esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Multi-Response Optimization Using Grey Relation Analysis  
Optimization Using GRA and ANOVA  
As seen above, the Taguchi technique is only capable of optimizing a single parameter at a time, and thus involves more cost, time, and effort. As a panacea, GRA was introduced, in which several parameters are combined and optimized. GRA is used to unravel real problems consisting of limited data. It is usually used to estimate the properties of indefinite systems without black and white solutions. GRG is largely applied in optimization problems dealing with several parameters and responses [40; 41]. Data preprocessing via GRA was executed on the responses (M\({}_{\text{L}}\), V\({}_{\text{L}}\), \(\mu\), and T\({}_{\text{s}}\)). The reference sequence and deviation sequence (0-1) of the responses were computed using Equations (3) and (4), respectively. Figures 15 and 16 show the reference sequence and deviation sequence values, respectively, after the data pre-processing. The GRC for each value of the response was computed (Equation (5)). Ultimately, the mean of the GRCs was calculated to establish the GRG, and the calculated values were utilized to produce equivalent SNRs. A higher SNR is useful and indicates that the trials lie in proximity to the actual normalized magnitude of the GRG. Once the ranks were found (Figure 17), a GRG response table was contrived. From Figure 18, it is clear that the eleventh trial possessed the highest SNR. Therefore, the first rank was designated to the eleventh trial. The GRG of each parameter at the selected level was chosen and the average computed to obtain the mean GRG for each separate parameter, for example, parameter G at level 1 in the first, fourth, and seventh runs of the test. The concomitant GRG values from Table 8 were used for computation, as indicated in Equation (8).  
The mean of the chosen GRGs was computed through the aforementioned method and put together to generate the response table (Table 8). The grades in Table 8 were utilized to serve as a measure between the comparability sequence and reference sequence of the GRA. Higher values for the mean GRGs exhibit a strong degree of correlation [42]. Hence, from Table 8, it is possible to achieve a combination of optimal parameters that maximizes the overall response. As observed in Table 8, the maximum GRG exists at L3, G1, SD3, and SS3. Therefore, to wrap it up, the optimal parameter settings for the useful abrasive tribological properties of PTFE-based composites are L at 9 N, G at 1000 mesh, sliding distance at 55 m, and 1000 mesh  
and sliding speed at 0.14 ms\({}^{-1}\), coded as L3G1 SD3 SS3. The ANOVA results of the GRG revealed that grit size has the maximum percentage contribution of 94.56% upon GRG, succeeded by SS with 2.71%, SD with 2.18%, and load with a minimal contribution of 0.55%. This signifies that grit size is the most significant parameter that influences the abrasive wear property of PTFE-based composites.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Related Work
#### 4.3.3 Real-time/QoS Scheduling  
The support of real-time flows is becoming dominant for novel Wi-Fi usage such as industrial applications (see Section 3.3). In particular, the MAC layer must be able to guarantee a bounded latency for high-priority flows and provide QoS to assign a larger bandwidth to critical flows. Hopefully, Wi-Fi 6 relies on a schedule of transmissions and may work without collision when the access point knows the requests of each station. [PERSON] et al. [PERSON] et al. (2019) considered latency-bound traffic with strict deadlines. They prioritized emergency messages and enqueued them in an emergency queue. Depending on the number of packets in the emergency queue and the maximum number of devices to transmit simultaneously, flows are scheduled in parallel and assigned to RUs to meet the waiting deadlines.  
Traffic asymmetry has already been identified as a key problem in legacy Wi-Fi ([PERSON] et al., 2019). Indeed, the stations and access point have the same priority for medium access, whereas the access point transmits more packets. [PERSON] et al. (2019) corroborated the inefficiency of trigger-based OFDMA while the Wi-Fi 6 access point was competing with legacy stations for channel access. To be able to give more transmission opportunities to the access point, they proposed to use different EDCA parameter sets for the access point and legacy stations.  
Reliability and data delivery are also critical for providing QoS. Cyclic Resource Assignment (CRA) ([PERSON] et al., 2019) targets real-time applications (RTA) to meet the critical latency and reliability constraints. Primarily, it randomly schedules stations and assigns 26-tone RUs to them. Therefore, it can schedule a large number of stations in parallel. In the case of collision, the access point specifies an RU to the stations to transmit without collision. The authors of ([PERSON] et al., 2019) focused on time-sensitive traffic. To predict data stream deadlines on the access point, the access point regularly retrieves the stations' queue sizes through BSR. This provides a realistic approximation of the stations' deadlines and then assigns the RUs to the stations based on the estimated deadlines. Thereby, it minimizes packet loss significantly.
## 3 Determination of HJC Model Parameters of Diorite in the Jinan Region
### Determination of Limit Surface Parameters  
(1) Determination of strength constant A  
If the effects of damage and strain rate are not considered, the limit surface equation of HJC model can be expressed as follows:  
\[\sigma^{*}=A+BP^{*N} \tag{15}\]  
Since Equation (15) does not consider the influence of the Rohde angle, the projection of the limit surface on the partial plane is the Mises circle. According to the plastic theory, when the Rhodes angle is \(60^{\circ}\), the compression meridian of the Mises criterion on the deviated plane is connected to the compression meridian of the M-C criterion. The expression of the M-C criterion is expressed as shown in Formula (16).  
\[\sigma=c+P\tan\varphi \tag{16}\]  
In this equation, \(c\) and \(\varphi\) are cohesive strength and internal friction angle, respectively. According to the data of triaxial experimental under different confining pressures, an envelope of this group of Mohr circles was obtained, and the cohesive strength \(c\) is \(49.12\) MPa.  
It can be seen from the above analysis that if the Mohr-Coulomb criterion satisfies the linear relationship, the strength constant \(A\) is equal to \(c/f_{c}\), and the quasi-static cohesive strength (when \(\overset{\bullet}{\varepsilon}=10^{-4}/\text{s}\)) can be obtained at the same time, \(A=0.251\). When normalized strain rates \(\overset{\bullet*}{\varepsilon}=1.0\), \(A=0.251/(1+Cln10^{-4})=0.254\) were obtained.  
(2) Determination of strength constants \(B\) and \(N\)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\rho\)/ & \(f_{c}\)/ & \(E\)/ & \(\
u\) & \(G\)/ & \(K\)/ & \(T\)/ \\ (**kg**\(\cdot\)**m\({}^{-3}\))** & **MPa** & **GPa** & & **GPa** & **GPa** & **MPa** \\ \hline
3013 & 195.5 & 109.7 & 0.267 & 43.3 & 78.5 & 12.26 \\ \hline \end{tabular}
\end{table}
Table 1: The basic physical and mechanical parameters of diorite.  
Triaxial compression tests were performed using a triaxial compression apparatus. Under different confining pressure conditions, according to Formulas (17) and (18), the different hydrostatic pressures \(p\) and the corresponding maximum differential pressure \(\Delta\sigma\) are measured. Normalizing maximum differential pressure \(\Delta\sigma\) as shown in Formula (19), a series of points (\(P^{*}\),\(\sigma^{*}\)) was obtained. Then, to obtain the values of the strength constants \(B\) and \(N\), the data points were fitted as shown in Formula (20).  
\[P=\frac{\sigma_{1}+2\sigma_{3}}{3} \tag{17}\]  
\[\Delta\sigma=\sigma_{1}-\sigma_{3} \tag{18}\]  
\[\sigma^{*}=\frac{\Delta\sigma}{f_{c}} \tag{19}\]  
\[\sigma^{*}=0.254+BP^{*N} \tag{20}\]  
Figure 3 shows the test data and fitting results. The strength constants \(B\) and \(N\) are 1.695 and 0.742, respectively.
## 3 Aircraft System Identification
### Aircraft Model Evaluation Method  
In order to choose the best model among the identified models, it is required to apply a model evaluation method to evaluate the performance of the models. The used method in this study is adopted from [PERSON] (2000), which is variance accounted for (VAF) and norm. Given a set of \(N\) data, VAF is defined by the equation below:  
\[VAF=\left\{1-\frac{\mathrm{var}\left(\mathbf{y}_{i}-\mathbf{\hat{y}}_{i} \right)}{\mathrm{var}\left(\mathbf{y}_{i}\right)}\right\}\;\times\;100\%,\;i \;=\;1,\;2,\;3,\;\ldots,\;N \tag{29}\]  
where \(\mathrm{var}(. )\) is the variance of the variable in the bracket, \(\mathbf{y}_{i}\) is the measured output, and \(\mathbf{\hat{y}}_{i}\) is the predicted output of the identified model. VAF is expressed in percentage where 100% means the outputs of identified model fit the measured output perfectly. However, there is a possibility to encounter with negative percentage, simply indicating that the outputs of the identified model do not fit the measured output. The norm is given by:\[NRM=\left(\frac{\hat{\mathbf{y}}_{i}-\mathbf{y}_{i}}{\mathbf{y}_{i}}\right)^{T} \left(\frac{\hat{\mathbf{y}}_{i}-\mathbf{y}_{i}}{\mathbf{y}_{i}}\right),\;i=1, \;2,\;3,\;\ldots,N \tag{30}\]  
where a smaller value of norm will correspond to a better identified model. There are total of 55 longitudinal flight data collected, performed by using elevator doublet input, with adequate elevator deflection angles. The flight data are named on the form as shown below for the ease of data management and analysis, as an example _ft05 run11_ referred to the fifth flight test and the eleventh run. The aircraft distribution energy models were identified by imposing the performance requirements as shown below:  
\[\mathit{VAF}\geq 85\%\text{ and }NRM\leq 15\]  
The values of VAF have to be greater or equal to 85% and the norms must be smaller or equal to 15. There are total of 13 identified aircraft energy distribution models along with the trim conditions of the flight, VAF and norm. An example of an identified model _ft11 run10_ that fulfills the performance requirements is shown in Figure 5. Identified models will be short-listed, and cross-validation is performed to further evaluate the performance of the model. Figure 6 presents the cross-validation result of model _ft10 run07_ and data _ft10 run15_. Cross-validation of model _ft10 run07_ with other flight data is shown in Table 1. The discrete-time state-space of _ft10 run07_ model, identified at trim airspeed of 27.44 m/s and altitude of 150.8 m, is given by Equations (20)-(24). From Figure 5, the VAF value of \(\Delta q\) and \(\dot{L}_{\text{s}}\) are 92.31% and 94.52%, respectively, in which both values satisfy the minimum requirement of VAF at 85% based on the our experience. Figure 5: An example of identified model _ft11 run10_. Since model _f10 run07_ has better performance than others, it was adopted to be the model for control design. The identified discrete-time system matrices of model _f10 run07_ are shown below:
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 3 Results and Discussion
### Asphal Mixture Test Results
** & **\(|E^{\star}|\)** & **Phase** & **\(|E^{\star}|\)** & **Phase** & **\(|E^{\star}|\)** & **Phase** & **\(|E^{\star}|\)** & **Phase** \\ \({}^{\circ}\)**C** & **Hz** & **Angle** & **\(|E^{\star}|\)** & **Angle** & **\(|E^{\star}|\)** & **Angle** & **\(|E^{\star}|\)** & **Angle** & **\(|E^{\star}|\)** & **Angle** \\ \hline  & 0.1 & 12,482 & 17.7 & 11,685 & 20.4 & 12,055 & 20.1 & 14,009 & 18.7 & 11,744 & 20.7 \\
4 & 1 & 17,934 & 12.4 & 17,416 & 13.8 & 17,876 & 13.7 & 20,677 & 12.7 & 17,928 & 14.1 \\  & 10 & 23,354 & 8.8 & 23,190 & 9.4 & 23,643 & 9.3 & 27,015 & 8.8 & 24,087 & 9.6 \\ \hline  & 0.1 & 3066 & 33.1 & 2464 & 35.6 & 3123 & 35.5 & 1973 & 33.1 & 2746 & 35.2 \\
20 & 1 & 6243 & 27.3 & 5606 & 30.4 & 6686 & 28.9 & 7823 & 27 & 6215 & 29.4 \\  & 10 & 10,732 & 20.4 & 10,269 & 22.6 & 11,658 & 21.2 & 13,232 & 19.9 & 11,178 &Figure 8: Comparison of master curves for wearing course mixture from the plant (mixture 2). Figure 7: Comparison of master curves for base layer mixture from the site (mixture 1). The field mixtures collected from the construction site were expected to be aged slightly higher compared with the plant wearing mix since the former mixture was subjected to elevated temperatures for a longer time due to hauling from the plant to the site. However, this was not reflected in the dynamic modulus test results. The short-term aging protocol for the unmodified plant mixture 2 was slightly higher than that for the unmodified field base mixture 1. Aggregate gradation was different between the two mixtures and, thus, the difference in mix design could be a reason for the different extents of aging in the mixtures. Moreover, mixture 3 showed 4 h of duration to simulate the short-term aging yet with a different aggregate gradation and modified binder. In summary, for the three mixtures studied in this work, the short-term aging protocol was found to be mostly close to 4 h of conditioning at 135 \({}^{\circ}\)C. To further investigate and validate the protocol, a binder study was also undertaken.
## 6 Simulation Results  
In this section, we evaluate the performance of our joint routing and resource allocation scheme. The results of the proposed PM-DQN based joint design algorithm is compared with (i) Q-routing: (ii) DQN based scheme; and (iii) Fixed Power Q-routing (FP-Q-routing) [15] which does not consider power allocation and its transmission power is fixed. We set the bandwidth of DTC to be \(\text{W}=1\) MHz, and the bandwidth of CCC is 2 MHz. It is supposed that the AWGN power \(\sigma=10^{-7}\) mW, the packet size is \(2\times 10^{5}\) bit, and the link gain used in this paper is given by:  
\[h=L\text{F}\bigg{(}\frac{d}{d_{0}}\bigg{)}^{-n},\text{ for }d>d_{0} \tag{20}\]  
where \(L\) is a constant set to be \(10^{-6}\), and the shadowing factor \(F\) is subject to a lognormal distribution with a mean of 0 dB and variance of 6 dB, \(d\) is the actual distance between the transmitter and receiver, \(d_{0}\) is the reference distance, and \(n\) is the path loss exponent. In our simulation process, we set \(d_{0}=1\) and \(n=4\). To compare the performance of the algorithms, we consider two different kinds of experimental scenarios varying in the number of SU nodes, PU channels and transmission power levels. In the first experiment, a CRN with 10 SU nodes and 5 PU channels deployed in a 300 m \(\times\) 300 m region is considered, and the discrete transmission power of SU nodes is divided into 5 levels: \(\{50,\ 100,\ldots,250\ \text{mW}\}\). The size of state space is calculated as \(10\times 5=50\). In the second experiment, a CRN consisting of 50 SU nodes and 10 PUs uniformly distributed over a 1000 m \(\times\) 1000 m square area is considered, and the transmission power is divided into 10 levels: \(\{50,\ 100,\ldots,500\ \text{mW}\}\). The size of state space is \(50\times 10=500\) which is ten times larger than it in the first experiment. The expected mean and deviation of average PU departure rate \(\theta_{d}(\mu,\sigma)\) are set as \(\mu=0.1\) and \(\sigma=0.05\). In addition, we set the erasing parameters \(a\), \(b\), \(\lambda\) to 90, 60, 0.5 respectively.
## Abbreviations  
* 3T3-L1--mouse embryonic fibroblast cell line; 4-TZD--4-thiazoltiones; A549--epithelial lung carcinoma cell lines; AhR--aryl hydrocarbon receptor; BJ--human skin fibroblast cell line; CACO-2--human adenocarcinoma cell line; CRC--colorectal cancer; DMEM--Dulbeccoo's Modified Eagle Medium; FBS--total bovine serum; GW9662--2-chloro-5-nitro-N-phenylbenzamide; H\({}_{2}\)DCF-DA--2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofluorescein diacetate; LDH-- lactate dehydrogenase; Les-2194--_rel_-_N_-(2,4-dichlorophenyl)-2-[(5a_R_,11b_)R_]-2-oxo-5a,11b-dihydro-2\(H\),5_H_-chromoen[4\({}^{\prime}\),3\({}^{\prime}\),5][thioPyramel(2,3-4)[1],3]thiazol-3(6H)-ylacetamide; Les-236--52-(4-fluorobenzyl)-2-(4+hydroxyphenylamino)-thiazol-4-one; Les-2769--ethyl _rel_-[(5a_R_,11b_)R_]-10-bromo-2-oxo-5a,11b-dihydro-2\(H\),5_H_-chromoen[4\({}^{\prime}\),4-oxo-5a,11b-dirano[2,3-4][1,3]thiazol-3(6H)-yl] acetate; Les-3166--(2-[2-[3-(benzothiazol-2-ylamino)-4-oxo-2-thiosothiazolidin-5-ylidenemethyl]-4 chlorophenylophenoxy)-\(N\)-(4-methoxyphenyl)-acetamide); Les-3266--7,8-dimethoxy-1-oxo-1-\(H\)-isothiochrome-3-carboxylic acid (4-phenylthiazol-2-yl)-amide; Les-3640--3-[2-[5-(4-dimethylaminophenyl)-3-phenyl)-4,5-dihydroxypropylazol-1-yl]-4-oxo-4,5-dihydro-1,3-thiazol-5-ylidene)-2,3-dihydro-1_H_-indol-2-one; Les-4368--(Z)-5-[5-(2-hydroxyethylyphenyl)]-3-phenyl-4,5-dihydroxypropyl-1-\(\upmu\)Methylene]-3-(3-acetoxyphenyl)-2-thiosothiazolidin-4-one; Les-4370--(Z)-5-[5-(4-dimethylaminophenyl)-3-phenyl)-4,5-dihydroxypropylazol-1-ylmethylene]-3-(3-acetoxyphenyl)-2-thiosothiazolidin-4-one; Les-5935--7-oxa-10-thia-8-aza-cyclopenta[\(b\)] phenanthren-9-one; Les-5935--(9_H_-benzo[5,6]chromeon[2,3-4][1,3]thiazol-9-one); Les-6009--5-fluoro-3-(4-oxo-2-thiosothiazolidin-5-ylidenemethyl)-1_H_-indole-2-carboxylic acid methyl ester; Les-6166--methyl 5-fluoro-3-[2-(4-hydroxyanilineo)-4-oxo-4,5-dihydro-1,3-thiazol-5-ylidenemethyl]-1_H_-2-indolecarboxylate; Les-3288--5-bromo-3-[2-[5-(4-methoxyphenyl)-3-phenyl)-4,5-dihydroxypropylazol-1-yl]-4-oxo-4,5-dihydro-1,3-thiazol-5-ylidene)-2,3-dihydro-1_H_-indol-2-one; LPS--lipopolysaccharide; MDR--multidrug resistance; MEM--Minimum Essential Medium; MP--microparticles; mTOR--mammalian target of rapamycincin; NF-\(\upkappa\)B--nuclear factor kappa-light-chain-enhancer of activated B cells; PBS--phosphate-buffered saline; PPAR--peroxisome proliferatorator-activated receptors; PPAR--peroxisome proliferatoractivated receptor gamma; PXR--pregnane X receptor; RAW264.7--mouse mononuclear macrophage leukemia cells; ROS--reactive oxygen species; SH-SY5Y--neuroblastioma cell line; U251--glioma cell line; WM793--human melanoma cells.
## 3 Results and Discussion
### Importance of Input Variables and Processing Methods on WUE Estimates
#### 3.3.3 Impact of Crop Physiology on Inconsistencies in Ecosystem Water Use  
For EWUE methods, EWUEF (uses ET) and EWUEC (uses T) are compared for the different stages of alfalfa and maize. Earlier, it was noted that there were discrepancies between these methods for alfalfa, but these discrepancies varied among the four cuts throughout the growing season. More specifically, median half-hourly EWUEC was 73%, 57%, 23% and 43% greater than EWUEF for cuts 1-4, respectively (Table 2). The inconsistency in discrepancy between these two methods is likely caused by crop physiological responses to changes in temperature and microclimate conditions. At higher temperatures in C3 photosynthesis, greater losses of assimilated CO2 occur since rubisco fails to distinguish between CO2 and O2 [84]. The uptake of both CO2 and O2 therefore results in higher photorespiration, which in turn results in greater transpiration losses [65]. This was seen in T:ET ratios, where there was higher T:ET ratios later in the season whereas cut 1 experienced lower T:ET ratio.  
Although EWUEC was always greater than EWUEF, the magnitudes of the differences between the two methods were smaller in maize than were observed for alfalfa. Some of this occurred because the FVS partitioning program (Fluxpart) did not produce frequent estimates when maize was in its early growing season when the difference between these two methods was expected to be greatest, and is therefore a limitation of this study. However, half-hourly EWUES indicates that there should be a large increase in medianhalf-hourly EWUEF between GS1 and GS2 that is not captured by this data limitation. Moreover, since crop heights were lower, and soil was exposed (not shaded) due to planting in rows, and evaporation components of ET would have been much higher than T during GS1 [85]. A previous study has shown that reducing spacing between rows, enabling earlier canopy closure, can increase EWUE by up to 17% [86]. Following canopy development, less soil is exposed and the differences between the two methods are lessened [9; 85]. Moreover, higher leaf coverage results in more T per unit area [87]. Thus, the difference between these two methods should have been lower during GS2 than GS1, when it was approximately 32%, and even smaller during GS3 (17%) when it reached maximum height and LAI (Figure 4). However, the differences increased again during GS4 (31%) due to self-shading, and deterioration of leaves due to aging and senescence. As LAI increases, more light is intercepted; however, this can also have self-shading effects on lower canopy positions, where self-shading can be the dominant factor in determining the rate of change in photosynthesis and T, which are affected disproportionally [9]. This is complicated by leaf aging, which can cause decreases in WUE due to shifts in stomata conductance [88; 89; 90] explaining the larger variance in GS4. Comparatively, alfalfa did not experience as much soil exposure due to random sowing techniques or leaf aging effects due to multiple harvest practices.
## 3 Target Doppler Rate Estimation
Additionally, the outcomes are compared to the classical approach existing in the literature with particular emphasis on the usability in real-life data processing which is investigated in the next section.
## 3 Results and Discussion  
### Three-Dimensional Effect Analysis of Tunnel  
Figure 9 shows the relationship between the instantaneous pressure on the tunnel wall and propagation of the pressure wave when the maglev train passes through the tunnel at 600 km/h. In this figure, H denotes the train head displacement, W denotes the train tail displacement, C denotes the compression wave, and E denotes the expansion wave. Subscript H denotes the wave due to the evolution of the compressional wave induced by the entry of the train head, and W denotes the wave due to the evolution of the expansion wave induced by the entry of train tail, with the subscripts increasing when the wave is reflected. T6-1 to T6-6 are six measurement points in the same tunnel section that are arranged at different positions around the tunnel wall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tunnel Exit Pressure (Pa)** & **M-1** & **M-2** & **M-3** \\ \hline Moving model test & 1070.556 & 681.97 & 313.9677 \\ Numerical simulation calculations & 1021.85 & 641.26 & 292.22 \\ Error & 4.55\% & 5.97\% & 6.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of moving model test and numerical simulation results.  
Figure 8: Comparison of dynamic model test and numerical simulation time travel curve.  
At 0.03 s, the head of the train enters the tunnel. The air in front of the train is compressed, leading to the generation of the compression wave \(\mathrm{C_{H1}}\) and forward propagation at the speed of sound, reaching the measurement point at 0.0848 s, and resulting in an increase in pressure at A. The compression wave then reaches the tunnel entrance, and the expansion wave \(\mathrm{E_{H2}}\) is generated and arrives at the measurement point at 0.0894 s, resulting in a decrease in pressure at B. At 0.1097 s, the expansion wave \(\mathrm{E_{W1}}\) reaches the measurement point, and the measured pressure decreases again. When the expansion wave \(\mathrm{E_{W1}}\) reaches the tunnel exit, it generates a compression wave \(\mathrm{T_{W2}}\), which propagates towards the tunnel entrance, causing a pressure rise at D. During this process, the pressure profiles monitored at the different measurement points T6-1 to T6-6 overlap almost exactly, which is consistent with the results obtained by [PERSON] et al. [24] for a 350-km/h train. This indicates that significant one-dimensional effects still dominate the tunnel when the model used in this study is operated at 600 km/h. Therefore, in subsequent studies, it is sufficient to set up only one monitoring point for different sections, and discussion of the magnitude of the effect of different measurement points on the test results in the same section is not required.