esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations  
The study presents valuable insights into DIR systems but also reveals several limitations that warrant consideration for future research endeavors. Firstly, the sensitivity of OCR-based DIR systems to image quality poses a significant challenge. These systems may struggle with low-quality or distorted document images, affecting overall performance. Future research should prioritize enhancing the robustness of OCR engines to mitigate these issues, ultimately improving system performance across a broader range of image qualities. Another limitation arises from the specificity of the dataset used in this study, primarily consisting of university exam preparation books. While the findings offer valuable contributions, they may not fully capture the diversity of document images encountered in various domains. Future research should extend the evaluation of DIR systems to more extensive and diverse datasets to assess their generalizability and applicability across different document types and domains.  
The computational demands of strong DIR systems, particularly those relying on image features like SURF, raise concerns about real-time applications and scalability to large document databases. Addressing computational efficiency remains a critical challenge for future research and will ensure that DIR systems remain practical and accessible for various applications. Lastly, generalization is a critical consideration. The study's focus on specific DIR systems in the context of university exam preparation books should prompt further investigation into the applicability of these systems to a broader range of document typesand domains, ensuring their relevance and effectiveness in various real-world scenarios. Addressing these limitations will pave the way for more robust and versatile DIR systems.
## 4 Simulation Results and Discussion
### Effects of the OIS's Load Ratio on Various Aspects of Performance
#### 4.3.3 Average RSS  
Figure 24 shows the performance of the OIS's average RSS in relation to its load ratio in various channel environments. Regardless of the presence of fading effects in the channels, the toss-and-catch algorithm has a better average RSS than the two control groups at various OIS load ratios. For example, when the load ratio of the OIS is 50% and there are no fading effects in the channel, as shown in Figure 24a, the performances of Control Groups 1 and 2 were about 11 dB and about 17 dB lower, respectively, than that of the toss-and-catch algorithm. When Nakagami-3 fading is employed, the performances of Control Groups 1 and 2 were about 19 dB and about 20 dB lower, respectively, than that of the toss-and-catch algorithm; when Nakagami-1 fading is employed, the performances of Control Groups 1 and 2 were about 27 dB and about 29 dB lower, respectively, than that of the toss-and-catch algorithm; when Nakagami-1/2 fading is employed, the performances of Control Groups 1 and 2 were both about 26 dB lower than that of the toss-and-catch algorithm. All of the aforementioned outcomes are due to the lack of an effective design in both control groups, which resulted in a relatively lower average RSS. Therefore, when the level of fading increases, the control groups are less able to resist the impacts such that the declines in average RSS become more significant and even to the point of disconnection (according to the findings in Section 4.1, a signal cannot be demodulated when the RSS is lower than \(-99.8757\) dBm).  
Figure 23: Comparing the performance of the OIS’s handover frequency in relation to its load ratio using the three methods in (**a**) nonfading environments; (**b**) Nakagami-3 fading environments; (**c**) Nakagami-1 fading environments; and (**d**) Nakagami-1/2 fading environments.  
With regard to the toss-and-catch algorithm, when the load ratio of the OIS is between 0% and 50%, the performance of the algorithm in terms of average RSS increases in response to an increasing load ratio; whereas when the load ratio is between 50% and 100%, the performance of the algorithm decreases in response to an increasing load ratio. In the former, when the load ratio of the OIS increases, there are more chances for activating the overload support mechanism in which the ASBSs can undertake the loads of MSBSs that are closer to the OIS. Consequently, the OIS has a higher chance of selecting these MSBSs, thereby increasing its average RSS. In the latter, when the load ratio of the OIS increases, the overload support capacity of the ASBSs is gradually full, and the average RSS decreases slightly. Nevertheless, compared to the two control groups that lack an ASBS overload support mechanism, the performance of the toss-and-catch algorithm remains significantly better.
###### Abstract  
Tunnelling is a vital geotechnical engineering feature of underground transportation systems that is potentially hazardous if not properly investigated, studied, planned, and executed. A reliability, availability, and maintainability (RAM) analysis is one of the main practical techniques in machinery-based projects to recognize the failure and repair rates of machines during or after their operations. RAM analysis of mechanized tunneling can help to manage the project safety and cost, and improve the availability and performance of the machine. There are several methods to obtain and predict the RAM of a system, including the Markov chain simulation and other statistical methods; however, the result of the analysis can be affected by the selected method. This paper presents the results of a critical investigation on the RAM of the Earth pressure balance machines (EPBMs) used in developing an urban metro project in Isfahan, Iran. The five kilometer length of the first line of the Isfahan metro project was excavated using EPBMs over four years. After overhauling the EPBMs and making some minor changes, excavation of the second line started, and to date, about 1.2 km has been excavated by the refurbished machines. In the present study, a RAM analysis has been applied to electrical, mechanical, and cutter head subsystems of the EPBMs in Lines 1 and 2 of the Isfahan metro project over an 18- and 7-month period of machine operation, respectively. The results show that the estimated availability, A(t), determined by the Markov method, is closer to reality but cannot be propagated to reliability R(t) and maintainability M(t) analysis. It was also revealed that by predicting the required maintenance and proper planning, the overall availability of the EPBM was improved from 45% in Line 1 to 61% in Line 2. The outcomes of this study can be used in the future planning of urban tunneling projects to estimate machine, staff, and logistic performance with the least possible error, and appropriately arrange the factors involved in the system.  
EPBM; reliability; availability; maintainability; Markov method; network modeling +
Footnote †: journal: Applied Sciences  
The RAM analysis helps managers to estimate machine, staff, and logistic performance with the least possible error and adequately plan the performance of the factors involved in the system. In general, reliability R(t), availability A(t), and maintainability M(t) are obtained by three methods: renewable process (RP), non-homogeneous Poisson process (NHPP), and homogeneous Poisson process (HPPP) [3]. Each of these methods has specific conditions and, consequently, probability distributions. Continuous or repairable systems could be simulated by stochastic processes, such as Markov, semi-Markov, and semi-regenerative [4]. Markov processes represent a straightforward generalization of sequences of independent random variables. These are processes without after effect. Considering this, the evolution of the process after an arbitrary time point, \"t,\" only depends on \"t\" and on the state occupied at \"t\", not on the evolution of the process before \"t\" [3].
## 5 Empirical Test and Result Analysis
### Endogenous Treatment
#### 5.3.5 Robust Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline \multirow{4}{*}{_Lndigital_} & **HZESG** & _ESG_ & _ESG_ & _ESG_ & _ESG_ & _ESG_ & _ESG_ \\ \cline{2-8}  & 0.0188 * & 0.0145 ** & 0.0196 ** & 0.0103 *** & 0.0114 *** & 0.0207 *** & \\ \cline{2-8}  & (0.0101) & (0.0057) & (0.0076) & (0.0026) & (0.0032) & (0.0046) & \\ \hline \multirow{4}{*}{_DDD_} & \multicolumn{8}{c}{0.0466 ***} \\ \cline{2-8}  & 0.0466 *** & & & & & (0.0063) \\ \cline{2-8}  & 0.0466 *** & & & & & (0.0063) \\ \hline \multirow{4}{*}{_Lnsize_} & 0.1550 *** & 0.0673 *** & 0.0650 *** & 0.0694 *** & 0.0883 *** & 0.0692 *** & 0.0719 *** \\ \cline{2-8}  & (0.0130) & (0.0067) & (0.0088) & (0.0033) & (0.0041) & (0.0056) & (0.0030) \\ \hline \multirow{4}{*}{_Lev_} & \(-0.563\) *** & \(-0.0652\) & \(-0.0542\) & \(-0.0728\) *** & \(-0.128\) *** & \(-0.000780\) & \(-0.0591\) *** \\ \cline{2-8}  & (0.0808) & (0.0400) & (0.0491) & (0.0207) & (0.0277) & (0.0341) & (0.0185) \\ \hline \multirow{4}{*}{_Growth_} & \(-0.0223\) * & \(-0.0051\) & \(-0.0086\) & \(-0.0006\) & \(-0.0014\) & \(-0.0060\) & \(-0.0031\) \\ \cline{2-8}  & (0.0121) & (0.0043) & (0.0066) & (0.0029) & (0.0034) & (0.0062) & (0.0027) \\ \hline \multirow{4}{*}{_Roa_} & 0.8820 *** & \(-0.0898\) & \(-0.0105\) & \(-0.0678\) & \(-0.2030\) ** & \(-0.1210\) & \(-0.0688\) \\ \cline{2-8}  & (0.2290) & (0.0732) & (0.0908) & (0.0566) & (0.0823) & (0.0898) & (0.0489) \\ \hline \multirow{4}{*}{_Cashflow_} & \(-0.5270\) *** & 0.1380 ** & 0.1870 ** & 0.1200 ** & 0.0883 & 0.1760 ** & 0.1230 *** \\ \cline{2-8}  & (0.190) & (0.0629) & (0.0809) & (0.0471) & (0.0599) & (0.0838) & (0.0433) \\ \hline \multirow{4}{*}{_Mb_} & 0.0546 & 0.0147 & 0.0459 *** & 0.0122 & \(-0.0363\) *** & 0.0373 *** & 0.0064 \\ \cline{2-8}  & (0.0333) & (0.0146) & (0.0168) & (0.0089) & (0.0127) & (0.0126) & (0.0079) \\ \hline \multirow{4}{*}{_Fsh1_} & 0.0802 & 0.0265 & 0.0801 & \(-0.0111\) & 0.0199 & 0.0954 *** & 0.0811 *** \\ \cline{2-8}  & (0.0781) & (0.0417) & (0.0569) & (0.0190) & (0.0264) & (0.0321) & (0.0187) \\ \hline \multirow{4}{*}{_Dlzb_} & 0.3500 * & 0.0550 & \(-0.1370\) & 0.1050 ** & 0.2780 *** & \(-0.1810\) ** & 0.0285 \\ \cline{2-8}  & (0.207) & (0.0946) & (0.118) & (0.0513) & (0.0655) & (0.0781) & (0.0463) \\ \hline \multirow{4}{*}{_Big4_} & 0.2080 *** & 0.1480 *** & 0.1490 *** & 0.1320 *** & 0.1270 *** & 0.1550 *** & 0.1410 *** \\ \cline{2-8}  & (0.0393) & (0.0222) & (0.0306) & (0.0103) & (0.0119) & (0.0189) & (0.0095) \\ \hline \multirow{4}{*}{_Lange_} & 0.3530 *** & 0.0910 *** & 0.1180 *** & 0.0897 *** & 0.0809 *** & 0.1320 *** & 0.0941 *** \\ \cline{2-8}  & (0.0361) & (0.0197) & (0.0261) & (0.0091) & (0.0119) & (0.0145) & (0.0087) \\ \hline \end{tabular}
\end{table} 
Table 8: Robust regression (* \(p\) < 0.1, **_p_ < 0.05, *** \(p\) < 0.01).
## 5 Results and Discussion  
This section discusses the localization performance of iaABC for WSNs in a 3D environment. In this context, the proposed method is compared with the competitive algorithms  
\begin{table}
\begin{tabular}{l c c c} \hline
**Algorithm** & **CS** & **D** & **Others** \\ \hline Co-PPSO & 30 & 5 & \\ SSA & 30 & 5 & c1, c2, c3: 1.494 \\ SCA & 30 & 5 & r1, r2, r3, r4: random \\ AFPA & 30 & 5 & p\({}_{t}\): Dynamic and adaptive \\ ABC & 30 & 5 & Limit: 75 \\ aABC & 30 & 5 & Limit: 75 \\ iaABC & 30 & 5 & Limit: 75 \\ \hline \end{tabular}
\end{table}
Table 6: Parameter set for the algorithms for optimal location of target nodes.  
Figure 11: Node positioning in a 3D environment with ABC.  
Figure 10: Node positioning in a 3D environment with the AFPA.  
Figure 9: Node positioning in a 3D environment with the SCA.  
Table 7 contains the errors calculated for the results obtained by the competitive algorithms for each experiment. Figure 14 displays the average error values for these experiments. A review of Figure 14 reveals that the proposed method yields results with generally lower errors.  
The average processing time spent by the competitive algorithms for 30 experiments is shown in Figure 15. The processing time is presented in seconds and milliseconds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & **Number of Movements** & \begin{tabular}{c} **Localization** \\ **Error** \\ **(Max)** \\ \end{tabular} & \begin{tabular}{c} **Localization** \\ **Error** \\ **(Min)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Error** \\ **(Min)** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Located** \\ **Targets** \\ \end{tabular} \\ \hline AFPA & 1 & 4.2779 & 0.1011 & 0.9861 & 100 \\  & 2 & 5.4338 & 0.0977 & 1.2391 & 100 \\  & 3 & 7.1194 & 0.1191 & 1.4468 & 100 \\  & 4 & 6.7734 & 0.0855 & 1.7348 & 100 \\  & 5 & 6.4891 & 0.0958 & 1.6814 & 100 \\ ABC & 1 & 4.3911 & 0.1148 & 1.1328 & 100 \\  & 2 & 6.0124 & 0.1067 & 1.3045 & 100 \\  & 3 & 7.1842 & 0.1247 & 2.2559 & 100 \\  & 4 & 7.0528 & 0.1163 & 1.8309 & 100 \\  & 5 & 6.8703 & 0.1189 & 1.9671 & 100 \\ aABC & 1 & 4.5803 & 0.0914 & 0.7832 & 100 \\  & 2 & 5.2579 & 0.1011 & 0.8011 & 100 \\  & 3 & 5.7711 & 0.0933 & 0.7546 & 100 \\  & 4 & 6.9016 & 0.0754 & 0.7949 & 100 \\  & 5 & 6.4715 & 0.0994 & 0.7284 & 100 \\ iaABC & 1 & 4.1107 & 0.0811 & 0.9993 & 100 \\  & 2 & 5.3199 & 0.1181 & 0.8597 & 100 \\  & 3 & 5.5551 & 0.0733 & 0.6311 & 100 \\  & 4 & 5.6916 & 0.0848 & 0.6882 & 100 \\  & 5 & 5.4157 & 0.0969 & 0.6118 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 14: Average localization errors for competitive algorithms.  
Figure 15: Average processing time for competitive algorithms.
## 1 Introduction
### Basics of the Smart City
#### 1.1.5 Smart Farming  
Smart farming introduces information and communication technology (ICT) in the agricultural and farming sectors of smart cities [34]. The IoT, sensors, actuators, unmanned aerial vehicles (UAV), robots, geopositioning, big data, and other ICT applications are to be introduced into the cultivation process of the agricultural sector [34]. Several evolving technologies are to be introduced into smart farming like smart machines, crop sensors, cloud computing, as well as the IoT for the smart control, analysis, and planning of farming [35]. The process of smart farming consists of smart analysis and planning, smart sensing and monitoring, smart control, and storing big data in cloud devices [36]. Smart farming not only reduces labor requirements, but it also enhances the caliber of production [37].
# Discoloration Mechanisms of Natural Rubber and Its Control
## 5 Discoloration Mechanism of Natural Rubber
### Enzymatic Browning
#### 5.1.1 Polyphenol Oxidase (PPO)  
Total carotenoids from the neutral lipids have been identified as the primary pigments that result in a darker processed latex color [13]. [PERSON] et al. [14] have tried to purify and characterize the color substances extracted from various fractions of _Hevera_ rubber latex by certain methods, using high-resolution characterization techniques. It was found that the content of color substances extracted from FL, rubber cream, the BF, FW particles, and STR 20 was different [17].  
PPO is a copper-binding metalloproteinase widely distributed in plants, animals, fungi, and other cells, which plays a role in plant defense mechanism on the tapping wound of the rubber tree. Regarding substrate specificity and mechanism of action, PPOs are composed of three different types, i.e., tyrosinases, catechol oxidase, and lacases [25]. The enzymatic discoloration is caused by the naturally occurring phenols and amino phenols in NR latex [26]. PPO are the key enzymes in the natural coagulation and darkening of  
Figure 3: Brown pigment (melanin) formation from phenolic compounds.  
latex [27], and they are located inside the Frey-Wyssling particles. The PPO is responsible for the darkening of coagulated rubber on exposure to air or oxygen, i.e., in the presence of PPO, the phenols and amino phenols combining with oxygen from the air to form \(o\)-quinones. These \(o\)-quinones react with naturally occurring amino acids and proteins in the NR latex, giving colored compounds resembling melanin [28; 29].  
Both PPO and polyphenols are independently present in latex but do not react with each other. However, the Frey-Wyssling particles are destroyed during fractionation and solidification, and enzymes are released into the NR latex serum, leading to discoloration [30; 31].
# Influence of Medium Frequency Light/Dark Cycles on the Cultivation of _Auxenochlorella pyrenoidosa_
## 3 Results  
### Continuous Light  
_Auxenochlorella pyrenoidosis_ growth under different light sources (red, white, green, yellow, and blue) with various light intensities (50, 110, 220, and 440 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\)) for an incubation period of seven days is shown in Figure 3. The results obviously indicate that the microalgae biomass increased at first, and then decreased with the increase in light intensity. This means that there is an optimal light intensity to achieve the maximum biomass productivity. The growth of _A. pyrenoidosis_ in the light intensity of 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) was higher when compared with the light intensity of 50, 110, and 420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\). The light intensity of 50 and 110 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) was low to support the growth of _A. pyrenoidosis_, but the light intensity of 420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) was high enough to induce photo-inhibition. Hence, the light intensity of 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) is suitable for _A. pyrenoidosis_ cultivation, which agreed well with the previous study [16]. Excessive (420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\)) or insufficient (50 and 110 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\)) incident light constrains optimal performance in terms of biomass yield.  
At the light intensity of 50 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\), the blue light achieved the highest biomass productivity. The biomass productivity of blue light increased by 7.3%, 9.7%, 15%, and 16% when compared to white, red, yellow, and green, respectively. The red light achieved higher biomass growth than the other lights when the light intensity was greater than 110 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\). The maximum biomass productivity was 0.057 g/L, which was conducted at the red light with a light intensity of 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\). When compared with the white light, the red light improved the biomass productivity of 3.6%, 5.9%, and 12.4% at the light intensity of 110, 220, and 420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\), respectively.  
Figure 3: Cell growth of _Auxenochlorella pyrenoidosis_ at various light wavelengths and intensities: (**a**) 50, (**b**) 110, (**c**) 220, and (**d**) 420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\).
## 3 Results and Discussion
### Antioxidant Capacity
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Color Parameter/Content of** & \multicolumn{3}{c}{**Antioxidant Capacity**} \\ \cline{2-3}
**Biactive Compounds** & **DPPH Assay** & **ABTS Assay** \\ \hline L* parameter & \(-0.73\) & \(-0.75\) \\ a* parameter & \(-0.58\) & \(-0.61\) \\ b* parameter & \(-0.68\) & \(-0.68\) \\ TPC & \(0.96\) * & \(0.95\) * \\ TFC & \(0.74\) & \(0.78\) \\ Caffeic acid & \(0.78\) & \(0.84\) * \\ Chlorogenic acid & \(0.96\) * & \(0.95\) * \\ Ferulic acid & \(0.53\) & \(0.48\) \\ \(m\)-Coumaric acid & \(0.25\) & \(0.27\) \\ \(p\)-Coumaric acid & \(0.93\) * & \(0.96\) * \\ Vanillic acid & \(-0.70\) & \(-0.74\) \\ Sum of phenolic acids & \(0.97\) * & \(0.96\) * \\ Quercetin & \(0.61\) & \(0.59\) \\ Quercetin-3-O-glucose & \(0.70\) & \(0.75\) \\ Rutin & \(-0.70\) & \(-0.74\) \\ Sum of flavonoids & \(0.83\) * & \(0.84\) * \\ Lutein & \(0.95\) * & \(0.95\) * \\ Zeaxanthin & \(-0.80\) & \(-0.80\) \\ \(\beta\)-cryptoxanthin & \(0.44\) & \(0.44\) \\ \(15\)-_cis_-\(\beta\)-carotene & \(0.66\) & \(0.76\) \\ \(13\)-_cis_-\(\beta\)-carotene & \(-0.67\) & \(-0.74\) \\ \(\alpha\)-carotene & \(-0.68\) & \(-0.69\) \\ \(\beta\)-carotene & \(0.86\) * & \(0.92\) * \\
9-_cis_-\(\beta\)-carotene & \(0.00\) & \(0.06\) \\ Sum of carotenoids & \(0.88\) * & \(0.94\) * \\ \hline \hline \end{tabular}  
* Correlation coefficient statistically significant (\(p\leq 0.05\)); TPC—total phenolic content; TPC—total flavonoid content.  
\end{table} 
Table 5: Pearson correlation coefficients (\(r\)) showing the relationships between antioxidant capacity and color parameters and the content of bioactive compounds. coefficients (ranging from 0.61 to 0.97) between antioxidant activity and total carotenoid content. Furthermore, they observed a significant correlation between antioxidant activity and lutein content, a result also noted in the present study. However, [PERSON] and [PERSON] [59] found no significant correlation between antioxidant activity and the \(\beta\)-carotene or \(\alpha\)-carotene content in carrot samples.
## 5 Rate-Compatible IR-HARQ Transmission Scheme Based on PCPP Codes
### Initial Code-Rate and Number of Transmissions of the RC IR-HARQ Scheme  
A general method of constructing RC IR-HARQ transmission scheme for transmitting multiple data blocks over a time-varying channel is described as follows. Assuming the first data block is encoded by the PCPP coding scheme with the initial code-rate \(R_{1}\) (the rate of \(C_{1}(N_{1},R_{1},\mathcal{A}_{1}^{(1)})\)), after \(k\) transmissions the receiver can be decoded successfully and the final rate is \(R_{k}\). Then, a simple analysis of the number of transmissions corresponding to the different initial code-rates for the second data block \(C_{2}(N_{1},R_{1},\mathcal{A}_{1}^{(1)})\) is shown in Table 1.  
Obviously, in a continuous data block transmission over a time-varying channels scenario, \(R_{k}\) is closer to the real channel capacity than \(R_{1}\), which leads to a simpler structure of PCPP codes as well as less transmissions. Therefore, the key to simplifying the RC IR-HARQ scheme is to choose an optimal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Code-Rate** & **Channel Capacity** & **Result** \\ \hline \multirow{2}{*}{\(R_{1}\)} & increase & \((k-1)\) transmissions at most \\  & decrease & \((k+1)\) transmissions at least \\  & increase & 1 transmission \\ \multirow{2}{*}{\(R_{k}\)} & decrease & 2 transmissions at least \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relationship between the initial code-rate and the number of transmissions over a time-varying channel.  
initial code-rate that is close to the time-varying channel capacity \(I(W)\). However, the exact \(I(W)\) is unavailable for the sender in a practical wireless system.  
Let \([R_{k},R_{k-1})\) denote a capacity interval after the \(n\)-th data block is transmitted successfully and \(I(W)\in[R_{k},R_{k-1})\), where \(R_{k}\) is the final code-rate of the PCPP coding block \(C_{n}(N_{k},R_{k},\mathcal{A}_{k}^{(k)})\) and \(R_{k-1}\) is the code-rate of the last PCPP coding block that cannot be decoded. Then, the initial code-rate of \(C_{n+1}(N_{k},R_{1},\mathcal{A}_{1}^{(1)})\) for the \((n+1)\)-th data block can be chosen according to \([R_{k},R_{k-1})\) for the time-varying channel. Similarly, we can get such interval after the transmission of each data block and choose an optimal rate according to the interval for the next data block. The detail of the optimization method for determining the initial code-rate for the 1 st PCPP coding block of RC IR-HARQ transmission scheme is given in the next subsection.