esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study and Discussion
### Tests on Total Cooling Capacity and Number/Size of Chillers
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Chiller Number**} & **Chiller Plant Option** & **Operation Cost** & **Capital Cost** \\  & **(Size (kW) \(\times\) Number)** & **(10\({}^{3}\) HKD)** & **(10\({}^{3}\) HKD)** \\ \hline
2 & \(3200\times 1\)CSD + \(2800\times 1\)VSD & 3502 & 373 \\
3 & \(2100\times 2\)CSD + \(1800\times 1\)VSD & 3478 & 461 \\
4 & \(1550\times 3\)CSD + \(1350\times 1\)VSD & 3461 & 542 \\
4 & \(1600\times 2\)CSD + \(1400\times 2\)VSD & 3446 & 580 \\
5 & \(1250\times 4\)CSD + \(1000\times 1\)VSD & 3436 & 616 \\
5 & \(1300\times 3\)CSD + \(1050\times 2\)VSD & 3403 & 653 \\
6 & \(1050\times 5\)CSD + \(750\times 1\)VSD & 3435 & 685 \\
6 & \(1050\times 4\)CSD + \(900\times 2\)VSD & 3392 & 723 \\ \hline \hline \end{tabular} Remarks: CSD: constant-speed chiller, VSD: variable-speed chiller.  
\end{table} 
Table 5: Annual operation cost and annualized capital cost of different design options. cooling capacity) and four constant-speed chillers (1250 kW cooling capacity) gets the lowest total cost \(4237\times 10^{3}\) HKD, which has good robustness subject to uncertainties and reliability. It is therefore considered to be the best configuration corresponding to the total cooling capacity 6000 kW. If the penalty ratio is set to be 1 HKD/kW, the best configuration corresponding for the total cooling capacity is one variable-speed chiller (1800 kW cooling capacity) and two constant-speed chillers (2100 kW cooling capacity). Designers should select the best configuration according to the level of penalty. Table 7 summarizes the calculation results of lowest total costs corresponding to different total cooling capacities. When the total chiller plant cooling capacity increases from 5100 kW to 6300 kW, the total costs reduce due to the rapid decrease of availability risk costs when the optimal combination is identified. However, the total cost increases when the total cooling capacity is over 6300 kW. Compared with other options, results show that the combination with one variable-speed chiller (1100 kW cooling capacity) and four constant-speed chillers (1300 kW cooling capacity) achieves the lowest total cost 4222 \(\times\) \(10^{3}\) HKD. The major reason for achieving such a result is attributed to the lowest availability risk cost as a better robustness for uncertainties, and system reliability can be achieved by the selected option.
###### Abstract  
In this paper, we propose a joint power allocation, time switching (TS) factor and relay selection scheme for an energy harvesting two-way relaying communication network (TWRN), where two transceivers exchange information with the help of a wireless-powered relay. By exploiting the TS architecture at the relay node, the relay node needs to use additional time slots for energy transmission, reducing the transmission rate. Thus, we propose a joint resource allocation algorithm to maximize the max-min bidirectional instantaneous information rate. To solve the original non-convex optimization problem, the objective function is decomposed into three sub-problems and solved sequentially. The closed-form solution of the transmit power of two sources and the optimal TS factor can be obtained by the information rate balancing technology and the proposed time allocation scheme, respectively. At last, the optimal relay node can be obtained. Simulation results show that the performance of the proposed algorithm is better than the traditional schemes and power-splitting (PS) scheme.  
energy harvesting; two-way relaying network; max-min bidirectional information rate; power allocation; relay selection +
Footnote †: journal:
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 7 Conclusions  
This paper presents a review of the performances of CE schemes for candidate waveforms of xG wireless networks. Firstly, in Section 2, the design of digital filters is reviewed since the physical layer structure of these waveforms are based on filters developed from a well-designed prototype filter. In subsequent sections, the importance of CE for wireless communication networks is emphasised. As such, the CE techniques deployed for these waveforms in the literature are identified and classified, where the classification of CE schemes in application to filter and non-filter-based waveforms currently under consideration for xG networks is narrowed down. In addition, this paper summarises the performance comparison of CE schemes that are immensely considered as waveform candidates of xG networks. Finally, the performances of two linear CE schemes and three adaptive CE schemes for filter bank OFDM-OQAM and GFDM-OQAM based transceivers over slow and fast frequency selective Rayleigh fading channels are reviewed. The performance of these estimators is verified assuming NPR and Non-PR low-pass prototype filter design. It is observed from the simulation results presented in Section 5 that the adaptive CE schemes are more suitable as compared to the linear schemes for FBMC based transceivers. However, the need for more novel and application-specific CE schemes for FMBC transceivers is paramount, especially for Non-PR filter design. Thus, keeping in view the coverage of this review paper, we presumed that this paper will spring up possible research directions in improving the performances of the considered filter and non-filter based xG waveform candidates over frequency and dispersive time-varying Rayleigh fading channels.  
The review idea was conceived and implemented by [PERSON], [PERSON] and [PERSON] developed the structure of the review and write up the paper. [PERSON], [PERSON] and [PERSON] supervised the review presentation and the paper write up.  
This research received no external funding.  
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the NRF.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Panoramic Generative Tales
(**A**) shows the two view trajectories with the highest correlation (best match) in the spherical coordinates \(\phi\) and \(\theta\); we assumed \(r\) to be constant. (**B**) shows the two trajectories with the lowest correlation (poorest match). The color of the markers indicates the density value estimated by the KDE on the single trajectory at this point, and it allows us to visualize how much time the user spent observing this area. (**C**) Agglomerative clustering dendrogram, obtained using \(d(x,y)=1-corr(x,y)\) as a metric (where \(corr(x,y)\) is the Pearson correlation between the KDE of two trajectories), and the complete linking condition. The y-axis is the WCD, the linking condition value at which the clusters merge. We obtained four clusters by stopping the agglomerative clustering at the WCD = 0.98. To the right of the dendrogram, we present the Pearson correlation matrix between all view trajectory KDE couples. Bottom row: inspection of KDE clusters C0 and C2. \[\rho(u,v)=\rho(\hat{f}_{\sigma}(V|\mathbf{V}_{u}),\hat{f}_{\sigma}(V|\mathbf{V}_{v }))=\frac{\sum_{x,z}(\hat{f}_{\sigma}(V|\mathbf{V}_{u})-M_{u})(\hat{f}_{\sigma}( V|\mathbf{V}_{v})-M_{v})}{\sqrt{\sum_{x,z}(\hat{f}_{\sigma}(V|\mathbf{V}_{u})-M_{u})^{2} \sum_{x,z}(\hat{f}_{\sigma}(V|\mathbf{V}_{v})-M_{v})^{2}}} \tag{3}\]  
where \(M_{u}=\sum_{x,z}\hat{f}_{\sigma}(P|\mathbf{P}_{u})\). Given the Pearson correlation definition, we obtained the Pearson correlation matrix shown in Figure 9C. Figure 9A,B show the pair of view trajectories with the highest and lowest Pearson correlations computed on the corresponding 2D KDEs. Thus, we computed the agglomerative clustering model, with \(d(u,v)=1-\rho(u,v)\) and the complete linkage criterion, and obtained the dendrogram in Figure 9C. With a WCD threshold of 0.98, the dendrogram in Figure 9C gives two larger clusters (C0 and C2) and two smaller clusters. Figure 9 (bottom row) shows where the users in cluster C0 and cluster C2, respectively, focused the most in the Merkhet tool, elucidating how the two clusters were differentiated by their focus on two different portions of the larger house.
## 3 Experiments and Results
### Dynamic Target Ranging  
The retroreflector was set to move at a uniform speed of 500 mm/s. Limited by the storage depth of the DSO, the retroreflector was measured continuously for 5 ms. Figure 10 shows the results of the distance measurements for the moving target. In Figure 10a, the red triangles represent the distance measurements obtained using the proposed method, the black dots represent the distance measurements obtained using the traditional ranging method, and the green line represents the theoretical distance. Figure 10b shows the deviations between the measured and theoretical distances. The maximum ranging deviations for the proposed and traditional methods were \(-0.38\) mm and \(-1.26\) mm, respectively. Additionally, as the target distance increased, the ranging deviation of the traditional method reached a maximum, following the trend of the black dots shown in Figure 8b, with a periodicity of 600 mm. The ranging precisions (standard deviations) of the proposed and traditional methods were 0.13 mm and 0.20 mm, respectively. Compared to the static ranging results presented in the previous section, the ranging accuracy of the proposed method for the moving target was lower than that for the static target because random phase fluctuations occurred between the sampling clocks of the DSO and AWG when the DSO was triggered to sample the data, which disrupted the synchronization between the clocks.  
Figure 10: Measurement results for the relative distances of the moving target. (**a**) Ranging results. (**b**) Deviations between the measured and theoretical distances.  
Figure 9: Three groups of measurements of the distance of the stationary target. (**a**) The three groups of measurements. (**b**) Deviations after subtracting the average of the three groups of measurements.
## 4 Discussion  
We would like to stress that our working point at \(E_{c}\lesssim 1\) keV is unique in the literature, since most of the previous works and published papers show critical energies on the several to many keVs level. With a more appropriate UV detector, we would have been able to detect ultra-short bursts of ultraviolet radiation, demonstrating a promising radiation source in that range of frequencies. However, our experimental conditions are quite well explained by scaling laws found in the literature, as discussed in the previous section. Concerning the literature, we also would like to recall that with a similar laser system, with a longer off-axis parabola, and similar conditions for plasma density and injection scheme, other authors have demonstrated the same electron energies but higher critical energy (in the few keVs) and number of photons (\(\sim\)10\({}^{8}\)) [26]. In another work, with a similar experimental setup, larger electron energies up to 500 MeV have been demonstrated and larger critical energies of the betatron spectrum up to \(\lesssim\)10 keV [35], but smaller bunch charges. The reason for these differences may be attributed to different conditions for laser guiding and/or different injection schemes realized, e.g., with a mixture of gases, affecting the electron beam emittance, the bunch charge and the energy spread. A similar photon number but higher critical energy has been obtained in [36], obtained with a slightly longer off-axis parabola and higher electron plasma density. In conditions of direct laser-electron interaction, the critical energy may be even shifted towards \(\gamma\)-rays [30, 37, 38]. With comparable laser intensity but different systems with longer pulses, different regimes have been studied, as the self-modulated regime [39], demonstrating large photon number and critical energies on the tens of keVs. Further enhancing techniques for photon number and critical energy are based on the addition of wiggling sources and/or density tailoring [31, 32, 40, 41, 42, 43, 44, 45] or with different gas targets [46, 47].
## 4 Empirical Analysis
### Results Analysis and Discussion
#### 4.2.2 Spatial Autocorrelation Test and Aggregation Results
To clearly show the changes in the positions of each province's corresponding quadrants at the four time nodes, we summarized the specific changes, as shown in Table 5. Over time, L-L aggregation, such as applying to Jilin, Heilongjiang, Xinjiang and Tibet, occurred in economically underdeveloped border areas, especially in the remote northwestern regions of China. Here, economic development, traffic location and macroeconomic policies tend to lag behind those of the rest of the country, and make these areas have low-value in terms of _ITE_. In contrast, areas of H-H aggregation, such as Beijing, Shanghai and Tianjin, have advantageous conditions that are beneficial to transport development. These regions, therefore, have a relatively high level of _ITE_. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Moran’s I Value** & **\(Z\) Value** & **\(p\) Value** \\ \hline
2009 & 0.276 & 2.588 & 0.010 \\
2010 & 0.265 & 2.667 & 0.011 \\
2011 & 0.238 &  
Figure 7: Moran’s I scatter graphs of _ITEs_ in China. Note: The provinces are numbered as follows: 1. Beijing; 2. Tianjin; 3. Hebei; 4. Shanxi; 5. Inner Mongolia; 6. Liaoning; 7. Jilin; 8. Heilongjiang; 9. Shanghai; 10. Jiangsu; 11. Zhejiang; 12. Anhui; 13. Fujian; 14. Jiangxi; 15. Shandong; 16. Henan; 17. Hubei; 18. Hunan; 19. Guangdong; 20. Guangxi; 21. Hainan; 22. Chongqing; 23. Sichuan; 24. Guizhou; 25. Yunnan; 26. Tibet; 27. Shaanxi; 28. Gansu; 29. Ningxia; 30. Qinghai; 31. Xinjiang. The provinces represented by blue, orange and green dots are located in the eastern, central and western regions of China, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline Year & **2009** & **2011** & **2014** & **2016** \\ \hline H-H & Tianjin, Hebei, Shanghai, Jiangsu, Anhui, Shandong, Henan & Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Anhui, Shandong, Henan & Beijing, Tianjin, Hebei, Jiangsu, Anhui, Jiangxi, Henan, Hunan, Chongqing, Guizhou, Shanxi & Beijing, Tianjin, Hebei, Jiangxi, Inner Mongolia, Liaoning, Hanan, Anhui, Fujian, Jiangxi, Henan, Chongqing, Guizhou & Inner Mongolia, Liaoning, Hanan, Anhui, Fujian, Jiangxi, Henan, Chunqing, Guizhou, Shanxi & Anhui, Fujian, Jiangxi, Henan, Chunqing, Guizhou \\ \hline L-H & Beijing, Shanxi, Liaoning, Fujian, Heilongjiang, Heilongjiang, Zhejiang, Hubei, Huan, Hainan, Shaanxi, Ningxia & Shanxi, Zhejiang, Hubei, Shandong, Guangxi, Hainan, Shaanxi, Ningxia & Shanxi, Zhejiang, Hubei, Shandong, Guangxi, Hainan, Guangxi, Hainan, Shaanxi, Ningxia \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Changes in corresponding _ITE_ quadrants.
## 3 Infrared Image Segmentation
### Copper Rod Segmentation Based on the Region Growing Algorithm
#### 3.2.1 Selection of Initial Seeds  
In the grayscale image, all copper rods with a fixed width are placed vertically and equidistantly, which are darker than the electrolyte area. The vertical gray mean shows differences in \(x\)-axis direction and the copper rod areas have the characteristics of a low gray mean. So, the initial seed selection for each copper rod can be determined by analyzing the mean, variance, and jump-edge of the pixel set in the copper rod area [14].  
In this paper, we trim a 320 \(\times\) 100 captured image from an original 320 \(\times\) 320 grayscale image without isolated partitions and busbars. There are obvious differences between wide copper rod areas and electrolyte areas.  
Figure 7 shows the trimming procedure and distribution of vertical gray mean along x axis.  
The initial seed selection for wide copper rods can be determined by traversing the vertical gray mean on the x axis and analyzing the mean, variance, and jump-edge in the scatter plot [15]. The algorithm steps used to search for the initial seed for the wide copper rod is described as follows:  
Figure 6: The approximate vertical position of the cell wall edge in different conductive states.  
Figure 7: The original grayscale image, the captured image, and the scatter plot of the relationship between the horizontal position and vertical gray mean.  
1. Traverse all horizontal positions in the captured image, calculate the vertical gray mean at each position, and store it in array named verticalMean.
2. According to the width of the wide copper rod, set a variable named WinSlideSize for the sliding window length and initialize WinSlideSize to be equal to 20.
3. Traverse array verticalMean, enter step (4), and start to search for the position of the wide copper rod in the valley when the traversal results meet the following conditions: \[\left\{\begin{array}{l}verticalMean[i]\leq 130\\ verticalMean[i]-verticalMean[i+1]>5\\ verticalMean[i+1]-verticalMean[i+2]>5\end{array}\right.\] (1)
4. Traverse array _verticalMean_ from the index \(i\), and calculate the variance in the sliding window named _verticalMean_ [jj+WinSlideSize]. When the variance is less than 5 for the first time, record the position as the start. When the variance is greater than 5 for the first time, the position is recorded as the end.
5. The positions of the initial seed for the wide copper rod are (start + end)/2, which are recorded in the array named WideRodPos. Then, return to step (3) and continue to traverse until all initial seeds of all wide copper rods are found.  
Figure 8 shows an illustration of all initial seeds for the wide copper rod after applying the above algorithm. It also shows the initial seeds for narrow copper rods, which can be selected using the same method.
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 3 Development of New Energy-Based Penetration Formula
### Involved Energies in Impact Mechanism
#### 3.2.6 Tunneling-Resistant Energy (E\({}_{\rm T}\))  
When the projectile has a huge energy per unit area by hyper-velocity and/or large mass, it can penetrate deep into the concrete panel. The energy beyond that required for the spalling step can result in tunneling depth (\(x_{t}\)). The tunneling-resistant energy (\(E_{T}\)) can be obtained using the energy  
Figure 8: Spalled cone and truncated shape.  
density and bearing bond strength (Equations (112) and (113)). The force acting on the side of the cylindrical tunnel is shown in Equation (114). The impact energy of a projectile can be resisted by the bearing bond strength between the projectile and concrete. According to previous studies [48; 49; 50], the average bond stress under static loading is \(2.2(f^{r}{}_{c,dst})^{0.5}\), which is modified to \(2.2(f^{r}{}_{c,dyn})^{0.5}\) under dynamic loading. This stress is obtained from the interface between a deformed reinforcing bar and concrete. As there are no lugs on the projectile in many cases, the bearing stress should be reduced, and thus, it is assumed to be 50% in this study, as shown in Equation (115). If there are reinforcing bars in line of the projection, the shear stress of the reinforcing bar needs to be considered. Thus, \(E_{T}\) can be expressed as Equation (116).  
\[E_{T}=\underbrace{\frac{F_{t}}{A_{t}}V_{t}}_{\text{energy density}}+ \underbrace{\tau_{dyn}x_{t}}_{\text{energy of bond}} \tag{112}\]  
\[E_{T}=\underbrace{F_{t}x_{t}}_{\text{energy density}}+\underbrace{\tau_{dyn }x_{t}}_{\text{energy of bond}} \tag{113}\]  
\[F_{t}=N_{p}2\pi d_{p}x_{t}0.7f^{r}_{c,dyn} \tag{114}\]  
\[\tau_{dyn}=1.1\sqrt{f^{r}_{c,dyn}} \tag{115}\]  
\[E_{T}=\underbrace{\left(N_{p}2\pi d_{p}x_{t}0.7f^{r}_{c,dyn}\right)}_{\text {energy density}}+\underbrace{1.1\sqrt{f^{r}_{c,dyn}}x_{t}}_{\text{energy of bond}} \tag{116}\]  
where \(F_{T}\) is the tunneling-resistant force on the side of the cylindrical tunnel (N); \(d_{p}\) is the diameter of a projectile (mm); \(x_{t}\) is the length of the tunnel (mm); \(N_{p}\) is the nose shape factor; \(\tau_{dyn}\) is the bond stress under the dynamic loading (MPa); \(\rho_{p}\) is the density of a projectile (kg/m\({}^{3}\)); \(A_{t}\) is the cross-sectional area of the tunnel (mm\({}^{2}\)); and \(M_{p}\) is the mass of a projectile (kg).
## 3 Determination of a Reasonable Solution to Pass Through Fault F6
### Determination of Reasonable Solution  
Table 1 summarizes the advantages and disadvantages of the two solutions of the #3 up1101 coalface over the F6 fault, the solution of PDRs with rock pillars and the solution of a single PDR without rock pillars.  
In summary, the solution of PDRs with rock pillars provides superior control over the stability of the top and bottom plates and effectively mitigates the influence of mining pressure. This approach is particularly suited to scenarios involving complex geological conditions and stringent safety requirements. Conversely, the solution of a single PDR without rock pillars is better suited to stable geological conditions where improving mining efficiency is a priority, although it entails higher support costs and greater technical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methodology** & \multicolumn{2}{c}{**PDRs with Rock Pillars**} & \multicolumn{2}{c}{**A Single PDR Without Rock Pillars**} \\ \hline \multirow{6}{*}{Advantages} & ✓ & Placed rock columns disperse & ✓ & Maximize resource recovery, \\  & & ground pressure and prevent & & improve coal quality, and reduce \\  & & regional stress concentration. & & resource waste. \\  & & Set up rock pillars to disperse roof & ✓ & Coalface can be advanced more \\  & & pressure and prevent regional stress & & quickly, increasing production in the \\  & & concentration. & & short term. \\  & & Small cross-section tunnel supports & ✓ & No rock pillars are left, reducing the \\  & & low technical requirements, simple & & risk of spontaneous combustion due \\  & & construction. & & to poor ventilation. \\ \hline \multirow{6}{*}{Disadvantages} & \(\diamondsuit\) & Retention of rock pillars may result & \(\diamondsuit\) & Absence of rock pillars in roadways \\  & & in some coal resources not being & & and coalface demands enhanced \\  & & mined. & & support. \\ \cline{1-1}  & \(\diamondsuit\) & Multiple pre-dug roadways & \(\diamondsuit\) & High-standard support materials \\ \cline{1-1}  & & complicate ventilation, raising safety & & and techniques are required, \\ \cline{1-1}  & & risks with poor air flow. & & increasing costs. \\ \cline{1-1}  & \(\diamondsuit\) & Controlling layer levels becomes & \(\diamondsuit\) & Roof slab is prone to accidents such \\ \cline{1-1}  & & more challenging when crossing the & & as slab collapse, and safety risk \\ \cline{1-1}  & & F6 fault. & & is higher. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of two solutions.  
Figure 5: Solution of a single pre-driven roadway (PDR) without rock pillars.  
investment. For the #3 up1101 coalface, which crosses the F\({}_{6}\) fault--a complex geological situation--the primary focus must be on ensuring the safety and stability of both the coalface and the roadway during the advancement process. As such, solution 1 is deemed more appropriate for the #3 up1101 coalface crossing the F\({}_{6}\) fault.