\xi_{1,2}+\xi_{2,2}+\eta+b_{2,1}u_{1}+b_{2,2}u_{2}\\ y_{2}=\xi_{2,1}\\ \dot{\eta}=\xi_{1,2}+\xi_{2,1}+sin(\eta)+sin(t)\end{array} \right.\]  
where \(f_{1}\) and \(f_{2}\) are defined during the simulations, as: \(f_{1}=\xi_{1,1}+\xi_{2,1}+\eta+sin(\xi_{1,2}+\xi_{2,2})w_{1},\;\;f_{2}=\xi_{1,2 }+\xi_{2,2}+\eta+sin(\xi_{1,1}+\xi_{2,1})w_{2}\). Now, \(w_{1}\) and \(w_{2}\) are time-varying, which forces \(f_{1}\) and \(f_{2}\) to be time-varying, too. Moreover, it was mentioned previously that the coefficient \(b_{i,q}\) is approximate of \(g_{i,q}\) in the system with a \(\pm 50\%\) zone, where, during the simulations, we took the worse-case condition for the coefficients \(g_{1,1}(t),g_{1,2}(t),g_{2,1}(t),g_{2,2}(t)\), i.e., they are considered as time-varying coefficients with, \(g_{1,1}(t)=1+\frac{1}{10}sin(t),g_{1,2}(t)=1+\frac{1}{10}cos(t),\;\;g_{2,1}(t) =1+\frac{1}{10}2^{-t}\), and \(g_{2,2}(t)=-1\). This reflects a big mismatch between the nominal process, where no external disturbances are acting on it and it has no parameter variation, and the mathematical model used in the simulations with external disturbances and inherent parameter uncertainties. \(\bullet\) **Case (2): Input and State Decoupling**  
Two reference inputs \(r_{1}\) and \(r_{2}\) sequentially applied to the nonlinear system (Equation (42)) to check the disassociation of the input and state couplings between the two subsystems. The transient response of the outputs \(y_{1}\) and \(y_{2}\), in response to reference inputs \(r_{1}\) and \(r_{2}\), sequentially applied to the nonlinear system of Equation (42), are shown in Figures 12 and 13. As can be seen from these figures, the decoupling requirement stated in the problem statement was very well satisfied, with a smooth response on each output channel. The proposed decentralized control scheme converted the nonlinear system of Equation (1) into two non-interacted SISO subsystems. Figure 12: Cont. Figure 12: The output tracking of Equation (42) due to reference inputs \(r_{1}\) and \(r_{2}\) using decentralized control scheme with CADRC, (**a**) \((r_{1},r_{2})=(\sin(t),0)\), (**b**) \((r_{1},r_{2})=(0,\cos(t))\). Figure 13: The output tracking of (42) due to reference inputs \(r_{1}\) and \(r_{2}\), using a decentralized control scheme with IADRC, (**a**) \((r_{1},r_{2})=(\sin(t),0)\); (**b**) \((r_{1},r_{2})=(0,\cos(t))\).
# Evaluating Creativity: Can LLMs Be Good Evaluators in Creative Writing Tasks?
## Appendix C Score Distribution Analysis  
The frequency distributions of scores assigned to 200 writings are presented in Figures 11 and 12, evaluated by human evaluators and four different AI models across six creativity criteria: fluency, flexibility, elaboration, originality, usefulness, and specific creativity strategies. The graph illustrates how often each score, whether one point, two points, or three points, was assigned. This provides insights into the evaluation patterns for human writings and AI-generated writings.  
A distinct pattern emerges between human and AI evaluators. In human writing, scores are more evenly distributed across two points and three points, with a notable number of one points in categories such as usefulness and flexibility. This suggests that human writing exhibits a wider variability in perceived quality, particularly in practical applicability. Additionally, elaboration consistently receives the highest number of three points, indicating that human writing tends to be rated as highly detailed.  
In contrast, GPT-4-generated writing shows a more structured distribution, with a stronger tendency toward two points and three points, particularly in fluency and elaboration. This suggests that AI-generated texts maintain coherence and depth with high consistency. However, usefulness remains a category with relatively lower scores, reinforcing the notion that while GPT-generated writing is well structured, it may lack practical applicability or engagement. Additionally, AI evaluations display fewer one points overall, suggesting that its outputs rarely fall into the lowest-scoring bracket, unlike human writing, where evaluation is more varied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\chi^{2}\) & \(p\) \\ \hline
**Human writings** & 18,227.18 & \textless{}0.001 \\
**AI writings** & 15,992.43 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Chi-square test results for human and GPT-4 writings.  
Furthermore, AI models exhibit highly similar scoring distributions within their respective types. GPT-4o-based evaluators tend to assign scores in a more consistent manner, while GPT-3.5-based evaluators show similar tendencies among themselves. This pattern suggests that AI-based evaluation reflects learned assessment tendencies, resulting in predictable scoring distributions within the same model type. Conversely, human evaluators show greater variation in their ratings, highlighting the subjective nature of human judgment in creative writing evaluation.  
These findings indicate that while GPT-generated writing excels in fluency and elaboration, human writing demonstrates more diverse evaluation patterns, particularly in subjective aspects such as originality and usefulness. Additionally, the similarity in scoring distributions among AI models suggests that LLM-based evaluation may reinforce internal biases, leading to a more standardized but less nuanced assessment of creative writing.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Actor Critic. \\ AWGN & Additive White Gaussian Noise. \\ CBR & Coded Bit Rate. \\ CCT & CQI Correction Term. \\ CDR & Coded Data Rate. \\ CQI & Channel Quality Indicator. \\ CR & Code Rate. \\ CRC & Cyclic Redundancy Check. \\ DRL & Deep Reinforcement Learning. \\ DRLLA & Deep Reinforcement Learning for Link Adaptation. \\ GR & GNU Radio. \\ HMM & Hidden Markov Model. \\ LA & Link Adaptation. \\ LTS & Latent Thompson Sampling. \\ MCS & Modulation and Coding Scheme. \\ MIMO & Multiple Input and Multiple Output. \\ mMIMO & Massive MIMO. \\ ML & Machine Learning. \\ NAC & Natural Actor Critic. \\ NLOS & Non-Line of Sight. \\ NN & Neural Network. \\ NR & New Radio. \\ OFDM & Orthogonal Frequency-Division Multiplexing. \\ OLLA & Outer Loop Link Adaptation. \\ PDF & Probability Density Function. \\ PER & Packet Error Rate. \\ PPO & Proximal Policy Optimization. \\ PSR & Packet Success Rate. \\ RL & Reinforcement Learning. \\ RSSI & Received Signal Strength Indicator. \\ SC & Sub carrier. \\ SINR & Signal to Interference plus Noise Ratio. \\ SISO & Single Input and Single Output. \\ SNR & Signal to Noise Ratio. \\ TROLL & Training of Outer Loop Link Adaptation. \\ UE & User Equipment. \\ \end{tabular}
## 4 Results and Discussion
### Case Study Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Validation** & **ANN** & **Hybrid** & **RCGA** & **SOGA** & **Ensemble** & **Ensemble** \\  & **Ensemble** & **Ensemble** & **Ensemble** & **Ensemble** & **AVG** & **EB** \\ \hline
**MAE** & 0.0363 & 0.0361 & 0.0378 & 0.0374 & 0.0355 & 0.0355 \\
**MSE** & 0.0031 & 0.0031 & 0.0031 & 0.0030 & **0.0028** & **0.0028** \\
**Testing** & & & & & & \\ \hline
**MAE** & 0.0393 & 0.0394 & 0.0399 & 0.0391 & 0.0381 & 0.0381 \\
**MSE** & 0.0037 & 0.0037 & 0.0036 & 0.0034 & **0.0034** & **0.0034** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculated errors for each ensemble forecaster based on scores for Thessaloniki. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Validation** & **ANN** & **Hybrid** & **RCGA** & **SOGA** & **Ensemble AVG** & **Ensemble EB** \\ \hline
**MAE** & 0.0322 & 0.0324 & 0.0372 & 0.0365 & 0.0326 & 0.0319 \\
**MSE** & 0.0030 & 0.0028 & 0.0033 & 0.0032 & **0.0027** & **0.0027** \\
**Testing** & & & & & \\ \hline
**MAE** & 0.0412 & 0.0417 & 0.0466 & 0.0468 & 0.0427 & 0.0417 \\
**MSE** & 0.0043 & 0.0041 & 0.0047 & 0.0047 & **0.0040** & **0.0040** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculated errors for individual forecaster based on scores for Larissa. Figure 4: Forecasting results for Thessaloniki considering the two ensemble methods (AVG, EB) based on scores. (**a**) Validation, (**b**) Testing. AVG, simple average; EB, error-based. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Validation** & **ANN** & **Hybrid** & **RCGA** & **SOGA** & **Ensemble AVG** & **Ensemble EB** \\ \hline
**MAE** & 0.0337 & 0.0332 & 0.0371 & 0.0362 & 0.0329 & 0.0326 \\
**MSE** & 0.0032 & 0.0030 & 0.0032 & 0.0031 & 0.0027 & **0.0026** \\
**Testing** & & & & & & \\ \hline
**MAE** & 0.0428 & 0.0417 & 0.0458 & 0.0460 & 0.0426 & 0.0423 \\
**MSE** & 0.0048 & 0.0044 & 0.0045 & 0.0045 & 0.0041 & 0.0040 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Case (B) -Calculated errors for each ensemble forecaster based on scores for Larissa. Figure 5: Forecasting results for Larissa considering the two ensemble methods (AVG, EB) based on scores. (**a**) Validation, (**b**) Testing. Figure 6: _Cont._
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 2 Materials and Methods  
In this section, we describe several methods for dealing with global time warping. First, we explain the standard DTW algorithm for completeness, and then we describe four different ways to handle global time warping.  
Standard DTW estimates the alignment between two sequences \(x_{0}\), \(x_{1}\),...\(x_{N-1}\) and \(y_{0}\), \(y_{1}\),..., \(y_{M-1}\) in the following manner. First, a pairwise cost matrix \(C\in\mathbb{R}^{N\times M}\) is computed, where \(C(i,j)\) indicates the distance between \(x_{i}\) and \(y_{j}\) under a particular cost metric (e.g., Euclidean distance, cosine distance). Next, a cumulative cost matrix \(D\in\mathbb{R}^{N\times M}\) is computed with dynamic programming, where \(D(i,j)\) indicates the optimal cumulative path cost from \((0,0)\) to \((i,j)\) under a pre-defined set of allowable transitions and transition weights. For example, with a set of allowable transitions \(\{(1,1),\)\((1,2),\)\((2,1)\}\) and corresponding transition weights [2; 3; 3], the elements of \(D\) can be computed using the following recursion:  
\[D(0,0) =C(0,0) \tag{1}\] \[D(i,j) =min\begin{cases}D(i-1,j-1)+2\cdot C(i,j)\\ D(i-1,j-2)+3\cdot C(i,j)\\ D(i-2,j-1)+3\cdot C(i,j)\end{cases} \tag{2}\]  
During this dynamic programming stage, a backtrace matrix \(B\in\mathbb{Z}^{N\times M}\) is also computed, where \(B(i,j)\) indicates the optimal transition ending at \((i,j)\). Once \(D\) and \(B\) have been computed using dynamic programming, we can determine the optimal path through the cost matrix by following the backpointers in \(B\) starting at position \((N-1,M-1)\). The optimal path defines the predicted alignment between the two sequences.
## 5 Conclusions  
(1) In comparison with NA RA, the NS RA has a good modification effect as well. Whether it is original asphalt or aging asphalt, the softening point value of NS RA-modified asphalt is larger, and its penetration value is lower, in comparison with its base asphalt. At the same time, with the increase of NS RA content, its softening point and viscosity values are greatly increased; the penetration ratio is elevated, and its penetration value is greatly decreased. Therefore, NS RA can obviously improve the anti-rutting ability of the asphalt binder, and enhance the anti-aging performance of the asphalt binder as well. In comparison with the base asphalt, the low-temperature performance of NS RA modified asphalt slightly declines, and its low-temperature performance becomes weaker with an increase in NS RA content.  
(2) Considering the raw-material price of NS RA, and the anti-rutting performance of NS RA modified asphalt mixture, the optimum NS RA content is about 8%. For an NS RA content of 8%, the dynamic stability values of NS RA modified asphalt mixtures are far greater than the summer hot-area requirements of the dynamic stability value (no less than 2800 times/mm). Therefore, the anti-rutting performances of their modified asphalt mixtures are excellent. At the same time, NS RA-modified asphalt mixtures used in the middle layer of a pavement can obviously improve the anti-rutting performance of the pavement.  
(3) For the optimum NS RA content of 8%, its modified asphalt mixtures have good anti-water damage and ability anti-fatigue ability, and its anti-cracking performance meets the requirements for the zones with a minimum temperature of no less than \(-21.5\)\({}^{\circ}\)C. Therefore, it is feasible to determine the optimum NS RA content, based on its anti-rutting performance, and except for extremely low temperature areas, an effective method for solving the rutting problem of asphalt pavement could involve the use of NS RA-modified asphalt.  
Writing--original draft preparation, L.L.; project administration, Z.H.; analyzing test data and writing--review and editing, W.L.; performing the experiment, C.H.  
This research was funded by the Hunan Provincial Natural Science Foundation of China (Grant No. 2015J2073), Hunan Provincial Department of Education of China (Grant No. 16A082) and the construct program of applied characteristic discipline in Hunan University of Science and Engineering.  
The authors declare no conflict of interest.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 5 Analyzing the Challenges and Advantages of Protocols in Underwater Communication
### MAC Layer Protocols
#### 5.1.3 Hybrid MAC Protocol Design  
Hybrid Spatial Reuse (HSR-TDMA) was proposed in 2010. This technique has been applied in underwater ad-hoc networks which use the spatial reuse methods to improve the throughput level. The experimental result showed that the availability of underwater nodes increased to transmit the data [31]. Hybrid Medium Access Control Protocol (H-MAC) was developed in 2010. This protocol was proposed to improve the power efficiency in traffic, quality of service, channel utilization, etc. [32], Pattern-MAC (P-MAC) was developed in 2005. The sleep-wake schedule of underwater nodes was adaptively determined based on the traffic condition and the neighbor nodes [33]. UW-MAC, also known as CDMA-based energy control MAC, was proposed in 2010. This protocol focuses on delay, network throughput, and increased network lifetime [34].