esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
In this section, we summarize the related literature reviews based on the results of each study or group of studies, as shown in Table 1. These results illustrate a comparison in terms of the transmission carrier frequency bands, such as optical, RF, and acoustic waves.  
From the results presented in the table above, we can observe that optical communication has better performance in terms of the system capacity and throughput, BER performance, channel attenuation, and required transmuted power. However, it suffers from transmission distance challenges. In this study, the authors have focused on enhancing the transmission distance at a good BER performance and optimum number of active users.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Parameters** & **Optical** & **RF Waves** & **Acoustic Waves** \\ \hline \multirow{8}{*}{[14]} & \multirow{8}{*}{Attenuation} & 0.39 dB/m & \multirow{8}{*}{-} & \multirow{8}{*}{-} \\  & & (ocean)–11 dB/m & & \\  & & (turbid) & & \\ \cline{1-1}  & Speed (m/s) & \(2.26\times 10^{8}\) & \(2.26\times 10^{8}\) & \(2.26\times 10^{8}\) \\ \cline{1-1}  & Data rate & \(\sim\)Gbps & & \\ \cline{1-1}  & Latency & Low & & \\ \cline{1-1}  & Distance & 10–100 m & & \\ \hline \multirow{8}{*}{[13]} & \multirow{8}{*}{Attenuation} & \multirow{8}{*}{-} & Frequency band and \multirow{8}{*}{-} \\  & & & conductivity & \\ \cline{1-1}  & & & dependent & \\ \cline{1-1}  & & & (3.5–5) dB/m & \\ \cline{1-1}  & Speed (m/s) & & \(2.26\times 10^{8}\) & \\ \cline{1-1}  & Data rate & & \(\sim\)Mbps & \\ \cline{1-1}  & Latency & & Moderate & \\ \cline{1-1}  & Distance & & Up to 10 m & \\ \hline \multirow{8}{*}{[12]} & \multirow{8}{*}{Attenuation} & \multirow{8}{*}{-} & Distance- and \multirow{8}{*}{-} \\  & & & frequency-dependent \\ \cline{1-1}  & & & (0.1–4) dB/km & \\ \cline{1-1}  & Speed (m/s) & & 1500 \\ \cline{1-1}  & Data rate & & \(\sim\)Kbps & \\ \cline{1-1}  & Latency & & High \\ \cline{1-1}  & Distance & & Up to Kms \\ \hline \end{tabular}
\end{table}
Table 1: Literature review comparison of results for developed underwater wireless channels.
## 2 Materials and Methods
### \(h\)-Adaptation and Grid Independence Test  
For computing the results for the proposed DPHE, the \(h\)-adaptive finite element approach was employed. In the computational domain, an initial triangular mesh is generated. In the process of \(h\)-adaptation, firstly, the solution in terms of \(fRe\) is computed on the initial mesh, and the mesh is flagged on the basis of error estimates for uniform mesh refinement. In this refinement, each flagged triangle is further refined into four sub-triangles. A detailed description of this adaptive process is given in Section 3 of [15]. Figure 2 presents the results of the grid independence test for \(\tilde{R}=0.5\) and \(\beta=3^{\circ}\) with 4-32 diamond fins of height 40% of the annulus width. It may be noted that for each configuration of the diamond-shaped fin, the computational domain is altered, and hence, the triangular mesh and its adaptive form (as evident from the last column of Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(M\) & \(\mathbf{l}^{*}\) & \(\mathbf{D}_{h}\) & \(fRe\) & \(\overline{Nu}\) & \(jff\) & **Triangles** \\ \hline \multirow{6}{*}{4} & 0.2 & 1.3000 & 20.7725 & **4.9051** & 0.2685 & 4315 \\ \cline{2-6}  & 0.4 & 1.1389 & 20.4763 & 4.3981 & 0.2443 & 4189 \\ \cline{2-6}  & 0.6 & 1.0087 & 19.6619 & 4.2889 & 0.2481 & 3922 \\ \cline{2-6}  & 0.8 & 0.9015 & 17.3747 & 3.7919 & 0.2482 & 3783 \\ \cline{2-6}  & 1.0 & 0.8113 & 14.4670 & 3.0280 & 0.2380 & 3294 \\ \hline \multirow{6}{*}{8} & 0.2 & 1.1547 & 18.6648 & 3.5309 & 0.2151 & 2367 \\ \cline{2-6}  & 0.4 & 0.9216 & 18.4451 & 2.9066 & 0.1792 & 2319 \\ \cline{1-1} \cline{2-6}  & 0.6 & 0.7607 & 19.3996 & 3.2968 & 0.1933 & 2123 \\ \cline{1-1} \cline{2-6}  & 0.8 & 0.6426 & 18.3673 & **3.9105** & 0.2421 & 1894 \\ \cline{1-1} \cline{2-6}  & 1.0 & 0.5516 & 14.6700 & 3.2728 & 0.2537 & 1783 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the triangular- and rectangular-finned annulus results for \(\tilde{R}=0.5\) and \(\beta=3^{\circ}\).  
Figure 2: Grid independence test: \(fRe\) is plotted against the number of triangular mesh for \(\tilde{R}=0.5\), \(\beta=3^{\circ}\), and \(N\) varying from 4 to 32, having 40% height of the annulus. [Colored Figure].
# Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions
## 2 Preliminaries
### DCD Algorithms  
The DCD algorithm is one of many iterative techniques for solving the linear LS problem [19]. There are \(M_{b}\) bits to represent the elements of the solution vector. The amplitude of the element is limited to \([-\zeta,\zeta]\). The iterative search begins with the most significant bits of the solution \(\mathbf{x}\) and ends with the least significant bits updated. The execution is controlled by the step size \(\sigma\) (\(\sigma>0\)). \(\sigma\) starts at \(\sigma=\zeta\) and decreases as \(\sigma\rightarrow\sigma/2\) for less significant bits. DCD-based techniques usually use bit-shift to replace the multiplication/division operations, which is attractive for hardware implementation. The variants of the DCD algorithm: cyclic DCD algorithm and leading DCD algorithm, have different properties and been applied to different applications. These algorithms are described below.
## 3 Results
### Combination of the Staggered Arrangement of the Nozzles and Fan  
Figure 8 compares the coolant outlet temperatures during cooling process by the mutual combination of the nozzles in the staggered arrangement and the fan. The distances of the nozzles from the heat exchange surface of the cooler were in the range of 60 mm to 170 mm. We assumed that this combination of cooling equipment would be the most efficient of the options investigated. As with the combination of inline staggered nozzle and fan, no higher cooling efficiency was observed when the fan was used alone. All of the distances investigated achieved higher cooling efficiencies, but the outlet temperatures differed only minimally when compared with each other. The longest cooling time was recorded at a nozzle spacing of 70 mm; a value of \(T_{out}\) = 42.38 \({}^{\circ}\)C was reached at a time of 47 min. On the contrary, the nozzle spacing of 140 mm was the most effective, when the outlet temperature value was 1.08 \({}^{\circ}\)C lower (\(T_{out}\) = 41.40 \({}^{\circ}\)C) at the same time. With the distance of the staggered nozzles arranged 140 mm and the fan, the outlet temperature of the coolant was \(T_{out}\) = 50.36 \({}^{\circ}\)C at the time of 46 min (30 s after the start of the cooling process), which is 17.61 \({}^{\circ}\)C less than that of using only a separate fan.  
A single fan removes most of the heat only from the surroundings of the heat exchange surface or its second row of tubes, which makes its cooling efficiency significantly lower compared to a system of air pressure nozzles. Also, when only the fan is used, the entire heat exchange surface is not thoroughly covered with air due to the different construction shapes of the fan and the cooler. For this reason, a number of empty spots are created on the surface of the cooler, which are not sufficiently covered by the air flow from the fan. Another disadvantage of the fan itself is the lack of heat dissipation from the entire first row of cooler tubes, since the fan primarily removes hot air from behind the heat exchange surface and not from its core. To eliminate these deficiencies, we have developed a system of air pressure nozzles that cause complete removal of the hot air from the core of the heat exchange surface of the cooler. The airflow from the nozzles also covers the empty spots on the cooler from which the fan cannot remove the hot air. In addition to a more efficient cooling process (shorter coolant cooling time) by means of the air nozzle system, there is also the advantage of increasing the time for the necessary cooler re-cooling. Since, by cooling the entire cooler volume (not just part of it as in the case of the fan), the time for reheating the coolant in the small cooling circuit will be longer, causing a reduction in the number of cooling cycles and thus an increase in the efficiency and economy of the internal combustion engine operation.
## 3 Method  
### Data Post-Processing  
In order to assess the vertical error, the most important steps during post-processing are  
1. [leftmargin=*]
2. Merge and crop DEM, and then extract ICESat/GLAS and ICESat-2 altimetry points. The vertical and horizontal datum of all the datasets are given in Table 11, as shown in Appendix A. The WGS84 reference system and vertical datum are selected in this study.
3. Filter outliers for ground points. Select high precision ICESat/GLAS altimetry points according to the four flags mentioned in [20] and a 50-m elevation difference threshold. A total of 95,381 filtered ICESat/GLAS elevation points are acquired (Figure 0(a)). Due to the lower noise of the night collections, we selected the strong beam data acquired at night to ensure the quality of ICESat-2 elevation [46; 47; 48]. A slope corrected best-fit terrain height (h_te_Besftti) on the ATL08 data product was selected as the reference data, which represents the ground surface elevation. Furthermore, the flag of \"_cloud_flag_atm_\" was used to reduce the aerosols or clouds impacts of the acquisition. After the above processing, we collected a total of 49,707 ICESat-2 reference data points, and the corresponding footprints are shown in Figure 0(b).
4. Transform orthometric height to ellipsoidal height.  
For SRTM1/3 DEM, ASTER GDEM2 and AW3D30 DEM, the global geoid model provided by National Geospatial-Intelligence Agency (NGA) was used to evaluate the geoid undulation \((N)\), which means the distance from a point on the geoid (e.g., EGM-96) along the normal line to the ellipsoid (e.g., WGS84) [27; 28] and thus the ellipsoidal height can be calculated by Equation (1):  
\[h=H+N \tag{1}\]  
where \(h\) is the ellipsoidal height relative to the WGS84 ellipsoid, and \(H\) is the orthometric height with respect to EGM-96 geoid.  
The height of the ICESat/GLAS data is the ellipsoidal height with respect to the TOPEX/Poseidon (T/P) ellipsoid. Although the Geographic datum between ICESat/GLAS elevation and each DEM data is different, the horizontal displacements between T/P and WGS84 ellipsoid are negligible [19]. So only the transformation of vertical datum needs to be considered and the ellipsoidal height above WGS84 can be calculated by Equation (2):  
\[h_{\text{WGS84}}=h_{\text{T/P}}-0.707\text{ m} \tag{2}\]where \(h_{WGS84}\) is the ellipsoidal height with reference to WGS84 ellipsoid; \(h_{T/P}\) is the ellipsoidal height relative to the T/P reference system; and the constant value 0.707 m means the height difference between WGS84 and T/P ellipsoids.
## 2 Operation and Control
### Communications and Automation
#### 2.2.3 Communication Architecture  
The communications systems in the \(SG\) will be hybridized (with and without cable). In this sense, it is necessary to have simulators of different technologies, and their interaction with others, so [PERSON] et al. [31] propose a set of simulation models. This type of tools will allow having a piece of more advanced knowledge about the interaction between technologies and protocols.  
The coexistence of clients and distributors in the same domain, through an advanced architecture based on the wide area network (WAN) concept, is exposed in the work of [PERSON] et al. [32]. The model improves performance parameters, energetic efficiency in data delivery and latency, as in the work of [PERSON] and [PERSON] [29]. The problem of data collection is addressed through mixed integer linear programming and using cellular networks, as in the case of [PERSON] et al. [33].  
Another important aspect is the priority in data traffic, such as for control orders, multimedia traffic or reading of meters, and this aspect has been addressed in the work of [PERSON] et al. [34].  
An interesting work presented by [PERSON] et al. [35] addresses the reduction of the existing data volume, which is based on the reduction of data from different locations.  
It is possible to use cloud computing for the development of new solutions for SG, as reported by [PERSON] et al. [36]. Of all the suggested applications, the efficient storage on a large scale is perhaps the most interesting.  
CPS must be integrated into the communications infrastructure and deployed systems. CPS will allow fast and efficient interaction between devices and humans. In short, CPS will affect the SG and it is necessary to contemplate its integration.  
The interaction and integration of the physical and cybernetic world enables the Internet of Things (IoT) to be integrated into the SG. Therefore, CPS, IoT and SG must be developed and integrated together. IoT is critical in the SG, since the electrical infrastructure will be completely filled with heterogeneous devices.
## 3 LSDM Assembly Error Model in Microgravity Assembly on the Ground
### Assembly Error Propagation of Multi-Loop Closed Chain  
Based on the above analysis, the center coordinates of each hinge of the single loop closed chain structure under the influence of the three error factors can be calculated. The LSDM can be equivalent to the combination of multiple single-loop closed chains, as shown in Figure 10. I, II, and III are used to represent the three closed loop chains, respectively, and the assembly error propagates among the closed loops.  
The coordinates of hinges \(c\) and \(d\) in space can be solved in loop I, and then, the truss rod \(cd\) can be seen as the equivalent fixed length rod for the assembly error solving of loop II, as well loop III eventually being solved. Then, the coordinates of all the truss hinges in space in the weightless environment can be obtained.  
The LSDM's yaw angle \(\alpha\) in space is as follows:  
\[\alpha=\frac{\pi}{2}-\arccos\frac{\overset{\rightarrow}{\text{cj}}\overset{ \rightarrow}{\text{ab}}}{|\text{cj}|\times\left|\overset{\rightarrow}{\text{ ab}}\right|} \tag{11}\]  
where the vectors in the above equation can be expressed as follows:  
\[\left\{\begin{aligned} \overset{\rightarrow}{\text{ab}}& =\left(x_{s}^{b}-x_{s}^{a},z_{s}^{b}-z_{s}^{a}\right)\\ \overset{\rightarrow}{\text{cj}}&=\left(\dot{x}_{s}^{j }-x_{s}^{c},z_{s}^{j}-z_{s}^{c}\right)\end{aligned}\right. \tag{12}\]  
Thus, the error propagation model for the yaw angle \(\alpha\) affected by the three factors--positioning error, hinge clearance, and truss rod deformation in the X-O-Z plane--can be established. Similarily, the same method can be used to build the error propagation model of the pitch angle \(\beta\) in X-O-Y plane. Then, the pointing angle \(\theta\) can be calculated using Equation (1).  
Finally, the relationship between the positioning error of the ground assembly and the pointing accuracy in space can be established, which is used to guide the assembly operations for the workers, and it provides a basis for the dynamic tolerance optimization in the subsequent sections.  
Figure 10: The error propagation of the polycyclic closed chain structure.
## 2 Materials and Methods
### Physico-Chemical Analysis
#### 2.2.2 Color  
Instrumental color was measured using a spectrophotometer, HUNTERLAB ColorFlexTM, mod. A60-1010-615 (Reston, VA, USA). For each sample, L*, a* and b* parameters from the CIELAB scale were measured.  
Results were expressed as luminosity (L*), hue angle (h\({}^{\circ}\)) and chroma (C*). The latter two color parameters were calculated using Equations (1) and (2), respectively:  
\[\text{h}^{\circ}=[\text{ arctg (b*/a*) 2 \pi}]\,360 \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Treatment** & **Sample Code** \\ \hline Untreated & C \\ OD & OD \\ PEF & PEF \\ OD + PEF & OD/PEF \\ PEF + OD + PEF & PEF/OD/PEF \\ \hline \end{tabular}
\end{table}
Table 1: Overview of the combination of treatments applied for kiwifruit slices at each drying temperature (50, 60 and 70 \({}^{\circ}\)C) and relative sample codes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Drying** & & & & & \\
**Temperature [\({}^{\circ}\)C]** & & & & & \\ \hline  & **C** & **OD** & **PEF** & **PEF/OD** & **OD/PEF** & **PEF/OD/PEF** \\ \hline
**50** & 960 & 360 & 300 & 360 & 300 & 300 \\
**60** & 480 & 300 & 240 & 360 & 300 & 270 \\
**70** & 420 & 240 & 180 & 300 & 210 & 210 \\ \hline \end{tabular}
\end{table}
Table 2: Total drying time (min) of the differently pre-treated kiwifruit slices at the different temperatures (50, 60 and 70 \({}^{\circ}\)C).  
\[\text{C}^{*}=\sqrt{\left(\text{a}^{*2}\ +\ \text{b}^{*2}\right)} \tag{2}\]  
where: a* (red-green) and b* (yellow-blue) are parameters of the color measurement.  
The analyses were carried out at least in ten repetitions from each sample at each drying temperature.
## 4 Proposal of a Method of Level Calibration in Non-Laboratory Tests  
Judging by the results of the conducted experiment, it is highly unlikely that level calibration of a non-laboratory listening setup can be made simply by listening to a calibration sample of reproduced speech and setting the reproduction volume so that the speech would sound \"normally\" loud. The results of the experiment clearly show that the understanding of what normal speech level should be is highly individual. In any group of participants involved in a non-laboratory listening test (e.g., an online test), the reproduction level calibrated in this manner would vary considerably within the group, thus introducing a large amount of additional variability, i.e., error, into the results of such a test.  
On the other hand, the results of the experiment show that the differences between the subjects are considerably smaller when it comes to reading at a \"normal\" speech level. Therefore, a speech-based level calibration could be performed by reading a few lines of text to a listener involved in a listening test in non-laboratory conditions.  
The proposed level calibration method based on \"normal\" speech level, to be used in non-laboratory test setups, involves a test subject performing a non-laboratory listening test and a second person serving as a live reader. The task of the test subjects would be twofold: (1) after proper instruction, to find and choose a space in their environment that is suitable for performing the listening test, i.e., with typical room acoustic conditions and low background noise, and (2) to find a \"normal\" talker for the role of the reader, i.e., to exclude any candidates that are obviously too loud or too quiet talkers. The reader would be positioned within the personal space of the subject, e.g., at a conversational distance of 1 m, and they would need to read a sample of text prepared by the designer of the experiment. The subject would need to adjust the level of the pre-recorded sample of the same text read by a speaker, i.e., do a purely subjective loudness-matching procedure between the speech read live and the pre-recorded sample. This way, the test subjects would have a live reference based on which they would perform the calibration, and the effort they would have to make would be to remember what the live reader sounded like, i.e., they would have to resort only to short-term memory. In research performed in study [34], it is suggested that the listeners simply set the normal level of speech based on their long-term memory and experience on what that level should be. However, the results of this research do not favor this course of action.  
The results of the reading part of the experiment show that the level of speech at a conversational, personal distance of 1 m in an acoustically ordinary room with low background noise averages at 55.3 dBA with the standard deviation of 3.7 dBA. It is reasonable to expect that the proposed calibration method would yield the same average level of reference speech read by a live reader, i.e., 55 dBA.
## 4 Optimization Design
### Evaluation of Optimization  
After the optimization of the dam body, the material consumption per unit thickness is saved by 11.83%, the maximum settlement of the dam body is reduced by 2.60%, and the maximum compressive stress of the dam body is reduced by 37.35% compared to the initial dam body, achieving an improvement in stress state and economic benefits.  
The optimization method based on the BP neural network finds a better solution. Although the minimum value of the objective function is only 0.3% different from the value calculated by traditional optimization method, the time consumption is saved by 40.92% by using the optimization method based on BP neural network, greatly improving computational efficiency and saving computational resources.  
In the combination of neural network prediction and optimization design methods, the prediction error after neural network training is one of the key factors affecting the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\sigma_{y,hel}\) & \(\sigma_{max,appream}\) & \(\sigma_{dmmax}\) & \(\sigma_{hel}\) & \(\sigma_{tee}\) & \(\sigma_{heel}\) & \(\sigma_{heel}\)/\(\sigma_{tee}\) \\  & (MPa) & (MPa) & (MPa) & (MPa) & (MPa) & & \\ \hline Traditional method & \(-0.25\) & \(-0.06\) & \(1.49\) & \(0.27\) & \(0.20\) & \(1.35\) \\ Method based on BP & \(-0.26\) & \(-0.11\) & \(1.51\) & \(0.39\) & \(0.35\) & \(1.11\) \\ Requirement & \(<\)0 & \(<\)0 & \(<\)6 & - & - & \(<\)1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Check result.  
Figure 18: Comparison of sections of optimized and initial dam.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Size** & \(M_{1}\) & \(M_{2}\) & \(M_{3}\) & \(M_{4}\) & \(y_{dmmax}\) (cm) & \(\sigma_{dmmax}\) (MPa) & \(V_{dmax}\) (m\({}^{3}\)) &
\begin{tabular}{c} **Time** \\ **Consumption** \\ \end{tabular} \\ \hline Initial & 0.650 & 0.500 & 1.182 & 1.235 & 4.190 & 2.412 & 1429 & - \\ Optimization by traditional method & 0.517 & 0.403 & 0.651 & 0.647 & 4.083 & 1.492 & 1264 & 7 h 37 min \\ Optimization by method & 0.503 & 0.402 & 0.653 & 0.765 & 4.081 & 1.511 & 1260 & 4 h 30 min \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of the optimization.  
optimization effect. In this paper, the prediction effect is good because the data source is calculated by finite element software. The neural network model shows high accuracy in predicting relatively regular and almost noise-free datasets, which also makes the method of accelerating structural optimization design by neural network prediction model feasible and reasonable.