esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### All Cordierite versus All SiC
#### 3.1.1 Base Cases  
Figure 3 shows the maps of temperature, T, and propane mole fraction, X\({}_{\rm Propane}\), as computed when setting the inlet gas velocity, V\({}_{\rm in}\), equal to 0.5 m/s.  
Figure 3: Maps of temperature, T, and propane mole fraction, X\({}_{\rm Propane}\), at V\({}_{\rm in}\) = 0.5 m/s: All Cordierite; All SiC. A scaling factor equal to 2 was used to scale the computational domain along the transverse direction.  
Figure 2: Axial profiles of temperature along the center line of the inner wall at different grid resolutions: V\({}_{\rm in}\) = 0.5 m/s; All Cordierite.  
As expected, in the case of low thermal conductivity (cordierite), the thermal profile was much less uniform, and higher values of local temperature were attained. In addition, when looking at the inlet channel, it can be noticed that, in the case of SiC, there was a substantial symmetry with respect to the center line for temperature and, above all, for propane mole fraction. Conversely, in the case of cordierite, there was no symmetry.  
For both cases of Figure 3, Figure 4 shows the axial profiles of (catalytic) reaction rate along the inner and outer walls of the inlet channel.  
In the case of SiC, the reaction zone was located close to the inlet on both inner and outer sides. In the case of cordierite, symmetry was lost, the reaction zone being located close to the inlet on the inner side and further downstream on the outer side.  
On the inner side of the inlet channel, two mechanisms of heat recirculation allow for pre-heating of the cold feed. The first mechanism is that of transverse heat transfer, i.e., heat recirculation from the reaction zone: following reaction, the product mixture that flows down the channel first gets cooled due to the heat losses from the outer wall, but later, close to the outlet, it picks up the heat released in the reaction zone of the inlet channel and pre-heats the cold feed. The second mechanism of heat recirculation is the classical mechanism of axial conduction in the upstream direction along the solid wall. In the case of cordierite, despite its low thermal conductivity, this latter mechanism was facilitated by the presence of the outlet channel that also acted as a protection for the reaction zone. On the outer side of the inlet channel, which is directly exposed to the heat losses toward the external environment, the only pre-heating mechanism is that of axial heat conduction. However, in the case of cordierite, the thermal conductivity was too low and, thus, the reaction zone was pushed downstream.
## 3 Mathematical Background  
### Quaternions  
Quaternions, introduced by [PERSON] in 1844 [21], are extend the complex number system used to represent rigid-body rotations. A quaternion \(\mathbf{q}\) is the sum of a scalar \(s\) and a vector \(\mathbf{v}=(q_{1},\,q_{2},\,q_{3})\):  
\[\mathbf{q}=s+\mathbf{v}=s+q_{1}\mathbf{i}+q_{2}\mathbf{j}+q_{3}\mathbf{k}, \tag{1}\]  
where \(\mathbf{i},\,\mathbf{j},\,\mathbf{k}\) are unit vectors pointing in the \(\mathbf{x},\,y,\,\mathbf{z}\) directions.  
The conjugate of a quaternion \(\mathbf{q}\) is defined as:  
\[\mathbf{q}^{*}=s-\mathbf{v}=s-q_{1}\mathbf{i}-q_{2}\mathbf{j}-q_{3}\mathbf{k}, \tag{2}\]  
which is used to calculate the norm of a quaternion as follows:  
\[\parallel\mathbf{q}\parallel=\sqrt{\mathbf{q}^{*}\mathbf{q}}. \tag{3}\]  
Unit quaternions satisfy \(s^{2}+q_{1}^{2}+q_{2}^{2}+q_{3}^{2}=1\) or simply \(\parallel\mathbf{q}\parallel=1\).  
More information on quaternions can be found in Reference [22].
## 2 Related Work
### Search-Order Coding (SOC)  
In 1996, [17] utilized the high similarity between index values within a local region to further compress the VQ index table. The process is illustrated in Figure 2. Assuming that the black-dotted element is the current pixel under processing. Since the indices are recorded in the raster scan order, the indices in the gray area are available to be referred. The order of searching is as labeled in Figure 3. When a matched index is found within a predefined search range, the current index is encoded with the serial number of the matched index. The serial number of indices is assigned along the searching path with repeated index values skipped to improve the coding capacity.  
To distinguish between SOC representable and non-representable indices, an additional indicator bit is prefixed to an SOC serial number or an original index. The existing RDH schemes for SOC-encoded VQ indices focus on replacing the indicator bit with a secret bit and modifying its following code to fit the data type indicated. In cases where the indicator bit mismatches with the secret bit, the succeeding code may be significantly lengthened.  
An example of SOC is illustrated in Figure 3, where the index 36 at the center is under processing and the gray portion is the referable indices that have been compressed earlier. Suppose we apply four bits to represent the serial number of indices along the search path and an indicator bit valued 1 to indicate a compressible index. The first encountered index 30 is labeled as 0000. The next index is also 30. According to the rule of labeling, repeated indices are left unlabeled. Then, the process is proceeded step by step and labels the indices 31, 32, 33, 34, 35, (31), 37, 38, 40, 41, 39, 42, 43, 44, and 36 as 0001 to 1110. The parenthesized 31 is also a repetition index and therefore skipped. As a result, the current index 36 is compressed into five bits 11,110, where the first bit 1 is the indicator bit.  
Figure 1: An illustration of VQ image compression, where \(w\times h\) is the block size; \(K=w\times h\) is the codeword length of the codebook; \(i\), \(j\), and \(k\) are the indices of the leading blocks.
# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 4 Simulation and Analysis
### Comparison and Discussion  
To analyze the superiority of the proposed method, the maximum score (\(S_{score\_\_\_\_}\)max), the mean value (Mean) and the standard deviation (SD) of the four algorithms were compared. The performance comparison results of the four different algorithms are shown in Table 3. As can be seen in the table, the \(S_{score\_\_\_}\)max of MBA, ABAM, BCBA and BAO was between 92.394 and 92.396. The mean of the MBA optimized \(\alpha^{\prime}\) was 0.66\({}^{\circ}\). The mean values of ABAM, BCBA and BAO optimized \(\alpha^{\prime}\) were 0.659\({}^{\circ}\), 0.656\({}^{\circ}\), 0.655\({}^{\circ}\), respectively. The errors of the means were 0.6%, 0.77%, 1.5%, and 3.4%, respectively. The SDs of MBA, ABAM, BCBA and BAO were 1%, 2.1%, 2.5%, and 2.8%, respectively. Overall, the proposed MBA optimization algorithm converged quickly and with a small deviation.
## 5 Performance Evaluation
### Performance Trade-Offs  
The performance trade-offs of Re-Intar with selected protocols are listed in Table 5. WDFAD-DBR makes a routing decision according to two metrics: depth of current hop and depth of next expected forwarding node, to avoid void hole. However, considering these two metrics cannot eliminate void hole. Therefore, WDFAD-DBR shows low PDR, as shown in Figure 4, and high energy consumption due to high packet drop in the sparse network (Figure 5). Moreover, it selects a PFN with least depth as next forwarder, achieves low APD, as shown in Figure 7, and less energy consumption in dense network, as shown in Figure 5, but, due to holding time, shows high end-to-end delay, as shown in Figure 6. Intar protocol does not consider holding time of the packet and avoids void hole by following end-to-end path from source node to sink. Therefore, Intar protocol shows high PDR (Figure 4), less energy consumption in the sparse network (Figure 5) and low end-to-end delay (Figure 6) compared to WDFAD-DBR at the cost of high APD (Figure 7) and high energy consumption (Figure 7). Besides end-to-end path Re-Intar also uses one-hop backward transmission and reduces APD of the Intar resulting in high PDR, as shown in Figure 4, low end-to-end delay (Figure 6) and less energy consumption in the sparse network (Figure 5) at the cost of high APD than WDFAD-DBR, as shown in Figure 7.  
Figure 7: APD of the existing and proposed protocols with varying number of nodes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{10}{c}{**Number of Nodes**} \\ \cline{2-11}  & \multicolumn{2}{c}{**100**} & **150** & **200** & **250** & **300** & **350** & **400** & **450** & **500** \\ \hline \multirow{4}{*}{**WDFAD-DBR**} & **PDR** & 40.00\% & 31.08\% & 27.65\% & 18.05\% & 13.35\% & 9.46\% & 5.65\% & 4.06\% & 3.37\% \\ \cline{2-11}  & **Energy tax** & 66.62\% & 34.44\% & 15.23\% & 9.66\% & 4.71\% & 2.42\% & \(-\)0.057\% & \(-\)1.45\% & \(-\)1.63\% \\ \cline{2-11}  & **End-to-end delay** & 38.20\% & 37.22\% & 37.85\% & 40.54\% & 37.82\% & 36.74\% & 36.78\% & 35.24\% & 34.13\% \\ \cline{2-11}  & **APD** & \(-\)22.27\% & \(-\)14.48\% & \(-\)11.09\% & \(-\)6.46\% & \(-\)5.64\% & \(-\)4.85\% & \(-\)3.43\% & \(-\)3.38\% & \(-\)3.87\% \\ \hline \multirow{4}{*}{**Intar**} & **PDR** & 17.07\% & 15.67\% & 10.80\% & 7.16\% & 5.31\% & 3.84\% & 2.54\% & 1.71\% & 1.26\% \\ \cline{2-11}  & **Energy tax** & \(-\)2.81\% & 1.98\% & 5.44\% & 7.87\% & 9.36\% & 10.38\% & 11.98\% & 12.09\% & 12.55\% \\ \cline{2-11}  & **End-to-end delay** & \(-\)3.30\% & 3.23\% & 7.40\% & 10.30\% & 12.34\% & 13.79\% & 15.80\% & 15.98\% & 16.38\% \\ \cline{2-11}  & **APD** & \(-\)2.32\% & 2.46\% & 6.23\% & 8.88\% & 11.00\% & 12.12\% & 13.30\% & 13.90\% & 13.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance trade-offs made by Re-Intar with WDFAD-DBR and Intar in terms of percentage. Positive sign shows that the performance of Re-Intar is improved over respective protocol and negative sign shows that the performance of Re-Intar is reduced.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 5 Energy-Saving Features and AI-Assisted Radio Resource Management
### Q-Learning Definitions  
* **State:** It is a representation of the environment that an agent finds itself in and influences the actions of an agent. In this work, we considered a diverse set of heuristically determined input features for each agent listed in Table 3. Each agent observes a subset/complete set of features depending on its role and position in the hierarchy. In addition to the features listed in Table 3, each agent receives features that are more specific to their role. For instance, the local configuration adapter agent takes as input only those features that are more relevant at the site of interest. The site selector for RRA--a centralized agent--receives information from all sites along with the user distribution statistics at each site. On the other hand, the carrier adapter receives as input the carriers that could be activated or deactivated at each site.
* **Action space:** It corresponds to the decisions that an agent takes at each decision point. Each agent has a different action set closely tied to its objective. Table 2 describes the agent's actions.
* **Reward:** The reward signal is common to all the agents and is a function of the two optimization KPIs: network energy consumption and throughput. The reward is expressed as \[R=\beta*\lambda_{e}(\Delta E_{c})*\lambda_{t}(\Delta\tau)\] (20) Here, \(\Delta E_{c}\) and \(\Delta\tau\) correspond to the percentage change in the energy consumption and throughput, respectively. Here, \(\lambda_{e}\) and \(\lambda_{t}\) (\(\lambda_{e}>\lambda_{t}\)) are their _importance factors_. They indicate the relative importance of the two parameters in the optimization process. \(\beta\in[-4,4]\) is a fixed reward that is assigned based on the direction of change in the two KPIs.
# On IRS-Assisted Covert Communication with a Friendly UAV
## 4 Optimization Algorithm
### Phase Shift Optimization for IRS  
To optimize the phase shift of the IRS, \(h_{IB}^{H}[t]\Theta[t]h_{AI}[t]\) in (23) can be expressed as  
\[h_{IB}^{H}[t]\Theta[t]h_{AI}[t]=\frac{\beta_{0}\sum\limits_{m=1}^{M}e^{j\left( \theta_{m}[t]+\frac{2\pi}{\lambda}d(m-1)(\phi_{IB}[t]-\phi_{AI}[t])\right)}}{ \sqrt{d_{IB}^{a}[t]d_{AI}^{a}[t]}}. \tag{31}\]Then, [PERSON] can combine the signals transmitted from different paths via the IRS and the received power of the combined signals can be increased in order to improve the covert rate. The phase shift of the IRS can now be expressed as  
\[\theta_{1}[t] =\theta_{2}[t]+\frac{2\pi}{\lambda}d(\phi_{IB}[t]-\phi_{AI}[t])\] \[=\theta_{M}[t]+\frac{2\pi}{\lambda}d(M-1)(\phi_{IB}[t]-\phi_{AI}[t])\] \[=\omega, \tag{32}\]  
where \(\omega=[0,2\pi]\) is the direction to be aligned. Thus, the cell phase shift of the \(m\)-th IRS can be expressed as  
\[\theta_{m}[t]=\frac{2\pi}{\lambda}d(m-1)(\phi_{AI}[t]-\phi_{IB}[t])+\omega. \tag{33}\]  
For simplicity, we substitute (33) into (31); the maximum value can be approximated by deriving the upper bound on \(\left|h_{IB}^{H}[t]\Theta[t]h_{AI}[t]\right|^{2}\) with  
\[\left|h_{IB}^{H}[t]\Theta[t]h_{AI}[t]\right|^{2}=\left|\frac{\beta_{0}\sum \limits_{m=1}^{M}\frac{j^{\left(q_{m}[t]\right)+\frac{2\pi}{\lambda}d(m-1)(q_{ IB}[-\phi_{AI}[t]))}{\sqrt{d_{IB}^{H}[t]A_{AI}^{H}[t]}}}\right|^{2}=\left| \frac{\beta_{0}Me^{i\omega}}{\sqrt{d_{IB}^{H}[t]A_{AI}^{H}[t]}}\right|^{2}\leq \frac{\beta_{0}^{2}M^{2}}{d_{IB}^{H}[t]A_{AI}^{H}[t]}. \tag{34}\]  
Then, through phase alignment, we can obtain the best \(\Theta\).
## 4 FIAE-GAN Digital Watermarking Model
### Discriminator  
The discriminator \(DIS_{\delta}\) attempts to distinguish the encoded image \(F_{en}\) from the original image \(F_{cover}\). At the same time, the encoder aims to deceive \(DIS_{\delta}\) by making \(F_{en}\) resemble \(F_{cover}\), preventing accurate discrimination. Essentially, \(DIS_{\delta}\) checks for the presence of a watermark in the encoded image. This adversarial relationship enhances the encoded image quality through iterative training until Nash equilibrium is achieved.  
Training FIAE-GAN by reducing total loss, comprising encoding, decoding, and adversarial losses, involves updating the encoder and decoder parameters by minimizing their losses. The encoder's parameters are influenced by both encoding and decoding losses to embed the watermark in imperceptible and robust areas. If image features are not robust against noise, the decoder's feedback prompts the encoder to adjust. The discriminator updates its parameters using adversarial loss to evaluate image quality and guide the encoder to improve watermark invisibility. Consequently, the encoder, decoder, and discriminator are trained together to achieve optimal performance.
# Bond Strength Properties of GFRP and CFRP according to Concrete Strength
## 4 Results and Discussion
### Bond Strength and Slip Relationship  
Table 8 summarizes the experimental data of the bond strength of the GFRP and CFRP according to the water-cement ratio. The notation of the test specimens is as follows: free end slip is represented by the mean of the LVDTs installed at the bottom of both sides measured at maximum bond strength.  
CW_D#N  
C = type of rebar used in the test specimen  
W = water-cement ratio  
D = diameter of rebar used in the test specimen  
N = number of test specimens  
Figure 10 shows the relationship between bond strength and slip of the GFRP. It was found that the increase in bond strength of the GFRP also led to the increase in slip. At the water-cement ratio of 40%, the bond strength increased after maximum bond strength. This may be because the high compressive strength cracked the ribs on the surface of the GFRP, and the ribs remaining on the bonded surface between the concrete and GFRP led to the increase in bond strength. For the water-cement ratio of 50%, the bond strength increased slightly after maximum bond strength. On the other hand, for the water-cement ratio of 60%, the bond strength showed a gentle slope after maximum bond strength and then declined. Moreover, a lower water-cement ratio led to more slip of maximum bond strength.  
Figure 8: Average bond strength results of GFRP.  
Figure 9: Average bond strength results of CFRP.  
Figure 11 is a graph showing the relationship between bond strength and slip of the CFRP. For the water-cement ratio of 40%, similar to GFRP, high compressive strength cracked the ribs on the surface of the CFRP, and the ribs remaining on the bonded surface between the concrete and the CFRP led to an increase in bond strength. In addition, the concrete with 50% and 60% water-cement ratio showed a similar trend as the GFRP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen Name**} & \multicolumn{3}{c}{**Ultimate State**} \\ \cline{2-4}  & **Bond Strength** & **Free End Slip** & **Load** \\  & **(MPa)** & **(mm)** & **(kN)** \\ \hline G40\_13\#1 & 11.29 & 11.67 & 23.96 \\ \hline G40\_13\#2 & 10.69 & 11.01 & 22.69 \\ \hline G40\_13\#3 & 13.48 &
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 2 F-H Impacts on Photovoltaic Output and Load Forecasts  
On 7 December 2015, the Moderate Resolution Imaging Spectroradiometer on NASA's Aqua satellite captured an image of eastern China being inundated by thick F-H, seen in the gray area of Figure 1.  
Figure 1: The spatial distribution of fog and haze of eastern China on 7 December 2015.  
As shown in Figure 1, the thick F-H would certainly block out sunlight, which would affect the output of PV panels to a great extent. The previous day in Beijing, the Chinese government issued a first-ever \"red alert\" for the city, which resulted in power plant and factory closures and the change of power load.  
The generation output forecast (photovoltaic power, wind power, thermal power, _etc._) and load forecast of power systems are the basic tasks of dispatching, in which improved prediction accuracy would efficiently reduce the complexity and uncertainty of dispatch control to ensure the system's safe, economic and green operation. The impacts of F-H on photovoltaic output and power load are discussed as follows.
## 3 Numerical Implementation  
We have presented the problem of topology optimization in Equation (11). The pseudocode in Algorithm 1 shows the iterative solving process and contains a loop for the solution. The variational formulations of the partial differential equations and adjoint equations [45; 46] are solved by finite element method [47]. The distribution of \(\gamma\) is updated by MMA [48].  
The values of each parameter in topology optimization are set at the beginning of the procedure. In the process of solving, \(r_{f}\) is kept as 0.1; \(\beta\) with the initial value of 1 is doubled after every 30 iterations [49]. The maximum number of iterations is 315 and the corresponding value of \(\beta\) is \(2^{10}\). The greater the value of \(\beta\) is, the clearer the material interface is.  
In the iterative solving loop, \(n_{i}\) and \(n_{max}\) are the loop index and its maximal value, respectively. \(J_{n_{i}}\) is the specific value of \(J\) in the \(n_{i}\)-th iteration, and mod is an operator for taking the remainder. The loop will stop at the maximal number of iterations. It also will terminate when both the average variation of \(J\) for five consecutive iterations and the residual of \(v\) are less than the tolerance of \(10^{-5}\).  
```
Set \(u_{0}\)\(v_{0}\) Re \(h\)\(\eta\),  Set \(\left\{\begin{array}{l}\gamma\gets v_{0}\\ n_{i}\gets 1\\ n_{\max}\gets 315\\ q\gets 1\end{array}\right.\)  loop  Solve Equation (8) to derive \(\gamma_{f}\) by filtering \(\gamma\);  Project \(\gamma_{f}\) to derive \(\gamma_{p}\) and compute \(v_{i}\);  Solve \(\mathbf{u}\) and \(p\) from Equation (4), and evaluate \(J/J_{0}\);  Solve \(\mathbf{u}_{a}\), \(p_{a}\) and \(\gamma_{fa}\) from Equations (13) and (14);  Evaluate \(\delta J\) from Equation (12);  Solve \(\gamma_{fa}\) from Equation (16);  Evaluate \(\delta v\) from Equation (15);  Update \(\gamma\) based on \(\delta J\) and \(\delta v\); ifmod (\(n_{i}\), 30) == 0 \(\beta\gets 2\beta\) endif if (\(n_{i}==n_{\max}\)) or \(\left\{\begin{array}{l}\beta\text{=}2^{10}\\ 1/5\sum_{m=0}^{4}\left|J_{n_{i}}-J_{n_{i}-m}\right|/J_{0}\leq 10^{-5}\\ \left|v-v_{0}\right|\leq 10^{-5}\end{array}\right.\)  break; endif \(n_{i}\gets n_{i}+1\) endloop
```  
**Algorithm 1** Iterative solution of Equation (11). Pseudocode used to solve the topology optimization problem for the Navier-Stokes flows.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 3 The Proposed HMIPS
### Marker Generation
#### 3.2.1 QR Marker and Marker Recognition Method in HMIPS
We check whether the recognized marker ID exists in the database, and then extract the marker location from the database (see Figure 5d) to express its current position, as shown in Figure 5e. If the marker ID does not exist, no information is output and the application waits until the marker is recognized again. If the recognized marker ID does exist in the database, the database calculates the distance and direction from the location information of the recognized marker ID to the corresponding destination and delivers it to the application. The application recalculates the direction information among the received destination data and continues with directions to the destination.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML  
### Basic Principle  
Wireless communications use electromagnetic waves, which can propagate in free space, to carry data. Microwave refers to the electromagnetic wave with 300 MHz-300 GHz frequency, i.e., 1 mm-1 m wavelength. Microwave can be further divided into decimeter wave, centimeter wave, and millimeter wave. Normally, round directional antennas have stronger beam focusing characteristics than rectangular omnidirectional antennas. The communication link between two such directional antennas is called a microwave backhaul link, or microwave link for short. As shown in Figure 1, when the microwave propagates through the rain area, the transmitting power will be attenuated due to the scattering and absorption by raindrops [21; 22]. This type of microwave attenuation is called rain-induced attenuation, and the relative loss of power per unit path length is called specific attenuation, denoted as \(k\) (dB/km). Recommendation ITU-R P.838-3 [23] gives the relationship between the specific attenuation \(k\) and rain rate, denoted as \(R\) (mm/h), as follows:  
\[k=aR^{b}, \tag{1}\]  
where the coefficients \(a\) and \(b\) are functions of microwave signal frequency, polarization, and raindrop size distribution (DSD). In addition to rain-induced attenuation, non-rain factors, such as free space path loss, atmospheric attenuation and multipath effect, can also lead to microwave link attenuation. Therefore, the rain-induced attenuation can only be obtained by eliminating the non-rainfall induced attenuation from the total attenuation. CML-based rainfall measurement has the advantages of path-integrated measurement, high spatial density, less human intervention, low implementation cost, and true reflection of near-surface rainfall and thus can be used as an effective substitution or supplement to traditional rainfall measurement methods, such as RGs, weather radar, and satellite [20; 24; 25]. In addition, CML-based measurement can estimate not only rainfall, but also water vapor, solid particles, fog, snow, sleet, hail, and so on [26; 27; 28; 29].  
Figure 1: Basic operating principle of CML-based rainfall measurement.